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\begin{document}

\title{Transcriptome profiling research in urothelial cell carcinoma}



\author[1]{Umar Ahmad}%
\author[2]{Buhari Ibrahim}%
\author[3]{Mustapha Mohammed}%
\author[1]{Ahmed Faris Aldoghachi}%
\author[4]{Mahmood Usman}%
\author[5]{Abdulbasit Haliru Yakubu}%
\author[2]{Abubakar Sadiq Tanko}%
\author[6]{Khadijat Abubakar Bobbo}%
\author[2]{Usman Adamu Garkuwa}%
\author[2]{Abdullahi Adamu Faggo}%
\author[7]{Sagir Mustapha}%
\author[8]{Mahmoud Al-Masaeed}%
\author[1]{Syahril Abdullah}%
\author[9]{Yong Yoke Keong}%
\author[10]{Abhi Veerakumarasivam}%
\affil[1]{Universiti Putra Malaysia Medical Genetics Laboratory}%
\affil[2]{Bauchi State University Gadau}%
\affil[3]{Universiti Sains Malaysia Pusat Pengajian Sains Farmasi}%
\affil[4]{Ahmadu Bello University Faculty of Agriculture}%
\affil[5]{University of Maiduguri Faculty of Pharmacy}%
\affil[6]{University Putra Malaysia Institut Biosains}%
\affil[7]{Universiti Sains Malaysia School of Medical Sciences}%
\affil[8]{The University of Newcastle College of Health Medicine and Wellbeing}%
\affil[9]{Universiti Putra Malaysia Jabatan Anatomi Manusia}%
\affil[10]{Sunway University Department of Medical Sciences}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Urothelial cell carcinoma (UCC) is the ninth most common cancer that
accounts for 4.7\% of all the new cancer cases globally. UCC development
and progression are due to complex and stochastic genetic programmes. To
study the cascades molecular events underlying the poor prognosis that
are due to limited treatment options for advance disease and resistance
to conventional therapies in UCC, transcriptomics technology (RNA-Seq),
a method of analysing the RNA content of a sample using the modern
high-throughput sequencing platforms has been employed to address these
limitations. Here we review the principles of RNA-Seq technology and
summarize the recent studies on human bladder cancer that employed this
technique to unravel the pathogenesis of the disease, identify
biomarkers, discover pathways and classify the disease state. We list
the commonly used computational platforms and software that are publicly
available for RNA-Seq analysis. Moreover, we discussed the future
perspective for RNA-Seq studies on bladder cancer and recommend the
application of new technology called single cell sequencing (scRNA-Seq)
to further understand bladder cancer.%
\end{abstract}%



\sloppy


\textbf{Transcriptome profiling research in urothelial cell carcinoma}\selectlanguage{english}
\begin{longtable}[]{@{}l@{}}
\toprule
\textbf{Umar Ahmad\textsuperscript{1,2*}} ORCID:
https://orcid.org/0000-0002-3216-5171 \textsuperscript{1}Medical
Genetics Laboratory, Genetics and Regenerative Medicine Research Centre,
Faculty of Medicine and Health Sciences, Universiti Putra Malaysia,
Serdang, Selangor, 43400, Malaysia, \textsuperscript{2}Medical Genetics
Unit, Department of Anatomy, Faculty of Basic Medical Science, Bauchi
State University, Gadau, Bauchi, PMB 65, Nigeria. e-mail:
umarahmad@basug.edu.ng\tabularnewline
\midrule
\endhead
\textbf{Buhari Ibrahim\textsuperscript{3,4}} ORCID:
https://orcid.org/0000-0003-3559-3099 \textsuperscript{3}Department of
Imaging, Faculty of Medicine and Health Sciences, Universiti Putra
Malaysia, Serdang, Selangor, 43400, Malaysia,
\textsuperscript{4}Department of Physiology, Faculty of Basic Medical
Sciences, Bauchi State University, Gadau, Bauchi, PMB 65, Nigeria.
e-mail: ibrahimbuhari@basug.edu.ng\tabularnewline
\textbf{Mustapha Mohammed\textsuperscript{5,6}} ORCID:
https://orcid.org/0000-0002-5021-1610 \textsuperscript{5}School of
Pharmaceutical Sciences, Universiti Sains Malaysia, Penang, Pulau
Pinang, 11800, Malaysia, \textsuperscript{6}Department of Clinical
Pharmacy and Pharmacy Practice, Faculty of Pharmaceutical Sciences,
Ahmadu Bello University, Zaria, Kaduna, PMB 1045, Nigeria. e-mail:
macreener88@gmail.com\tabularnewline
\textbf{Ahmed Faris Aldoghachi\textsuperscript{7}} ORCID:
https://orcid.org/0000-0003-3236-1305 \textsuperscript{7}Medical
Genetics Laboratory, Department of Biomedical Science, Faculty of
Medicine and Health Sciences, Universiti Putra Malaysia, Serdang,
Selangor, 43400, Malaysia. e-mail:
ahmed.aldoghachi@gmail.com\tabularnewline
\textbf{Mahmood Usman\textsuperscript{8,9}} ORCID:
https://orcid.org/0000-0002-0273-9867 \textsuperscript{8}Department of
Human Anatomy, Faculty of Basic Medical Sciences, Ahmadu Bello
University, Zaria, Kaduna, PMB 1045, Nigeria,
\textsuperscript{9}Department of Human Anatomy, Faculty of Basic Medical
Sciences, Yusuf Maitama Sule University, Kano, PMB 3220, Nigeria.
e-mail: mahmoodusman13@gmail.com\tabularnewline
\textbf{Abdulbasit Haliru Yakubu\textsuperscript{10,11}} ORCID:
https://orcid.org/0000-0001-6018-9931 \textsuperscript{10}Faculty of
Pharmacy, University of Maiduguri, Maiduguri, Borno, PMB 1069, Nigeria,
\textsuperscript{11}Department of Pharmaceutical Service, University of
Maiduguri Teaching Hospital, Maiduguri, Borno, PMB 1414, Nigeria.
e-mail: pharmahy071@gmail.com\tabularnewline
\textbf{Abubakar Sadiq Tanko\textsuperscript{12}} ORCID:
https://orcid.org/0000-0001-8415-0740 \textsuperscript{12}Department of
Biochemistry, Faculty of Science, Bauchi State University, Gadau,
Bauchi, PMB 65, Nigeria. e-mail:
abubakarstanko@basug.edu.ng\tabularnewline
\textbf{Khadijat Abubakar Bobbo\textsuperscript{13,14}} ORCID:
https://orcid.org/0000-0001-7765-0317 \textsuperscript{13}UPM-MAKANA
Cancer Research Laboratory, Institute of Bioscience, Universiti Putra
Malaysia, Serdang, Selangor, 43400, Malaysia,
\textsuperscript{14}Department of Anatomy, Faculty of Basic Medical
Sciences, Gombe State University, Gombe, PMB 127, Nigeria. e-mail:
khadijantii@gmail.com\tabularnewline
\textbf{Usman Adamu Garkuwa\textsuperscript{15,4}} ORCID:
https://orcid.org/0000-0001-9089-9950 \textsuperscript{15}Department of
Human Physiology, Faculty of Basic Medical Sciences, Ahmadu Bello
University, Zaria, Kaduna, PMB 1045, Nigeria,
\textsuperscript{4}Department of Physiology, Faculty of Basic Medical
Sciences, Bauchi State University, Gadau, Bauchi, PMB 65, Nigeria.
e-mail: uagarkuwa@basug.edu.ng\tabularnewline
\textbf{Abdullahi Adamu Faggo\textsuperscript{16}} ORCID:
https://orcid.org/0000-0001-5413-3093 \textsuperscript{16}Department of
Microbiology, Faculty of Science, Bauchi State University, Gadau,
Bauchi, PMB 65, Nigeria. e-mail:
abdullahiadamufaggo@gmail.com\tabularnewline
\textbf{Sagir Mustapha\textsuperscript{17}} ORCID:
https://orcid.org/0000-0003-4185-1680 \textsuperscript{17}Department of
Pharmacology, School of Medical Sciences, Universiti Sains Malaysia,
Kota Bharu, Kelantan, 16150, Malaysia. e-mail:
maimunat001@gmail.com\tabularnewline
\textbf{Mahmoud Al-Masaeed\textsuperscript{18,19}} ORCID:
\textsuperscript{18}Department of Nursing, Faculty of Health and
Medicine, University of Newcastle, Callaghan, NSW, 2308, Australia,
\textsuperscript{19}Faculty of Medicine and Health Sciences, Universiti
Putra Malaysia, Serdang, Selangor, 43400, Malaysia. e-mail:
mahmoud.almasaeed@uon.edu.au\tabularnewline
\textbf{Syahril Abdullah\textsuperscript{13,1*}} ORCID:
https://orcid.org/0000-0003-2718-6266 \textsuperscript{13}UPM-MAKANA
Cancer Research Laboratory, Institute of Bioscience, Universiti Putra
Malaysia, Serdang, Selangor, 43400, Malaysia, \textsuperscript{1}Medical
Genetics Laboratory, Genetics and Regenerative Medicine Research Centre,
Faculty of Medicine and Health Sciences, Universiti Putra Malaysia,
Serdang, Selangor, 43400, Malaysia. e-mail:
syahril@upm.edu.my\tabularnewline
\textbf{Yong Yoke Keong\textsuperscript{20}} ORCID:
https://orcid.org/0000-0002-9442-7456 \textsuperscript{20}Department of
Human Anatomy, Faculty of Medicine and Health Sciences, Universiti Putra
Malaysia, Serdang, Selangor, 43400, Malaysia. e-mail:
yoke\_keong@upm.edu.my\tabularnewline
\textbf{Abhi Veerakumarasivam\textsuperscript{21}} ORCID:
https://orcid.org/0000-0002-2676-1815 \textsuperscript{21}Department of
Medical Sciences, School of Science and Technology, Sunway University,
Bandar Sunway, Selangor, 47500, Malaysia. e-mail:
abhiv@sunway.edu.my\tabularnewline
\bottomrule
\end{longtable}

\textsuperscript{\#}Corresponding author and lead contact (UA):

Medical Genetics Unit, Department of Anatomy, Faculty of Basic Medical
Science, Bauchi State University, Gadau, Bauchi, PMB 65,
Nigeria.\emph{umarahmad@basug.edu.ng}

\textsuperscript{*}These authors contributed equally

\textbf{Abstract}

Urothelial cell carcinoma (UCC) is the ninth most common cancer that
accounts for 4.7\% of all the new cancer cases globally. UCC development
and progression are due to complex and stochastic genetic programmes. To
study the cascades molecular events underlying the poor prognosis that
are due to limited treatment options for advance disease and resistance
to conventional therapies in UCC, transcriptomics technology (RNA-Seq),
a method of analysing the RNA content of a sample using the modern
high-throughput sequencing platforms has been employed to address these
limitations. Here we review the principles of RNA-Seq technology and
summarize the recent studies on human bladder cancer that employed this
technique to unravel the pathogenesis of the disease, identify
biomarkers, discover pathways and classify the disease state. We list
the commonly used computational platforms and software that are publicly
available for RNA-Seq analysis. Moreover, we discussed the future
perspective for RNA-Seq studies on bladder cancer and recommend the
application of new technology called single cell sequencing (scRNA-Seq)
to further understand bladder cancer.

