RUM 2.0.5_06 – Comparative Analysis of RNA-Seq Alignment Algorithms and the RNA-Seq Unified Mapper

RUM 2.0.5_06

:: DESCRIPTION

RUM (RNA-Seq Unified Mapper) is an alignment, junction calling, and feature quantification pipeline specifically designed for Illumina RNA-Seq data.

::DEVELOPER

the Computational Biology and Informatics Laboratory at the University of Pennsylvania

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux/ MacOsX
Perl

:: DOWNLOAD

RUM

:: MORE INFORMATION

Citation:

Gregory R. Grant, Michael H. Farkas, Angel Pizarro, Nicholas Lahens, Jonathan Schug, Brian Brunk, Christian J. Stoeckert Jr, John B. Hogenesch and Eric A. Pierce.
Comparative Analysis of RNA-Seq Alignment Algorithms and the RNA-Seq Unified Mapper (RUM)
Bioinformatics. 2011 Sep 15;27(18):2518-28. Epub 2011 Jul 19.

RNASeqPowerCalculator – Calculate the Power and Sample size for RNA-Seq Differential Expression

RNASeqPowerCalculator

:: DESCRIPTION

RNASeqPowerCalculator captures the dispersion in the data and can serve as a practical reference under the budget constraint of RNA-Seq experiments.

::DEVELOPER

Lana Garmire Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows
R

:: DOWNLOAD

RNASeqPowerCalculator

:: MORE INFORMATION

Citation

RNA. 2014 Nov;20(11):1684-96. doi: 10.1261/rna.046011.114. Epub 2014 Sep 22.
Power analysis and sample size estimation for RNA-Seq differential expression.
Ching T, Huang S, Garmire LX

StringTie 2.0.3 – Transcript Assembly and Quantification for RNA-Seq

StringTie 2.0.3

:: DESCRIPTION

StringTie is a fast and highly efficient assembler of RNA-Seq alignments into potential transcripts. It uses a novel network flow algorithm as well as an optional de novo assembly step to assemble and quantitate full-length transcripts representing multiple splice variants for each gene locus.

::DEVELOPER

The Center for Computational Biology at Johns Hopkins University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Mac OsX

:: DOWNLOAD

StringTie

:: MORE INFORMATION

Citation

Nat Biotechnol. 2015 Mar;33(3):290-5. doi: 10.1038/nbt.3122. Epub 2015 Feb 18.
StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.
Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL

isomiR-SEA 1.60 – RNA-Seq analysis tool

isomiR-SEA 1.60

:: DESCRIPTION

isomiR-SEA is the first tool implemented in order to perform reads alignment on miRNAs databases by considering the miRNA:mRNA interaction pairing aspects.

::DEVELOPER

isomiR-SEA team

:: SCREENSHOTS

N/A

::REQUIREMENTS

Linux / Windows/ MacOsX
C++ Compiler

:: DOWNLOAD

isomiR-SEA

:: MORE INFORMATION

Citation

isomiR-SEA: an RNA-Seq analysis tool for miRNAs/isomiRs expression level profiling and miRNA-mRNA interaction sites evaluation.
Urgese G, Paciello G, Acquaviva A, Ficarra E.
BMC Bioinformatics. 2016 Mar 31;17(1):148. doi: 10.1186/s12859-016-0958-0

BinPacker 1.1 – Packing-Based De Novo Transcriptome Assembly from RNA-seq Data

BinPacker 1.1

:: DESCRIPTION

BinPacker is a novel de novo assembler by modeling the transcriptome assembly problem as tracking a set of trajectories of items with their sizes representing coverage of their corresponding isoforms by solving a series of bin-packing problems.

::DEVELOPER

BinPacker team

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

BinPacker

:: MORE INFORMATION

Citation

BinPacker: Packing-Based De Novo Transcriptome Assembly from RNA-seq Data.
Liu J, Li G, Chang Z, Yu T, Liu B, McMullen R, Chen P, Huang X.
PLoS Comput Biol. 2016 Feb 19;12(2):e1004772. doi: 10.1371/journal.pcbi.1004772.

SpliceJumper – Splicing Junction Calling from RNA-Seq data

SpliceJumper

:: DESCRIPTION

SpliceJumper is a classification based approach for calling splicing junctions from RNA-seq data

::DEVELOPER

Simon C Chu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux

:: DOWNLOAD

SpliceJumper

:: MORE INFORMATION

Citation:

BMC Bioinformatics. 2015;16 Suppl 17:S10. doi: 10.1186/1471-2105-16-S17-S10. Epub 2015 Dec 7.
SpliceJumper: a classification-based approach for calling splicing junctions from RNA-seq data.
Chu C, Li X, Wu Y.

