HTSFilter protocols

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HTSFilter specifications


Unique identifier OMICS_14779
Name HTSFilter
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License Artistic License version 2.0
Computer skills Advanced
Version 1.20.0
Stability Stable
methods, stats, graphics, Biobase, utils, testthat, BiocStyle, grDevices, R(>=3.4), edgeR(>=3.9.14), DESeq2(>=1.10.1), DESeq(>=1.22.1), BiocParallel(>=1.4.3), EDASeq(>=2.1.4)
Maintained Yes



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  • person_outline Andrea Rau <>

Publication for HTSFilter

HTSFilter in pipelines

PMCID: 5206491
PMID: 27998927
DOI: 10.1084/jem.20151414

[…] and niche cell data were generated using umi-seq, as described previously (). to remove noise from lowly expressed genes, count datasets were subjected to data-driven gene filtering using the htsfilter r package (; tables s3 and s5)., differential expression (de) analysis was done by applying the deseq2 package () using the likelihood ratio to test for differential expression in 14,029 […]

PMCID: 5135139
PMID: 27911943
DOI: 10.1371/journal.ppat.1006044

[…] matches (same best score for several read-pairs) were removed. finally, between 8.2 and 23.4m non ambiguous read-pairs were obtained. mapped reads were imported into r environment. the package htsfilter was used to eliminate very low-expressed genes from the analysis. a total of 5157 out the predicted 5307 genes were thus kept in. r package deseq2 was used to normalize and complete […]

PMCID: 4721924
PMID: 26418546
DOI: 10.1371/journal.pone.0136765

[…] python tool with the default “union” mode []. to enhance the statistical power for identifying differentially expressed genes (degs), we removed those genes with weak expression levels using the htsfilter package []. the deseq2 package [] was employed to distinguish degs between the low and high rfi groups. deseq2 first used empirical bayes shrinkage method to estimate dispersions and fold […]

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HTSFilter in publications

PMCID: 5796731
PMID: 29360820
DOI: 10.1371/journal.pgen.1007180

[…] []. a summary of raw read counts mapped to each gene and time point is available at the geo repository (gse94915)., for each pair-wise comparison (72h ael vs. 96h ael and 96h ael vs. 120h ael) htsfilter [] was used with default parameters to filter out genes with very low expression in all samples. for the remaining genes in each pair-wise comparison, differential expression was calculated […]

PMCID: 5744907
PMID: 29281677
DOI: 10.1371/journal.pone.0185511

[…] was performed using r version 3.0.1 (r development core team, 2013) with the bioconductor package deseq2 [], as described by le guillou et al. []. data were filtered using the bioconductor package htsfilter []. this method aims to identify the threshold that maximizes the filtering similarity among biological replicates, or in other words that where most genes tend to have either normalized […]

PMCID: 5682283
PMID: 29129929
DOI: 10.1038/s41467-017-01475-7

[…] trimmed using cutadapt 1.0. potential pcr duplicates were removed using samtools 1.3. reads were then aligned on the grch38 human genome using star 2.4.2a. differential expression on filtered genes (htsfilter 1.7.1), were performed using deseq2 in r 3.3.1. genes were declared differentially expressed with a false discovery rate < 5%. for heatmap, genes differentially expressed with fold […]

PMCID: 5647381
PMID: 29044138
DOI: 10.1038/s41598-017-13171-z

[…] (v1.4.5-p1) was used to perform read summarization at gene level, with the strand-specific option “reversely stranded”. statistical analysis of the read counts was performed with r, using the htsfilter package, to remove low expressed genes, and the noiseq package, to perform differential expression analysis. gene ontology enrichment analysis of the differentially expressed genes […]

PMCID: 5552707
PMID: 28798312
DOI: 10.1038/s41598-017-07532-x

[…] expression counts were then calculated by using featurecounts (version 1.4.6-p5). raw counts were imported in r and, following tmm normalization, the lowly expressed genes were filtered out with the htsfilter package. differential expression analysis of filtered genes was carried out with the noiseq package. gene ontology enrichment analysis of differentially expressed genes was carried […]

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HTSFilter institution(s)
INRA, UMR, Génétique animale et biologie intégrative, Jouy-en-Josas, France; AgroParisTech, UMR1313 Génétique animale et biologie intégrative, Paris, France; Inria Saclay - Île-de-France, Orsay, France
HTSFilter funding source(s)
This work was supported by the French National Research Agency [ANR-09-GENM-006, Biocart project].

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