{ "info": { "author": "David Lawrence", "author_email": "davmlaw@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 5 - Production/Stable", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9" ], "description": "## PyReference ##\n\n[![PyPi version](https://img.shields.io/pypi/v/pyreference.svg)](https://pypi.org/project/pyreference/) [![Python versions](https://img.shields.io/pypi/pyversions/pyreference.svg)](https://pypi.org/project/pyreference/) [![PyReference](https://img.shields.io/pypi/dm/pyreference.svg)](https://pypi.org/project/pyreference/)\n\nA Python library for working with reference gene annotations.\n\nPyReference loads GTF annotations extremely rapidly, and makes it easy to write code which can be run against different genomes.\n\n## Example ##\n\n import numpy as np\n import pyreference\n\t\n\treference = pyreference.Reference()\n\n\tmy_gene_ids = [\"MSN\", \"GATA2\", \"ZEB1\"]\n\tfor gene in reference[my_gene_ids]:\n\t\taverage_length = np.mean([t.length for t in gene.transcripts])\n\t\tprint(\"%s average length = %.2f\" % (gene, average_length))\n\t\tprint(gene.iv)\n\t\tfor transcript in gene.transcripts:\n\t\t\tif transcript.is_coding:\n\t\t\t\tthreep_utr = transcript.get_3putr_sequence()\n\t\t\t\tprint(\"%s end of 3putr: %s\" % (transcript.get_id(), threep_utr[-20:]))\n\nOutputs:\n\n\tMSN (MSN) 1 transcripts average length = 3970.00\n\tchrX:[64887510,64961793)/+\n\tNM_002444 end of 3putr: TAAAATTTAGGAAGACTTCA\n\n\tGATA2 (GATA2) 3 transcripts average length = 3367.67\n\tchr3:[128198264,128212030)/-\n\tNM_001145662 end of 3putr: AATACTTTTTGTGAATGCCC\n\tNM_001145661 end of 3putr: AATACTTTTTGTGAATGCCC\n\tNM_032638 end of 3putr: AATACTTTTTGTGAATGCCC\n\n\tZEB1 (ZEB1) 6 transcripts average length = 6037.83\n\tchr10:[31608100,31818742)/+\n\tNM_001174093 end of 3putr: CTTCTTTTTCTATTGCCTTA\n\tNM_001174094 end of 3putr: CTTCTTTTTCTATTGCCTTA\n\tNM_030751 end of 3putr: CTTCTTTTTCTATTGCCTTA\n\tNM_001174096 end of 3putr: CTTCTTTTTCTATTGCCTTA\n\tNM_001174095 end of 3putr: CTTCTTTTTCTATTGCCTTA\n\tNM_001128128 end of 3putr: CTTCTTTTTCTATTGCCTTA\n\nThis takes less than 4 seconds to load via a network drive on my machine.\n\n## Installation ##\n\n sudo pip install pyreference\n\nChoose your annotation:\n\n # Latest Ensembl GRCh37\n wget ftp://ftp.ensembl.org/pub/grch37/release-87/gff3/homo_sapiens/Homo_sapiens.GRCh37.87.gff3.gz\n\n # Latest Ensembl GRCh38\n wget ftp://ftp.ensembl.org/pub/release-104/gff3/homo_sapiens/Homo_sapiens.GRCh38.104.gff3.gz\n\n # Latest RefSeq GRCh37\n wget http://ftp.ncbi.nlm.nih.gov/refseq/H_sapiens/annotation/annotation_releases/105.20201022/GCF_000001405.25_GRCh37.p13/GCF_000001405.25_GRCh37.p13_genomic.gff.gz\n\n # Latest RefSeq GRCh38\n http://ftp.ncbi.nlm.nih.gov/refseq/H_sapiens/annotation/annotation_releases/109.20210514/GCF_000001405.39_GRCh38.p13/GCF_000001405.39_GRCh38.p13_genomic.gff.gz\n\nPre-process your GFF3 or GTF files to create genes.gtf.json.gz (~1/20th the size of the input GTF file)\n \n pyreference_gff_to_json.py --gff3 genes.gff.gz\n\nCreate a ~/pyreference.cfg file pointing to your references.\n\n\t[global]\n\tdefault_build=hg19\n\n\t[hg19]\n\tgenes_json=/data/reference/hg19/genes.gtf.json.gz\n\tmature_mir_sequence_fasta=/data/reference/hg19/mature.fa\n\tgenome_sequence_fasta=/data/reference/hg19/genome.fa\n\n\t[mm10]\n\tgenes_json=/data/reference/mm10/genes.gtf.json.gz\n\tmature_mir_sequence_fasta=/data/reference/mm10/mature.fa\n\tgenome_sequence_fasta=/data/reference/mm10/genome.fa\n\n\n## Command line arguments ##\n\nSubstitute ArgumentParser with pyreference.ReferenceArgumentParser to add a --build option to your command line arguments. \n\nargs.reference is now initialised to the correct 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