Broader functional annotation of genetic variation is a valuable means for prioritising phenotypically-important variants in further disease studies and large-scale genotyping projects. We developed SNPnexus to meet this need by assessing the potential significance of known and novel SNPs on the major transcriptome, proteome, regulatory and structural variation models. Since its previous release in 2012, we have made significant improvements to the annotation categories and updated the query and data viewing systems. The most notable changes include broader functional annotation of noncoding variants and expanding annotations to the most recent human genome assembly GRCh38/hg38. SNPnexus has now integrated rich resources from ENCODE and Roadmap Epigenomics Consortium to map and annotate the noncoding variants onto different classes of regulatory regions and noncoding RNAs as well as providing their predicted functional impact from eight popular non-coding variant scoring algorithms and computational methods. A novel functionality offered now is the support for neo-epitope predictions from leading tools to facilitate its use in immunotherapeutic applications. These updates to SNPnexus are in preparation for its future expansion towards a fully comprehensive computational workflow for disease-associated variant prioritization from sequencing data, placing its users at the forefront of translational research. SNPnexus is freely available at http://www.snp-nexus.org.

Broader functional annotation of single nucleotide variations is a valuable mean for prioritizing targets in further disease studies and large-scale genotyping projects. We originally developed SNPnexus to assess the potential significance of known and novel SNPs on the major transcriptome, proteome, regulatory and structural variation models in order to identify the phenotypically important variants. Being committed to providing continuous support to the scientific community, we have substantially improved SNPnexus over time by incorporating a broader range of variations such as insertions/deletions, block substitutions, IUPAC codes submission and region-based analysis, expanding the query size limit, and most importantly including additional categories for the assessment of functional impact. SNPnexus provides a comprehensive set of annotations for genomic variation data by characterizing related functional consequences at the transcriptome/proteome levels of seven major annotation systems with in-depth analysis of potential deleterious effects, inferring physical and cytogenetic mapping, reporting information on HapMap genotype/allele data, finding overlaps with potential regulatory elements, structural variations and conserved elements, and retrieving links with previously reported genetic disease studies. SNPnexus has a user-friendly web interface with an improved query structure, enhanced functional annotation categories and flexible output presentation making it practically useful for biologists. SNPnexus is freely available at http://www.snp-nexus.org.

Broader functional annotation of known as well as putative genetic variations is a valuable mean for prioritizing targets in disease studies and large-scale genotyping projects. In this article, we present a practical guide to SNPnexus, a web-based tool that provides an aggregate set of functional annotations for genomic variation data by characterizing related consequences at the transcriptome/proteome levels with in-depth analysis of potential deleterious effects, inferring physical and cytogenetic mapping, reporting related HapMap data, finding overlaps with potential regulatory, structural as well as conserved elements and retrieving links with previously reported genetic disease studies. We focus on the SNPnexus query system, its annotation categories and the biological interpretation of results.

The protein folding problem remains one of the most challenging open problems in computational biology. Simplified models in terms of lattice structure and energy function have been proposed to ease the computational hardness of this optimization problem. Heuristic search algorithms and constraint programming are two common techniques to approach this problem. The present study introduces a novel hybrid approach to simulate the protein folding problem using constraint programming technique integrated within local search.

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) is the only device currently available for mining of pancreatic cancer literature data. It brings together the largest collection of multidimensional pancreatic data from the literature including genomic, proteomic, microRNA, methylomic and transcriptomic profiles. PED allows the user to ask specific questions on the observed levels of deregulation among a broad range of specimen/experimental types including healthy/patient tissue and body fluid specimens, cell lines and murine models as well as related treatments/drugs data. Here we provide an update to PED, which has been previously featured in the Database issue of this journal. Briefly, PED data content has been substantially increased and expanded to cover methylomics studies. We introduced an extensive controlled vocabulary that records specific details on the samples and added data from large-scale meta-analysis studies. The web interface has been improved/redesigned with a quick search option to rapidly extract information about a gene/protein of interest and an upload option allowing users to add their own data to PED. We added a user guide and implemented integrated graphical tools to overlay and visualize retrieved information. Interoperability with biomart-compatible data sets was significantly improved to allow integrative queries with pancreatic cancer data.