\textbf{Keywords} : Transcriptome profiling, RNA-sequencing, genomics,
bioinformatics, bladder cancer



\section*{Introduction}

{\label{introduction}}

Cancer represents one of the most life-threatening diseases posing a
major health challenge all over the world(Li, Xu, Ding, \& Tang, 2019).
Urothelial cell carcinoma (UCC) also known as bladder cancer is among
the principal causes of cancer-related deaths globally(Garg, 2016;
Kobayashi, 2016). The incidence of bladder cancer is ranked
9\textsuperscript{th} with a total number of 549,393 new cases and
199,922 deaths globally(Bray et al., 2018; Saginala et al., 2020). These
numbers are forecasted to double in 2040, particularly in the developed
countries, signifying a severe health crisis in the future which can
lead to financial burden on countries due to the exorbitant treatments,
high recurrence rates and subsequent need for long-term follow-up(Leal,
Luengo-Fernandez, Sullivan, \& Witjes, 2016; Soria et al., 2018).
Urothelial cell carcinoma is reported to be the second most frequent
malignant tumour of the genitourinary tract with men at risk four times
higher than women. The most common bladder cancer is transitional cell
carcinoma (TCC) or urothelial cell carcinoma (UCC), classified into;
non-muscle-invasive bladder cancers (NMIBCs) and muscle-invasive bladder
cancers (MIBCs). The NMIBCs accounts for about 70-80 percent of all
diagnosed patients, with the tendency of future recurrences and may
advance into muscle-invasive bladder cancers(Gao et al., 2020). Whereas
MIBCs accounts for about 20-25 percent of patients, signifying an
active, locally invasive carcinoma with a metastatic
potential.\textsuperscript{10}

Despite diagnostic and therapeutic advances for UCC, if it is not
detected early, will remain a big challenge. Although there have been
great achievements in its management and treatments that include
surgical procedures, radiotherapy, pre- and post-operative treatments
and chemotherapy have been made, there has been no significant progress
in survival rates for bladder cancer
patients.\textsuperscript{11}Moreover, these treatments are presented
with various side effects that lead to other health
problems.\textsuperscript{4} As a result, healthcare providers receive
substantial number of cases with relapse and progression, leading to
long-term follow-up. All these challenges made bladder cancer an
expensive disease to manage.\textsuperscript{7}The need to identify
novel molecular markers in bladder cancer in order to predict medical
outcomes, especially in patients with relapse has made researchers in
recent years to focus more on the molecular aspects of the disease with
the aim of boosting its biological understanding in order to unravel the
molecular factors that can be utilized as targets for therapies and
guiding assessment of risk and help in informed decision making in
clinical practice.\textsuperscript{12}

Transcriptomic technologies employ methods that study and quantify
organism's transcriptome,\textsuperscript{13} a complete set of RNA
transcripts in a cell for a specific developmental stage or
physiological state. Understanding transcriptome is essential for
interpreting the functional elements of the genome and revealing the
molecular constituents of the cells and tissues to better understand the
development of diseases.\textsuperscript{14} Identifying transcripts and
quantifying the level of gene expression have been the major focus in
molecular biology since the discovery of the RNA as an essential
intermediate between the genome and the proteome.\textsuperscript{15}The
last decades have witnessed the use of microarray technology and
bioinformatic analysis in screening genetic changes at the genome level
which has greatly assisted in the identification of differentially
expressed genes (DEGs) and functional pathways implicated in
cancer.\textsuperscript{16} However, with technological advancement and
elucidation of noncoding RNAs, transcriptomic approaches have given room
for deeper understanding of the intricacies of the regulation of gene
expression, alternate splicing events, functions of noncoding RNAs and
ascertaining the importance of such approaches in the correct
construction and annotation of complex genomes.\textsuperscript{1}

Furthermore, high-throughput RNA sequencing (RNA-Seq) has opened
opportunities for transcriptomic studies and has advanced into a
standard technique used in biomedical studies. This technique is
currently utilized in the estimation of gene expression, identification
of noncoding genes, discovery of new genomic characteristics and drug
discoveries. It is well established that RNA-Seq has strong advantages
over the previously \selectlanguage{english}developed sequencing
techniques.\textsuperscript{17} This makes RNA-Seq a vital tool in
cancer biology that can be exploited for tumour classification, patient
stratification and monitoring of patients' response to
therapy.\textsuperscript{18} However, despite the high level of
correlation that exists between RNA-Seq and other techniques such as
microarray, studies have strongly emphasised the merits of RNA-Seq over
the other techniques.\textsuperscript{19} The discovery of gene fusion
and differential expression of RNA transcripts that are known for
causing diseases are some of the prospects of the
RNA-Seq.\textsuperscript{20} In this review, we summarized the current
literature status of transcriptomic studies carried out on urothelial
cell carcinoma and identified the possible areas for future research.

\section*{Transcriptome sequencing
technologies}

{\label{transcriptome-sequencing-technologies}}

Transcriptome sequencing technology is a computational method that
determines the identity and abundance of RNA sequences in a biological
sample. This technology has been widely used to study dynamic gene
expression in diverse human tissues, including human bladder cancer. The
general workflow of RNA-Seq begins with isolation of RNAs from tissue
samples, library preparation, sequencing and computational analysis
(\textbf{Figure 1} ). Below we summarize and discuss RNA-Seq techniques,
narrowing down to its application in human bladder cancer studies.

\textbf{2.1 Principle of RNA-Seq technology}

RNA-Seq~is a transcriptome~profiling technology that utilizes
next-generation sequencing (NGS) platforms.\textsuperscript{21} The
principle of RNA-Seq involve the reverse-transcription of RNA-Seq
transcripts into cDNA, ligation of adapters to each end of the cDNA,
sequencing and then aligning them~to a reference genome or assembling to
obtain~\emph{de novo~} transcripts, proving a genome-wide expression
profile.\textsuperscript{22} The quality and quantity of the starting
RNA material are the most important aspects to consider when deciding on
the methods to generate RNA-Seq libraries.\textsuperscript{23,24} While
working with RNA, it is critical to avoid RNase contamination by using
sterile, RNase-free solutions, and plastic ware. It is also important to
include quality control (QC) in all stages of
operation.\textsuperscript{23} The method of RNA isolation affects the
ability to detect differentially expressed
transcripts.\textsuperscript{25} Various methods of RNA extraction exist
in the literature; the most popular and widely used methods are the
TRIzol and RNAeasy.\textsuperscript{25} Other methods include density
gradient centrifugation, magnetic bead technology, lithium chloride and
urea isolation, Oligo(dt)-cellulose column chromatography, and
non-column poly (A)+ purification/isolation.\textsuperscript{26} TRIzol
employs the guanidinium-acid-phenol extraction and regarded as the `gold
standard' but chemicals used are corrosive and toxic, whilst, the
RNAeasy (and variants) employs the glass fibre filter and silica
technology that is fast, simple and safe to use but
expensive.\textsuperscript{24,25}

The extracted RNA is treated in solution or on-column with DNase to
remove traces of genomic DNA and to prevent contamination of RNA-Seq
libraries by DNA. DNA-free RNA could then be assessed for both quality
and quantity.\textsuperscript{24} In assessing the quality of the RNA,
most laboratories utilize the electrophoretic-based
system.\textsuperscript{22,24} The quality of the RNA produced is
measured through the RNA integrity score, RNA Integrity Numbers (RIN)
that varies from one instrument to the other.\textsuperscript{23,24} The
measure reads per kilobase of exon model per million reads (RPKM) and
its variance are the most frequently adopted in the measurement of
RNA-Seq expression value.\textsuperscript{27} The acquisition of RNA-Seq
data consists of several steps and in each of these steps, specific QC
checks are applied to monitor the quality of the data output
produced.\textsuperscript{15}

\textbf{2.2 RNA-Seq library preparation}

In the last decade, there has been a considerable understanding of the
dynamic nature of organism's genomes,\textsuperscript{13,28} this is not
limited to the modifications of DNA sequence but also to the
quantitative and qualitative measurements of RNA
sequences.\textsuperscript{13} Before sequencing a sample, a library
must be prepared for that sample.\textsuperscript{13,28,29} A library is
a collection of randomly sized DNA fragments representing the sample
input.\textsuperscript{28} Depending on the type of NGS application,
different library preparation steps are
available.\textsuperscript{28-30} Also, the sample preparation required
for RNA sample sequencing vary depending on the type of
RNA.\textsuperscript{28,30,31} For RNA-Seq, basically there are some
steps that are required which are; selection of transcript with poly(A)
tail, rRNA depletion,\textsuperscript{28,30} fragmentation followed by
cDNA synthesis, purification and
amplification.\textsuperscript{30,32}mRNA and lncRNA \textgreater{}200
nt contains a poly(A) tail,\textsuperscript{28,29} convenient for the
enrichment of poly(A) + RNAs from total cellular RNA, which is carried
out with oligo-dT nuclease.\textsuperscript{28,29} This is followed by
rRNA depletion,\textsuperscript{32} this can be done based on
sequence-specific probes which can hybridize to rRNA then depleted with
streptavidin beads.\textsuperscript{28} RNA samples are fragmented to a
certain size range before RT,\textsuperscript{32} this is necessary due
to size limitation of most sequencing
platforms.\textsuperscript{28}Fragmentation of RNA can be done with
alkaline solution or divalent cations on an elevated
temperature.\textsuperscript{28} Enzymes such as RNase III can also be
used to fragment RNAs but this can introduce bias due to its preference
for double-stranded RNA sequence.\textsuperscript{28} cDNA synthesized
from the RNA fragments using RT are ligated to DNA adapters before
amplification and sequencing.\textsuperscript{28,29,32}

