Small proteins is the general term for proteins with length shorter than 100 amino acids. Identification and functional studies of small proteins have advanced rapidly in recent years, and several studies have shown that small proteins play important roles in diverse functions including development, muscle contraction and DNA repair. Identification and characterization of previously unrecognized small proteins may contribute in important ways to cell biology and human health. Current databases are generally somewhat deficient in that they have either not collected small proteins systematically, or contain only predictions of small proteins in a limited number of tissues and species. Here, we present a specifically designed web-accessible database, small proteins database (SmProt, http://bioinfo.ibp.ac.cn/SmProt), which is a database documenting small proteins. The current release of SmProt incorporates 255 010 small proteins computationally or experimentally identified in 291 cell lines/tissues derived from eight popular species. The database provides a variety of data including basic information (sequence, location, gene name, organism, etc.) as well as specific information (experiment, function, disease type, etc.). To facilitate data extraction, SmProt supports multiple search options, including species, genome location, gene name and their aliases, cell lines/tissues, ORF type, gene type, PubMed ID and SmProt ID. SmProt also incorporates a service for the BLAST alignment search and provides a local UCSC Genome Browser. Additionally, SmProt defines a high-confidence set of small proteins and predicts the functions of the small proteins.

MOTIVATION: Long non-coding RNAs (lncRNAs) are essential in many molecular pathways, and are frequently associated with disease but the mechanisms of most lncRNAs have not yet been characterized. Genetic variations, including single nucleotide polymorphisms (SNPs) and structural variations, are widely distributed in the genome, including lncRNA gene regions. As the number of studies on lncRNAs grows rapidly, it is necessary to evaluate the effects of genetic variations on lncRNAs. RESULTS: A modification sites of lncRNAs and provided comprehensive effects of SNPs on transcription and modification of lncRNAs. We collected putatively translated open reading frames (ORFs) in lncRNAs, and identified both synonymous and non-synonymous SNPs in ORFs. We also collected expression quantitative trait loci of lncRNAs from the literature. Furthermore, we identified lncRNAs in CNV regions as prognostic biomarker candidates of cancers and predicted lncRNA gene fusion events from RNA-seq data from cell lines. The LncVar database can be used as a resource to evaluate the effects of the variations on the biological function of lncRNAs. AVAILABILITY AND IMPLEMENTATION: LncVar is available at http://bioinfo.ibp.ac.cn/LncVar CONTACT: [email protected] information: Supplementary materials are available at Bioinformatics online.

UNLABELLED: We here present BioCircos.js, an interactive and lightweight JavaScript library especially for biological data interactive visualization. BioCircos.js facilitates the development of web-based applications for circular visualization of various biological data, such as genomic features, genetic variations, gene expression and biomolecular interactions. AVAILABILITY AND IMPLEMENTATION: BioCircos.js and its manual are freely available online at http://bioinfo.ibp.ac.cn/biocircos/ CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: PIWI-interacting RNAs (piRNAs) are a class of small non-coding RNAs that are highly abundant in the germline. One important role of piRNAs is to defend genome integrity by guiding PIWI proteins to silence transposable elements (TEs), which have a high potential to cause deleterious effects on their host. The mechanism of piRNA-mediated post-transcriptional silencing was also observed to affect mRNAs, suggesting that piRNAs might play a broad role in gene expression regulation. However, there has been no systematic report with regard to how many protein-coding genes might be targeted and regulated by piRNAs. RESULTS: We trained a support vector machine classifier based on a combination of Miwi CLIP-Seq-derived features and position-derived features to predict the potential targets of piRNAs on mRNAs in the mouse. Reanalysis of a published microarray dataset suggested that the expression level of the 2587 protein-coding genes predicted as piRNA targets showed significant upregulation as a whole after abolishing the slicer activity of Miwi, supporting the conclusion that they are subject to piRNA-mediated regulation. AVAILABILITY AND IMPLEMENTATION: A web version of the method called pirnaPre as well as our results for browse is available at http://www.regulatoryrna.org/software/piRNA/piRNA_target_mRNA/index.php CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.