An SQL-based solution for large-scale genomic analysis
Project description
pysequila
pysequila is a Python entrypoint to SeQuiLa, an ANSI-SQL compliant solution for efficient sequencing reads processing and genomic intervals querying built on top of Apache Spark. Range joins, depth of coverage and pileup computations are bread and butter for NGS analysis but the high volume of data make them execute very slowly or even failing to compute.
Requirements
Python 3.7, 3.8, 3.9
Features
custom data sources for bioinformatics file formats (BAM, CRAM, VCF)
depth of coverage calculations
pileup calculations
reads filtering
efficient range joins
other utility functions
support for both SQL and Dataframe/Dataset API
Setup
$ python -m pip install --user pysequila or (venv)$ python -m pip install pysequila
Usage
$ python
>>> from pysequila import SequilaSession
>>> ss = SequilaSession \
.builder \
.config("spark.jars.packages", "org.biodatageeks:sequila_2.12:1.1.0") \
.config("spark.driver.memory", "2g") \
.getOrCreate()
>>> ss.sql(
f"""
CREATE TABLE IF NOT EXISTS reads
USING org.biodatageeks.sequila.datasources.BAM.BAMDataSource
OPTIONS(path "/features/data/NA12878.multichrom.md.bam")
"""
>>> ss.sql ("SELECT * FROM coverage('reads', 'NA12878','/features/data/Homo_sapiens_assembly18_chr1_chrM.small.fasta")
>>> # or using DataFrame/DataSet API
>>> ss.coverage("/features/data/NA12878.multichrom.md.bam", "/features/data/Homo_sapiens_assembly18_chr1_chrM.small.fasta")
ChangeLog
0.1.0 (2020-09-16)
Initial release.