\textbf{2.3 Sequencing platforms}

The method that has been in place for the past few decades is Sanger
sequencing, this has been characterized by its simplicity but is capital
intensive and the running takes longer time than
necessary.\textsuperscript{33} Novel sequencing technologies that
immediately evolved after Sanger sequencing are collectively termed as
next-generation sequencing (NGS) and are often described as throughput,
accurate, cost-effective and reliable techniques that can examine the
whole genome within shortest period of
time.\textsuperscript{33,34}Generally, NGS platforms are grouped into
second and third generations but the most widely used platforms for
second generation sequencing include Ion Torrent and Illumina, on the
contrary, Pacific Biosystems and Oxford Nanopore Technologies are the
most popular platforms for third generation
sequencing.\textsuperscript{35} Also, 454 FLX is among the second
generation platforms that was released in 2005 and works based on
pyrosequencing (i.e. sequencing-by-synthesis technique) like
Illumina.\textsuperscript{36} The most established Illumina platforms
include MiSeq, HiSeqs\textsuperscript{37} and NovaSeq, the newest
platform that is used for large-scale whole genome sequencing
analysis.\textsuperscript{38} MiSeq works as personalized sequencer that
sequence very small genomes with high speed and complete its operation
within 4 hours. On the other hand, HiSeqs such as HiSeq 2500 is designed
for ``high-throughput'' usage and mostly finish its cycle in 6 days
period.\textsuperscript{37} Unlike Illumina and 454 sequencing platforms
that are sequenced by synthesis, sequencing by oligonucleotide ligation
and detection (SOLiD) developed by Applied Biosystems (which later
became Life Technologies) is sequenced by ligation through hybridization
of short probes with the template DNA strand.\textsuperscript{36}Despite
its apparent advantages, its reads length and depth are not as
sophisticated as that of Illumina, thereby causing major assembly
challenge.\textsuperscript{39,40} Throughout all Illumina platforms,
only 1\% error rate is recorded and substitution is regarded as their
major error,\textsuperscript{37} providing in depth sequencing capacity
that allows detection of very small quantity of transcripts in the
sample.\textsuperscript{41}

Nevertheless, the third NGS sequencing platforms provide two essential
advantages over the second generation, including their ability to
increase reads length, avoid partiality of PCR in the amplification
process and the capacity to sequence single molecules at a given
time.\textsuperscript{36} Despite all advantages of NGS, the technique
has faced numerous challenges including numerous GC content, big size of
genome and the presence of homopolymers, however, various alternatives
have been put in place to overcome the current
challenges.\textsuperscript{28} Moreover, third NGS sequencing platforms
such as single-molecule real-time (SMRT) sequencing was developed by
Pacific Biosciences (PacBio), uses long reads that provides solution to
the myriads challenges faced by second generation platforms due to their
short reads length that make them unable to accurately detect gene
isoform, poor genome assembly and resolution of complicated genomic
region. On the contrary, PacBio sequencing (SMRT) is limited by high
percentage error, too costly per base and has
low-throughput.\textsuperscript{42} These challenges can be overcome by
the use of platforms with lower error rates as low as
3\%\textsuperscript{38} and alternatively the use of hybrid Sanger
sequencing and PacBio sequencing technology have been
proposed.\textsuperscript{42} In addition, Helicos is one of the
single-molecule-based platforms with 5\% error rate, reducing the number
of usable reads, making the reference genome extremely hard to be
matched with the sequence reads, and causing the loss of miRNA reads at
the alignment level.\textsuperscript{41} Due to their low error rate
(\textgreater{}1\%), Illumina or SOLiD platforms are regarded as the
best alternative for miRNA sequencing because of its small
size.\textsuperscript{41} Nanopore sequencing platforms such as MinION,
PromethION and GridION\textsuperscript{37} belong to third generation
sequencing platforms developed by Oxford Nanopore Technologies, which
operates by passing a single stranded molecule of DNA or RNA via a
protein nanopore at the rate of 30 bases per seconds, through an
electrical current allowing direct sequencing of the molecule, thus
offering greater advantages.\textsuperscript{43} However, a major
setback of these platforms is having high error rate up to 10\% to 20\%
compare to other platforms with high-throughput.\textsuperscript{37,44}

Most recently, several studies have reported remarkable achievements in
the diagnosis of different cancers including bladder cancer using
RNA-Seq.\textsuperscript{45-47} Transcriptomics (RNA-Seq) refers to the
application of any NGS platforms for the examination of
RNA\textsuperscript{41} and has become the best method for
whole-transcriptome profiling over the last
decade.\textsuperscript{48}The selection criteria for each platform
depend on the purpose of the experiments. The techniques operate similar
to DNA sequencing except for the library preparation and their data
analysis which comprises assembly of transcript, uncovering novel
transcripts and calculation of transcripts, among
others.\textsuperscript{41} Additionally, RNA-sequencing gives
comprehensive, high-throughput, precise, accurate and impartial view of
the transcriptome analysis that overcome the inherent limitations of
real time PCR and microarray techniques.\textsuperscript{49} These
limitations include: the need for previous knowledge of the sequence in
question, inability to calculate low and high expressed genes with great
accuracy, among others.\textsuperscript{22} RNA-Seq though highly
throughput, it is too expensive,\textsuperscript{50} especially for
clinical settings.

\section*{3.0 RNA-Seq analysis
pipelines}

{\label{rna-seq-analysis-pipelines}}

Over the years, microarray and gene-chip technologies provide an insight
into understanding the genetic changes in biological samples. However,
these techniques are known to have certain limitations related to
dynamic range, resolution and accuracy.\textsuperscript{51} Advances in
transcriptome technology have allowed deeper understanding of the
intricacies of gene expression regulation, particularly high-throughput
RNA sequencing technology that made it possible to observe whole
transcriptome variations, discover novel splicing sites and events,
functions of noncoding RNAs as well as proving correct construction and
annotation of complex genomes.\textsuperscript{52} It also aids to
qualitatively ascertain the RNA transcripts present, RNA editing sites,
and to quantitatively know how much of the individual transcripts
expressed.\textsuperscript{53} Thus, it is paramount to overview
pipelines and workflows applied to bladder cancer RNA-Seq analyses.

A number of computational pipelines and workflows are being used for the
pre-processing of RNA-Seq data in cancer studies and other experimental
purposes.\textsuperscript{50,54,55} A typical RNA-Seq workflow consists
of seven steps; (1) pre-processing of raw data, (2) alignment of reads
to the reference (3) transcriptome reconstruction, (4) quantifications
of transcripts or genes level (5) differential expression analysis (6)
functional profiling and (7) advanced analysis (\textbf{Figure 2}
).\textsuperscript{56} These stages in the RNA-Seq workflow that
includes quality control (QC) and data analysis can be done using
varieties of computational platforms or tools. For example, read counts
may be aligned using different tools such as spliced transcript
alignment to a reference (STAR) or Tophat.\textsuperscript{57,58} Then,
the aligned read counts can be obtained using either HTSeq or Rsubread
R/Bioconductor package.\textsuperscript{59,60} The advantage of Rsubread
over HTSeq is that the former is faster, requires less memory and
summarizes the read counts that are more closely related to a true
value.\textsuperscript{61,62}

RNA-Seq raw data often have quality problems that can distort analytical
findings significantly and lead to incorrect
conclusions.\textsuperscript{63} For instance, the quality of raw
RNA-Seq data could be altered by residue of ribosomal RNA, degradation
of RNA and variation in read coverage.\textsuperscript{63} Hence, in
order to obtain accurate transcripts or genes measurements and proper
acquisition of information from the data, raw RNA-Seq data must be
reviewed and evaluated by quality control measures before subsequent
analyses are conducted.\textsuperscript{27,63} Presently, the most
widely and commonly used computational tools available for RNA-Seq QC
include; FASTQC and MultiQC. FASTQC processes one sample at a time,
while MultiQC can generate a single report that visualizes the output of
several samples from multiple tools thereby giving room for easy
comparison.\textsuperscript{64,65} Other important and commonly used
computational software for QC are comprises of RseQC, RNA-seQC and
RNA-QC-Chain.\textsuperscript{66-68} Although both RseQC and RNAseQC can
offer QC statistics of aligned read counts, RseQC partially relies on
the University of California Senta Crus (UCSC) Genome
Browser.\textsuperscript{67} Moreover, they are slow and unable to
provide sequence trimming and filtration of contaminants. However,
RNA-QC-Chain can remove low quality reads and contamination, in addition
to providing fast and reliable QC to produce data for downstream
analysis.\textsuperscript{63} RNA-Seq data analyses steps totally depend
on the data quality and specific aims of the study. These analyses steps
were reviewed in detail elsewhere.\textsuperscript{27,69}

The system of RNA-Seq analysis employs high-computational tool
applications for the development of pipelines that orchestrate the
entire workflow and optimize usage of available computational
resources.\textsuperscript{67} The development of such analytic tools
for RNA-Seq data has expanded owing to complex nature of transcriptome
data, and thus, selecting the correct processing pipeline and
normalization strategy has a significant impact on downstream
analysis.\textsuperscript{70} This pipeline consists of multiple
independent analytical software packages, tools and platforms which
employ R and Python, Unix/Bash, Java script, Perl and C++. Being that
these software are in programmable environment; they provide flexible
manipulation of data and methods. However, they required the user to
have expertise in programming languages especially the bash language or
Unix Commands Line.\textsuperscript{71} With the growing application of
RNA-Seq in biomedical research, an integrated user friendly platforms
are needed to overcome the barriers encountered when using code-bond
platforms, the Graphical user interface(GUI) or web-based platforms
provides convenient and enabling environment for non-expert with
advantages for quick exploratory analysis, even though not on the scale
of large datasets.\textsuperscript{71} \textbf{Table 1} provides a
summary of the various computational tools and their associated
platforms used in RNA-Seq analyses.

