Month: March 2017

CancerLocator

[PMID:28335812] [Genome Biology]

CancerLocator: non-invasive cancer diagnosis and tissue-of-origin prediction using methylation profiles of cell-free DNA

“a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data.”

Somatic Mutations in Embryo

[PMID:28329761] [Nature]

Somatic mutations reveal asymmetric cellular dynamics in the early human embryo

“Here we use whole-genome sequences of normal blood from 241 adults to identify 163 early embryonic mutations. We estimate that approximately three base substitution mutations occur per cell per cell-doubling event in early human embryogenesis and these are mainly attributable to two known mutational signatures. We used the mutations to reconstruct developmental lineages of adult cells and demonstrate that the two daughter cells of many early embryonic cell-doubling events contribute asymmetrically to adult blood at an approximately 2:1 ratio.”

LINSIGHT

[PMID:28288115] [Nature Genetics]

Fast, scalable prediction of deleterious noncoding variants from functional and population genomic data

“substantially improves the prediction of noncoding nucleotide sites at which mutations are likely to have deleterious fitness consequences, and which, therefore, are likely to be phenotypically important. LINSIGHT combines a generalized linear model for functional genomic data with a probabilistic model of molecular evolution. The method is fast and highly scalable, enabling it to exploit the ‘big data’ available in modern genomics. “

HRDetect

[PMID:28288110] [Nature Methods]

HRDetect is a predictor of BRCA1 and BRCA2 deficiency based on mutational signatures

“Recently, somatic substitution, insertion/deletion and rearrangement patterns, or ‘mutational signatures’, were associated with BRCA1/BRCA2 dysfunction. Herein we used a lasso logistic regression model to identify six distinguishing mutational signatures predictive of BRCA1/BRCA2 deficiency. A weighted model called HRDetect was developed to accurately detect BRCA1/BRCA2-deficient samples.”