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Photo of Patrik Edén

Patrik Edén

Senior lecturer

Photo of Patrik Edén

A community effort to identify and correct mislabeled samples in proteogenomic studies

Author

  • Seungyeul Yoo
  • Zhiao Shi
  • Bo Wen
  • SoonJye Kho
  • Renke Pan
  • Hanying Feng
  • Hong Chen
  • Anders Carlsson
  • Patrik Edén
  • Weiping Ma
  • Michael Raymer
  • Ezekiel J. Maier
  • Zivana Tezak
  • Elaine Johansson
  • Denise Hinton
  • Henry Rodriguez
  • Jun Zhu
  • Emily Boja
  • Pei Wang
  • Bing Zhang
Show all

Summary, in English

Sample mislabeling or misannotation has been a long-standing problem in scientific research, particularly prevalent in large-scale, multi-omic studies due to the complexity of multi-omic workflows. There exists an urgent need for implementing quality controls to automatically screen for and correct sample mislabels or misannotations in multi-omic studies. Here, we describe a crowdsourced precisionFDA NCI-CPTAC Multi-omics Enabled Sample Mislabeling Correction Challenge, which provides a framework for systematic benchmarking and evaluation of mislabel identification and correction methods for integrative proteogenomic studies. The challenge received a large number of submissions from domestic and international data scientists, with highly variable performance observed across the submitted methods. Post-challenge collaboration between the top-performing teams and the challenge organizers has created an open-source software, COSMO, with demonstrated high accuracy and robustness in mislabeling identification and correction in simulated and real multi-omic datasets.

Department/s

  • Computational Biology and Biological Physics - Has been reorganised
  • Computational Science for Health and Environment
  • Centre for Environmental and Climate Science (CEC)

Publishing year

2021-05-14

Language

English

Publication/Series

Patterns

Volume

2

Issue

5

Document type

Journal article

Publisher

Cell Press

Topic

  • Bioinformatics and Systems Biology

Keywords

  • proteomics, genomics, mislabeling

Status

Published

Research group

  • Computational Science for Health and Environment

ISBN/ISSN/Other

  • ISSN: 2666-3899