Gene expression regulation is a sophisticated, multi-stage process, and its robustness is critical to normal cell function and the survival of an organism. Previous studies indicate that differential gene expression at the RNA level is typically attenuated at the protein level through translational regulation. However, how post-transcriptional regulation (PTR) influences expression change during the RNA maturation process remains unclear. In this study, we investigated this by quantifying the magnitude of expression change in precursor RNA and mature RNA across a vast range of different biological conditions. We analyzed bulk tissue RNA sequencing data from 4689 samples, including healthy and diseased tissues from human, chimpanzee, rhesus macaque, and murine sources. We demonstrated that PTR tends to support homeostatic expression of mature RNA by amplifying normal tissue-specific expression of precursor RNA, while reducing expression change of precursor RNA in disease contexts. Our study provides insight into the general influence of PTR on gene expression homeostasis. Our analysis also suggests that intronic reads in RNA-seq studies may contain under-utilized information about disease associations. Additionally, our findings may assist in identifying new disease biomarkers and more effective ways of altering gene expression as a therapeutic strategy.

MOTIVATION: Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts. RESULTS: Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches. AVAILABILITY AND IMPLEMENTATION: Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0.

Motivation: Genome sequencing has had a remarkable impact on our ability to study the effects of human genetic variation, however, variant interpretation remains the major bottleneck. Understanding the potential impact of variants, including structural variants, requires extensive annotation from disparate sources of knowledge, and in silico prediction algorithms. Results: We introduce Seave, an intuitive web platform that enables all types of variants to be securely stored, annotated and filtered. Variants are annotated with allele frequencies and pathogenicity assessments from many popular databases and in silico pathogenicity prediction scores. Seave enables filtering of variants with specific inheritance patterns, including somatic variants, by quality, allele frequencies and gene lists which can be curated and saved. Seave was made for whole genome data and is capable of storing and querying copy number and structural variants. Availability and implementation: To demo Seave with public data, see https://www.seave.bio. Source code is available at http://code.seave.bio and extensive documentation is available at http://documentation.seave.bio. Seave can be locally installed on an Apache server with PHP and MySQL, or we provide an Amazon Machine Image for quick deployment. For commercial and clinical diagnostic licensing, contact the corresponding author. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Advanced high-throughput sequencing technologies have produced massive amount of reads data, and algorithms have been specially designed to contract the size of these datasets for efficient storage and transmission. Reordering reads with regard to their positions in de novo assembled contigs or in explicit reference sequences has been proven to be one of the most effective reads compression approach. As there is usually no good prior knowledge about the reference sequence, current focus is on the novel construction of de novo assembled contigs. RESULTS: We introduce a new de novo compression algorithm named minicom. This algorithm uses large k-minimizers to index the reads and subgroup those that have the same minimizer. Within each subgroup, a contig is constructed. Then some pairs of the contigs derived from the subgroups are merged into longer contigs according to a (w, k)-minimizer-indexed suffix-prefix overlap similarity between two contigs. This merging process is repeated after the longer contigs are formed until no pair of contigs can be merged. We compare the performance of minicom with two reference-based methods and four de novo methods on 18 datasets (13 RNA-seq datasets and 5 whole genome sequencing datasets). In the compression of single-end reads, minicom obtained the smallest file size for 22 of 34 cases with significant improvement. In the compression of paired-end reads, minicom achieved 20-80% compression gain over the best state-of-the-art algorithm. Our method also achieved a 10% size reduction of compressed files in comparison with the best algorithm under the reads-order preserving mode. These excellent performances are mainly attributed to the exploit of the redundancy of the repetitive substrings in the long contigs. AVAILABILITY AND IMPLEMENTATION: https://github.com/yuansliu/minicom. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motivation: The branchpoint element is required for the first lariat-forming reaction in splicing. However current catalogues of human branchpoints remain incomplete due to the difficulty in experimentally identifying these splicing elements. To address this limitation, we have developed a machine-learning algorithm-branchpointer-to identify branchpoint elements solely from gene annotations and genomic sequence. Results: Using branchpointer, we annotate branchpoint elements in 85% of human gene introns with sensitivity (61.8%) and specificity (97.8%). In addition to annotation, branchpointer can evaluate the impact of SNPs on branchpoint architecture to inform functional interpretation of genetic variants. Branchpointer identifies all published deleterious branchpoint mutations annotated in clinical variant databases, and finds thousands of additional clinical and common genetic variants with similar predicted effects. This genome-wide annotation of branchpoints provides a reference for the genetic analysis of splicing, and the interpretation of noncoding variation. Availability and implementation: Branchpointer is written and implemented in the statistical programming language R and is freely available under a BSD license as a package through Bioconductor. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: Comparing transcriptomic data with proteomic data to identify protein-coding sequences is a long-standing challenge in molecular biology, one that is exacerbated by the increasing size of high-throughput datasets. To address this challenge, and thereby to improve the quality of genome annotation and understanding of genome biology, we have developed an integrated suite of programs, called Pinstripe. We demonstrate its application, utility and discovery power using transcriptomic and proteomic data from publicly available datasets. RESULTS: To demonstrate the efficacy of Pinstripe for large-scale analysis, we applied Pinstripe's reverse peptide mapping pipeline to a transcript library including de novo assembled transcriptomes from the human Illumina Body Atlas (IBA2) and GENCODE v10 gene annotations, and the EBI Proteomics Identifications Database (PRIDE) peptide database. This analysis identified 736 canonical open reading frames (ORFs) supported by three or more PRIDE peptide fragments that are positioned outside any known coding DNA sequence (CDS). Because of the unfiltered nature of the PRIDE database and high probability of false discovery, we further refined this list using independent evidence for translation, including the presence of a Kozak sequence or functional domains, synonymous/non-synonymous substitution ratios and ORF length. Using this integrative approach, we observed evidence of translation from a previously unknown let7e primary transcript, the archetypical lncRNA H19, and a homolog of RD3. Reciprocally, by exclusion of transcripts with mapped peptides or significant ORFs (>80 codon), we identify 32 187 loci with RNAs longer than 2000 nt that are unlikely to encode proteins. AVAILABILITY AND IMPLEMENTATION: Pinstripe (pinstripe.matticklab.com) is freely available as source code or a Mono binary. Pinstripe is written in C# and runs under the Mono framework on Linux or Mac OS X, and both under Mono and .Net under Windows. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Genome-wide expression studies have developed exponentially in recent years as a result of extensive use of microarray technology. However, expression signals are typically calculated using the assignment of "probesets" to genes, without addressing the problem of "gene" definition or proper consideration of the location of the measuring probes in the context of the currently known genomes and transcriptomes. Moreover, as our knowledge of metazoan genomes improves, the number of both protein-coding and noncoding genes, as well as their associated isoforms, continues to increase. Consequently, there is a need for new databases that combine genomic and transcriptomic information and provide updated mapping of expression probes to current genomic annotations. RESULTS: GATExplorer (Genomic and Transcriptomic Explorer) is a database and web platform that integrates a gene loci browser with nucleotide level mappings of oligo probes from expression microarrays. It allows interactive exploration of gene loci, transcripts and exons of human, mouse and rat genomes, and shows the specific location of all mappable Affymetrix microarray probes and their respective expression levels in a broad set of biological samples. The web site allows visualization of probes in their genomic context together with any associated protein-coding or noncoding transcripts. In the case of all-exon arrays, this provides a means by which the expression of the individual exons within a gene can be compared, thereby facilitating the identification and analysis of alternatively spliced exons. The application integrates data from four major source databases: Ensembl, RNAdb, Affymetrix and GeneAtlas; and it provides the users with a series of files and packages (R CDFs) to analyze particular query expression datasets. The maps cover both the widely used Affymetrix GeneChip microarrays based on 3' expression (e.g. human HG U133 series) and the all-exon expression microarrays (Gene 1.0 and Exon 1.0). CONCLUSIONS: GATExplorer is an integrated database that combines genomic/transcriptomic visualization with nucleotide-level probe mapping. By considering expression at the nucleotide level rather than the gene level, it shows that the arrays detect expression signals from entities that most researchers do not contemplate or discriminate. This approach provides the means to undertake a higher resolution analysis of microarray data and potentially extract considerably more detailed and biologically accurate information from existing and future microarray experiments.

