ArticlePloS one2014
Genome-wide identification of somatic aberrations from paired normal-tumor samples.
Article in PloS one, 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, 9 citations in OpenAlex.
- Precise identification of somatic and germline variants in the absence of matched normal samples.Briefings in bioinformatics · 2024Article
- TOSCA: an automated Tumor Only Somatic CAlling workflow for somatic mutation detection without matched normal samples.Bioinformatics advances · 2022Article
- A computational approach to distinguish somatic vs. germline origin of genomic alterations from deep sequencing of cancer specimens without a matched normal.PLoS computational biology · 2018Article
- Application of high-resolution genomic profiling in the differential diagnosis of liposarcoma.Molecular cytogenetics · 2017Article
- Better tools, new problems: New technologies help to advance research in the life sciences, but the quantities of data generated are proving hard to manage and interpret.EMBO reports · 2015Article
- DeAnnCNV: a tool for online detection and annotation of copy number variations from whole-exome sequencing data.Nucleic acids research · 2015Article
- TAFFYS: An Integrated Tool for Comprehensive Analysis of Genomic Aberrations in Tumor Samples.PloS one · 2015Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genomic copy number alteration and allelic imbalance are distinct features of cancer cells, and recent advances in the genotyping technology have greatly boosted the research in the cancer genome. However, the complicated nature of tumor usually hampers the dissection of the SNP arrays. In this study, we describe a bioinformatic tool, named GIANT, for genome-wide identification of somatic aberrations from paired normal-tumor samples measured with SNP arrays. By efficiently incorporating genotype information of matched normal sample, it accurately detects different types of aberrations in cancer genome, even for aneuploid tumor samples with severe normal cell contamination. Furthermore, it allows for discovery of recurrent aberrations with critical biological properties in tumorigenesis by using statistical significance test. We demonstrate the superior performance of the proposed method on various datasets including tumor replicate pairs, simulated SNP arrays and dilution series of normal-cancer cell lines. Results show that GIANT has the potential to detect the genomic aberration even when the cancer cell proportion is as low as 5∼10%. Application on a large number of paired tumor samples delivers a genome-wide profile of the statistical significance of the various aberrations, including amplification, deletion and LOH. We believe that GIANT represents a powerful bioinformatic tool for interpreting the complex genomic aberration, and thus assisting both academic study and the clinical treatment of cancer.
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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.