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

A family-based probabilistic method for capturing de novo mutations from high-throughput short-read sequencing data.

Statistical applications in genetics and molecular biology - 6 Jan 2012

Cartwright Reed A, Hussin Julie, Keebler Jonathan E M, Stone Eric A, Awadalla Philip

Abstract excerpt

Recent advances in high-throughput DNA sequencing technologies and associated statistical analyses have enabled in-depth analysis of whole-genome sequences. As this technology is applied to a growing number of individual human genomes, entire families are now being sequenced. Information containe...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computer Simulation
Share to Family

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genome, Human

Share to High-Throughput Nucleotide Sequencing

Share to Models, Genetic

Share to Probability

Share to ROC Curve

Source

Open in PubMed Open full text

Identifiers

PMID: 22499693
DOI: 10.2202/1544-6115.1713
PMCID: PMC3728889

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 4 Sept 2026. Prepared record publication-22499693-2026-09-04.2. Manifest publication-graph-cohort-2026-09-04.2.

Source receipt: PubMed via Helena Literature Corpus, retrieved 4 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

A family-based probabilistic method for capturing de novo mutations from high-throughput short-read sequencing data.PMID 22499693Read ↗

A likelihood-based framework for variant calling and de novo mutation detection in families.Semantic proximity Read ↗

A framework for the detection of de novo mutations in family-based sequencing data.Semantic proximity Read ↗

Joint variant and de novo mutation identification on pedigrees from high-throughput sequencing data.Semantic proximity Read ↗

Joint Variant and <i>De Novo</i> Mutation Identification on Pedigrees from High-Throughput Sequencing DataSemantic proximity Read ↗

Rare variant detection using family-based sequencing analysis.Semantic proximity Read ↗

Human de novo mutation rates from a four-generation pedigree reference.Semantic proximity Read ↗

Leveraging Identity-by-Descent for Accurate Genotype Inference in Family Sequencing Data.Semantic proximity Read ↗

Genotype-Frequency Estimation from High-Throughput Sequencing Data.Semantic proximity Read ↗

A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data.Semantic proximity Read ↗

Family-based association tests for genomewide association scans.Semantic proximity Read ↗

In silico method for inferring genotypes in pedigrees.Semantic proximity Read ↗

A full-likelihood method for the evaluation of causality of sequence variants from family data.Semantic proximity Read ↗

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