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Machado: Open source genomics data integration framework.

Submitted by karopka on Wed, 2021/10/20 - 10:50
TitleMachado: Open source genomics data integration framework.
Publication TypeJournal Article
Year of Publication2020
AuthorsMudadu, Mde Alvaren, Zerlotini, A
JournalGigascience
Volume9
Issue9
Date Published2020 09 14
ISSN2047-217X
Abstract

BACKGROUND: Genome projects and multiomics experiments generate huge volumes of data that must be stored, mined, and transformed into useful knowledge. All this information is supposed to be accessible and, if possible, browsable afterwards. Computational biologists have been dealing with this scenario for more than a decade and have been implementing software and databases to meet this challenge. The GMOD's (Generic Model Organism Database) biological relational database schema, known as Chado, is one of the few successful open source initiatives; it is widely adopted and many software packages are able to connect to it.FINDINGS: We have been developing an open source software package named Machado, a genomics data integration framework implemented in Python, to enable research groups to both store and visualize genomics data. The framework relies on the Chado database schema and, therefore, should be very intuitive for current developers to adopt it or have it running on top of already existing databases. It has several data-loading tools for genomics and transcriptomics data and also for annotation results from tools such as BLAST, InterproScan, OrthoMCL, and LSTrAP. There is an API to connect to JBrowse, and a web visualization tool is implemented using Django Views and Templates. The Haystack library integrated with the ElasticSearch engine was used to implement a Google-like search, i.e., single auto-complete search box that provides fast results and filters.CONCLUSION: Machado aims to be a modern object-relational framework that uses the latest Python libraries to produce an effective open source resource for genomics research.

DOI10.1093/gigascience/giaa097
Alternate JournalGigascience
PubMed ID32930331
PubMed Central IDPMC7490629
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