Chunxuan Shao | bioRxiv Science

bioRxiv2016

Inference of maternal allele inheritance via hierarchical Bayesian model in noninvasive prenatal diagnosis

Noninvasive prenatal diagnosis (NIPD) poses a promising solution for detecting genetic alterations in fetus genome. However, the inference of the maternal allele inheritance in monogenic autosomal recessive disease is still challenging. Here the Bayesian hierarchical model is proposed to deduce the allele inheritance basing on haplotype frequency. The Bayesian approach, which does not depend on the knowledge of fetus DNA proportion in maternal plasma, provides accurate estimations on both real and simulated data; moreover, it is most robust than current methods in analyzing noisy or even erroneous data.

Bioinformatics↗

bioRxiv2016

Synlet: an R package for systemically analyzing synthetic lethal RNA interference screen data

SummaryHigh-throughput synthetic lethal RNA interference (RNAi) screen experiments shed important insights on the filed of cancer researches and drug discovery, but a comprehensive software for analyzing the data was not available yet. We present synlet, an R package provided a complete pipeline to process the synthetic lethal RNAi screens data. Synlet provides several methods to access the screen quality, including Z factor and data visualization. B-score and fraction of control or samples normalization methods are implemented in the package. More importantly, synlet facilitates the process of hits selection by implementing several algorithms, providing the possibility to identify high confidence targets.\n\nAvailabilityThe source code is freely available in Bioconductor (http://bioconductor.org/).\n\nContactc.shao@Dkfz-Heidelberg.de

Bioinformatics↗