Bio4j: a high-performance cloud-enabled graph-based data platform

BioRxiv : the Preprint Server for Biology
Pablo Pareja-TobesEduardo Pareja-Tobes

Abstract

Background Next Generation Sequencing and other high-throughput technologies have brought a revolution to the bioinformatics landscape, by offering sheer amounts of data about previously unaccessible domains in a cheap and scalable way. However, fast, reproducible, and cost-effective data analysis at such scale remains elusive. A key need for achieving it is being able to access and query the vast amount of publicly available data, specially so in the case of knowledge-intensive, semantically rich data: incredibly valuable information about proteins and their functions, genes, pathways, or all sort of biological knowledge encoded in ontologies remains scattered, semantically and physically fragmented. Methods and Results Guided by this, we have designed and developed Bio4j. It aims to offer a platform for the integration of semantically rich biological data using typed graph models. We have modeled and integrated most publicly available data linked with proteins into a set of interdependent graphs. Data querying is possible through a data model aware Domain Specific Language implemented in Java, letting the user write typed graph traversals over the integrated data. A ready to use cloud-based data distribution, based on the Ti...Continue Reading

Associated Proceedings Papers

Oct 19, 2020·Xiaolin HanHongtai Cao

Related Concepts

Biochemical Pathway
Genes
High Throughput Screening
Bio-Informatics
Massively-Parallel Sequencing
probe gene fragment
Analysis
Landscapes
Transcriptional Activation Domain
Protein Expression

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