***************************************
Overview
***************************************
We want an extremely fast, lightweight way to access gene data stored in GTF format.
The parsed data is held in an intuitive
Gene
-> transcript
-> transcript
with exons being stored as intervals
Our aim is to
* cache data in binary format, which can be
* re-read in < 10s for even the largest genomes
Currently initial parsing Ensembl Homo sapiens release 56 takes around 4.5 minutes.
The binary data can be reloaded in < 10s.
This contains *all* of the data structure in the original GTF file
Note that we sacrifice memory usage for speed. This is seldom a problem for modern computers
and genome sizes (There are around ~400,000 exons but there are stored as intervals / int pairs)
***************************************
A Simple example
***************************************
::
gene_structures = t_parse_gtf("Mus musculus")
#
# used cached data for speed
#
ignore_cache = False
#
# get all protein coding genes only
#
genes_by_type = gene_structures.get_genes(gtf_file, logger, ["protein_coding"], ignore_cache = ignore_cache)
#
# print out gene counts
#
t_parse_gtf.log_gene_types (logger, genes_by_type)
return genes_by_type
Overview
***************************************
We want an extremely fast, lightweight way to access gene data stored in GTF format.
The parsed data is held in an intuitive
Gene
-> transcript
-> transcript
with exons being stored as intervals
Our aim is to
* cache data in binary format, which can be
* re-read in < 10s for even the largest genomes
Currently initial parsing Ensembl Homo sapiens release 56 takes around 4.5 minutes.
The binary data can be reloaded in < 10s.
This contains *all* of the data structure in the original GTF file
Note that we sacrifice memory usage for speed. This is seldom a problem for modern computers
and genome sizes (There are around ~400,000 exons but there are stored as intervals / int pairs)
***************************************
A Simple example
***************************************
::
gene_structures = t_parse_gtf("Mus musculus")
#
# used cached data for speed
#
ignore_cache = False
#
# get all protein coding genes only
#
genes_by_type = gene_structures.get_genes(gtf_file, logger, ["protein_coding"], ignore_cache = ignore_cache)
#
# print out gene counts
#
t_parse_gtf.log_gene_types (logger, genes_by_type)
return genes_by_type
Metadata
Release files for gtf_to_genes 1.40
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Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| gtf_to_genes-1.40.tar.gz | 35.6 kB | Details |
Release files / gtf_to_genes-1.40.tar.gz
| Download URL | gtf_to_genes-1.40.tar.gz |
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