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

Analysis of insertion-deletion from deep-sequencing data: software evaluation for optimal detection.

Briefings in bioinformatics - 1 Jan 2013

Neuman Joseph A, Isakov Ofer, Shomron Noam

Abstract excerpt

Insertion and deletion (indel) mutations, the most common type of structural variance in the human genome, affect a multitude of human traits and diseases. New sequencing technologies, such as deep sequencing, allow massive throughput of sequence data and greatly contribute to the field of disease causing mutation detection, in general, and indel detection, specifically. In order to infer indel presence (indel...

Read the complete abstract on PubMed

Topics

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

Share to Computational Biology
Share to Computer Simulation
Share to DNA Mutational Analysis

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to INDEL Mutation

Share to Mutagenesis, Insertional

Share to Sequence Analysis, DNA

Share to Sequence Deletion

Share to Software

Source

Identifiers

PMID: 22707752
DOI: 10.1093/bib/bbs013

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 2 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Analysis of insertion-deletion from deep-sequencing data: software evaluation for optimal detection.PMID 22707752Read ↗

Tool evaluation for the detection of variably sized indels from next generation whole genome and targeted sequencing data.Semantic proximity Read ↗

Variant calling tool evaluation for variable size indel calling from next generation whole genome and targeted sequencing dataSemantic proximity Read ↗

Benchmarking long-read aligners and SV callers for structural variation detection in Oxford nanopore sequencing data.Semantic proximity Read ↗

Machine learning-based detection of insertions and deletions in the human genomeSemantic proximity Read ↗

Kled: an ultra-fast and sensitive structural variant detection tool for long-read sequencing data.Semantic proximity Read ↗

Investigating the performance of Oxford Nanopore long-read sequencing with respect to Illumina microarrays and short-read sequencingSemantic proximity Read ↗

Identification of indels in next-generation sequencing data.Semantic proximity Read ↗

Automating resequencing-based detection of insertion-deletion polymorphisms.Semantic proximity Read ↗

Reducing INDEL calling errors in whole-genome and exome sequencing dataSemantic proximity Read ↗

Comprehensive evaluation of structural variant genotyping methods based on long-read sequencing data.Semantic proximity Read ↗

Split-Read Indel and Structural Variant Calling Using PINDEL.Semantic proximity Read ↗

DiSNPindel: improved intra-individual SNP and InDel detection in direct amplicon sequencing of a diploid.Semantic proximity Read ↗

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