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

Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data

8 Oct 2014

Abstract excerpt

MOTIVATION: Most tumor samples are a heterogeneous mixture of cells, including admixture by normal (non-cancerous) cells and subpopulations of cancerous cells with different complements of somatic aberrations. This intra-tumor heterogeneity complicates the analysis of somatic aberrations in DNA sequencing data from tumor samples. RESULTS: We describe an algorithm called THetA2 that infers the composition of a...

Read the complete abstract on PubMed

Topics

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

Share to Cancer Genomics and Diagnostics
Share to Genetic factors in colorectal cancer
Share to Pancreatic and Hepatic Oncology Research

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 25297070
DOI: 10.1093/bioinformatics/btu651

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing dataPMID 25297070Read ↗

THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing dataSemantic proximity Read ↗

TPES: tumor purity estimation from SNVsSemantic proximity Read ↗

SomatiCA: identifying, characterizing and quantifying somatic copy number aberrations from cancer genome sequencing data.Semantic proximity Read ↗

Reconstructing tumor clonal heterogeneity and evolutionary relationships based on tumor DNA sequencing data.Semantic proximity Read ↗

Accurity: accurate tumor purity and ploidy inference from tumor-normal WGS data by jointly modelling somatic copy number alterations and heterozygous germline single-nucleotide-variantsSemantic proximity Read ↗

FACETS: Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing.Semantic proximity Read ↗

PurityEst: estimating purity of human tumor samples using next-generation sequencing data.Semantic proximity Read ↗

A combinatorial approach for analyzing intra-tumor heterogeneity from high-throughput sequencing data.Semantic proximity Read ↗

Reconstructing subclonal composition and evolution from whole genome sequencing of tumorsSemantic proximity Read ↗

Computational Methods for Characterizing Cancer Mutational HeterogeneitySemantic proximity Read ↗

Deconvolving tumor purity and ploidy by integrating copy number alterations and loss of heterozygositySemantic proximity Read ↗

TrAp: a tree approach for fingerprinting subclonal tumor composition.Semantic proximity Read ↗

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