The assumption that RNA can be readily classified into either protein-coding or non-protein-coding categories has pervaded biology for close to 50 years. Until recently, discrimination between these two categories was relatively straightforward: most transcripts were clearly identifiable as protein-coding messenger RNAs (mRNAs), and readily distinguished from the small number of well-characterized non-protein-coding RNAs (ncRNAs), such as transfer, ribosomal, and spliceosomal RNAs. Recent genome-wide studies have revealed the existence of thousands of noncoding transcripts, whose function and significance are unclear. The discovery of this hidden transcriptome and the implicit challenge it presents to our understanding of the expression and regulation of genetic information has made the need to distinguish between mRNAs and ncRNAs both more pressing and more complicated. In this Review, we consider the diverse strategies employed to discriminate between protein-coding and noncoding transcripts and the fundamental difficulties that are inherent in what may superficially appear to be a simple problem. Misannotations can also run in both directions: some ncRNAs may actually encode peptides, and some of those currently thought to do so may not. Moreover, recent studies have shown that some RNAs can function both as mRNAs and intrinsically as functional ncRNAs, which may be a relatively widespread phenomenon. We conclude that it is difficult to annotate an RNA unequivocally as protein-coding or noncoding, with overlapping protein-coding and noncoding transcripts further confounding this distinction. In addition, the finding that some transcripts can function both intrinsically at the RNA level and to encode proteins suggests a false dichotomy between mRNAs and ncRNAs. Therefore, the functionality of any transcript at the RNA level should not be discounted.