Variations in the RNA-Seq analysis results might be observed due to
usage of different platforms and analytical framework. The number of
computational tools and bioinformatics methods that are currently in
use, add more challenges to the analysis and interpretation of the
RNA-Seq data. In order to solve these challenges caused by variations in
RNA-Seq analysis techniques, standard pipelines need to be enforced and
re-designed in order to integrate analysis of multiple experiments.
Workflow constructions software packages such as
Chipster,\textsuperscript{72} Anduril\textsuperscript{73,74} and
Galaxy\textsuperscript{75} could be very much relevant in solving some
of these challenges. For example, Anduril was developed for designing
complex RNA-Seq pipelines with large-scale datasets which require
automated parallelization. While Chipster and Galaxy are powerful in
data integrative visualization which makes it very useful for data
exploration and interpretation. Other workflows and management
frameworks for RNA-Seq analysis are KNIME\textsuperscript{76} which aid
in visual assembly and interactive execution of data pipeline and
Snakemake,\textsuperscript{77} which is a Python-based workflow
management engine that provides a powerful execution environment.
Workflow management framework that specifically focuses on RNA-Seq data
analysis is reviewed by.\textsuperscript{83} In addition, the
large-scale nature of the data analyses associated with RN-Seq brought
many challenges that are beyond the scope of this review. Han and
colleagues \textsuperscript{78} reviewed these challenges
comprehensively and proposed solutions. Moreover, results from RNA-Seq
study on tumours revealed the presence of molecular subsets of cellular
signatures, microenvironment and facilitates choices to circumvent
treatment failure.\textsuperscript{79} Thus, single-cell sequencing
(scRNA-Seq) may prove the correct method to understand tumour
progression, pathogenesis and discovery of biomarkers that could lead to
a better treatment and management of bladder cancer.

\section*{4.0 Review of RNA-Seq studies on bladder
cancer}

{\label{review-of-rna-seq-studies-on-bladder-cancer}}

RNA sequencing has emerged as a powerful next-generation sequencing
(NGS) technology tool for unbiased identification of gene expression,
discovery and quantification of novel transcripts, and identification of
alternatively spliced genes.\textsuperscript{22,41} Consequently, recent
progress in RNA-Seq has broadened the understanding of the molecular
pathogenesis of cancers, including bladder cancer. RNA-Seq has been
successfully applied in bladder cancer research for earlier detection,
establishing pathological origin, and defining the aberrant genes and
dysregulated molecular pathways across patient groups. Thus far, eight
studies have been reported on bladder cancer that made use of the
RNA-Seq in understanding the bladder cancer pathology. These studies are
summarised based on sample, bladder cancer grades and library
preparation techniques employed (\textbf{Table 2} ).

\textbf{4.1 Pathogenesis of bladder cancer}

The aetiology and pathogenesis of bladder cancer are still less
understood even though it has been characterized to have a high degree
of malignancy and relapse even after
surgery.\textsuperscript{80-82}Several findings suggest that in humans,
microbiome could be one factor that can influence the development of
cancer, including bladder cancer.\textsuperscript{83,84} For example,
\emph{Campylobacter}genus\emph{,} an opportunistic bacterium of the
urinary tract was found to have pathogenic potential, as it was able to
invade epithelial cells, produce toxins that inhibit NK cells
cytotoxicity hence, promoting evasion of an immune
response.\textsuperscript{83} This genus has the ability to generate a
pro-inflammatory environment that supports tumour
progression.\textsuperscript{83} The development of bladder cancer has
also been reported to be highly correlated with abnormal expression of
noncoding RNAs and protein-coding genes. \textsuperscript{82} Wang et
al\textsuperscript{82} constructed three-layer network of miRNA-lncRNA
data from several microRNAs and long noncoding RNAs databases to
calculate the topology attributes of nodes and concluded that\emph{E2F1}
and \emph{E2F2} are important target genes of miRNA-93 while\emph{AKT3}
is an important target gene of miRNA-195 and that their dysregulation
may be closely related to cell proliferation and apoptosis in bladder
cancer. Similar findings reported that the dysregulation of lncRNA and
circRNA are important in bladder cancer pathogenesis and
progression.\textsuperscript{85}

\textbf{4.2 Biomarkers of bladder cancer}

Cancer biomarkers are biological molecules available in tissues, body
fluids, or blood that helps in cancer prognosis, diagnosis, prediction
of response to treatment, and monitoring disease
progression.\textsuperscript{86,87} To date, there have been several
studies conducted to determine a potential bladder cancer biomarkers in
order to enhance the diagnostic accuracy,\textsuperscript{87,88}however,
due to the false-positive and false-negative results, the use of these
biomarkers have sparked clinical controversy.\textsuperscript{89}
Nevertheless, utilizing the potential biomarkers to enhance the
therapeutic surveillance and outcome has been investigated. A study on
105 NMIBC patients showed the alteration of certain genes that includes;
\emph{TP53} , \emph{PIK3CA, FGFR, TERT}promoter, \emph{STAG2, ARIDIA,}
and \emph{KDM6A} . Among these genes, the\emph{TERT} promoter accounted
for 73\% of alteration in NMBIC.\textsuperscript{90} While in MIBC,
RNA-Seq analysis on high-grade bladder cancer (urine sample) revealed
high expression of 15 genes such as \emph{PLEKHS1, CP, WNT5A, RARRES1,
MYBPC1, AR, ROBO1, SLC14A1, AKR1C2, FBLN1, IGFBP5, STEAP2, ENTPD5,
GPD1L,} and\emph{SYBU.} \textsuperscript{91} Recently,
Sucularli\textsuperscript{92}determined genes differentially expressed
among bladder cancer (n=404) compared to the normal bladder (n=28) and
found 559 genes were downregulated and 171 were upregulated . Six genes
were associated with the patient's survival that includes \emph{CDC20,
PTTG1, PLK1, SFN, CCNB1,} and \emph{BUB1B.} \textsuperscript{92}

Furthermore, Shen et al\textsuperscript{93} conducted a study on cancer
stem cells to determine the potential of \emph{Sox4} as a biomarker for
bladder cancer. Findings from the study showed a reduction in sphere
formation and an elevation in the levels of aldehyde dehydrogenase in
cells and the tumour forming ability upon the knockdown of \emph{Sox4} .
Moreover, the elevated expression of \emph{Sox4} was found to be
correlated with stages of cancer and a reduction in the rate of
survival, making it a potential biomarker in the aggressive bladder
cancer phenotype.\textsuperscript{93}

Recent research has incorporated circular RNA to develop functional
biomarkers for bladder cancer. The involvement of circRNA in gene
transcription as well as translation suggests the potential
participation of circRNA in the process of disease progression including
cancer.\textsuperscript{94} In a study by Li et
al\textsuperscript{95}RNA-Seq results demonstrated down regulation of
circular RNA (circHIPK3) in cell lines and tissues of bladder cancer.
CircHIPK3 can sponge miR-558 which in turn suppresses heparanase
(\emph{HPSE} ) expression as demonstrated mechanistically. The circHIPK3
overexpression was found to halt the bladder cell angiogenesis,
migration, and invasion in vitro and inhibit the metastasis and growth
of bladder cancer in vivo, suggesting a potential biomarker for bladder
cancer therapy.\textsuperscript{95}Similarly, a study by Dong et
al,\textsuperscript{94} utilized a novel circRNA and circACVR2A of which
their overexpression is linked with migration, proliferation and
invasion of bladder cancer cells.\textsuperscript{94} It was
demonstrated that circACVR2A is able to interact directly with miR-626,
thus acting as a miRNA sponge which in turn regulates the expression of
\emph{EYA4.} \textsuperscript{94}Despite the above findings and the
potential of several genes as bladder cancer biomarkers, to date, there
have been no available prognostic and diagnostic biomarkers that have
successfully been translated clinically, implying the need for further
future studies to improvise the available biomarkers.

\textbf{4.3 Identified molecular pathways in bladder cancer}

Modification of different molecular pathways and changes in living
organisms such as mutations, alterations in gene control and epigenetic
alterations are the driving forces for malignancy and its
development.\textsuperscript{96} Alterations of signaling pathways,
metabolic pathways, cytoskeleton and DNA repair pathways have been
identified to be associated with tumorigenesis and progression of
different types of cancers.\textsuperscript{97} Moreover, signaling
pathways that control cell growth, proliferation, cell specialization
and apoptosis, when damaged could lead to carcinogenesis and disease
progression.\textsuperscript{97-99} Activation of focal adhesion and
MAPK signaling pathways and subsequent dysregulating genetic processes
are further uncovered in the development of bladder
cancer.\textsuperscript{100}

Data generated by RNA sequencing and bioinformatics analyses revealed
that 17 differentially expressed genes were down-regulated in 5637 cell
line whereas 44 were up-regulated in comparison to
T24.\textsuperscript{46} Similarly, down-regulation of WNT9A and WNT10A
was confirmed in both cell lines which have been found to be associated
with alteration of Wnt signaling pathways that contribute to bladder
cancer initiation and development\textsuperscript{1,9} Using RNA-Seq,
the most significantly enriched pathway involved in the development of
bladder cancer was found to be the cell adhesion molecules (CAMs)
pathway (FDR= 2.67E-08).\textsuperscript{101} Further enrichment of
bladder cancer pathway, focal adhesion, and extracellular matrix (ECM)
receptor interaction pathways revealed that genes such as \emph{PTPRF}
,\emph{VEGFA} and \emph{CLDN7} participated in all the pathways
including bladder cancer.\textsuperscript{101} The upregulation of genes
such as\emph{ITGA} , \emph{F3} , \emph{ANXA1} have been reported in
cancer-related pathways associated with cell growth, cellular cycle and
apoptosis. However, downregulation of \emph{GRB7} , \emph{VEGP} genes
was also noted in the same pathways.\textsuperscript{102}

Further, upregulation of Interferon-g, angiogenesis, and inflammatory
pathways was observed in NEURAL, mesenchymal-like (MES), and
squamous-cell carcinoma-like (SCC).\textsuperscript{103} Nevertheless,
downregulation of several DEGs have been reported in immune activation
pathways like NF-kappa B signaling pathway, MAPK signaling pathway and
PI3K-Akt signaling pathway.\textsuperscript{104} Interleukin-10
production, lymphocyte chemotaxis and aberrant IFN\selectlanguage{greek}γ, \selectlanguage{english}NF-\selectlanguage{greek}κ\selectlanguage{english}B and ERK
signalling networks are the major pathways identified in the immune
transcriptome related to programmed death-ligand 1 (PD- L1) inhibitors
status and may participate in the evasion of immunity in high- grade
muscle invasive urothelial carcinoma of the bladder
(HGUC).\textsuperscript{105} Similarly, PIK3/AKT/MTOR pathway is
activated by ERBB2 and FGFR3 (types of receptor tyrosine kinases) and is
found to control essential carcinogenesis stages and progression of
tumour.\textsuperscript{106} In a different study, key signaling
pathways including vascular endothelial growth factor (VEGF/VEGFR)
pathway and PI3K-Akt-mTOR pathway were identified and have been linked
to promote muscle-invasive bladder carcinoma. Also, through activation
of Janus kinase-signal transducer and activator of transcription
(JAK/STAT) signaling pathway, carcinogenesis of urothelial carcinoma was
improved by Insulin-like growth factor binding protein 4--1 (IGFBP4--1).
Upregulation of IGFBP4--1 in bladder tissue might play a significant
role in the initiation and progression of bladder
cancer.\textsuperscript{71} Similarly, L-type amino acid transporter 1
(LAT1) which transport leucine (essential amino acid) control mammalian
Target of Rapamycin (mTOR) signaling pathway and has been linked to the
initiation and progression of urothelial carcinoma.\textsuperscript{107}