SimSeq 1.4.0 – Nonparametric Simulation of RNA-Seq Data

SimSeq 1.4.0

:: DESCRIPTION

SimSeq a data-based simulation algorithm for RNA-seq data. The vector of read counts simulated for a given experimental unit has a joint distribution that closely matches the distribution of a source RNA-seq dataset provided by the user.

::DEVELOPER

Samuel Benidt <sbenidt at iastate.edu>

:: SCREENSHOTS

N/a

:: REQUIREMENTS

Windows / Linux
R

:: DOWNLOAD

SimSeq

:: MORE INFORMATION

Citation

SimSeq: A Nonparametric Approach to Simulation of RNA-Sequence Datasets.
Benidt S, Nettleton D.
Bioinformatics. 2015 Feb 26. pii: btv124.

ASARP 0.9 – Identification of Allele-Specific Alternative mRNA Processing in RNA-Seq data

ASARP 0.9

:: DESCRIPTION

ASARP is a pipeline for accurate identification of allele-specific alternative mRNA processing

::DEVELOPER

XIAO LAB

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux
Perl

:: DOWNLOAD

ASARP

:: MORE INFORMATION

Citation

Nucleic Acids Res. 2012 Jul;40(13):e104. doi: 10.1093/nar/gks280. Epub 2012 Mar 29.
Identification of allele-specific alternative mRNA processing via transcriptome sequencing.
Li G1, Bahn JH, Lee JH, Peng G, Chen Z, Nelson SF, Xiao X.

GSAA 2.0 / GSAA-SNP / GSAA-Seq – Gene Set Association Analysis / Analysis-SNP / for RNA-Seq

GSAA 2.0 / GSAA-SNP / GSAA-Seq

:: DESCRIPTION

GSAA (Gene Set Association Analysis) is a computational method that integrates gene expression analysis with genome wide association studies to determine whether an a priori defined sets of genes shows statistically significant, concordant differences with respect to gene expression profiles and genotypes between two biological states. Gene sets are generally a group of genes that are putatively functionally related, co-regulated, or tightly linked on the same chromosome.

GSAA-SNP (Gene Set Association Analysis-SNP) is a computational method that determines whether an a priori defined sets of genes shows statistically significant, concordant differences with respect to genotypes between two biological states. Gene sets are generally a group of genes that are putatively functionally related, co-regulated, or tightly linked on the same chromosome.

GSAA-Seq (Gene Set Association Analysis for RNA-Seq) is a computational method that evaluates whether an a priori defined sets of genes shows statistically significant, concordant differences with respect to RNA-Seq gene expression profiles between two biological states. Gene sets are generally a group of genes that are putatively functionally related, co-regulated, or tightly linked on the same chromosome.

::DEVELOPER

Furey Lab at The University of North Carolina at Chapel Hill and Mukherjee Lab at Duke University.

:: SCREENSHOTS

:: REQUIREMENTS

Windows/Linux/MacOsX
Java

:: DOWNLOAD

GSAA / GSAA-SNP / GSAA-Seq

:: MORE INFORMATION

Citation

GSAASeqSP: a toolset for gene set association analysis of RNA-Seq data.
Xiong Q, Mukherjee S, Furey TS.
Sci Rep. 2014 Sep 12;4:6347. doi: 10.1038/srep06347.

Genome Res. 2012 Feb;22(2):386-97. doi: 10.1101/gr.124370.111. Epub 2011 Sep 22.
Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets.
Xiong Q, Ancona N, Hauser ER, Mukherjee S, Furey TS.

contamDE 1.0 – Differential Expression analysis of RNA-seq data for Contaminated Tumor Samples

contamDE 1.0

:: DESCRIPTION

The R package ‘contamDE’ conducts differential expression (DE) analysis using high throughput next-generation RNA-seq read count data generated from contaminated tumor samples that are either matched or unmatched with normal samples, which estimates the proportion of pure tumor cells in each contaminated tumor sample, and provides tumor vs.

::DEVELOPER

Hong Zhang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

Linux / Windows/ MacOsX
R

:: DOWNLOAD

contamDE

:: MORE INFORMATION

Citation

contamDE: Differential expression analysis of RNA-seq data for contaminated tumor samples.
Shen Q, Hu J, Jiang N, Hu X, Luo Z, Zhang H.
Bioinformatics. 2015 Nov 9. pii: btv657.