Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Comprehensive benchmarking of SNV callers for highly admixed tumor data.

PloS one - 1 Jan 2017

Bohnert Regina, Vivas Sonia, Jansen Gunther

Abstract excerpt

Precision medicine attempts to individualize cancer therapy by matching tumor-specific genetic changes with effective targeted therapies. A crucial first step in this process is the reliable identification of cancer-relevant variants, which is considerably complicated by the impurity and heterogeneity of clinical tumor samples. We compared the impact of admixture of non-cancerous cells and low somatic allele...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Algorithms
Share to Benchmarking
Share to Computer Simulation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Databases, Genetic

Share to Germ Cells

Share to Reference Standards

Share to Reproducibility of Results

Source

Open in PubMed Open full text

Identifiers

PMID: 29020110
DOI: 10.1371/journal.pone.0186175
PMCID: PMC5636151

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 6 Sept 2026. Manifest publication-graph-cohort-2026-09-06.1.

Source receipt: pubmed, retrieved 6 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Comprehensive benchmarking of SNV callers for highly admixed tumor data.PMID 29020110Read ↗

Comprehensive benchmarking of somatic single-nucleotide variant and indel detection at ultra-low allele fractions using short- and long-read dataSemantic proximity Read ↗

Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs.Semantic proximity Read ↗

Scalable Open Science Approach for Mutation Calling of Tumor Exomes Using Multiple Genomic Pipelines.Semantic proximity Read ↗

Comparison of somatic mutation calling methods in amplicon and whole exome sequence data.Semantic proximity Read ↗

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples.Semantic proximity Read ↗

In-depth comparison of somatic point mutation callers based on different tumor next-generation sequencing depth data.Semantic proximity Read ↗

TNscope: Accurate Detection of Somatic Mutations with Haplotype-based Variant Candidate Detection and Machine Learning FilteringSemantic proximity Read ↗

Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers.Semantic proximity Read ↗

A benchmarking study of individual somatic variant callers and voting-based ensembles for whole-exome sequencingSemantic proximity Read ↗

SCANBIT facilitates identification of tumor cell populations in scRNAseq data using pseudobulked SNV callsSemantic proximity Read ↗

Accurate Reference-Free Somatic Variant-Calling by Integrating Genomic, Sequencing and Population DataSemantic proximity Read ↗

Accuracy and Reproducibility of Somatic Point Mutation Calling in Clinical-Type Targeted Sequencing DataSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.