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EDASeq

A package of within-lane normalization procedures to adjust for GC-content effect on read counts. EDAseq provides numerical summaries and graphical representations of some key features for GC content bias to adjust for sequencing depth and possibly other differences in distribution. EDASeq has two-step process: read-level EDA helps in discovering lanes with low sequencing depths, quality issues, and unusual nucleotide frequencies, while gene-level EDA can capture mislabeled lanes, issues with distributional assumptions (e.g., over-dispersion), and GC-content bias.

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EDASeq classification

EDASeq specifications

Unique identifier:
OMICS_01231
Software type:
Package/Module
Restrictions to use:
None
Operating system:
Unix/Linux, Mac OS, Windows
License:
Artistic License version 2.0
Version:
2.8
Requirements:
Biobase, ShortRead
Name:
Exploratory Data Analysis and Normalization for RNA-Seq
Interface:
Command line interface
Input data:
RNA-Seq read data
Programming languages:
R
Computer skills:
Advanced
Stability:
Stable
Maintained:
Yes

EDASeq distribution

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EDASeq support

Documentation

Credits

Institution(s)

Department of Statistical Sciences, University of Padua, Italy; Department of Genetics, Stanford University, Standford, CA, USA; Division of Biostatistics and Department of Statistics, University of California, Berkeley, CA, USA

Funding source(s)

This work was funded by grant R01 HG03468 from the NHGRI at the NIH and grant CPDA094285 from the University of Padua.

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