PyGMQL: scalable data extraction and analysis for heterogeneous genomic datasets

BMC Bioinformatics

Table 1 Mapping between PyGMQL methods and GMQL operators or utilities

PyGMQL function	Description	GMQL operator
load_from_path	UTIL, loads a dataset from local repository	SELECT
load_from_remote	UTIL, loads a dataset from remote repository	SELECT
load_from_file	UTIL, loads a bed file from local repository
selectreg_selectmeta_select	UNOP, filters samples using region and/or metadata predicates	SELECT
projectreg_projectmeta_project	UNOP, projects (in/out) attributes of regions or metadata. Creates new attributes by means of expressions	PROJECT
extend	UNOP, creates a new metadata attribute by aggregation of region data	EXTEND
covernormal_coverflat_coversummit_coverhistogram_cover	UNOP, collapses regions from several samples into regions of a single sample, based on min/max accumulation indexes	COVER
order	UNOP, orders the samples of a dataset based on regions and/or metadata attributes	ORDER
merge	UNOP, merges all the samples of a dataset into a single one	MERGE
groupmeta_groupreg_group	UNOP, groups regions and/or metadata with the same values	GROUP
join	BINOP, joins the regions of two datasets based on distance-based predicates	JOIN
map	BINOP, computes aggregate values from overlapping regions of two datasets	MAP
union	BINOP, builds the union of regions and metadata of two datasets	UNION
difference	BINOP, keeps the regions of a dataset not intersecting with regions of another one	DIFFERENCE
materialize	UTIL, triggers the query execution for the specified dataset and stores the result after query completion	MATERIALIZE
head	UTIL, Shows the first lines of a dataset

For every method we provide a concise explanation (UNOP stands for unary operator, BINOP stands for binary operator and UTIL identifies an utility function)

ISSN: 1471-2105