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Sherlock: an open-source data platform to store, analyze and integrate Big Data for computational biologists.

Balazs Bohar1,2, David Fazekas1,2, Matthew Madgwick1,3

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|March 8, 2023
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Sherlock is an open-source, cloud-based platform streamlining bioinformatics data management. It simplifies storing, converting, querying, and sharing diverse biological data for large-scale research projects.

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Computational biologyData lakeNetwork biologySoftwareSystems biologybig datadata integrationdata management

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Big Data Analytics

Background:

  • Biological research heavily relies on Big Data, but data collection and preparation are time-consuming.
  • Managing diverse data structures from multiple public databases poses significant challenges for computational biologists.
  • Inefficient data management hinders timely analysis and discovery in large-scale bioinformatics projects.

Purpose of the Study:

  • To introduce Sherlock, an open-source, cloud-based platform designed to streamline bioinformatics data management.
  • To provide a solution for efficient storage, conversion, querying, sharing, and generation of biological data.
  • To simplify the use of big data technologies for computational biologists.

Main Methods:

  • Developed Sherlock, an open-source, cloud-based big data platform utilizing technologies like Docker and PrestoDB.
  • Implemented a gap-filling approach for data management, enabling users to store, convert, query, and share biological data.
  • Integrated loader scripts for structured data sources (genomics, interaction, expression) and conversion to optimized formats like Optimized Row Columnar (ORC).

Main Results:

  • Sherlock offers a user-friendly interface for leveraging big data technologies in biological research.
  • The platform efficiently handles diverse structured data types (interaction, localization, genomic sequence) from multiple sources.
  • Sherlock facilitates rapid, distributed analytical queries on large datasets and enables seamless dataset sharing.

Conclusions:

  • Sherlock empowers computational biologists by providing an integrated platform for data management, analytics, and collaboration.
  • The platform significantly streamlines bioinformatics workflows, reducing the time spent on data preparation.
  • Sherlock's open-source nature and utilization of modern big data technologies make it a valuable resource for large-scale bioinformatics projects.