RNAseqCNV: analysis of large-scale copy number variations from RNA-seq data
Jazyk angličtina Země Velká Británie, Anglie Médium print-electronic
Typ dokumentu časopisecké články, práce podpořená grantem, Research Support, N.I.H., Extramural
Grantová podpora
R35 CA197695
NCI NIH HHS - United States
K99 CA241297
NCI NIH HHS - United States
P30 CA021765
NCI NIH HHS - United States
R00 CA241297
NCI NIH HHS - United States
P50 GM115279
NIGMS NIH HHS - United States
PubMed
35351983
PubMed Central
PMC9177690
DOI
10.1038/s41375-022-01547-8
PII: 10.1038/s41375-022-01547-8
Knihovny.cz E-zdroje
- MeSH
- algoritmy MeSH
- karyotypizace MeSH
- lidé MeSH
- sekvenování transkriptomu MeSH
- variabilita počtu kopií segmentů DNA * genetika MeSH
- vysoce účinné nukleotidové sekvenování * metody MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
Transcriptome sequencing (RNA-seq) is widely used to detect gene rearrangements and quantitate gene expression in acute lymphoblastic leukemia (ALL), but its utility and accuracy in identifying copy number variations (CNVs) has not been well described. CNV information inferred from RNA-seq can be highly informative to guide disease classification and risk stratification in ALL due to the high incidence of aneuploid subtypes within this disease. Here we describe RNAseqCNV, a method to detect large scale CNVs from RNA-seq data. We used models based on normalized gene expression and minor allele frequency to classify arm level CNVs with high accuracy in ALL (99.1% overall and 98.3% for non-diploid chromosome arms, respectively), and the models were further validated with excellent performance in acute myeloid leukemia (accuracy 99.8% overall and 99.4% for non-diploid chromosome arms). RNAseqCNV outperforms alternative RNA-seq based algorithms in calling CNVs in the ALL dataset, especially in samples with a high proportion of CNVs. The CNV calls were highly concordant with DNA-based CNV results and more reliable than conventional cytogenetic-based karyotypes. RNAseqCNV provides a method to robustly identify copy number alterations in the absence of DNA-based analyses, further enhancing the utility of RNA-seq to classify ALL subtype.
Department of Computational Biology St Jude Children's Research Hospital Memphis TN 38105 USA
Department of Pathology St Jude Children's Research Hospital Memphis TN 38105 USA
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