QuasiSeq pipeline

QuasiSeq specifications

Information


Unique identifier OMICS_01963
Name QuasiSeq
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0, GNU General Public License version 2.0
Computer skills Advanced
Version 1.0-10-1
Stability Stable
Maintained Yes

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Maintainer


  • person_outline Long Qu <>

Publication for QuasiSeq

QuasiSeq IN pipelines

 (3)
2015
PMCID: 4687377
PMID: 26689712
DOI: 10.1186/s12864-015-2318-2

[…] joint genome institute and read counts for each gene model were obtained using the htseq program developed by anders and co-workers [21]. differential expression analysis was performed using the quasiseq package in r [20]. for de analysis, pathogenic isolate (fo40) was compared against non-pathogenic isolate (fo36) at each time point. upper quartile normalization was used to normalize […]

2015
PMCID: 4687377
PMID: 26689712
DOI: 10.1186/s12864-015-2318-2

[…] de analysis, pathogenic isolate (fo40) was compared against non-pathogenic isolate (fo36) at each time point. upper quartile normalization was used to normalize the read counts for library size. in quasiseq, the qlspline method was used to account for over-dispersion effects. to account for multiple testing, we estimated the qvalues for each gene model. to control false discovery rate at 5 %, […]

2014
PMCID: 4027157
PMID: 24598257
DOI: 10.1093/nar/gku173

[…] background, the log-scale 75th percentile of library size normalization method was used to normalize wild-type and mutant rna-seq reads (30). the normalized reads were counted by htseq(v0.5.4). the quasiseq (r package) was used to identify differentially expressed genes by negative binomial quasi-likelihood estimation (31). genes were considered differentially expressed between wild-type […]

QuasiSeq institution(s)
Statistical Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD, USA; Department of Statistics, Iowa State University, Ames, IA, USA; University of Oxford, Oxford, UK; Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
QuasiSeq funding source(s)
Supported by the National Research Initiative of the USDA-CSREES Grant No. 2008-35600-18786; the National Science Foundation grant number 0820610; the General Sir John Monash Scholarship; and the National Health and Medical Research Council Program Grant 490037.

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