Interestingly, RNA-sequencing has shown significant upregulation (fold
change \textgreater{} 2.0) of 1793 genes, on the other hand,
downregulation of 1759 genes was recorded after knockdown of
Methyltransferase-like 3 (METTL3), suggesting that methylation of METTL3
may halt the process of \emph{N\textsuperscript{6}} -methyladenosine
(m6A) methylation which could lead to progression of urothelial
carcinoma.\textsuperscript{108} The functional enrichment analysis
revealed that strong (significant) negative correlation was observed in
MYC oncogene and TNF-\selectlanguage{greek}α/\selectlanguage{english}NF-\selectlanguage{greek}κ\selectlanguage{english}B target genes pathways while strong positive
correlation was noticed in other signaling pathways in relation to
METTL3 knockout.\textsuperscript{108}

Moreover, activation of RAS pathway was observed more in all BC159-T
samples than TCGA-urothelial bladder carcinoma (TCGA- BLCA)
samples.\textsuperscript{79} The alterations of DNA in urothelial
carcinoma are controlled by the activation of the Ras--MEK--ERK and PI3
kinase--AKT--mTOR pathways either by tumour suppressor genes or
oncogenes and thus, RB1-dependent G1-S cell cycle restriction point,
anabolic metabolism, and cell survival RB1-dependent G1/S checkpoint,
cell continuity (survival) and building aspect of metabolism (anabolism)
regulate carcinogenesis through these pathways.\textsuperscript{109} In
about 20\% of muscle-invasive bladder neoplasm, mutation in fibroblast
growth factor (FGFR3) was activated, triggering activation of receptor
that encourage profuse growth through downstream activation of
ERKs.\textsuperscript{109,110} Genetically altered FGFR3 (mutant FGFR3)
has been reported to activate RAS-MAPK pathway which led to abnormal
proliferation of cancer cells.\textsuperscript{106}

Several attempts have been put in place to avert proliferation of cancer
cells by targeting the specific molecular pathways for the development
of bladder cancer. For example, anticancer drugs like infigratinib and
dasatinib were used to target Erk1/2 and Src signalling pathways and
thus overcome most resistance observed in cancer
cells.\textsuperscript{111} Similarly, three combination of anticancer
drugs (romidepsin + gemcitabine + cisplatin) have been used to target
the ERK pathway by increasing the level of reactive oxygen species (ROS)
which trigger cysteine-aspertic proteases (caspases) that played a vital
role in cell apoptosis.\textsuperscript{112} Therefore, understanding
integrated metabolomic and transcriptomic pathways related to bladder
cancer could offer alternative ways for the treatment of bladder
cancer.\textsuperscript{113}

\textbf{4.4 Bladder cancer classification}

Bladder cancer was earlier annotated with mutation recurrence in 32
genes\textsuperscript{114} and characterized with signature genetic
metabolism predispositions that includes; N-acetyl transferase editosome
due to ``apolipoprotein B mRNA editing enzyme, catalytic
polypeptide-like'' (APOBEC)-cytidine
deaminases.\textsuperscript{10}Profiling of 58-genes and genes fusion of
784 genes have identified APOBEC signature
mutations,\textsuperscript{115} that are highest in bladder cancer due
to overexpression of APOBEC3B genes than in all solid
tumours\textsuperscript{109} . However, cigarette smoking associated
gene (GSTM1-null) has been found more susceptible in females
and\emph{FGFR3} mutation which is more frequent in non-muscle invasive
bladder cancer (NMIBC) types.\textsuperscript{10} Accurate association
of bladder cancer histological variants pathological features and
specific molecular mutational pattern might provide promising target for
developing therapy.\textsuperscript{116}\textbf{~} So far, the European
Association of Urology has provided guidelines on identifying
evidence-based prognosis and progression\textsuperscript{115} as
different morphological subtype has different clinical course and
response to treatment.\textsuperscript{117} Lin and
colleagues\textsuperscript{102} recommend the use of radiomics and
transcriptomics in further identification of prognostic signature
markers for the different variants of bladder cancer.

The two main types of bladder cancer include the non-muscle-invasive
bladder cancer (NMIBCs), which is a low-grade
tumour,\textsuperscript{103,115} accounting for about 75\% of all
diagnosed cases\textsuperscript{10,103} and has been associated with a
relative stable genomic \emph{FGFR} mutation\textsuperscript{10,103} and
homozygous deletion of \emph{CDKN2A} .\textsuperscript{118} Gene
expression profiling has identified 3 subtypes of NMIBCs from a luminal
to basal type marker progression shift from class 1 to class
3.\textsuperscript{119} Other type of bladder cancer is the
muscle-invasive bladder cancer (MIBC), although less frequently
diagnosed, it is genetically predisposed as unstable and divergent
type\textsuperscript{109,119}. MIBC progress from a flat lesion and
gradually acquire gene mutations that promote cell proliferation and
survival\textsuperscript{120} and are associated with
aneuploidy\emph{TP53} mutations.\textsuperscript{10,103} Other mutations
observed with MIBCs include p53 expression stability, upregulated
cytokeratin 20 (\emph{CK20} ) and \emph{HER2} /neu genes and
downregulated \emph{PTEN}expression that is associated with upregulation
of phosphatidylinositol 3-kinase (PI3K) pathway.\textsuperscript{10}
Additionally, the common RB1 deletion and 6P amplification are
associated with MIBCs development\textsuperscript{118} with an
unfortunate less than 50\% 5-year survival.\textsuperscript{103,117}
However, the NMIBCs exhibits a papillary growth pattern of low
malignancy potential, however, they requires an expensive clinical
management which includes local resection due to high tendency of
recurrence.\textsuperscript{115,120}

Nevertheless, the earlier anatomical based classification of bladder
tumour (NMIBC; Tis, Ta \&T1 and MIB; T2, T3 \& T4) had limitation in the
understanding of the diverse prognosis within same tumour
type.\textsuperscript{121} Several studies have speculated that tumour
heterogeneity might be associated with diagnosis, metastasis and
pre-existing treatment resistance especially in the case of
MIBCs.\textsuperscript{121} Therefore, for an optimized patient
precision therapeutic management, identification of an accurate
histopathological\textsuperscript{116,122} and specific transcriptomic
biomarkers are encouraged to be adopted.\textsuperscript{123} Based on
this, bladder cancer has been classified into urothelial carcinoma (UC),
urothelial carcinoma with variant (UCV), squamous cell carcinoma (SCC),
and adenocarcinoma (AD)
subtypes.\textsuperscript{124,125}Transcriptomics and other molecular
techniques have led to the identification of urothelial carcinoma (UC)
that account for 75\%\textsuperscript{116} or even up to
90\%\textsuperscript{126} of the bladder cancer. Whereas the remaining
25\%, comprises of other histological variants known as the
nonurothelial cancer.\textsuperscript{116} Invasive UC have a tendency
to progress into divergent variants of nonurothelial tumours through
progressive histological differentiation.\textsuperscript{126}
Nonurothelial tumours are also known to consist of different range of
rare variants based on the WHO 2016 classification that include the pure
squamous, glandular, neuroendocrine, sarcomatoid, micropapillary,
plasmacytoid, microcystic, clear cell and pure histological
variants.\textsuperscript{116,117} The pasmacytoid variant has been
reported to have the worst overall survival
mean.\textsuperscript{116,124} In addition, other sub-populated tumours
with either the pure squamous cell or glandular differentiation are
shown not to be sensitive to adjuvant chemotherapy unlike their
counterpart that are populated with small cell carcinoma which are
however aggressive and sensitive to neo-adjuvant
chemotherapy.\textsuperscript{124}

Moreover, UC is more prevalent in developed countries like Japan, North
America, Western countries,\textsuperscript{122} while squamous cell
carcinoma (SCC) and adenocarcinoma have a higher prevalence in countries
like Egypt.\textsuperscript{125} Zhang et al\textsuperscript{101} used
high-throughput transcriptomic profiling in cisplatin resistant UCs, to
unravel differential splicing in more than 300 genes with the specific
dysregulation in five candidate genes were validated with real time PCR.
These including upregulation of \emph{CDH1} , \emph{VEGFA} ,
\emph{PTPRF} ,\emph{CLDN7} genes and downregulation of \emph{MMP2} gene
that are all together involved in cell adhesion molecules (CAMs), focal
adhesion and bladder cancer progression pathways.\textsuperscript{101}
In rare UC cases, fusion transcripts associated with chromosomal region
rearrangements (17q25, 15q26.3 and 1p36.22) have been identified
as\emph{SET9/CYHR, IGF1R/TTC23, SYT8/TNN12 and CASZ1/DFFA} transcripts
respectively.\textsuperscript{47} Similarly, about 25-30\% of UC of the
bladder possess histological variant which are predominantly populated
with SCC known as the urothelial carcinoma with variant
(UCV),\textsuperscript{125} which are associated with an enrichment
of\emph{MAPK} signaling pathway\textsuperscript{121}. Although SCC of
the bladder represent a small fraction (2\%-5\% in the United State) of
prevalence in the western countries based on the National Cancer
Database (NCDB), it attributes the worse prognosis both in terms of
stage-for-stage, grad, relapse and overall survival and to the
UC.\textsuperscript{116,127} Both the UC and SCC are derived from UCV
and have been demonstrated to have identical driving genes but
completely different transcriptional profiles.\textsuperscript{128} On
the other hand, SCC has also been attributed to be less responsive to
chemotherapy, thus its standard pre-existing treatment is still radical
cystectomy (RC) which might provide the maximal 5-year
survival.\textsuperscript{129} Unlike UC, SCC is shown to have a
negative expression of immune inhibitors biomarkers such as PD-L1, thus
its higher grade and aggressive association. Owyong \emph{et
al\textsuperscript{129}} suggests the increase in the expression of
PD-L1might improve the immune response associated with treatment
outcome. Primary adenocarcinoma represents 0.5-2\% of the all diagnosed
bladder cancer, like SCC it has a poor prognosis and has a weak TM
immunoexpression score than UC.\textsuperscript{130}

