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

Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers.

BMC bioinformatics - 3 Jan 2017

Hofmann Ariane L, Behr Jonas, Singer Jochen, Kuipers Jack, Beisel Christian, Schraml Peter, Moch Holger, Beerenwinkel Niko

Abstract excerpt

BACKGROUND: Next-generation sequencing of matched tumor and normal biopsy pairs has become a technology of paramount importance for precision cancer treatment. Sequencing costs have dropped tremendously, allowing the sequencing of the whole exome of tumors for just a fraction of the total treatment costs. However, clinicians and scientists cannot take full advantage of the generated data because the accuracy of...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to DNA, Neoplasm

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genomics

Share to High-Throughput Nucleotide Sequencing

Share to Kidney Neoplasms

Share to Polymorphism, Single Nucleotide

Share to Sequence Analysis, DNA

Source

Open in PubMed Open full text

Identifiers

PMID: 28049408
DOI: 10.1186/s12859-016-1417-7
PMCID: PMC5209852

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 5 Sept 2026. Manifest publication-graph-cohort-2026-09-05.1.

Source receipt: pubmed, retrieved 5 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Detailed simulation of cancer exome sequencing data reveals differences and common limitations of variant callers.PMID 28049408Read ↗

Comprehensive benchmarking of SNV callers for highly admixed tumor data.Semantic proximity Read ↗

SNVSniffer: an integrated caller for germline and somatic single-nucleotide and indel mutations.Semantic proximity Read ↗

A comparative analysis of algorithms for somatic SNV detection in cancer.Semantic proximity Read ↗

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

MOV&RSim: computational modelling of cancer-specific variants and sequencing reads characteristics for realistic tumoral sample simulation.Semantic proximity Read ↗

SNVMix: predicting single nucleotide variants from next-generation sequencing of tumors.Semantic proximity Read ↗

The consequences of variant calling decisions in secondary analyses of cancer sequencing dataSemantic proximity Read ↗

JointSNVMix: a probabilistic model for accurate detection of somatic mutations in normal/tumour paired next-generation sequencing data.Semantic proximity Read ↗

Comparative Evaluation of Mutect2, Strelka2, and FreeBayes for Somatic SNV Detection in Synthetic and Clinical Whole-Exome Sequencing Data.Semantic proximity Read ↗

Variant callers for next-generation sequencing data: a comparison study.Semantic proximity Read ↗

Detection of oncogenic and clinically actionable mutations in cancer genomes critically depends on variant calling tools.Semantic proximity Read ↗

Reliable Analysis of Clinical Tumor-Only Whole-Exome Sequencing Data.Semantic proximity Read ↗

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