Treffer: Omilayers: a Python package for efficient data management to support multi-omic analysis.

Title:
Omilayers: a Python package for efficient data management to support multi-omic analysis.
Authors:
Kioroglou D; Integrative Genomics Lab, Center for Cooperative Research in Biosciences (CIC bioGUNE), Basque Research and Technology Alliance (BRTA), Bizkaia Technology Park, Derio, Basque Country, Spain. dkioroglou@cicbiogune.es.
Source:
BMC bioinformatics [BMC Bioinformatics] 2025 Feb 06; Vol. 26 (1), pp. 40. Date of Electronic Publication: 2025 Feb 06.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: BioMed Central Country of Publication: England NLM ID: 100965194 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2105 (Electronic) Linking ISSN: 14712105 NLM ISO Abbreviation: BMC Bioinformatics Subsets: MEDLINE
Imprint Name(s):
Original Publication: [London] : BioMed Central, 2000-
MeSH Terms:
Software* , Computational Biology*/methods , Data Management*/methods , Database Management Systems*, Programming Languages ; Databases, Factual ; Multiomics
References:
Nat Genet. 2016 Oct;48(10):1284-1287. (PMID: 27571263)
Genome Biol. 2018 Feb 6;19(1):15. (PMID: 29409532)
Nucleic Acids Res. 2007 Jan;35(Database issue):D521-6. (PMID: 17202168)
Front Med (Lausanne). 2022 Jan 25;8:784455. (PMID: 35145977)
Nucleic Acids Res. 2023 Jan 6;51(D1):D942-D949. (PMID: 36420896)
Nucleic Acids Res. 2022 Jan 7;50(D1):D777-D784. (PMID: 34788838)
Grant Information:
CEX2021-001136-S Severo Ochoa Centre of Excellence Program
Contributed Indexing:
Keywords: Data management; Databases; Multi-omics; Python
Entry Date(s):
Date Created: 20250206 Date Completed: 20250506 Latest Revision: 20250506
Update Code:
20250506
PubMed Central ID:
PMC11800426
DOI:
10.1186/s12859-025-06067-7
PMID:
39915756
Database:
MEDLINE

Weitere Informationen

Multi-omic integration involves the management of diverse omic datasets. Conducting an effective analysis of these datasets necessitates a data management system that meets a specific set of requirements, such as rapid storage and retrieval of data with varying numbers of features and mixed data-types, ensurance of reliable and secure database transactions, extension of stored data row and column-wise and facilitation of data distribution. SQLite and DuckDB are embedded databases that fulfil these requirements. However, they utilize the structured query language (SQL) that hinders their implementation by the uninitiated user, and complicates their use in repetitive tasks due to the necessity of writing SQL queries. This study offers Omilayers, a Python package that encapsulates these two databases and exposes a subset of their functionality that is geared towards frequent and repetitive analytical procedures. Synthetic data were used to demonstrate the use of Omilayers and compare the performance of SQLite and DuckDB.
(© 2025. The Author(s).)

Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.