Annotating genes and genomes with DNA sequences extracted from biomedical articles

Motivation: Increasing rates of publication and DNA sequencing make the problem of finding relevant articles for a particular gene or genomic region more challenging than ever. Existing text-mining approaches focus on finding gene names or identifiers in English text. These are often not unique and do not identify the exact genomic location of a study

BBP: Brucella genome annotation with literature mining and curation

BACKGROUND: Brucella species are Gram-negative, facultative intracellular bacteria that cause brucellosis in humans and animals. Sequences of four Brucella genomes have been published, and various Brucella gene and genome data and analysis resources exist. A web gateway to integrate these resources will greatly facilitate Brucella research. Brucella genome data in current databases is largely derived from computational analysis without experimental validation typically found in peer-reviewed publications. It is partially due to the lack of a literature mining and curation system able to efficiently incorporate the large amount of literature data into genome annotation. It is further hypothesized that literature-based Brucella gene annotation would increase understanding of complicated Brucella pathogenesis mechanisms. RESULTS: The Brucella Bioinformatics Portal (BBP) is developed to integrate existing Brucella genome data and analysis tools with literature mining and curation. The BBP InterBru database and Brucella Genome Browser allow users to search and analyze genes of 4 currently available Brucella genomes and link to more than 20 existing databases and analysis programs. Brucella literature publications in PubMed are extracted and can be searched by a TextPresso-powered natural language processing method, a MeSH browser, a keywords search, and an automatic literature update service. To efficiently annotate Brucella genes using the large amount of literature publications, a literature mining and curation system coined Limix is developed to integrate computational literature mining methods with a PubSearch-powered manual curation and management system. The Limix system is used to quickly find and confirm 107 Brucella gene mutations including 75 genes shown to be essential for Brucella virulence. The 75 genes are further clustered using COG. In addition, 62 Brucella genetic interactions are extracted from literature publications. These results make possible more comprehensive investigation of Brucella pathogenesis. Other BBP features include publication email alert service, Brucella researchers' contact database, and discussion forum. CONCLUSION: BBP is a gateway for Brucella researchers to search, analyze, and curate Brucella genome data originated from public databases and literature. Brucella gene mutations and genetic interactions are annotated using Limix leading to better understanding of Brucella pathogenesis

Text-mining assisted regulatory annotation

Text-mining technologies can be integrated with genome annotation systems, increasing the availability of annotated cis-regulatory data

Translational web robots for pathogen genome analysis

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No wisdom in the crowd: genome annotation at the time of big data - current status and future prospects

Science and engineering rely on the accumulation and dissemination of knowledge to make discoveries and create new designs. Discovery-driven genome research rests on knowledge passed on via gene annotations. In response to the deluge of sequencing big data, standard annotation practice employs automated procedures that rely on majority rules. We argue this hinders progress through the generation and propagation of errors, leading investigators into blind alleys. More subtly, this inductive process discourages the discovery of novelty, which remains essential in biological research and reflects the nature of biology itself. Annotation systems, rather than being repositories of facts, should be tools that support multiple modes of inference. By combining deduction, induction and abduction, investigators can generate hypotheses when accurate knowledge is extracted from model databases. A key stance is to depart from ‘the sequence tells the structure tells the function’ fallacy, placing function first. We illustrate our approach with examples of critical or unexpected pathways, using MicroScope to demonstrate how tools can be implemented following the principles we advocate. We end with a challenge to the reader

DES-mutation : system for exploring links of mutations and diseases

During cellular division DNA replicates and this process is the basis for passing genetic information to the next generation. However, the DNA copy process sometimes produces a copy that is not perfect, that is, one with mutations. The collection of all such mutations in the DNA copy of an organism makes it unique and determines the organism's phenotype. However, mutations are often the cause of diseases. Thus, it is useful to have the capability to explore links between mutations and disease. We approached this problem by analyzing a vast amount of published information linking mutations to disease states. Based on such information, we developed the DES-Mutation knowledgebase which allows for exploration of not only mutation-disease links, but also links between mutations and concepts from 27 topic-specific dictionaries such as human genes/proteins, toxins, pathogens, etc. This allows for a more detailed insight into mutation-disease links and context. On a sample of 600 mutation-disease associations predicted and curated, our system achieves precision of 72.83%. To demonstrate the utility of DES-Mutation, we provide case studies related to known or potentially novel information involving disease mutations. To our knowledge, this is the first mutation-disease knowledgebase dedicated to the exploration of this topic through text-mining and data-mining of different mutation types and their associations with terms from multiple thematic dictionaries

A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE)

Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies

Semantic-enabled Hybrid Genetic Disease Diagnostics in Next-Generation Sequenced Data

Next Generation Sequencing is a technology for genome sequencing used in genetics for diseased diagnosis. NGS provides the list of all mutations in a genome, so identifying the one which causes a disease is not trivial. A number of applications for variant prioritization was developed, but the data they provide is rather a suggestion than a diagnosis, moreover they suffer from issues as identifying nonpathogenic variant as a causal one or inability to identify the causal gene. These issues inspired us to create a strategy for variant prioritization which includes the use of Exomiser and OmimExplorer result sets improved by semantic analysis of abstracts and articles freely available from PubMed and PubMed Central databases. For the wider scope of scientific articles Google Scholar repository will be used. Described approach enables to present latest and most accurate information about potential pathogenic variants