Evidence map›Paper›PMID 31470794›Full record

ArticleBMC genomics2019

SigProfilerMatrixGenerator: a tool for visualizing and exploring patterns of small mutational events.

Erik N Bergstrom, Mi Ni Huang, Uma Mahto, Mark Barnes, Michael R Stratton, Steven G Rozen, Ludmil B Alexandrov

Abstract read
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 209 papers.

0numbers the graph read from it
0cells of the map it votes in
209citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

209 citing papers in PubMed.

  1. Trial
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  3. Clonal hematopoiesis in sickle cell disease.The Journal of clinical investigation · 2022
    Trial
  4. Clonal selection of somatic mutations shapes NK cell memory.bioRxiv : the preprint server for biology · 2026
    Article
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149 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Erik N BergstromDepartment of Cellular and Molecular Medicine and Department of Bioengineering and Moores Cancer Center, University of California, San Diego, La Jolla, CA, 92093, USA.
Mi Ni HuangCentre for Computational Biology and Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, 8 College Rd, Singapore, 169857, Singapore.
Uma MahtoDepartment of Cellular and Molecular Medicine and Department of Bioengineering and Moores Cancer Center, University of California, San Diego, La Jolla, CA, 92093, USA.
Mark BarnesDepartment of Cellular and Molecular Medicine and Department of Bioengineering and Moores Cancer Center, University of California, San Diego, La Jolla, CA, 92093, USA.
Michael R StrattonCancer, Ageing and Somatic Mutation, Wellcome Trust Sanger Institute, Hinxton, Cambridgeshire, CB10 1SA, UK.
Steven G RozenCentre for Computational Biology and Programme in Cancer & Stem Cell Biology, Duke-NUS Medical School, 8 College Rd, Singapore, 169857, Singapore.
Ludmil B AlexandrovDepartment of Cellular and Molecular Medicine and Department of Bioengineering and Moores Cancer Center, University of California, San Diego, La Jolla, CA, 92093, USA. L2alexandrov@ucsd.edu.ORCID http://orcid.org/0000-0003-3596-4515

Funding

GENETIC ANALYSIS OF THE ACTIVATION OF A TYROSINE KINASEK08CA001422 · NCI · OREGON HEALTH AND SCIENCE UNIVERSITY · PI DRUKER, BRIAN J · 1990 to 1994
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Cancer Research UK Grand Challenge Award C98/A24032Singapore National Medical Research Council MOH-000032/MOH-CIRG18may-0004Singapore National Medical Research Council NMRC/CIRG/1422/2015
6 · The paper itself

Abstract

backgroundCancer genomes are peppered with somatic mutations imprinted by different mutational processes. The mutational pattern of a cancer genome can be used to identify and understand the etiology of the underlying mutational processes. A plethora of prior research has focused on examining mutational signatures and mutational patterns from single base substitutions and their immediate sequencing context. We recently demonstrated that further classification of small mutational events (including substitutions, insertions, deletions, and doublet substitutions) can be used to provide a deeper understanding of the mutational processes that have molded a cancer genome. However, there has been no standard tool that allows fast, accurate, and comprehensive classification for all types of small mutational events.

resultsHere, we present SigProfilerMatrixGenerator, a computational tool designed for optimized exploration and visualization of mutational patterns for all types of small mutational events. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. SigProfilerMatrixGenerator produces fourteen distinct matrices by considering transcriptional strand bias of individual events and by incorporating distinct classifications for single base substitutions, doublet base substitutions, and small insertions and deletions. While the tool provides a comprehensive classification of mutations, SigProfilerMatrixGenerator is also faster and more memory efficient than existing tools that generate only a single matrix.

conclusionsSigProfilerMatrixGenerator provides a standardized method for classifying small mutational events that is both efficient and scalable to large datasets. In addition to extending the classification of single base substitutions, the tool is the first to provide support for classifying doublet base substitutions and small insertions and deletions. SigProfilerMatrixGenerator is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/ .

Indexed as

MutationSoftwareGenomicsHumansINDEL MutationNeoplasmsMutational patternsMutational signaturesSomatic mutations

Identifiers

PMID31470794
PMCPMC6717374

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.