UTAP: User-friendly Transcriptome Analysis Pipeline.

BACKGROUND: RNA-Seq technology is routinely used to characterize the transcriptome, and to detect gene expression differences among cell types, genotypes and conditions. Advances in short-read sequencing instruments such as Illumina Next-Seq have yielded easy-to-operate machines, with high throughput, at a lower price per base. However, processing this data requires bioinformatics expertise to tailor and execute specific solutions for each type of library preparation. RESULTS: In order to enable fast and user-friendly data analysis, we developed an intuitive and scalable transcriptome pipeline that executes the full process, starting from cDNA sequences derived by RNA-Seq [Nat Rev Genet 10:57-63, 2009] and bulk MARS-Seq [Science 343:776-779, 2014] and ending with sets of differentially expressed genes. Output files are placed in structured folders, and results summaries are provided in rich and comprehensive reports, containing dozens of plots, tables and links. CONCLUSION: Our User-friendly Transcriptome Analysis Pipeline (UTAP) is an open source, web-based intuitive platform available to the biomedical research community, enabling researchers to efficiently and accurately analyse transcriptome sequence data.

A combined sequence and structure based method for discovering enriched motifs in RNA from in vivo binding data.

RNA binding proteins (RBPs) play an important role in regulating many processes in the cell. RBPs often recognize their RNA targets in a specific manner. In addition to the RNA primary sequence, the structure of the RNA has been shown to play a central role in RNA recognition by RBPs. In recent years, many experimental approaches, both in vitro and in vivo, were developed and employed to identify and characterize RBP targets and extract their binding specificities. In vivo binding techniques, such as CrossLinking and ImmunoPrecipitation (CLIP)-based methods, enable the characterization of protein binding sites on RNA targets. However, these methods do not provide information regarding the structural preferences of the protein. While methods to obtain the structure of RNA are available, inferring both the sequence and the structure preferences of RBPs remains a challenge. Here we present SMARTIV, a novel computational tool for discovering combined sequence and structure binding motifs from in vivo RNA binding data relying on the sequences of the target sites, the ranking of their binding scores and their predicted secondary structure. The combined motifs are provided in a unified representation that is informative and easy for visual perception. We tested the method on CLIP-seq data from different platforms for a variety of RBPs. Overall, we show that our results are highly consistent with known binding motifs of RBPs, offering additional information on their structural preferences.

Three interacting genomic loci incorporating two novel mutations underlie the evolution of diet-induced diabetes.

We investigated the pathophysiology of diet-induced diabetes in the Cohen diabetic rat (CDs/y) from its induction to its chronic phase, using a multi-layered integrated genomic approach. We identified by linkage analysis two diabetes-related quantitative trait loci on RNO4 and RNO13. We determined their functional contribution to diabetes by chromosomal substitution, using congenic and consomic strains. To identify within these loci genes of relevance to diabetes, we sequenced the genome of CDs/y and compared it to 25 other rat strains. Within the RNO4 locus, we detected a novel high impact deletion in the Ndufa4 gene that was unique to CDs/y. Within the RNO13 locus, we found multiple SNPs and INDELs that were unique to CDs/y but were unable to prioritize any of the genes. Genome wide screening identified a novel third locus not detected by linkage analysis that consisted of a novel high impact deletion on RNO11 that was unique to CDs/y and that involved the Sdf2l1 gene. Using co-segregation analysis, we investigated in silico the relative contribution to the diabetic phenotype and the interaction between the three genomic loci on RNO4, RNO11 and RNO13. We found that the RNO4 locus plays a major role during the induction of diabetes, whereas the genomic loci on RNO13 and RNO11, while interacting with the RNO4 locus, contribute more significantly to the diabetic phenotype during the chronic phase of the disease. The mechanisms whereby the mutations on RNO4 and 11 and the RNO13 locus contribute to the development of diabetes are under continuing investigation.

Searching for protein signatures using a multilevel alphabet.

