ArticleBioinformatics (Oxford, England)2014
CLImAT: accurate detection of copy number alteration and loss of heterozygosity in impure and aneuploid tumor samples using whole-genome sequencing data.
Article in Bioinformatics (Oxford, England), 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Coexisting genomic aberrations associated with lymph node metastasis in breast cancer.The Journal of clinical investigation · 2018Trial
- HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data.Genome biology · 2024Article
- HATCHet2: clone- and haplotype-specific copy number inference from bulk tumor sequencing data.bioRxiv : the preprint server for biology · 2023Article
- Accucopy: accurate and fast inference of allele-specific copy number alterations from low-coverage low-purity tumor sequencing data.BMC bioinformatics · 2021Article
- Analysis of Aneuploidy Spectrum From Whole-Genome Sequencing Provides Rapid Assessment of Clonal Variation Within Established Cancer Cell Lines.Cancer informatics · 2021Article
- Accurate quantification of copy-number aberrations and whole-genome duplications in multi-sample tumor sequencing data.Nature communications · 2020Article
- SimuSCoP: reliably simulate Illumina sequencing data based on position and context dependent profiles.BMC bioinformatics · 2020Article
- Hierarchical discovery of large-scale and focal copy number alterations in low-coverage cancer genomes.BMC bioinformatics · 2020Article
- MFCNV: A New Method to Detect Copy Number Variations From Next-Generation Sequencing Data.Frontiers in genetics · 2020Article
- Accurate Inference of Tumor Purity and Absolute Copy Numbers From High-Throughput Sequencing Data.Frontiers in genetics · 2020Article
- Comprehensive genomic variation profiling of cervical intraepithelial neoplasia and cervical cancer identifies potential targets for cervical cancer early warning.Journal of medical genetics · 2019Article
- Detection of copy number variants and loss of heterozygosity from impure tumor samples using whole exome sequencing data.Oncology letters · 2018Article
- Accurity: accurate tumor purity and ploidy inference from tumor-normal WGS data by jointly modelling somatic copy number alterations and heterozygous germline single-nucleotide-variants.Bioinformatics (Oxford, England) · 2018Article
- Discovery of targetable genetic alterations in advanced non-small cell lung cancer using a next-generation sequencing-based circulating tumor DNA assay.Scientific reports · 2017Article
- Quantification of Multiple Tumor Clones Using Gene Array and Sequencing Data.The annals of applied statistics · 2017Article
- Segmentum: a tool for copy number analysis of cancer genomes.BMC bioinformatics · 2017Article
- CLImAT-HET: detecting subclonal copy number alterations and loss of heterozygosity in heterogeneous tumor samples from whole-genome sequencing data.BMC medical genomics · 2017Article
- CloneCNA: detecting subclonal somatic copy number alterations in heterogeneous tumor samples from whole-exome sequencing data.BMC bioinformatics · 2016Article
- DeAnnCNV: a tool for online detection and annotation of copy number variations from whole-exome sequencing data.Nucleic acids research · 2015Article
- Copy number gain of granulin-epithelin precursor (GEP) at chromosome 17q21 associates with overexpression in human liver cancer.BMC cancer · 2015Article
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5 authors.
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Abstract
motivationWhole-genome sequencing of tumor samples has been demonstrated as an efficient approach for comprehensive analysis of genomic aberrations in cancer genome. Critical issues such as tumor impurity and aneuploidy, GC-content and mappability bias have been reported to complicate identification of copy number alteration and loss of heterozygosity in complex tumor samples. Therefore, efficient computational methods are required to address these issues.
resultsWe introduce CLImAT (CNA and LOH Assessment in Impure and Aneuploid Tumors), a bioinformatics tool for identification of genomic aberrations from tumor samples using whole-genome sequencing data. Without requiring a matched normal sample, CLImAT takes integrated analysis of read depth and allelic frequency and provides extensive data processing procedures including GC-content and mappability correction of read depth and quantile normalization of B-allele frequency. CLImAT accurately identifies copy number alteration and loss of heterozygosity even for highly impure tumor samples with aneuploidy. We evaluate CLImAT on both simulated and real DNA sequencing data to demonstrate its ability to infer tumor impurity and ploidy and identify genomic aberrations in complex tumor samples. AVAILABILITY AND IMPLEMENTATION: The CLImAT software package can be freely downloaded at http://bioinformatics.ustc.edu.cn/CLImAT/.
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