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PennSeq (RRID:SCR_001763)


http://sourceforge.net/projects/pennseq/

Software for isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution. Instead of making parametric assumptions, they give adequate weight to the underlying data by the use of a non-parametric approach. The rationale is that regardless what factors lead to non-uniformity, whether it is due to hexamer priming bias, local sequence bias, positional bias, RNA degradation, mapping bias or other unknown reasons, the probability that a fragment is sampled from a particular region will be reflected in the aligned data. This empirical approach thus maximally reflects the true underlying non-uniform read distribution.


Keywords

isoform, gene expression, rna-seq

Resource ID

SCR_001763

Alternate IDs

OMICS_01946

Website Status

Last checked up

Parent Organization

SourceForge

Abbreviation(s)

PennSeq

Resource Type

Resource, software resource

Availability

Free, Public

Proper citation

(PennSeq, RRID:SCR_001763)

Reference

PMID:24362841

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PennSeq: accurate isoform-specific gene expression quantification in RNA-Seq by modeling non-uniform read distribution.

  • Hu Y
  • Nucleic Acids Res.
  • 2014 Feb 11

Correctly estimating isoform-specific gene expression is important for understanding complicated biological mechanisms and for mapping disease susceptibility genes. However, estimating isoform-specific gene expression is challenging because various biases present in RNA-Seq (RNA sequencing) data complicate the analysis, and if not appropriately corrected, can affect isoform expression estimation and downstream analysis. In this article, we present PennSeq, a statistical method that allows each isoform to have its own non-uniform read distribution. Instead of making parametric assumptions, we give adequate weight to the underlying data by the use of a non-parametric approach. Our rationale is that regardless what factors lead to non-uniformity, whether it is due to hexamer priming bias, local sequence bias, positional bias, RNA degradation, mapping bias or other unknown reasons, the probability that a fragment is sampled from a particular region will be reflected in the aligned data. This empirical approach thus maximally reflects the true underlying non-uniform read distribution. We evaluate the performance of PennSeq using both simulated data with known ground truth, and using two real Illumina RNA-Seq data sets including one with quantitative real time polymerase chain reaction measurements. Our results indicate superior performance of PennSeq over existing methods, particularly for isoforms demonstrating severe non-uniformity. PennSeq is freely available for download at http://sourceforge.net/projects/pennseq.

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