TopHat 2.1.1 – Short Read Aligner for RNA-Seq Experiments

TopHat 2.1.1

:: DESCRIPTION

TopHat is a fast splice junction mapper for RNA-Seq reads. It aligns RNA-Seq reads to mammalian-sized genomes using the ultra high-throughput short read aligner Bowtie, and then analyzes the mapping results to identify splice junctions between exons.

::DEVELOPER

The Center for Computational Biology at Johns Hopkins University

:: SCREENSHOTS

N/A

:: REQUIREMENTS

:: DOWNLOAD

TopHat

:: MORE INFORMATION

Citation

Trapnell C, Pachter L, Salzberg SL.
TopHat: discovering splice junctions with RNA-Seq.
Bioinformatics (2009) 25(9): 1105-1111.

Genome Biology 2013, 14:R36
TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions
Daehwan Kim, Geo Pertea, Cole Trapnell, Harold Pimentel, Ryan Kelley and Steven L Salzberg

Bowtie 1.3.0 / Bowtie2 2.4.4 – Ultrafast Memory-efficient Short Read Aligner

Bowtie 1.3.0 / Bowtie2 2.4.4

:: DESCRIPTION

Bowtie is an ultrafast, memory-efficient short read aligner. For the human genome, Burrows-Wheeler indexing allows Bowtie to align more than 25 million reads per CPU hour with a memory footprint of approximately 1.3 gigabytes. Bowtie extends previous Burrows-Wheeler techniques with a novel quality-aware backtracking algorithm that permits mismatches. Multiple processor cores can be used simultaneously to achieve even greater alignment speeds.

Bowtie 2 is an ultrafast and memory-efficient tool for aligning sequencing reads to long reference sequences. It is particularly good at aligning reads of about 50 up to 100s or 1,000s of characters, and particularly good at aligning to relatively long (e.g. mammalian) genomes.

::DEVELOPER

the Center for Bioinformatics and Computational Biology

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows /Mac OsX

:: DOWNLOAD

Bowtie / Bowtie2

:: MORE INFORMATION

Citation

Langmead B, Trapnell C, Pop M, Salzberg SL.|
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Genome Biology 10:R25.

SGS v2 – Analysis of Spatial Genetic and Phenotypic Structures of Individuals and Population

SGS v2

:: DESCRIPTION

SGS (Spatial Genetic Software) is a computer program for analysis of spatial genetic and phenotypic structures of individuals and populations

::DEVELOPER

Bernd Degen

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows

:: DOWNLOAD

 SGS

:: MORE INFORMATION

Citation

Degen, Petit, Kremer (2001),
SGS – Spatial Genetic Software: a computer program for analysis of spatial genetic and phenotypic structures of individuals and populations”,
Journal of Heredity, 92(5):447-448.

Genome Workbench 3.6.0 – View & Analyze Sequence Data

Genome Workbench 3.6.0

:: DESCRIPTION

Gbench (NCBI Genome Workbench) is an integrated application for viewing and analyzing sequence data. With Genome Workbench, you can view data in publically available sequence databases at NCBI, and mix this data with your own private data.Genome Workbench can display sequence data in many ways, including graphical sequence views, various alignment views, phylogenetic tree views, and tabular views of data. It can also align your private data to data in public databases, display your data in the context of public data, and retrieve BLAST results.

::DEVELOPER

Genome Workbench Team

:: SCREENSHOTS

:: REQUIREMENTS

  • Linux / Windows / Mac OsX

:: DOWNLOAD

Genome Workbench

:: MORE INFORMATION

SiGPAT 0.1 – Finding significant Expression Patterns of Gene Set

SiGPAT 0.1

:: DESCRIPTION

SiGPAT is a useful tool based on gene set analysis for microarray data. It can find significant expression patterns of gene sets by pri-defined biological knowledge. To be unique to other tools, SiGPAT assignes two statistics for each gene sets and classifies expression patterns of gene sets form two dimension distribution of set-level statistics. The tool was evaluated with better performance than current tools such as GSEA and SAM-GS

::DEVELOPER

Zuguang Gu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package

:: DOWNLOAD

  SiGPAT

:: MORE INFORMATION

CePa 0.7.0 – Centrality-based Pathway Enrichment

CePa 0.7.0

:: DESCRIPTION

CePa is a useful tool to find significant pathways from pathway structure information. The motivation of the software is that the traditional over-representation analysis (ORA) methods find significant pathways without the topological information. The tool also emphasizes that multiple choices to look for important nodes in network is necessary.

