A flexible statistical framework is developed for the analysis of read counts from RNA-Seq gene expression studies. It provides the ability to analyse complex experiments involving multiple treatment conditions and blocking variables while still taking full account of biological variation. Biological variation between RNA samples is estimated separately from the technical variation associated with sequencing technologies. Novel empirical Bayes methods allow each gene to have its own specific variability, even when there are relatively few biological replicates from which to estimate such variability. The pipeline is implemented in the edgeR package of the Bioconductor project. A case study analysis of carcinoma data demonstrates the ability of generalized linear model methods (GLMs) to detect differential expression in a paired design, and even to detect tumour-specific expression changes. The case study demonstrates the need to allow for gene-specific variability, rather than assuming a common dispersion across genes or a fixed relationship between abundance and variability. Genewise dispersions de-prioritize genes with inconsistent results and allow the main analysis to focus on changes that are consistent between biological replicates. Parallel computational approaches are developed to make non-linear model fitting faster and more reliable, making the application of GLMs to genomic data more convenient and practical. Simulations demonstrate the ability of adjusted profile likelihood estimators to return accurate estimators of biological variability in complex situations. When variation is gene-specific, empirical Bayes estimators provide an advantageous compromise between the extremes of assuming common dispersion or separate genewise dispersion. The methods developed here can also be applied to count data arising from DNA-Seq applications, including ChIP-Seq for epigenetic marks and DNA methylation analyses.

/pdf/differential-expression-analysis-of-multifactor-rna-seq-29me7d29uk.pdf

Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

The role of non-coding RNAs (ncRNAs) in disease is best understood for microRNAs in cancer. However, there is increasing interest in the disease-related roles of other ncRNAs — including piRNAs, snoRNAs, T-UCRs and lncRNAs — and in using this knowledge for therapy.

Non-coding RNAs in human disease

Molecular Mechanisms of Long Noncoding RNAs

Although microRNAs (miRNAs), other non-coding RNAs (ncRNAs) (e.g. lncRNAs, pseudogenes and circRNAs) and competing endogenous RNAs (ceRNAs) have been implicated in cell-fate determination and in various human diseases, surprisingly little is known about the regulatory interaction networks among the multiple classes of RNAs. In this study, we developed starBase v2.0 (http://starbase.sysu.edu.cn/) to systematically identify the RNA-RNA and protein-RNA interaction networks from 108 CLIP-Seq (PAR-CLIP, HITS-CLIP, iCLIP, CLASH) data sets generated by 37 independent studies. By analyzing millions of RNA-binding protein binding sites, we identified ∼9000 miRNA-circRNA, 16 000 miRNA-pseudogene and 285,000 protein-RNA regulatory relationships. Moreover, starBase v2.0 has been updated to provide the most comprehensive CLIP-Seq experimentally supported miRNA-mRNA and miRNA-lncRNA interaction networks to date. We identified ∼10,000 ceRNA pairs from CLIP-supported miRNA target sites. By combining 13 functional genomic annotations, we developed miRFunction and ceRNAFunction web servers to predict the function of miRNAs and other ncRNAs from the miRNA-mediated regulatory networks. Finally, we developed interactive web implementations to provide visualization, analysis and downloading of the aforementioned large-scale data sets. This study will greatly expand our understanding of ncRNA functions and their coordinated regulatory networks.

/pdf/starbase-v2-0-decoding-mirna-cerna-mirna-ncrna-and-protein-28tbqe1yoz.pdf

starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data

Tumor Metastasis: Molecular Insights and Evolving Paradigms

Disclosed are primer and probe compositions, PCR assays, methods and kits for the specific detection of E. coli O157:H7 and not E. coli O55:H7 from a variety of samples. In some embodiments, methods for specifically detecting E. coli O157:H7 comprise: hybridizing at least a first pair of polynucleotide primers to at least a first target polynucleotide sequence, hybridizing at least a second pair of polynucleotide primers to at least a second target polynucleotide sequence, amplifying said at least first and said at least second target polynucleotide sequences, and detecting said at least first and said at least second amplified target polynucleotide sequence products, wherein the detection of both the first amplified target polynucleotide sequence product and the second amplified target polynucleotide sequence product is indicative of the presence of E. coli O157:H7 in a sample and not E. coli O55:H7.

Nucleic acid compositions and nucleic acid amplification based methods for detecting E. coli O157:H7

Disclosed are methods and kits for the specific detection of E. coli O157:H7 and not E. coli O55:H7 from samples such as: complex food matrices, water, beverages, fermentation broths, forensic & biological samples, and environmental samples including food processing and manufacturing surfaces. In some embodiments, a method of the disclosure comprises: hybridizing at least a first pair of polynucleotide primers to at least a first target polynucleotide sequence, hybridizing at least a second pair of polynucleotide primers to at least a second target polynucleotide sequence, amplifying the at least first and at least second target polynucleotide sequences, and detecting the first and second amplified target polynucleotide sequence products, wherein the detection of both the first amplified target polynucleotide sequence product and the second amplified target polynucleotide sequence product is indicative of the presence of E. coli O157:H7 in a sample and not E. coli O55:H7.

Nucleic acid amplification methods for specific detection of E. coli O157:H7 without co-detection of E. coli O55:H7

La presente invention concerne un essai d'amplification multi-amorces, un procede et des kits pour detecter des especes de mycoplasme et des especes etroitement apparentees en utilisant une pluralite d'amorces oligonucleotidiques en contact avec un echantillon dans un recipient unique et la detection du produit d'amplification, ou la presence d'un produit d'amplification indique la presence de mycoplasme dans l'echantillon.

Pius Brzoska

Papers

Nucleic acid compositions and nucleic acid amplification based methods for detecting E. coli O157:H7

Nucleic acid amplification methods for specific detection of E. coli O157:H7 without co-detection of E. coli O55:H7

Essai multi-amorces pour détection de mycoplasme