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

SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.

BMC genomics - 14 Nov 2016

Spinella Jean-François, Mehanna Pamela, Vidal Ramon, Saillour Virginie, Cassart Pauline, Richer Chantal, Ouimet Manon, Healy Jasmine, Sinnett Daniel

Abstract excerpt

BACKGROUND: Next-generation sequencing (NGS) allows unbiased, in-depth interrogation of cancer genomes. Many somatic variant callers have been developed yet accurate ascertainment of somatic variants remains a considerable challenge as evidenced by the varying mutation call rates and low concordance among callers. Statistical model-based algorithms that are currently available perform well under ideal scenarios,...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Computational Biology

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to High-Throughput Nucleotide Sequencing

Share to Machine Learning

Share to Reproducibility of Results

Share to Software

Share to Web Browser

Share to Workflow

Source

Open in PubMed Open full text

Identifiers

PMID: 27842494
DOI: 10.1186/s12864-016-3281-2
PMCID: PMC5109690

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

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

SNooPer: a machine learning-based method for somatic variant identification from low-pass next-generation sequencing.PMID 27842494Read ↗

Feature-based classifiers for somatic mutation detection in tumour-normal paired sequencing data.Semantic proximity Read ↗

DeepSomatic: Accurate somatic small variant discovery for multiple sequencing technologiesSemantic proximity Read ↗

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

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

Detection of somatic structural variants from short-read next-generation sequencing data.Semantic proximity Read ↗

Detection of somatic structural variants from short-read next-generation sequencing dataSemantic proximity Read ↗

Azurify integrates cancer genomics with machine learning to classify the clinical significance of somatic variantsSemantic proximity Read ↗

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

Machine learning-based somatic variant calling in cell-free DNA of metastatic breast cancer patients using large NGS panelsSemantic proximity Read ↗

ClairS: a deep-learning method for long-read somatic small variant callingSemantic proximity Read ↗

Ensemble-Based Somatic Mutation Calling in Cancer Genomes.Semantic proximity Read ↗

ClairS: a deep-learning method for long-read tumor-normal pair somatic small variant calling.Semantic proximity Read ↗

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