On the other hand, small cell carcinoma (SmCC) is a rare bladder cancer
variant (less than 1\% of all bladder diagnosis) and belongs to the
neuroendocrine carcinoma (NEBC) family, usually presented at a late
stage.\textsuperscript{126} Neuroendocrine carcinoma is somewhat similar
and sometimes intermingle with UC, although relatively rare (0.5-1.2\%
of new cases) it is highly lethal with a 1-year survival
progression.\textsuperscript{126,131} Small cell carcinoma of the
bladder is associated with a depletion of \emph{CCND1} and an
amplification of \emph{CDKN2A} genes whereas in UC, \emph{CDKN2A} gene
is depleted while \emph{CCND1} gene is
amplified.\textsuperscript{131}This variant has neuronal marker
signatures including upregulation of\emph{NESTIN} , \emph{TUBB2B} ,
\emph{PEG10} gene with low or no expression of basal or luminal
biomarkers.\textsuperscript{131,132}Another identified novel marker of
SmCC is dysregulated PVT1-ERBB2 affecting ERBB2 gene expression which
compliments the general MIBC signature markers; TP53 and RB1
depletion,\textsuperscript{126,133}and are evident in their histological
features.\textsuperscript{131}Although SmCC/ NEBC may express basal or
luminal biomarkers, a few of them have a basal molecular feature which
might explain their positive response to
chemotherapy\textsuperscript{126,132} or radiotherapy in improving
survival.\textsuperscript{132} Study by Shen and others, shows that NEBC
were sensitive to P13k inhibitor (GDC-0941) and FGFR inhibitor
(NVP-BGJ398). While Wang et al\textsuperscript{126} recommended
utilizing the common RB1 gene depletion as a potential therapeutic
target.

\textbf{5.0 Challenges and limitations of RNA-Seq method}

RNA sequencing (RNA-Seq) has become an essential tool for analysing
differential gene expression (DGE) utilized in characterizing specific
tissues,\textsuperscript{134} a development that has given room for
deeper insight into the complexity of the protein-coding transcriptome
than previously understood. Some studies have preferred RNA-Seq over the
earlier developed high-throughput RNA analysis by microarrays due to its
numerous advantages.\textsuperscript{17} One of such promising
opportunities is the detection of gene fusions and differential
expression of transcripts known to cause disease. Lee et
al\textsuperscript{52} reported that dysregulation of long non-coding
RNAs had been associated with development of some diseases such as
cancer, myocardial infarction and diabetes. Other advantages include
increased dynamic range of expression, measurement of focal changes
(such as single nucleotide variants (SNVs), insertions and deletions),
detection of rare and novel transcript isoforms, splice variants and
chimeric gene fusions (that include genes and transcripts hitherto not
identified).\textsuperscript{20}

Despite the merits of the RNA-Seq, some limitations especially
associated with the library preparation protocols can cause biases and
overvaluation of results.\textsuperscript{27,135} Because sequencing is
sensitive to the quantity of transcripts, this can make abundant mRNAs
to be overly represented in RNA-Seq libraries and can have influence on
the majority of the reads. These mRNAs are evaluated with low stochastic
variability between samples and can be found significant by differential
expression analysis (DEA). Conversely, transcripts low in abundance
receive few reads, which can subject them to noise and reduce their
chances of being selected.\textsuperscript{27}

Another factor that influences the transcript detection in RNA-Seq
experiments is the length of the transcript itself, because a longer
transcript has a higher possibility of being detected in the library, it
will be considered significant after DEA which can subsequently affect
the functional annotation of significant
genes.\textsuperscript{136,137}Excitingly, evidences have proven that
microarrays can outperform RNA-Seq in detecting small non-coding RNAs
like microRNAs. Another limitation of the RNA-Seq is the errors that
arise from the quality of the RNA, as significant percentage of the
total RNA come from ribosomal RNA (rRNA) while a very low percentage
come from the mRNA, as a result, special considerations must be made in
the methods in order to either enrich the mRNA (polyA selection) or
reduce the rRNA levels.\textsuperscript{17} It is possible to avoid some
of these issues with proper study design, however, bias and
inconsistency associated with adapter ligation, cDNA synthesis, and
amplification could be primarily dependent on library preparation and
pre-processing procedures.\textsuperscript{138} Therefore, standardized
procedures for addressing the limitations of the RNA-Seq and
ascertaining the accuracy, reproducibility and precision in a variant
clinically important settings are pertinent to enable the adoption of
RNA-Seq tests.



\section*{6.0 Conclusion and future
perspective}

{\label{conclusion-and-future-perspective}}

Urothelial cell carcinoma (UCC) is among the prime causes of
malignancy-associated deaths globally and its aetiology is poorly
understood. The bladder cancer progression is a multifaceted process
that is extremely heterogeneous and complicated. Thus, requiring the use
of advanced transcriptome technology like RNA-sequencing (RNA-Seq) to
better understand the disease phenotypes. The RNA-Seq studies of bladder
cancer reviewed in this paper reveal genome-wide changes among different
classes of bladder cancer and its pathogenicity that includes;
dysregulated genes and pathways. Moreover, the possibilities to better
understand the molecular mechanism underlying UCC progression has been
successful through application of RNA-sequencing as this helps in
understanding pathogenesis, discovering biomarkers and uncovering the
mechanism of drug resistance in bladder cancer.

Despite the progressive improvement provided by RNA-Seq technologies in
understanding the molecular basis for UCC pathogenesis, there is still a
lot to be done to prevent a recurrence, prevent further metastasis of
the malignant cells and reduce cancer-related death due to UCC. A good
number of dysregulated genes and biomarkers have been implicated in
bladder cancer, and the uses of these genes clinically have provided
controversial results. Hence, there has not been a single prognostic and
diagnostic biomarker that are successfully translated into clinical
application. This opens up an avenue for a potential research space to
fully understand bladder cancer. To address the current limitations,
there is a need to have a standard pipeline and integrated multiple
experiments analyses as a reference point for all studies due to the
high recurrence nature of UCC. The existence of molecular subsets of
cellular signatures and microenvironment promotes measures which could
avoid therapeutic failures exposed by RNA-Seq study. Although, the
acceptability of RNA-Seq regarding gene expression in UCC led to
existence of incompatibility due to RNA-Seq techniques and variability
in platform application.

In summary, RNA-Seq is regarded as the golden standard for large scale
high-throughput genome-wide and gene expression studies. It has provided
us an unknown insight into the transcriptional changes in bladder cancer
diagnosis and management. As the cost of sequencing is gradually
decreasing and more precision is being obtained in most of the
computational tools, RNA-Seq technology will continue to be applied in
studying bladder cancer transcriptomics and disease state. Consequently,
an in-depth exploration of the complex and stochastic genetic nature of
bladder cancer could possibly result in a novel biomarker discovery and
identifying new therapeutic strategies. Moreover, single cell sequencing
(scRNA-Seq) may offer a better option for comprehensive understanding of
UCC development and recurrence and could effectively help in improving
bladder cancer management.

\section*{Authors contributions}

{\label{authors-contributions}}

UA conceived and designed the study; BI, MM, AFA, MU, AHY, analyse the
entire articles that was collected and wrote some component of the
paper; AST, BKA and AHY draw the images and RNA-Seq pipeline; UAG, AAF,
SM, MA, and UA wrote the manuscript; SA, YYK and AV proofread the paper
and provide grant for the work.

\section*{Conflict of interest}

{\label{conflict-of-interest}}

Declarations of interest: none

\textbf{Acknowledgement}

Support for title page creation and format was provided by
AuthorArranger, a tool developed at the National Cancer Institute (NCI),
United States. All figures in this manuscript are designed and created
with BioRender.com.

\section*{Abbreviations}

{\label{abbreviations}}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
AD & Adenocarcinoma\tabularnewline
CAMs & Cell Adhesion Molecules\tabularnewline
cDNA & Complimentary Deoxynucleic Acid\tabularnewline
circRNA & Circular RNA\tabularnewline
DEA & Differential Expression Analysis\tabularnewline
DEGs & Differentially expressed genes\tabularnewline
DNA & Deoxynucleic Acid\tabularnewline
ECM & Extracellular Matrix\tabularnewline
GUI & Graphical User Interface\tabularnewline
HGUC & High-grade Urothelial Carcinoma\tabularnewline
lncRNA & Long non-coding RNA\tabularnewline
MES & Mesenchymal-like\tabularnewline
MIBC & Muscle Invasive Bladder Cancer\tabularnewline
miRNA & microRNA\tabularnewline
mTOR & Mammalian Target of Rapamycin\tabularnewline
NCDB & National Cancer Database\tabularnewline
NCI & National Cancer Institute\tabularnewline
NEBC & Neuroendocrine Carcinoma\tabularnewline
NGS & Next-generation Sequencing\tabularnewline
NK & Natural Killer\tabularnewline
NMIBC & Non-muscle Invasive Bladder Cancer\tabularnewline
Nt & Nucleotide\tabularnewline
PacBio & Pacific Biosciences\tabularnewline
PCR & Polymerase Chain Reaction\tabularnewline
QC & Quality Control\tabularnewline
RC & Radical Cystectomy\tabularnewline
RIN & RNA Integrity Number\tabularnewline
RNA & Ribonucleic Acid\tabularnewline
RNA-Seq & RNA Sequencing\tabularnewline
ROS & Reactive Oxygen Species\tabularnewline
RPKM & Reads Per Kilobase of Exon Model Per Million Reads\tabularnewline
rRNA & Ribosomal RNA\tabularnewline
RT & Reverse Transcriptase\tabularnewline
SCC & Squamous Cell Carcinoma\tabularnewline
scRNA-Seq & Single Cell RNA Sequencing\tabularnewline
SMRT & Single-Molecule Real-Time\tabularnewline
SmSCC & Small Cell Carcinoma\tabularnewline
SNVs & Single Nucleotide Variants\tabularnewline
SOLiD & Sequencing by Oligonucleotide Ligation and
Detection\tabularnewline
STAR & Spliced Transcript Alignment to a Reference\tabularnewline
UA & Umar Ahmad\tabularnewline
UC & Urothelial Carcinoma\tabularnewline
UCC & Urothelial Cell Carcinoma\tabularnewline
UCSC & University of California Senta Crus\tabularnewline
UCV & Urothelial Carcinoma with Variant\tabularnewline
\bottomrule
\end{longtable}



\section*{References}

{\label{references}}

Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R. L., Torre, L. A., \&
Jemal, A. (2018). Global cancer statistics 2018: GLOBOCAN estimates of
incidence and mortality worldwide for 36 cancers in 185
countries.\emph{CA: a cancer journal for clinicians, 68} (6), 394-424.

