Statistical Tests for Detecting Differential RNA-Transcript Expression from Read Counts

As a fruit of the current revolution in sequencing technology, transcriptomes can now be analyzed at an unprecedented level of detail. These advances have been exploited for detecting differential expressed genes across biological samples and for quantifying the abundances of various RNA transcripts within one gene. However, explicit strategies for detecting the hidden differential abundances of RNA transcripts in biological samples have not been defined. In this work, we present two novel statistical tests to address this issue: a 'gene structure sensitive' Poisson test for detecting differential expression when the transcript structure of the gene is known, and a kernel-based test called Maximum Mean Discrepancy when it is unknown. We analyzed the proposed approaches on simulated read data for two artificial samples as well as on factual reads generated by the Illumina Genome Analyzer for two _C. elegans_ samples. Our analysis shows that the Poisson test identifies genes with differential transcript expression considerably better that previously proposed RNA transcript quantification approaches for this task. The MMD test is able to detect a large fraction (75%) of such differential cases without the knowledge of the annotated transcripts. It is therefore well-suited to analyze RNA-Seq experiments when the genome annotations are incomplete or not available, where other approaches have to fail

Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data : From Seventh International Society for Computational Biology (ISCB) Student Council Symposium 2011 Vienna, Austria. 15 July 2011

First published by BioMed Central: Schultheiss, Sebastian J.; Jean, Géraldine; Behr, Jonas; Bohnert, Regina; Drewe, Philipp; Görnitz, Nico; Kahles, André; Mudrakarta, Pramod; Sreedharan, Vipin T.; Zeller, Georg; Rätsch, Gunnar: Oqtans: a Galaxy-integrated workflow for quantitative transcriptome analysis from NGS Data - In: BMC Bioinformatics. - ISSN 1471-2105 (online). - 12 (2011), suppl. 11, art. A7. - doi:10.1186/1471-2105-12-S11-A7

Fine-mapping of prostate cancer susceptibility loci in a large meta-analysis identifies candidate causal variants

© 2018 The Author(s). Prostate cancer is a polygenic disease with a large heritable component. A number of common, low-penetrance prostate cancer risk loci have been identified through GWAS. Here we apply the Bayesian multivariate variable selection algorithm JAM to fine-map 84 prostate cancer susceptibility loci, using summary data from a large European ancestry meta-analysis. We observe evidence for multiple independent signals at 12 regions and 99 risk signals overall. Only 15 original GWAS tag SNPs remain among the catalogue of candidate variants identified; the remainder are replaced by more likely candidates. Biological annotation of our credible set of variants indicates significant enrichment within promoter and enhancer elements, and transcription factor-binding sites, including AR, ERG and FOXA1. In 40 regions at least one variant is colocalised with an eQTL in prostate cancer tissue. The refined set of candidate variants substantially increase the proportion of familial relative risk explained by these known susceptibility regions, which highlights the importance of fine-mapping studies and has implications for clinical risk profiling

Germline variation at 8q24 and prostate cancer risk in men of European ancestry

Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10−15), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62–4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification

SDN enabled QoS provision for online streaming services in residential ISP networks

In this paper we present an idea of a propriety Software Defined residential Network (SDrN) and we show as a use case, a multicast streaming service that can be hosted on such networks. To verify the feasibility of the service in the context of quality of service, we offer to the providers of online streaming services (in some cases the ISPs themselves), APIs to control and validate the QoS of the users in the service. The QoS control APIs were tested on SDN based simulation environment