ArticleBriefings in bioinformatics2016
Evaluation of somatic copy number estimation tools for whole-exome sequencing data.
Article in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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.
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.
Who cites it
26 citing papers in PubMed.
- Adenosine Pathway-Based Prognostic Signature for Predicting Clinical Outcomes and Immune Microenvironment Characteristics in Epithelial Ovarian Cancer.Journal of clinical practice and research · 2026Article
- EMcnv: enhancing CNV detection performance through ensemble strategies with heterogeneous meta-graph neural networks.Briefings in bioinformatics · 2025Article
- Evaluation of somatic copy number variation detection by NGS technologies and bioinformatics tools on a hyper-diploid cancer genome.Genome biology · 2024Article
- Assessing the impact of sequencing platforms and analytical pipelines on whole-exome sequencing.Frontiers in genetics · 2024Article
- Multicentric pilot study to standardize clinical whole exome sequencing (WES) for cancer patients.NPJ precision oncology · 2023Article
- Computational analysis of cancer genome sequencing data.Nature reviews. Genetics · 2022Review
- Novel Partial Exon 51 Deletion in the Duchenne Muscular Dystrophy Gene IdentifiedFrontiers in genetics · 2021Article
- Article
- DNA Copy Number Changes in Diffuse Large B Cell Lymphomas.Frontiers in oncology · 2020Review
- SECNVs: A Simulator of Copy Number Variants and Whole-Exome Sequences From Reference Genomes.Frontiers in genetics · 2020Article
- Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology.Cancers · 2019Review
- Bioinformatics for precision oncology.Briefings in bioinformatics · 2019Review
- New Recurrent Structural Aberrations in the Genome of Chronic Lymphocytic Leukemia Based on Exome-Sequencing Data.Frontiers in genetics · 2019Article
- TargetClone: A multi-sample approach for reconstructing subclonal evolution of tumors.PloS one · 2018Article
- Informatics for cancer immunotherapy.Annals of oncology : official journal of the European Society for Medical Oncology · 2017Review
- Prevalence and detection of low-allele-fraction variants in clinical cancer samples.Nature communications · 2017Article
- Anaconda: AN automated pipeline for somatic COpy Number variation Detection and Annotation from tumor exome sequencing data.BMC bioinformatics · 2017Article
- Reliability of algorithmic somatic copy number alteration detection from targeted capture data.Bioinformatics (Oxford, England) · 2017Article
- High resolution measurement of DUF1220 domain copy number from whole genome sequence data.BMC genomics · 2017Article
- Gene-based comparative analysis of tools for estimating copy number alterations using whole-exome sequencing data.Oncotarget · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Whole-exome sequencing (WES) has become a standard method for detecting genetic variants in human diseases. Although the primary use of WES data has been the identification of single nucleotide variations and indels, these data also offer a possibility of detecting copy number variations (CNVs) at high resolution. However, WES data have uneven read coverage along the genome owing to the target capture step, and the development of a robust WES-based CNV tool is challenging. Here, we evaluate six WES somatic CNV detection tools: ADTEx, CONTRA, Control-FREEC, EXCAVATOR, ExomeCNV and Varscan2. Using WES data from 50 kidney chromophobe, 50 bladder urothelial carcinoma, and 50 stomach adenocarcinoma patients from The Cancer Genome Atlas, we compared the CNV calls from the six tools with a reference CNV set that was identified by both single nucleotide polymorphism array 6.0 and whole-genome sequencing data. We found that these algorithms gave highly variable results: visual inspection reveals significant differences between the WES-based segmentation profiles and the reference profile, as well as among the WES-based profiles. Using a 50% overlap criterion, 13-77% of WES CNV calls were covered by CNVs from the reference set, up to 21% of the copy gains were called as losses or vice versa, and dramatic differences in CNV sizes and CNV numbers were observed. Overall, ADTEx and EXCAVATOR had the best performance with relatively high precision and sensitivity. We suggest that the current algorithms for somatic CNV detection from WES data are limited in their performance and that more robust algorithms are needed.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.