Gao, X., Wen, X., He, H., Zheng, L., Yang, Y., Yang, J., . . . Zhang, S.
(2020). Knockdown of CDCA8 inhibits the proliferation and enhances the
apoptosis of bladder cancer cells. \emph{PeerJ, 8} , e9078.
doi:10.7717/peerj.9078

Garg, M. (2016). Epithelial plasticity in urothelial carcinoma: Current
advancements and future challenges. \emph{World J Stem Cells, 8} (8),
260-267. doi:10.4252/wjsc.v8.i8.260

Kobayashi, T. (2016). Understanding the biology of urothelial cancer
metastasis. \emph{Asian J Urol, 3} (4), 211-222.
doi:10.1016/j.ajur.2016.09.005

Leal, J., Luengo-Fernandez, R., Sullivan, R., \& Witjes, J. A. (2016).
Economic Burden of Bladder Cancer Across the European Union. \emph{Eur
Urol, 69} (3), 438-447. doi:10.1016/j.eururo.2015.10.024

Li, X., Xu, M., Ding, L., \& Tang, J. (2019). MiR-27a: A Novel Biomarker
and Potential Therapeutic Target in Tumors. \emph{J Cancer, 10} (12),
2836-2848. doi:10.7150/jca.31361

Saginala, K., Barsouk, A., Aluru, J. S., Rawla, P., Padala, S. A., \&
Barsouk, A. (2020). Epidemiology of Bladder Cancer. \emph{Med Sci
(Basel), 8} (1). doi:10.3390/medsci8010015

Soria, F., Droller, M. J., Lotan, Y., Gontero, P., D'Andrea, D., Gust,
K. M., . . . Shariat, S. F. (2018). An up-to-date catalog of available
urinary biomarkers for the surveillance of non-muscle invasive bladder
cancer. \emph{World J Urol, 36} (12), 1981-1995.
doi:10.1007/s00345-018-2380-x

\par\null

\section*{Figure Legends}

{\label{figure-legends}}

\textbf{Figure 1: Overview of RNA-sequencing on bladder cancer} . RNA is
first extracted from the bladder cancer tissues or cells samples
followed by the cDNA synthesis through reverse transcription. Then, the
cDNAs are amplified for library preparation before subjecting it to
next-generation sequencing (NGS) to generate an output of raw FASTQ data
files (reads) that could be used for downstream computational and
bioinformatics analyses.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2: General RNA-Seq analysis pipeline} . A highlight of
the stepwise procedure involved in RNA-Seq data analysis. A typical
RNA-Seq analysis workflow consists these seven steps with each step
having some bioinformatics analysis to perform before going downstream
to the target objectives of the study.

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/577360/articles/619781-transcriptome-profiling-research-in-urothelial-cell-carcinoma}