Short motifs are known to play diverse roles in proteins, such as in mediating the interactions with other molecules, binding to membranes, or conducting a specific biological function. Standard approaches currently employed to detect short motifs in proteins search for enrichment of amino acid motifs considering mostly the sequence information. Here, we presented a new approach to search for common motifs (protein signatures) which share both physicochemical and structural properties, looking simultaneously at different features. Our method takes as an input an amino acid sequence and translates it to a new alphabet that reflects its intrinsic structural and chemical properties. Using the MEME search algorithm, we identified the proteins signatures within subsets of protein which encompass common sequence and structural information. We demonstrated that we can detect enriched structural motifs, such as the amphipathic helix, from large datasets of linear sequences, as well as predicting common structural properties (such as disorder, surface accessibility, or secondary structures) of known functional-motifs. Finally, we applied the method to the yeast protein interactome and identified novel putative interacting motifs. We propose that our approach can be applied for de novo protein function prediction given either sequence or structural information.

Simulations of the NK cell immune synapse reveal that activation thresholds can be established by inhibitory receptors acting locally.

NK cell activation is regulated by a balance between activating and inhibitory signals. To address the question of how these signals are spatially integrated, we created a computer simulation of activating and inhibitory NK cell immunological synapse (NKIS) assembly, implementing either a "quantity-based" inhibition model or a "distance-based" inhibition model. The simulations mimicked the observed molecule distributions in inhibitory and activating NKIS and yielded several new insights. First, the total signal is highly influenced by activating complex dissociation rates but not by adhesion and inhibitory complex dissociation rates. Second, concerted motion of receptors in clusters significantly accelerates NKIS maturation. Third, when the potential of a cis interaction between Ly49 receptors and MHC class I on murine NK cells was added to the model, the integrated signal as a function of receptor and ligand numbers was only slightly increased, at least up to the level of 50% cis-bound Ly49 receptors reached in the model. Fourth, and perhaps most importantly, the integrated signal behavior obtained when using the distance-based inhibition signal model was closer to the experimentally observed behavior, with an inhibition radius of the order 3-10 molecules. Microscopy to visualize Vav activation in NK cells on micropatterned surfaces of activating and inhibitory strips revealed that Vav is only locally activated where activating receptors are ligated within a single NK cell contact. Taken together, these data are consistent with a model in which inhibitory receptors act locally; that is, that every bound inhibitory receptor acts on activating receptors within a certain radius around it.

Giardia lamblia miRNAs as a new diagnostic tool for human giardiasis.

BACKGROUND: Giardia lamblia is a very common cause of gastrointestinal symptoms worldwide. There are several methods for the diagnosis of Giardia infection, however none are ideal. We aim to find a new, microRNA-based method that will improve the currently available diagnostic methods for giardiasis. METHODS: Deep-sequence profiling of Giardia small-RNA revealed that miR5 and miR6 are highly expressed in Giardia. These miRNAs were tested by qRT-PCR in duodenal biopsies of patients with giardiasis who were positive by microscopic pathological evaluation. The gastric biopsies of the same patients served as negative control tissues. Additionally, these miRNAs were evaluated in stool samples of patients with proven giardiasis. RESULTS: All histologically proven duodenal biopsies of patients with Giardia infection were positive for Giardia miR5, with a mean threshold cycle (Ct) of 23.7, as well as for Giardia DNA qPCR (16S-like gene, mean Ct 26.3). Gastric biopsies which were tested as a control all were negative. Stool evaluation of miR6 in patients with giardiasis showed 90% specificity but only 66% sensitivity, and a lower accuracy rate was obtained with miR5. CONCLUSION: Giardia miR5 testing in duodenal biopsies may be a new method for the diagnosis of giardiasis. It seems to be more sensitive when compared with testing for Giardia DNA by qPCR in duodenal biopsies. It will be important to investigate the contribution of routine Giardia miRNA testing in duodenal biopsies from patients with persistent abdominal symptoms.