Analyzing the type and frequency of patient‐specific mutations that give rise to Duchenne muscular dystrophy (DMD) is an invaluable tool for diagnostics, basic scientific research, trial planning, and improved clinical care. Locus‐specific databases allow for the collection, organization, storage, and analysis of genetic variants of disease. Here, we describe the development and analysis of the TREAT‐NMD DMD Global database (http://umd.be/TREAT_DMD/). We analyzed genetic data for 7,149 DMD mutations held within the database. A total of 5,682 large mutations were observed (80% of total mutations), of which 4,894 (86%) were deletions (1 exon or larger) and 784 (14%) were duplications (1 exon or larger). There were 1,445 small mutations (smaller than 1 exon, 20% of all mutations), of which 358 (25%) were small deletions and 132 (9%) small insertions and 199 (14%) affected the splice sites. Point mutations totalled 756 (52% of small mutations) with 726 (50%) nonsense mutations and 30 (2%) missense mutations. Finally, 22 (0.3%) mid‐intronic mutations were observed. In addition, mutations were identified within the database that would potentially benefit from novel genetic therapies for DMD including stop codon read‐through therapies (10% of total mutations) and exon skipping therapy (80% of deletions and 55% of total mutations).

Snakes are limbless predators, and many species use venom to help overpower relatively large, agile prey. Snake venoms are complex protein mixtures encoded by several multilocus gene families that function synergistically to cause incapacitation. To examine venom evolution, we sequenced and interrogated the genome of a venomous snake, the king cobra (Ophiophagus hannah), and compared it, together with our unique transcriptome, microRNA, and proteome datasets from this species, with data from other vertebrates. In contrast to the platypus, the only other venomous vertebrate with a sequenced genome, we find that snake toxin genes evolve through several distinct co-option mechanisms and exhibit surprisingly variable levels of gene duplication and directional selection that correlate with their functional importance in prey capture. The enigmatic accessory venom gland shows a very different pattern of toxin gene expression from the main venom gland and seems to have recruited toxin-like lectin genes repeatedly for new nontoxic functions. In addition, tissue-specific microRNA analyses suggested the co-option of core genetic regulatory components of the venom secretory system from a pancreatic origin. Although the king cobra is limbless, we recovered coding sequences for all Hox genes involved in amniote limb development, with the exception of Hoxd12. Our results provide a unique view of the origin and evolution of snake venom and reveal multiple genome-level adaptive responses to natural selection in this complex biological weapon system. More generally, they provide insight into mechanisms of protein evolution under strong selection. Significance Snake venoms are toxic protein cocktails used for prey capture. To investigate the evolution of these complex biological weapon systems, we sequenced the genome of a venomous snake, the king cobra, and assessed the composition of venom gland expressed genes, small RNAs, and secreted venom proteins. We show that regulatory components of the venom secretory system may have evolved from a pancreatic origin and that venom toxin genes were co-opted by distinct genomic mechanisms. After co-option, toxin genes important for prey capture have massively expanded by gene duplication and evolved under positive selection, resulting in protein neofunctionalization. This diverse and dramatic venom-related genomic response seemingly occurs in response to a coevolutionary arms race between venomous snakes and their prey.

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/

Despite its high prevalence and mortality, little is known about the pathogenesis of rheumatoid arthritis-associated interstitial lung disease (RA-ILD). Given that familial pulmonary fibrosis (FPF) and RA-ILD frequently share the usual pattern of interstitial pneumonia and common environmental risk factors, we hypothesised that the two diseases might share additional risk factors, including FPF-linked genes. Our aim was to identify coding mutations of FPF-risk genes associated with RA-ILD.We used whole exome sequencing (WES), followed by restricted analysis of a discrete number of FPF-linked genes and performed a burden test to assess the excess number of mutations in RA-ILD patients compared to controls.Among the 101 RA-ILD patients included, 12 (11.9%) had 13 WES-identified heterozygous mutations in theTERT,RTEL1,PARNorSFTPCcoding regions.The burden test, based on 81 RA-ILD patients and 1010 controls of European ancestry, revealed an excess ofTERT,RTEL1,PARNorSFTPCmutations in RA-ILD patients (OR 3.17, 95% CI 1.53–6.12; p=9.45×10⁻⁴). Telomeres were shorter in RA-ILD patients with aTERT,RTEL1orPARNmutation than in controls (p=2.87×10⁻²).Our results support the contribution of FPF-linked genes to RA-ILD susceptibility.

With the rapidly developing high-throughput sequencing technologies known as next generation sequencing or NGS, our approach to gene hunting and diagnosis has drastically changed. In <10 years, these technologies have moved from gene panel to whole genome sequencing and from an exclusively research context to clinical practice. Today, the limit is not the sequencing of one, many or all genes but rather the data analysis. Consequently, the challenge is to rapidly and efficiently identify disease-causing mutations within millions of variants. To do so, we developed the VarAFT software to annotate and pinpoint human disease-causing mutations through access to multiple layers of information. VarAFT was designed both for research and clinical contexts and is accessible to all scientists, regardless of bioinformatics training. Data from multiple samples may be combined to address all Mendelian inheritance modes, cancers or population genetics. Optimized filtration parameters can be stored and re-applied to large datasets. In addition to classical annotations from dbNSFP, VarAFT contains unique features at the disease (OMIM), phenotypic (HPO), gene (Gene Ontology, pathways) and variation levels (predictions from UMD-Predictor and Human Splicing Finder) that can be combined to optimally select candidate pathogenic mutations. VarAFT is freely available at: http://varaft.eu.