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Protocols

tximport specifications

Information


Unique identifier OMICS_11762
Name tximport
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
Computer skills Advanced
Version 1.8.0
Stability Stable
Requirements
limma, utils, testthat, edgeR, rmarkdown, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, tximportData, rhdf5, jsonlite, readr(>=0.2.2), DESeq2(>=1.11.6)
Maintained Yes

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Documentation


Maintainer


  • person_outline Charlotte Soneson

Publication for tximport

tximport citations

 (22)
library_books

Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia

2018
Oncotarget
PMCID: 5929421
PMID: 29731978
DOI: 10.18632/oncotarget.24808

[…] pressed. Differential expression analysis was performed with the Kallisto software (v0.42.1)+ [] on the raw FASTQ files with default parameters, followed by differential expression analysis using the TXimport (v1.0.3) [] and edgeR (v3.14.0)+ [] R packages. All hDEGs and DEGs within the model are presented in . The GRCh37 human reference genome assembly was used in all steps of the analyses. The ge […]

library_books

Gene level differential analysis at transcript level resolution

2018
Genome Biol
PMCID: 5896116
PMID: 29650040
DOI: 10.1186/s13059-018-1419-z

[…] DESeq2 1.14.1 and sleuth 0.29.0 were used in R version 3.4.1 to perform differential analyses. Tximport 1.2.0 was used to sum transcript counts within genes to perform gene-level differential expression with DESeq2. We implemented Fisher’s method and Lancaster method with the chisq and gamma fu […]

call_split

Long Non Coding RNAs Associated with Metabolic Traits in Human White Adipose Tissue

2018
PMCID: 5952343
PMID: 29580841
DOI: 10.1016/j.ebiom.2018.03.010
call_split See protocol

[…] M-CAT. Then the gene-level estimates, representing the overall transcriptional output of each gene, were obtained by summing the corresponding transcript-level estimates using a Bioconductor package, Tximport (). Differential gene expression analysis was conducted at the gene level using EdgeR, after applying the filter for at least half of the samples above the detection level (cpm > 1), selectin […]

library_books

Identification of Two Distinct Classes of the Human INO80 Complex Genome Wide

2018
PMCID: 5873900
PMID: 29432129
DOI: 10.1534/g3.117.300504

[…] G2 cells (). Expression analysis was carried out on single end 50bp reads. Gene expression levels were quantitated using kallisto (). These data were converted to counts and summarized per gene using tximport (). When comparing genes associated with the two classes, we assigned genes to the nearest peak. […]

call_split

Drosophila Fezf coordinates laminar specific connectivity through cell intrinsic and cell extrinsic mechanisms

2018
eLife
PMCID: 5854465
PMID: 29513217
DOI: 10.7554/eLife.33962.028
call_split See protocol

[…] ipts Per Million (TPM) measurements per isoform were generated using Salmon. Differential expression at the gene level was called with DESeq2. We used the counts per gene estimated from the Salmon by tximport as input to DESeq2 as quantitating at the isoform level has been shown to produce more accurate results at the gene level. As described in the DESeq2 documentation, the differential expressio […]

library_books

Co regulation of transcription by BRG1 and BRM, two mutually exclusive SWI/SNF ATPase subunits

2017
PMCID: 5740901
PMID: 29273066
DOI: 10.1186/s13072-017-0167-8

[…] Libraries were sequenced on Hiseq 4000 (50-bp reads). Gene expression levels were quantitated using kallisto []. These data were converted to counts and summarized per gene using tximport [], and differential expression was carried out using DESeq2 [] using an FDR of 0.05 and no explicit fold change cutoff. Raw count matrices used as input to DESeq2 and output of DESeq2 are sh […]


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tximport institution(s)
Institute for Molecular Life Sciences, University of Zurich, Zurich, Switzerland; SIB Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland; Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA
tximport funding source(s)
This work was supported by the “RNA & Disease” National Center of Competence in Research, an SNSF project grant (143883) by the European Commission through the 7th Framework Collaborative Project RADIANT (Grant Agreement Number: 305626), and by NIH grant 5T32CA009337-35.

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