::DEVELOPER

Zuguang Gu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package

:: DOWNLOAD

 CePa

:: MORE INFORMATION

Citation

CePa: an R package for finding significant pathways weighted by multiple network centralities.
Gu Z, Wang J.
Bioinformatics. 2013 Mar 1;29(5):658-60. doi: 10.1093/bioinformatics/btt008. Epub 2013 Jan 10.

ChIP-PED 0.99.2 – Predict Biological Contexts in which a TF is Functionally Active

ChIP-PED 0.99.2

:: DESCRIPTION

ChIP-PED is designed to enhance the analysis of ChIP-chip and ChIP-seq (ChIPx) data. Given the target genes of a TF in one or more cell types, ChIP-PED searches for new biological systems potentially enriched with regulatory activity of the TF by superimposing ChIPx data on large amounts of Publicly available human and mouse gene Expression Data from a diverse collection of biological systems.

::DEVELOPER

George Wu

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows/Linux/MacOsX
  • R package

:: DOWNLOAD

 ChIP-PED

:: MORE INFORMATION

Citation

Bioinformatics. 2013 May 1;29(9):1182-9. doi: 10.1093/bioinformatics/btt108. Epub 2013 Mar 1.
ChIP-PED enhances the analysis of ChIP-seq and ChIP-chip data.
Wu G, Yustein JT, McCall MN, Zilliox M, Irizarry RA, Zeller K, Dang CV, Ji H.

SC2P 1.0 – Two-phase differential Expression for single-cell RNA-seq

SC2P 1.0

:: DESCRIPTION

SC2P is a package designed for testing differential expression (DE) for data from single-cell RNA-seq experiment.

::DEVELOPER

Hao Wu, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows /Linux/ MacOsX
  • R

:: DOWNLOAD

SC2P

:: MORE INFORMATION

Citation

Wu Z, Zhang Y, Stitzel ML, Wu H.
Two-phase differential expression analysis for single cell RNA-seq.
Bioinformatics. 2018 Oct 1;34(19):3340-3348. doi: 10.1093/bioinformatics/bty329. PMID: 29688282; PMCID: PMC6157076.

POWSC – Power Evaluation and Sample Size Estimation in single cell RNA-seq.

POWSC

:: DESCRIPTION

POWSC is a R package designed for scRNA-seq with a wild range of usage.

::DEVELOPER

Hao Wu, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows /Linux/ MacOsX
  • R

:: DOWNLOAD

POWSC

:: MORE INFORMATION

Citation

Su K, Wu Z, Wu H.
Simulation, power evaluation and sample size recommendation for single-cell RNA-seq.
Bioinformatics. 2020 Dec 8;36(19):4860-4868. doi: 10.1093/bioinformatics/btaa607. PMID: 32614380; PMCID: PMC7824866.

FEAST – Feature selection for scRNA-seq Clustering

FEAST

:: DESCRIPTION

FEAST is a framework designed for ranking features and selecting an optimized feature set as an input for scRNA-seq clustering.

::DEVELOPER

Hao Wu, Ph.D.

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Windows /Linux/ MacOsX
  • R

:: DOWNLOAD

FEAST

:: MORE INFORMATION

Citation

Su K, Yu T, Wu H.
Accurate feature selection improves single-cell RNA-seq cell clustering.
Brief Bioinform. 2021 Feb 22:bbab034. doi: 10.1093/bib/bbab034. Epub ahead of print. PMID: 33611426.