Apr

17

SeqGSEA – Gene Set Enrichment Analysis of RNA-Seq Data

Filed Under Pathway Analysis | Leave a Comment

SeqGSEATranscriptome sequencing (RNA-Seq) has become a key technology in transcriptome studies because it can quantify overall expression levels and the degree of alternative splicing for each gene simultaneously. Many methods and tools, including quite a few R / Bioconductor packages, have been developed to deal with RNA-Seq data for differential expression analysis and thereafter functional analysis aiming at novel biological and biomedical discoveries. However, those tools mainly focus on each gene’s overall expression and may miss the opportunities for discoveries regarding alternative splicing or the combination of the two.

SeqGSEA is novel R / Bioconductor package to derive biological insight by integrating differential expression (DE) and differential splicing (DS) from RNA-Seq data with functional gene set analysis. Due to the digital feature of RNA-Seq count data, the package utilizes negative binomial distributions for statistical modeling to first score differential expression and splicing in each gene, respectively.

Then, integration strategies are applied to combine the two scores for integrated gene set enrichment analysis. See the publication Wang and Cairns (2013) for more details. The SeqGSEA package can also give detection results of differentially expressed genes and differentially spliced genes based on sample label permutation.

Availability – The package can be accessed at the URL: http://bioconductor.org/packages/rel…l/SeqGSEA.html

Documentation – http://bioconductor.org/packages/release/bioc/vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf

Questions, comments, or suggestions -  xi.wang@newcastle.edu.au

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Mar

18

Methods to study Event/Isoform Expression and Alternative Splicing from RNA-Seq

Filed Under Analysis Pipelines, Expression and Quantification, Other Tools, Pathway Analysis, Splicing and Junction Mapping, Transcriptome Assembly Tools, Unspliced Mapping Tools | Leave a Comment

The RegulatoryGenomics website posts and updates a comprehensive list of tools for RNA-Seq analysis.

This is their current version.

Spliced-mappers

Method

Reference

Web-site

TopHap

(Trapnell et al. 2009)

http://tophat.cbcb.umd.edu/

MapSplice

(Wang et al. 2010)

http://www.netlab.uky.edu/p/bioinfo/MapSplice

SpliceMap

(Auger et al. 2010)

http://www.stanford.edu/group/wonglab/SpliceMap/

HMMSplicer

(Dimon et al. 2010)

http://derisilab.ucsf.edu/index.php?software=105

TrueSight

(Li et al. 2012b)

http://bioen-compbio.bioen.illinois.edu/TrueSight/

SOAPsplice

(Huang et al. 2011)

http://soap.genomics.org.cn/soapsplice.html

PASSion

(Zhang et al. 2012)

https://trac.nbic.nl/passion

PALMapper

(Jean et al. 2010)

http://galaxy.raetschlab.org/

SplitSeek

(Ameur et al. 2010)

http://solidsoftwaretools.com/gf/project/splitseek

Supersplat

(Bryant et al. 2010)

http://mocklerlab-tools.cgrb.oregonstate.edu/

SeqSaw

(Wang et al. 2011)

http://bioinfo.au.tsinghua.edu.cn/software/seqsaw

MapNext

(Bao et al. 2009)

http://evolution.sysu.edu.cn/english/software/mapnext.htm

STAR

(Dobin et al. 2012)

http://gingeraslab.cshl.edu/STAR/

GSNAP

(Wu et al. 2010)

http://research-pub.gene.com/gmap/

QPALMA

(De Bona et al. 2008)

http://www.raetschlab.org/suppl/qpalma

OSA

(Hu et al. 2012)

http://omicsoft.com/osa/
  Read more

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Mar

1

Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods

Filed Under Other Tools, Pathway Analysis | Leave a Comment

Gene set analysis (GSA) is used to elucidate genome-wide data, in particular transcriptome data. A multitude of methods have been proposed for this step of the analysis, and many of them have been compared and evaluated. Unfortunately, there is no consolidated opinion regarding what methods should be preferred, and the variety of available GSA software and implementations pose a difficulty for the end-user who wants to try out different methods.

To address this, researchers at Chalmers University of Technology, Sweden have developed the R package Piano, that collects a range of GSA methods into the same system, for the benefit of the end-user. Further on they refine the GSA workflow by using modifications of the gene-level statistics. This enables them to divide the resulting gene set P-values into three classes, describing different aspects of gene expression directionality at gene set level.

Piano RNA-SeqThe researchers demonstrate their fully implemented workflow by investigating the impact of the individual components of GSA by using microarray and RNA-seq data. The results show that the evaluated methods are globally similar and the major separation correlates well with our defined directionality classes. As a consequence of this, they suggest to use a consensus scoring approach, based on multiple GSA runs. In combination with the directionality classes, this constitutes a more thorough basis for an enriched biological interpretation.

Availability – Piano is available, together with a user manual, for download at www.sysbio.se/piano.

  • Väremo L, Nielsen J, Nookaew I. (2013) Enriching the gene set analysis of genome-wide data by incorporating directionality of gene expression and combining statistical hypotheses and methods. Nucleic Acids Res [Epub ahead of print]. [article]

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Jan

28

GSVA – gene set variation analysis for microarray and RNA-Seq data

Filed Under Pathway Analysis | Leave a Comment

Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets.

To address this challenge, researchers at Hospital del Mar Medical Research Institute (IMIM),  Spain have developed Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. They demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise en-richment methods. Further, they provide examples of its utility in differential pathway activity and survival analysis. Lastly, the researchers show how GSVA works analogously with data from both microarray and RNA-seq experiments.

GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA

Availability – GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org/packages/release/bioc/html/GSVA.html.

  • Hänzelmann S, Castelo R, Guinney J. (2013) GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14(1), 7. [abstract]

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