\textbf{Table 1: List of platforms for RNA-Seq analysis}\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{S/N}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Software}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Platform}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Link (URL)}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Description}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\textbf{Reference}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
1\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Python, R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
UTAP\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
~\emph{https://utap.readthedocs.io}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
UTAP (User-friendly Transcriptome Analysis Pipeline) is an open source,
web-based intuitive and scalable transcriptome pipeline that executes
the full process, starting from sequences (RNA-Seq and bulk MARS-Seq),
ending with sets of differentially expressed genes and sophisticated
reports, and requiring minimal user expertise.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Kohen et al., 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
2\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Python\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
RASflow\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://github.com/zhxiaokang/RASflow}.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
RASflow (RNA-Seq Analysis Snakemake Workflow) is a lightweight and
easy-to-manage RNA-Seq analysis workflow. It includes the complete
workflow for RNA-Seq analysis, starting with QC of the raw FASTQ files,
going through optional trimming, alignment and feature counting (if the
reads are mapped to a genome), pseudo alignment (if transcriptome is
used as mapping reference), gene- or transcript- level DEA, and
visualization of the output from DEA.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Zhang and Jonassen, 2020.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
3\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
ARMOR\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
ARMOR (Automated Reproducible MOdular RNA-Seq) is a Snakemake workflow,
aimed at performing a typical RNA-seq workflow in a reproducible,
automated, and partially contained manner. It is implemented such that
alternative or similar analysis can be added or removed.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Orjuela et al., 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
4\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R, Python, Unix, C++, java, Perl\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
VIPER\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
~\emph{https://bitbucket.org/cfce/viper/}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
VIPER (Visualization Pipeline for RNA-Seq) is a comprehensive RNA-Seq
analysis tool built using snakemake which allows for ease of use,
optimal speed, and a highly modular code that can be further added onto
and customized by experienced users. VIPER combines the use of several
dozen RNA-Seq tools, suites, and packages to create a complete pipeline
that takes RNA-Seq analysis from raw sequencing data all the way through
alignment, quality control, unsupervised analysis, differential
expression, and downstream pathway analysis.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Cornwell et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
5\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
web\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Biojupies\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
http://biojupies.cloud.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
BioJupies is a web application that enables the automated creation,
storage, and deployment of Jupyter Notebooks containing RNA-Seq data
analyses. Through an intuitive interface, novice users can rapidly
generate tailored reports to analyse and visualize their own raw
sequencing files, gene expression tables, or fetch data from
\textgreater{}9,000 published studies containing \textgreater{}300,000
pre-processed RNA-Seq samples.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Torre et al., 2018.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
6\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Perl, R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
hppRNA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
https://sourceforge.net/projects/hpprna/.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
hppRNA package is dedicated to the RNA-Seq analysis for a large number
of samples simultaneously from the very beginning to the very end, which
is formulated in Snakemake pipeline management system. It starts from
fastq files and will produce gene/isoform expression matrix,
differentially-expressed-genes, sample clusters as well as detection of
SNP and fusion genes by combination of the state-of-the-art
software.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Wang, 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
7\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Python\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
RNA Cocktail\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{http://bioinform.github.io/rnacocktail/}.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
The RNACocktail pipeline is composed of high-accuracy tools for
different steps of RNA-Seq analysis. It performs a broad-spectrum
RNA-Seq analysis on both short- and long-read technologies to enable
meaningful insights from transcriptomic data. It was developed after
analysing a variety of RNA-Seq samples (ranging from germline, cancer to
stem cell datasets) and technologies using a multitude of tool
combinations to determine a pipeline which is comprehensive, fast and
accurate\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Sahraeian et al., 2017\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
8\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Python\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
aRNApipe\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://github.com/HudsonAlpha/aRNAPipe}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
aRNApipe is a project-oriented pipeline for processing of RNA-seq data
in high performance cluster environments. The provided framework is
highly modular and has been designed to be deployed on HPC environments
using IBM Platform LSF, although it can be easily migrated to any other
workload manager.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Alonso et al., 2017\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
9\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Python, R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
UTAP\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://utap.readthedocs.io}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
BISR-RNAseq (Bioinformatics Shared Resource Group-RNAseq) is a
consistent workflow that allows for the analysis (alignment, QC,
gene-wise counts generation) of raw RNAseq data and seamless integration
of quality analysis and differential expression results into a
configurable R shiny web application.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Kohen et al., 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
10\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Python, R, Shell\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
RASflow\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://github.com/zhxiaokang/RASflow}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
TRAPLINE (Transparent, Reproducible and Automated PipeLINE) supports
NGS-based research by providing a workflow that requires no
bioinformatics skills, decreases the processing time of the analysis,
and works in the cloud\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Zhang and Jonassen, 2020\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
11\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Python, R \& Bash\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
BISR-RNAseq\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://github.com/MPiet11/BISR-RNAseq}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
The docker4seq package was developed to facilitate the use of computing
demanding applications in the field of NGS data analysis. It uses docker
containers that embed demanding computing tasks (e.g., short reads
mapping) into isolated containers.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Gadepalli et al., 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
12\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
web\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
TRAPLINE\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://usegalaxy.org/u/mwolfien/p/trapline---manual}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
QuickRNASeq is an open-source based pipeline for large scale RNA-Seq
data analysis. It takes advantage of parallel computing resources, a
careful selection of previously published algorithms for RNA-Seq read
mapping, counting and quality control, and a three-stage strategy to
build a fully automated workflow.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Wolfien et al., 2016\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
13\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Docker4seq\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{http://reproducible-bioinformatics.org}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
IRIS-EDA (Interactive RNA-Seq Interpretation System for Expression Data
Analysis) provides a user-friendly interactive platform to analyse gene
expression data comprehensively and to generate interactive summary
visualizations readily. In contrast to other analysis platforms,
IRIS-EDA provides the user with a more comprehensive and multi-level
analysis platform.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Kulkarn et al., 2018.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
14\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Bash scripting, Perl, R, JavaScript\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
QuickRNAseq\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{http://quickrnaseq.sourceforge.net}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
bcbioRNASeq is a Bioconductor package that provides ready-to-render
templates, objects and wrapper functions to post-process bcbio RNA
sequencing output data. It helps automate the generation of high-level
RNA-Seq reports, facilitating the quality control analyses,
identification of differentially expressed genes and functional
enrichment analyses.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
He et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
15\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
web\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
IRIS-EDA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
~\emph{http://bmbl.sdstate.edu/IRIS/}.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
START (Shiny Transcriptome Analysis Resource Tool) provides researchers
with increased flexibility to easily upload and visualize RNA-Seq data.
The App visualizes data in multiple ways that will be useful for
scientists to understand their data. Critical to facilitating data
sharing capabilities, the App can be utilized within a web browser
environment for easy access as well as enabling seamless sharing of data
between collaborators.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Monier et al., 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
16\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
bcbioRNASeq\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://github.com/hbc/bcbioRNASeq}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
iDEP (integrated Differential Expression and Pathway analysis) enables
users to conduct in-depth bioinformatics analysis of transcriptomic data
through a GUI. The two use cases demonstrated that it can help pinpoint
molecular pathways from large genomic datasets, thus eliminating some
barriers for modern biologists.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Steinbaugh et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
17\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
START\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://kcvi.shinyapps.io/START}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
DEApp (Differential Expression App) interactive and dynamic web
application for differential expression analysis of count based NGS
data. It enables models selection, parameter tuning, cross validation
and visualization of results in a user-friendly interface.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Nelson et al., 2017\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
18\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
iDEP\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{http://ge-lab.org/idep/}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
GENAVi (Gene Expression Normalization Analysis and Visualization)
provides a user-friendly interface for normalization and differential
expression analysis (DEA) of human or mouse feature count level RNA-Seq
data. It is a GUI based tool that combines Bioconductor packages in a
format for scientists without bioinformatics expertise.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Ge et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
19\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
DEApp\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://yanli.shinyapps.io/DEApp}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
TCC-GUI (Graphical User Interface for TCC) is a browser-based
application for DE analysis of RNA-Seq data. It enables non-R users to
perform the TCC package without installation. In addition to the
functionalities originally implemented in TCC, TCC-GUI provides plenty
of interactive visualization functions. The powerful in-built functions
would also be satisfactory for experienced R users.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Li and Andrade, 2017\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
20\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
GENAVi\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
\emph{https://junkdnalab.shinyapps.io/GENAVi/}\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
BEAVR (Browser-based tool for the Exploration and Visualization of
RNA-Seq data) is an easy-to-use tool that facilitates interactive
analysis and exploration of RNA-Seq data. It is developed in R and uses
DESeq2 as its engine for differential gene expression (DGE) analysis,
but assumes users have no prior knowledge of R or DESeq2. BEAVR allows
researchers to easily obtain a table of differentially expressed genes
with statistical testing and then visualize the results in a series of
graphs, plots and heatmaps.\strut
\end{minipage} & \begin{minipage}[t]{0.16\columnwidth}\raggedright\strut
Rayes et al., 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
21\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Web, R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
TCC-GUI\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://infinityloop.shinyapps.io/TCC-GUI/}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
iSeq, an R-based Web server, for RNA-Seq data analysis and
visualization. iSeq is a streamlined Web-based R application under the
Shiny framework, featuring a simple user interface and multiple data
analysis modules. Users without programming and statistical skills can
analyse their RNA-Seq data and construct publication-level graphs
through a standardized yet customizable analytical pipeline.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Su et al., 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
22\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
BEAVR\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://github.com/developerpiru/BEAVR}~and~\emph{https://hub.docker.com/r/pirunthan/beavr}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
DaMiRseq package is a structured and convenient workflow to effectively
identify transcriptional biomarkers and exploit them for classification
purposes.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Perampalam and Dick,2020\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
23\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
iSeq\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
http://iseq.cbi.pku.edu.cn.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
DEsubs is a network-based systems biology R package that extracts
disease-perturbed sub pathways within a pathway network as recorded by
RNA-Seq experiments. It contains an extensive and customized framework
with a broad range of operation modes at all stages of the sub pathway
analysis, enabling a case-specific approach.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Zhang et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
24\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
DaMiRseq\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://bioconductor.org/packages/DaMiRseq/}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
CANEapp (Comprehensive automated Analysis of Next-generation sequencing
Experiments App) is a unique suite that combines a Graphical User
Interface (GUI) and an automated server-side analysis pipeline that is
platform-independent, making it suitable for any server
architecture.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Chiesa et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
25\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
DEsubs\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{http://bioconductor.org/packages/DEsubs/}.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
DiCoExpress is an R script-based tool allowing users to perform a full
RNA-Seq analysis from quality controls to co-expression analysis through
differential analysis based on contrasts inside generalized linear
models. DiCoExpress focuses on the statistical modelling of gene
expression according to the experimental design and facilitates the data
analysis leading the biological interpretation of the results.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Vrahatis et al., 2016\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
26\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Python, Java\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
CANEapp\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{http://psychiatry.med.miami.edu/research/laboratory-of-translational-rna-genomics/CANE-app}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
IRIS-DGE (Integrated RNA-Seq Data Analysis and Interpretation System for
Differential Gene Expression) is a server-based DGE analysis tool
developed using Shiny. It provides a straightforward, user-friendly
platform for performing comprehensive DGE analysis, and crucial analyses
that help design hypotheses and to determine key genomic features\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Velmeshev et al., 2016\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
27\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
DicoExpress\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
~\emph{https://forgemia.inra.fr/GNet/dicoexpress}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
SPARTA (Simple Program for Automated reference-based bacterial RNA-Seq
Transcriptome Analysis) is a reference-based bacterial RNA-Seq analysis
workflow application for single-end Illumina reads. SPARTA is turnkey
software that simplifies the process of analysing RNA-Seq data sets,
making bacterial RNA-Seq analysis a routine process that can be
undertaken on a personal computer or in the classroom.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Lambert et al., 2020.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
28\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
IRIS-DGE\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
http://bmbl.sdstate.edu/IRIS/\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
RAP (RNA-Seq Analysis Pipeline) is a web application implementing a
fully automated analysis workflow, designed to integrate in-house
developed scripts as well as open-source analysis tools into one
pipeline.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Monier et al., 2018.\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
29\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Python\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
SPARTA\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{http://sparta.readthedocs.org}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Shiny-Seq provides a guided and easy to use comprehensive RNA-Seq data
analysis pipeline. It has many features such as batch effect estimation
and removal, quality check with several visualization options,
enrichment analysis with multiple biological databases, identification
of patterns using advanced methods such as weighted gene co-expression
network analysis, summarizing analysis as PowerPoint presentation and
all results as tables via a one-click feature\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Johnson et al., 2016\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
30\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
web\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
RAP\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
~\emph{http://bioinformatics.cineca.it/rap/}.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
The Cancer Genome Atlas (TCGA) is a large-scale study that has
catalogued genomic data accumulated for many different types of cancers,
and includes mutations, copy number variation, mRNA and miRNA gene
expression, and DNA methylation. Being publicly distributed, it has
become a major resource for cancer researchers in target discovery and
in the biological interpretation and assessment of the clinical impact
of genes of interest.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
D'Antonio et al., 2015\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
31\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Shiny-seq\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
~\emph{https://szenitha.shinyapps.io/shiny-seq3/}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
A multitude of large-scale studies, e.g., TCGA and GTEx, have recently
generated an unprecedented volume of RNA-Seq data. The RNA-Seq
expression data from different studies typically are not directly
comparable, due to differences in sample and data processing and other
batch effects. Here, we developed a pipeline that processes and unifies
RNA-Seq data from different studies. Using the pipeline, we have
processed data from the GTEx and TCGA and have successfully corrected
for study-specific biases, allowing comparative analysis across
studies.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Sundararajan et al., 2019\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
32\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
TCGA RNA seq\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://github.com/srp33/TCGA\_RNASeq\_Clinical}.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
The Toil RNA-Seq workflow converts RNA sequencing data into gene- and
transcript-level expression quantification.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Mumtahena et al., 2015\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
33\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
R\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
RNAseqDB\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
\emph{https://github.com/mskcc/RNAseqDB/}\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
BioJupies is a web application that enables the automated creation,
storage, and deployment of Jupyter Notebooks containing RNA-Seq data
analyses. Through an intuitive interface, novice users can rapidly
generate tailored reports to analyse and visualize their own raw
sequencing files, gene expression tables, or fetch data from
\textgreater{}9,000 published studies containing \textgreater{}300,000
pre-processed RNA-Seq samples.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Wang et al., 2018\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
34\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
python\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
ToilRNA seq\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
https://github.com/BD2KGenomics/toil-rnaseq\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
hppRNA package is dedicated to the RNA-Seq analysis for a large number
of samples simultaneously from the very beginning to the very end, which
is formulated in Snakemake pipeline management system. It starts from
fastq files and will produce gene/isoform expression matrix,
differentially-expressed-genes, sample clusters as well as detection of
SNP and fusion genes by combination of the state-of-the-art
software.\strut
\end{minipage} & \begin{minipage}[t]{0.14\columnwidth}\raggedright\strut
Vivian et al., 2017\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}





\textbf{Table 2: Summary of the current RNA-Seq studies on bladder
cancer}\selectlanguage{english}
\begin{longtable}[]{@{}lllllllllll@{}}
\toprule
\begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{S/N}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{Sample}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{Source}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{Grade}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{Library Preparation}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{Library Selection}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{Library layout}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{Sequencing} \textbf{Platform}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{Instrument Model}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{Average Mean reads Pair-end length (bp)}\strut
\end{minipage} & \begin{minipage}[t]{0.09\columnwidth}\raggedright\strut
\textbf{Reference}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
1.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Tissue\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
UCC\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Ta/T1/T2-4/CIS\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
ScriptSeq\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
cDNA\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Paired-end\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina HiSeq 2000\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
101 + 7 + 101\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Hedegaard et al. (2016)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
2.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Tissue\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
UCC\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Ribo-Zero Magnetic Gold Kit (Illumina, USA) and the NEBNext\selectlanguage{ngerman}® Ultra\selectlanguage{english} RNA
Library Prep Ki\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
cDNA\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Paired-end\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
HiSeqX\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Li et al. (2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
3.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Tissue\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
UCC\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
TruSeq Stranded mRNA Lib Prep Kit\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
cDNA\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Paired-end\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
NextSeq 500\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
72\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Maeda et al. (2018)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
4.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Cell\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Urine\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Ovation RNA Seq System V2 kit\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
cDNA\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Paired-end/ Single\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina HiSeq 2000\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Sin et al. (2017)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
5.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Cell\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
TruSeq Stranded Total RNA kit\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
cDNA\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina HiSeq 2500\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Shen et al. (2015)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
6.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Tissue\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
RiboZero rRNA Removal Kit\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
cDNA\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
HiSeq2000\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Li et al. (2017)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
7.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Tissue\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
UCC\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
TruSeq\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
cDNA\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Pair-end\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Genome Analyzer IIx (GAIIx)\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
380\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Zhang et al (2014)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
8.\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Tissue/cell\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
UC\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
T1, T2--T4a, N0--3, M0--1\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
DNA/RNA AllPrep kit (QIAGEN)/ mirVana miRNA isolation kit\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
cDNA, miRNA\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
N/A\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Illumina HiSeq 2500\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
76\strut
\end{minipage} & \begin{minipage}[t]{0.07\columnwidth}\raggedright\strut
Robertson etal (2017)\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

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