QuasiSeq protocols

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QuasiSeq statistics

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QuasiSeq specifications


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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Publication for QuasiSeq

QuasiSeq in pipelines

PMCID: 4508525
PMID: 26257749
DOI: 10.3389/fpls.2015.00536

[…] of the raw reads) to a gene. raw reads and read counts are available through the ncbi gene expression omnibus with accession number gse67588. differentially expressed genes were identified using quasiseq (lund et al., ) with the quartile normalization method (bullard et al., ). the replicate number was used as a cofactor, and genes were filtered if they did not have an average gene […]

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

[…] from joint genome institute and read counts for each gene model were obtained using the htseq program developed by anders and co-workers []. differential expression analysis was performed using the quasiseq package in r []. for 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 […]

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 (). 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 (). genes were considered differentially expressed between wild-type […]

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QuasiSeq in publications

PMCID: 5817962
PMID: 29491871
DOI: 10.3389/fpls.2018.00108

[…] edger, and bayseq have superior specificity and sensitivity, and seem to outperform the limma and poissonseq methods. the worst method seems to be cuffdiff. burden et al. () concluded that the quasiseq tool achieves a low fdr providing the number of replicates in each condition is at least 4. the next best performing packages are edger and deseq2. in other studies, both edger and deseq […]

PMCID: 5550749
PMID: 28790203
DOI: 10.1128/mBio.00709-17

[…] by the spiked-in rna counts of individual samples to normalize the counts to the weight of the original fecal samples. statistics were all completed with counts normalized to spiked-in rna counts. quasiseq v. 1.0-8, an r bioconductor package, was used to compare samples treated with carbadox-treated and untreated samples. the statistical model used normalized read counts as the dependent […]

PMCID: 5333127
PMID: 28233777
DOI: 10.1038/ncomms14573

[…] were used to independently calculate the read depth of each annotated gene. genes with an average of at least one uniquely mapped read across samples were tested for differential expression using quasiseq (http://cran.r-project.org/web/packages/quasiseq). the generalized linear model quasi-likelihood spline method assuming negative binomial distribution of read counts implemented […]

PMCID: 4724083
PMID: 26801403
DOI: 10.1186/s12864-016-2395-x

[…] the statistical programming language r (version 3.1.0) was used for all statistical analyses, unless indicated otherwise. differential expression analysis was carried out by using the r package “quasiseq” (version 1.0-4) []. for each of the 12280 genes in the final count table, we used quasiseq to fit a full generalized linear model with a negative binomial response and a log link […]

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

[…] downstream analyses were only performed for soybean and not for f. oxysporum. the abundance of soybean transcripts are expressed in upper quartile normalized counts as calculated by htseq-count and quasiseq programs [, ]. one of the three biological replicates for the fo36 inoculated sample at 96 hpi was excluded for further expression level analysis due to a relatively small number of reads. […]

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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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