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Who is this gene and what does it do? A toolkit for munging transcriptomics data in python

https://doi.org/10.1101/299107
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s12064-012-0162-3openalex-snapshot
Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples
inferredcites →https://doi.org/10.1016/j.tig.2013.05.010openalex-snapshot
Human housekeeping genes, revisited
inferredcites →https://doi.org/10.1038/75556openalex-snapshot
Gene Ontology: tool for the unification of biology
inferredcites →https://doi.org/10.1038/nbt.3838openalex-snapshot
Reproducible RNA-seq analysis using recount2
inferredcites →https://doi.org/10.1089/bio.2015.0032openalex-snapshot
A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project
inferredcites →https://doi.org/10.1089/bio.2015.29031.hmmopenalex-snapshot
The Genotype-Tissue Expression (GTEx) Project
inferredcites →https://doi.org/10.1093/bioinformatics/btt432openalex-snapshot
TFcheckpoint: a curated compendium of specific DNA-binding RNA polymerase II transcription factors
inferredcites →https://doi.org/10.1093/biostatistics/kxr034openalex-snapshot
Using control genes to correct for unwanted variation in microarray data
inferredcites →https://doi.org/10.1093/biostatistics/kxv026openalex-snapshot
Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed
inferredcites →https://doi.org/10.1093/nar/gkh036openalex-snapshot
The Gene Ontology (GO) database and informatics resource
inferredcites →https://doi.org/10.1093/nar/gkm881openalex-snapshot
The HGNC Database in 2008: a resource for the human genome
inferredcites →https://doi.org/10.1186/1471-2105-12-449openalex-snapshot
ReCount: A multi-experiment resource of analysis-ready RNA-seq gene count datasets

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1101/574723openalex-snapshot
Deep learning of representations for transcriptomics-based phenotype prediction
inferred← citeshttps://doi.org/10.1186/s12859-020-3427-8openalex-snapshot
Standard machine learning approaches outperform deep representation learning on phenotype prediction from transcriptomics data
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