Evidence map›Paper›PMID 28968643›Full record

ArticleBioinformatics (Oxford, England)2018

CGmapTools improves the precision of heterozygous SNV calls and supports allele-specific methylation detection and visualization in bisulfite-sequencing data.

Weilong Guo, Ping Zhu, Matteo Pellegrini, Michael Q Zhang, Xiangfeng Wang, Zhongfu Ni

Open access · bronzeAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.

0numbers the graph read from it
0cells of the map it votes in
110citing papers in PubMed
5.1field-weighted citation impact, top 4% of its field
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

110 citing papers in PubMed, 218 citations in OpenAlex.

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50 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

6 authors at 4 institutions in 2 countries.

Weilong GuoState Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China.
Ping ZhuState Key Laboratory of Experimental Hematology, Institute of Hematology and Blood Disease Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China.
Matteo PellegriniDepartment of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095, USA.
Michael Q ZhangDepartment of Molecular and Cell Biology, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX 75080, USA.
Xiangfeng WangBeijing Advanced Innovation Center for Food Nutrition and Human health, China Agricultural University, Beijing 100193, China.
Zhongfu NiState Key Laboratory for Agrobiotechnology, Key Laboratory of Crop Heterosis and Utilization, Beijing Key Laboratory of Crop Genetic Improvement, China Agricultural University, Beijing 100193, China.
China Agricultural University · CNChinese Academy of Medical Sciences & Peking Union Medical College · CNThe University of Texas at Dallas · USUniversity of California, Los Angeles · US

Funding

DNA methylation and its relationship to gene expression and physiologic traitsR01GM095656 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI PELLEGRINI, MATTEO · 2011 to 2014
$1.8M
NIGMS NIH HHS R01 GM095656
6 · The paper itself

Abstract

Motivation: DNA methylation is important for gene silencing and imprinting in both plants and animals. Recent advances in bisulfite sequencing allow detection of single nucleotide variations (SNVs) achieving high sensitivity, but accurately identifying heterozygous SNVs from partially C-to-T converted sequences remains challenging. Results: We designed two methods, BayesWC and BinomWC, that substantially improved the precision of heterozygous SNV calls from ∼80% to 99% while retaining comparable recalls. With these SNV calls, we provided functions for allele-specific DNA methylation (ASM) analysis and visualizing the methylation status on reads. Applying ASM analysis to a previous dataset, we found that an average of 1.5% of investigated regions showed allelic methylation, which were significantly enriched in transposon elements and likely to be shared by the same cell-type. A dynamic fragment strategy was utilized for DMR analysis in low-coverage data and was able to find differentially methylated regions (DMRs) related to key genes involved in tumorigenesis using a public cancer dataset. Finally, we integrated 40 applications into the software package CGmapTools to analyze DNA methylomes. This package uses CGmap as the format interface, and designs binary formats to reduce the file size and support fast data retrieval, and can be applied for context-wise, gene-wise, bin-wise, region-wise and sample-wise analyses and visualizations. Availability and implementation: The CGmapTools software is freely available at https://cgmaptools.github.io/. Contact: guoweilong@cau.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

Indexed as

AllelesDNA MethylationHeterozygoteSoftwareAnimalsEukaryotaGenomicsHumansSequence Analysis, DNASulfiteshydrogen sulfiteSulfites

Identifiers

PMID28968643
PMCPMC6454434
OpenAlexW2754256904

What OpenQuestion holds

Textmetadata
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.