S/N |
Software |
Platform |
Link
(URL) |
Description |
Reference |
1 |
Python, R |
UTAP |
https://utap.readthedocs.io
|
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. |
Kohen et
al., 2019 |
2 |
Python |
RASflow |
https://github.com/zhxiaokang/RASflow. |
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. |
Zhang and Jonassen,
2020. |
3 |
R |
ARMOR |
|
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. |
Orjuela et al., 2019 |
4 |
R, Python, Unix, C++, java, Perl |
VIPER |
https://bitbucket.org/cfce/viper/
|
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. |
Cornwell et al.,
2018 |
5 |
web |
Biojupies |
http://biojupies.cloud. |
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 >9,000 published studies
containing >300,000 pre-processed RNA-Seq samples. |
Torre
et al., 2018. |
6 |
Perl, R |
hppRNA |
https://sourceforge.net/projects/hpprna/. |
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. |
Wang, 2018 |
7 |
Python |
RNA Cocktail |
http://bioinform.github.io/rnacocktail/. |
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 |
Sahraeian et al., 2017 |
8 |
Python |
aRNApipe |
https://github.com/HudsonAlpha/aRNAPipe |
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. |
Alonso et al., 2017 |
9 |
Python, R |
UTAP |
https://utap.readthedocs.io |
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. |
Kohen et al.,
2019 |
10 |
Python, R, Shell |
RASflow |
https://github.com/zhxiaokang/RASflow |
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 |
Zhang and Jonassen, 2020 |
11 |
Python, R & Bash |
BISR-RNAseq |
https://github.com/MPiet11/BISR-RNAseq |
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. |
Gadepalli et al.,
2019 |
12 |
web |
TRAPLINE |
https://usegalaxy.org/u/mwolfien/p/trapline—manual |
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. |
Wolfien et al., 2016 |
13 |
R |
Docker4seq |
http://reproducible-bioinformatics.org |
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. |
Kulkarn et al., 2018. |
14 |
Bash scripting, Perl, R, JavaScript |
QuickRNAseq |
http://quickrnaseq.sourceforge.net |
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. |
He et al., 2018 |
15 |
web |
IRIS-EDA |
http://bmbl.sdstate.edu/IRIS/. |
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. |
Monier et al., 2019 |
16 |
R |
bcbioRNASeq |
https://github.com/hbc/bcbioRNASeq |
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. |
Steinbaugh et al., 2018 |
17 |
R |
START |
https://kcvi.shinyapps.io/START |
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. |
Nelson et
al., 2017 |
18 |
R |
iDEP |
http://ge-lab.org/idep/ |
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. |
Ge et al.,
2018 |
19 |
R |
DEApp |
https://yanli.shinyapps.io/DEApp |
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. |
Li and Andrade,
2017 |
20
|
R
|
GENAVi
|
https://junkdnalab.shinyapps.io/GENAVi/
|
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.
|
Rayes et al., 2019
|
21 |
Web, R |
TCC-GUI |
https://infinityloop.shinyapps.io/TCC-GUI/ |
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. |
Su et
al., 2019 |
22 |
R |
BEAVR |
https://github.com/developerpiru/BEAVR and https://hub.docker.com/r/pirunthan/beavr
|
DaMiRseq package is a structured and convenient workflow to
effectively identify transcriptional biomarkers and exploit them for
classification purposes. |
Perampalam and Dick,2020 |
23 |
R |
iSeq |
http://iseq.cbi.pku.edu.cn. |
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. |
Zhang et al.,
2018 |
24 |
R |
DaMiRseq |
https://bioconductor.org/packages/DaMiRseq/ |
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. |
Chiesa et al., 2018 |
25 |
R |
DEsubs |
http://bioconductor.org/packages/DEsubs/. |
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. |
Vrahatis et al., 2016 |
26 |
Python, Java |
CANEapp |
http://psychiatry.med.miami.edu/research/laboratory-of-translational-rna-genomics/CANE-app |
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 |
Velmeshev et al., 2016 |
27 |
R |
DicoExpress |
https://forgemia.inra.fr/GNet/dicoexpress
|
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. |
Lambert et al.,
2020. |
28 |
R |
IRIS-DGE |
http://bmbl.sdstate.edu/IRIS/ |
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. |
Monier et al.,
2018. |
29 |
Python |
SPARTA |
http://sparta.readthedocs.org |
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 |
Johnson et al.,
2016 |
30 |
web |
RAP |
http://bioinformatics.cineca.it/rap/. |
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. |
D’Antonio et al., 2015 |
31 |
R |
Shiny-seq |
https://szenitha.shinyapps.io/shiny-seq3/
|
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. |
Sundararajan et al., 2019 |
32 |
R |
TCGA RNA seq |
https://github.com/srp33/TCGA_RNASeq_Clinical. |
The Toil RNA-Seq workflow converts RNA sequencing data into gene- and
transcript-level expression quantification. |
Mumtahena et al.,
2015 |
33 |
R |
RNAseqDB |
https://github.com/mskcc/RNAseqDB/ |
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
>9,000 published studies containing >300,000
pre-processed RNA-Seq samples. |
Wang et al., 2018 |
34 |
python |
ToilRNA seq |
https://github.com/BD2KGenomics/toil-rnaseq |
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. |
Vivian et al., 2017 |