ArticleBMC genomics2014
seqCNA: an R package for DNA copy number analysis in cancer using high-throughput sequencing.
Article in BMC genomics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 12 citations in OpenAlex.
- Identification of Copy Number Alterations from Next-Generation Sequencing Data.Advances in experimental medicine and biology · 2022Article
- Assessing the performance of methods for copy number aberration detection from single-cell DNA sequencing data.PLoS computational biology · 2020Article
- Intraventricular meningiomas frequently harbor NF2 mutations but lack common genetic alterations in TRAF7, AKT1, SMO, KLF4, PIK3CA, and TERT.Acta neuropathologica communications · 2019Article
- Clinically Actionable Insights into Initial and Matched Recurrent Glioblastomas to Inform Novel Treatment Approaches.Journal of oncology · 2019Article
- Informatics for cancer immunotherapy.Annals of oncology : official journal of the European Society for Medical Oncology · 2017Review
- VEGAWES: variational segmentation on whole exome sequencing for copy number detection.BMC bioinformatics · 2015Article
- Base resolution methylome profiling: considerations in platform selection, data preprocessing and analysis.Epigenomics · 2015Review
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeviations in the amount of genomic content that arise during tumorigenesis, called copy number alterations, are structural rearrangements that can critically affect gene expression patterns. Additionally, copy number alteration profiles allow insight into cancer discrimination, progression and complexity. On data obtained from high-throughput sequencing, improving quality through GC bias correction and keeping false positives to a minimum help build reliable copy number alteration profiles.
resultsWe introduce seqCNA, a parallelized R package for an integral copy number analysis of high-throughput sequencing cancer data. The package includes novel methodology on (i) filtering, reducing false positives, and (ii) GC content correction, improving copy number profile quality, especially under great read coverage and high correlation between GC content and copy number. Adequate analysis steps are automatically chosen based on availability of paired-end mapping, matched normal samples and genome annotation.
conclusionsseqCNA, available through Bioconductor, provides accurate copy number predictions in tumoural data, thanks to the extensive filtering and better GC bias correction, while providing an integrated and parallelized workflow.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.