Evidence map›Paper›PMID 27147817›Full record

ArticleCancer informatics2016

RefCNV: Identification of Gene-Based Copy Number Variants Using Whole Exome Sequencing.

Lun-Ching Chang, Biswajit Das, Chih-Jian Lih, Han Si, Corinne E Camalier, Paul M McGregor, Eric Polley

Open access · goldAbstract read
In one paragraph

Article in Cancer informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.9field-weighted citation impact, top 19% 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

3 citing papers in PubMed, 8 citations in OpenAlex.

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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 at 1 institution in 1 country.

Lun-Ching ChangDivision of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.
Biswajit DasMolecular Characterization and Clinical Assay Development Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Chih-Jian LihMolecular Characterization and Clinical Assay Development Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Han SiMolecular Characterization and Clinical Assay Development Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Corinne E CamalierMolecular Characterization and Clinical Assay Development Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Paul M McGregorMolecular Characterization and Clinical Assay Development Laboratory, Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD, USA.
Eric PolleyDivision of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.
National Cancer Institute · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With rapid advances in DNA sequencing technologies, whole exome sequencing (WES) has become a popular approach for detecting somatic mutations in oncology studies. The initial intent of WES was to characterize single nucleotide variants, but it was observed that the number of sequencing reads that mapped to a genomic region correlated with the DNA copy number variants (CNVs). We propose a method RefCNV that uses a reference set to estimate the distribution of the coverage for each exon. The construction of the reference set includes an evaluation of the sources of variability in the coverage distribution. We observed that the processing steps had an impact on the coverage distribution. For each exon, we compared the observed coverage with the expected normal coverage. Thresholds for determining CNVs were selected to control the false-positive error rate. RefCNV prediction correlated significantly (r = 0.96-0.86) with CNV measured by digital polymerase chain reaction for MET (7q31), EGFR (7p12), or ERBB2 (17q12) in 13 tumor cell lines. The genome-wide CNV analysis showed a good overall correlation (Spearman's coefficient = 0.82) between RefCNV estimation and publicly available CNV data in Cancer Cell Line Encyclopedia. RefCNV also showed better performance than three other CNV estimation methods in genome-wide CNV analysis.

Indexed as

copy number variationmethodologynext-generation sequencingwhole exome sequencing

Identifiers

PMID27147817
PMCPMC4849420
OpenAlexW2343555061

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.