In silico prediction of lncRNA function using tissue specific and evolutionary conserved expression

BACKGROUND: In recent years long non coding RNAs (lncRNAs) have been the subject of increasing interest. Thanks to many recent functional studies, the existence of a large class of lncRNAs with potential regulatory functions is now widely accepted. Although an increasing number of lncRNAs is being characterized and shown to be involved in many biological processes, the functions of the vast majority lncRNA genes is still unknown. Therefore computational methods able to take advantage of the increasing amount of publicly available data to predict lncRNA functions could be very useful. RESULTS: Since coding genes are much better annotated than lncRNAs, we attempted to project known functional information regarding proteins onto non coding genes using the guilt by association principle: if a gene shows an expression profile that correlates with those of a set of coding genes involved in a given function, that gene is probably involved in the same function. We computed gene coexpression for 30 human tissues and 9 vertebrates and mined the resulting networks with a methodology inspired by the rank product algorithm used to identify differentially expressed genes. Using different types of reference data we can predict putative new annotations for thousands of lncRNAs and proteins, ranging from cellular localization to relevance for disease and cancer. CONCLUSIONS: New function of coding genes and lncRNA can be profitably predicted using tissue specific coexpression, as well as expression of orthologous genes in different species. The data are available for download and through a user-friendly web interface at www.funcpred.com. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1535-x) contains supplementary material, which is available to authorized users. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1535-x) contains supplementary material, which is available to authorized users

Roar: detecting alternative polyadenylation with standard mRNA sequencing libraries

BACKGROUND: Post-transcriptional regulation is a complex mechanism that plays a central role in defining multiple cellular identities starting from a common genome. Modifications in the length of 3’UTRs have been found to play an important role in this context, since alternative 3’ UTRs could lead to differences for example in regulation by microRNAs and cellular localization of the transcripts thus altering their fate. RESULTS: We propose a strategy to identify the genes undergoing regulation of 3’ UTR length using RNA sequencing data obtained from standard libraries, thus widely applicable to data originally obtained to perform classical differential expression analyses. We decided to exploit previously annotated APA sites from public databases, in contrast with other approaches recently proposed in which the location of the APA site is inferred from the data together with the relative abundance of the isoforms. We demonstrate the reliability of our method by comparing it to the results of other microarray based or specific RNA-seq libraries methods and show that using APA sites databases results in higher sensitivity compared to de novo site prediction approach. CONCLUSIONS: We implemented the algorithm in a Bioconductor package to facilitate its broad usage in the scientific community. The ability of this approach to detect shortening from libraries with a number of reads comparable to that needed for differential expression analyses makes it useful for investigating if alternative polyadenylation is relevant in a certain biological process without requiring specific experimental assays. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1254-8) contains supplementary material, which is available to authorized users

Disease-gene discovery by integration of 3D gene expression and transcription factor binding affinities

Abstract Motivation: The computational evaluation of candidate genes for hereditary disorders is a non-trivial task. Several excellent methods for disease-gene prediction have been developed in the past 2 decades, exploiting widely differing data sources to infer disease-relevant functional relationships between candidate genes and disorders. We have shown recently that spatially mapped, i.e. 3D, gene expression data from the mouse brain can be successfully used to prioritize candidate genes for human Mendelian disorders of the central nervous system. Results: We improved our previous work 2-fold: (i) we demonstrate that condition-independent transcription factor binding affinities of the candidate genes' promoters are relevant for disease-gene prediction and can be integrated with our previous approach to significantly enhance its predictive power; and (ii) we define a novel similarity measure—termed Relative Intensity Overlap—for both 3D gene expression patterns and binding affinity profiles that better exploits their disease-relevant information content. Finally, we present novel disease-gene predictions for eight loci associated with different syndromes of unknown molecular basis that are characterized by mental retardation. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online

A new computational approach to analyze human protein complexes and predict novel protein interactions

We propose a new approach to identify interacting proteins based on gene expression data. By using hypergeometric distribution and extensive Monte-Carlo simulations, we demonstrate that looking at synchronous expression peaks in a single time interval is a high sensitivity approach to detect co-regulation among interacting proteins. Combining gene expression and Gene Ontology similarity analyses enabled the extraction of novel interactions from microarray datasets. Applying this approach to p21-activated kinase 1, we validated α-tubulin and early endosome antigen 1 as its novel interactors