Tools for munging genomics data
Tools for munging genomic data such as: - Converting between different types of gene identifiers - Searching for terms in the Gene Ontology (GO) associated with a keyword - Looking up housekeeping genes and transcription factors - Getting a list of GO terms associated with a given gene - Looking up how a gene is expressed across tissues - Normalizing a matrix of gene expression data by converting to TPM
When we’re not developing super awesome open source packages like genemunge, we help biopharma partners use unsupervised deep learning to extract insights from their omics data. Learn more at unlearn.health.
This library is accompanied by the following data sources: - The Gene Ontology. The current version used here is the 2018-03-27 release. - recount2 data for GTEx. - HGNC gene symbols. - A list of transcription factors. - A list of housekeeping genes.
Installing this package through pip (pip install genemunge from PyPI, pip install . from GitHub) will use the static data that accompanies this repository.
If you wish to use the latest data from the above sources, you may install in “develop” mode from GitHub with pip -e install .. Notably, this will download and process the recount2 GTEx data, requiring R and the recount package from bioconductor:
Please cite the following papers if you make use of genemunge for a publication.
Gene Ontology: Ashburner et al. Gene ontology: tool for the unification of biology (2000) Nat Genet 25(1):25-9 GO Consortium, Nucleic Acids Res., 2017
recount2: Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B, Leek JT. Reproducible RNA-seq analysis using recount2. Nature Biotechnology, 2017.
HGNC: Gray KA, Yates B, Seal RL, Wright MW, Bruford EA. genenames.org: the HGNC resources in 2015. Nucleic Acids Res. 2015 Jan;43(Database issue):D1079-85.
Transcription factors: TFcheckpoint: a curated compendium of specific DNA-binding RNA polymerase II transcription factors Konika Chawla; Sushil Tripathi; Liv Thommesen; Astrid Laegreid; Martin Kuiper Bioinformatics 2013.
Housekeeping genes: E. Eisenberg and E.Y. Levanon, Trends in Genetics 29, (2013)
If you know of similar tools that would be helpful references for users, please contribute an attribution to them here.
GO evidence codes
Experiment: - Inferred from Experiment (EXP) - Inferred from Direct Assay (IDA) - Inferred from Physical Interaction (IPI) - Inferred from Mutant Phenotype (IMP) - Inferred from Genetic Interaction (IGI) - Inferred from Expression Pattern (IEP) Computational: - Inferred from Sequence or structural Similarity (ISS) - Inferred from Sequence Orthology (ISO) - Inferred from Sequence Alignment (ISA) - Inferred from Sequence Model (ISM) - Inferred from Genomic Context (IGC) - Inferred from Biological aspect of Ancestor (IBA) - Inferred from Biological aspect of Descendant (IBD) - Inferred from Key Residues (IKR) - Inferred from Rapid Divergence(IRD) - Inferred from Reviewed Computational Analysis (RCA) Literature: - Traceable Author Statement (TAS) - Non-traceable Author Statement (NAS) Other: - Inferred by Curator (IC) - No biological Data available (ND) evidence code - Inferred from Electronic Annotation (IEA)
Common gene id types
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|Filename, size & hash||File type||Python version||Upload date|
|genemunge-0.0-py3-none-any.whl (39.4 MB) View hashes||Wheel||py3|
|genemunge-0.0.tar.gz (39.2 MB) View hashes||Source||None|