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

ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.

Genome medicine - 29 Jun 2017

Kalatskaya Irina, Trinh Quang M, Spears Melanie, McPherson John D, Bartlett John M S, Stein Lincoln

Abstract excerpt

BACKGROUND: A key step in cancer genome analysis is the identification of somatic mutations in the tumor. This is typically done by comparing the genome of the tumor to the reference genome sequence derived from a normal tissue taken from the same donor. However, there are a variety of common scenarios in which matched normal tissue is not available for comparison. RESULTS: In this work, we describe an algorithm...

Read the complete abstract on PubMed

Topics

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

Share to DNA Mutational Analysis
Share to High-Throughput Nucleotide Sequencing

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Mutation

Share to Neoplasms

Share to Supervised Machine Learning

Source

Open in PubMed Open full text

Identifiers

PMID: 28659176
DOI: 10.1186/s13073-017-0446-9
PMCID: PMC5490163

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

ISOWN: accurate somatic mutation identification in the absence of normal tissue controls.PMID 28659176Read ↗

Evaluating somatic tumor mutation detection without matched normal samples.Semantic proximity Read ↗

SomaticSniper: identification of somatic point mutations in whole genome sequencing data.Semantic proximity Read ↗

SomaticSeq: An Ensemble and Machine Learning Method to Detect Somatic Mutations.Semantic proximity Read ↗

Precise identification of somatic and germline variants in the absence of matched normal samples.Semantic proximity Read ↗

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

Detection of Somatic Mutations in Exome Sequencing of Tumor-only Samples.Semantic proximity Read ↗

Robust Cancer Mutation Detection with Deep Learning Models Derived from Tumor-Normal Sequencing DataSemantic proximity Read ↗

RNAIndel: discovering somatic coding indels from tumor RNA-Seq dataSemantic proximity Read ↗

AIVariant: a deep learning-based somatic variant detector for highly contaminated tumor samples.Semantic proximity Read ↗

ClairS-TO: A deep-learning method for long-read tumor-only somatic small variant callingSemantic proximity Read ↗

Using Machine Learning to Identify True Somatic Variants from Next-Generation SequencingSemantic proximity Read ↗

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

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