Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing-Reference-Cited by-同舟云学术

Integration of intra-sample contextual error modeling for improved detection of somatic mutations from deep sequencing

Published:2020-12-11 Issue:50 Volume:6 Page:
ISSN:2375-2548
Container-title:Science Advances
language:en
Short-container-title:Sci. Adv.

Author:

Abelson Sagi¹²^ORCID,Zeng Andy G. X.²³,Nofech-Mozes Ido¹²^ORCID,Wang Ting Ting³⁴,Ng Stanley W. K.⁵,Minden Mark D.³⁴^ORCID,Pugh Trevor J.¹³⁴^ORCID,Awadalla Philip¹²,Shlush Liran I.⁶⁷,Murphy Tracy³,Chan Steven M.³⁴^ORCID,Dick John E.²³^ORCID,Bratman Scott V.³⁴⁸^ORCID

Affiliation:

1. Ontario Institute for Cancer Research, Toronto, ON, Canada.

2. Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada.

3. Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.

4. Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.

5. Cancer Genome Project, Wellcome Trust Sanger Institute, Hinxton, UK.

6. Division of Hematology, Rambam Healthcare Campus, Haifa, Israel.

7. Department of Immunology, Weizmann Institute of Science, Rehovot, Israel.

8. Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.

Abstract

Espresso efficiently distinguishes true mutations from background noise common in deep sequencing data.

Funder

Genome Canada

Canadian Cancer Society Research Institute

Terry Fox Foundation

Canadian Institutes for Health Research

Benjamin Pearl fellowship, the McEwen Centre for Regenerative Medicine

Ontario Institute for Cancer Research - Investigator Award

Princess Margaret Cancer Centre - Gattuso-Slaight Personalized Cancer Medicine Fund

CIHR Vanier Scholarship

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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