Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

MetaGraph: Indexing and Analysing Nucleotide Archives at Petabase-scale

View ORCID ProfileMikhail Karasikov, View ORCID ProfileHarun Mustafa, Daniel Danciu, Marc Zimmermann, Christopher Barber, View ORCID ProfileGunnar Rätsch, View ORCID ProfileAndré Kahles
doi: https://doi.org/10.1101/2020.10.01.322164
Mikhail Karasikov
1Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland
2Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
3Swiss Institute of Bioinformatics, Zurich, Switzerland
4Department of Biology, ETH Zurich, Zurich, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Mikhail Karasikov
Harun Mustafa
1Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland
2Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
3Swiss Institute of Bioinformatics, Zurich, Switzerland
4Department of Biology, ETH Zurich, Zurich, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Harun Mustafa
Daniel Danciu
1Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland
2Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Marc Zimmermann
1Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland
2Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Christopher Barber
1Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland
2Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gunnar Rätsch
1Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland
2Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
3Swiss Institute of Bioinformatics, Zurich, Switzerland
4Department of Biology, ETH Zurich, Zurich, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Gunnar Rätsch
  • For correspondence: raetsch@inf.ethz.ch andre.kahles@inf.ethz.ch
André Kahles
1Biomedical Informatics Group, Department of Computer Science, ETH Zurich, Zurich, Switzerland
2Biomedical Informatics Research, University Hospital Zurich, Zurich, Switzerland
3Swiss Institute of Bioinformatics, Zurich, Switzerland
4Department of Biology, ETH Zurich, Zurich, Switzerland
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for André Kahles
  • For correspondence: raetsch@inf.ethz.ch andre.kahles@inf.ethz.ch
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

The amount of biological sequencing data available in public repositories is growing exponentially, forming an invaluable biomedical research resource. Yet, making all this sequencing data searchable and easily accessible to life science and data science researchers is an unsolved problem. We present MetaGraph, a versatile framework for the scalable analysis of extensive sequence repositories. MetaGraph efficiently indexes vast collections of sequences to enable fast search and comprehensive analysis. A wide range of underlying data structures offer different practically relevant trade-offs between the space taken by the index and its query performance. MetaGraph provides a flexible methodological framework allowing for index construction to be scaled from consumer laptops to distribution onto a cloud compute cluster for processing terabases to petabases of input data. Achieving compression ratios of up to 1,000-fold over the already compressed raw input data, MetaGraph can represent the content of large sequencing archives in the working memory of a single compute server. We demonstrate our framework’s scalability by indexing over 1.4 million whole genome sequencing (WGS) records from NCBI’s Sequence Read Archive, representing a total input of more than three petabases.

Besides demonstrating the utility of MetaGraph indexes on key applications, such as experiment discovery, sequence alignment, error correction, and differential assembly, we make a wide range of indexes available as a community resource, including those over 450,000 microbial WGS records, more than 110,000 fungi WGS records, and more than 20,000 whole metagenome sequencing records. A subset of these indexes is made available online for interactive queries. All indexes created from public data comprising in total more than 1 million records are available for download or usage in the cloud.

As an example of our indexes’ integrative analysis capabilities, we introduce the concept of differential assembly, which allows for the extraction of sequences present in a foreground set of samples but absent in a given background set. We apply this technique to differentially assemble contigs to identify pathogenic agents transfected via human kidney transplants. In a second example, we indexed more than 20,000 human RNA-Seq records from the TCGA and GTEx cohorts and use them to extract transcriptome features that are hard to characterize using a classical linear reference. We discovered over 200 trans-splicing events in GTEx and found broad evidence for tissue-specific non-A-to-I RNA-editing in GTEx and TCGA.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Methods section and supplemental material were extended.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
Back to top
PreviousNext
Posted November 03, 2020.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
MetaGraph: Indexing and Analysing Nucleotide Archives at Petabase-scale
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
MetaGraph: Indexing and Analysing Nucleotide Archives at Petabase-scale
Mikhail Karasikov, Harun Mustafa, Daniel Danciu, Marc Zimmermann, Christopher Barber, Gunnar Rätsch, André Kahles
bioRxiv 2020.10.01.322164; doi: https://doi.org/10.1101/2020.10.01.322164
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
MetaGraph: Indexing and Analysing Nucleotide Archives at Petabase-scale
Mikhail Karasikov, Harun Mustafa, Daniel Danciu, Marc Zimmermann, Christopher Barber, Gunnar Rätsch, André Kahles
bioRxiv 2020.10.01.322164; doi: https://doi.org/10.1101/2020.10.01.322164

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4095)
  • Biochemistry (8784)
  • Bioengineering (6493)
  • Bioinformatics (23382)
  • Biophysics (11765)
  • Cancer Biology (9166)
  • Cell Biology (13286)
  • Clinical Trials (138)
  • Developmental Biology (7421)
  • Ecology (11383)
  • Epidemiology (2066)
  • Evolutionary Biology (15113)
  • Genetics (10408)
  • Genomics (14020)
  • Immunology (9141)
  • Microbiology (22102)
  • Molecular Biology (8792)
  • Neuroscience (47429)
  • Paleontology (350)
  • Pathology (1423)
  • Pharmacology and Toxicology (2483)
  • Physiology (3711)
  • Plant Biology (8061)
  • Scientific Communication and Education (1433)
  • Synthetic Biology (2213)
  • Systems Biology (6020)
  • Zoology (1251)