Evidence map›Paper›PMID 31296206›Full record

ArticleBMC medical genomics2019

CoMutPlotter: a web tool for visual summary of mutations in cancer cohorts.

Po-Jung Huang, Hou-Hsien Lin, Chi-Ching Lee, Ling-Ya Chiu, Shao-Min Wu, Yuan-Ming Yeh, Petrus Tang, Cheng-Hsun Chiu, Ping-Chiang Lyu, Pei-Chien Tsai

Abstract read
In one paragraph

Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Article
  2. A Tale of Two Centers: Visual Exploration of Health Disparities in Cancer Care.IEEE Pacific Visualization Symposium : [proceedings]. IEEE Pacific Visualisation Symposium · 2022
    Article
  3. Article
  4. Article
  5. Review
  6. Article
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

10 authors.

Po-Jung HuangDepartment of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan.
Hou-Hsien LinInstitute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.
Chi-Ching LeeDepartment and Graduate Institute of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan.
Ling-Ya ChiuInstitute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan.
Shao-Min WuGraduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Yuan-Ming YehGenomic Medicine Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan.
Petrus TangGraduate Institute of Biomedical Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan.
Cheng-Hsun ChiuGenomic Medicine Core Laboratory, Chang Gung Memorial Hospital, Linkou, Taiwan.
Ping-Chiang LyuInstitute of Bioinformatics and Structural Biology, National Tsing Hua University, Hsinchu, Taiwan. pclyu@mx.nthu.edu.tw.
Pei-Chien TsaiDepartment of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan. pctsai@gap.cgu.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCoMut plot is widely used in cancer research publications as a visual summary of mutational landscapes in cancer cohorts. This summary plot can inspect gene mutation rate and sample mutation burden with their relevant clinical details, which is a common first step for analyzing the recurrence and co-occurrence of gene mutations across samples. The cBioPortal and iCoMut are two web-based tools that allow users to create intricate visualizations from pre-loaded TCGA and ICGC data. For custom data analysis, only limited command-line packages are available now, making the production of CoMut plots difficult to achieve, especially for researchers without advanced bioinformatics skills. To address the needs for custom data and TCGA/ICGC data comparison, we have created CoMutPlotter, a web-based tool for the production of publication-quality graphs in an easy-of-use and automatic manner.

resultsWe introduce a web-based tool named CoMutPlotter to lower the barriers between complex cancer genomic data and researchers, providing intuitive access to mutational profiles from TCGA/ICGC projects as well as custom cohort studies. A wide variety of file formats are supported by CoMutPlotter to translate cancer mutation profiles into biological insights and clinical applications, which include Mutation Annotation Format (MAF), Tab-separated values (TSV) and Variant Call Format (VCF) files.

conclusionsIn summary, CoMutPlotter is the first tool of its kind that supports VCF file, the most widely used file format, as its input material. CoMutPlotter also provides the most-wanted function for comparing mutation patterns between custom cohort and TCGA/ICGC project. Contributions of COSMIC mutational signatures in individual samples are also included in the summary plot, which is a unique feature of our tool. CoMutPlotter is freely available at http://tardis.cgu.edu.tw/comutplotter .

Indexed as

InternetMutationCohort StudiesComputational BiologyComputer GraphicsHumansNeoplasmsCancer mutational profile mutational signature TCGA

Identifiers

PMID31296206
PMCPMC6624176

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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.