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Background The development of high throughput sequencing techniques provides us with the possibilities to obtain large data sets, which capture the effect of dynamic perturbations on cellular processes. However, because of the dynamic nature of these processes, the analysis of the results is challenging. Therefore, there is a great need for bioinformatics tools that address this problem. Results Here we present DynOVis, a network visualization tool that can capture dynamic dose-over-time effects in biological networks. DynOVis is an integrated work frame of R packages and JavaScript libraries and offers a force-directed graph network style, involving multiple network analysis methods such as degree threshold, but more importantly, it allows for node expression animations as well as a frame-by-frame view of the dynamic exposure. Valuable biological information can be highlighted on the nodes in the network, by the integration of various databases within DynOVis. This information includes pathway-to-gene associations from ConsensusPathDB, disease-to-gene associations from the Comparative Toxicogenomics databases, as well as Entrez gene ID, gene symbol, gene synonyms and gene type from the NCBI database. Conclusions DynOVis could be a useful tool to analyse biological networks which have a dynamic nature. It can visualize the dynamic perturbations in biological networks and allows the user to investigate the changes over time. The integrated data from various online databases makes it easy to identify the biological relevance of nodes in the network. With DynOVis we offer a service that is easy to use and does not require any bioinformatics skills to visualize a network.

The Gene Ontology is the most developed and relevant biomolecular ontology available. In a species-independent manner, it defines the functional categories, i.e., the biological concepts represented by the terms of its controlled vocabulary, to which the genes or gene products related to some biological process, molecular function, or cellular component belong. Using the functional classification of genes or gene products in Gene Ontology categories provided by several databases (e.g., Entrez Gene and Uniprot), it is possible to perform semantic evaluations on a given gene or protein list, such as those produced by high-throughput experiments, which may foster new biological discoveries.

Microorganisms produce an enormous variety of chemical compounds. It is of general interest for microbiology and biotechnology researchers to have means to explore information about molecular and genetic basis of functioning of different microorganisms and their ability for bioproduction. To enable such exploration, we compiled 45 topic-specific knowledgebases (KBs) accessible through DESM portal (www.cbrc.kaust.edu.sa/desm). The KBs contain information derived through text-mining of PubMed information and complemented by information data-mined from various other resources (e.g. ChEBI, Entrez Gene, GO, KOBAS, KEGG, UniPathways, BioGrid). All PubMed records were indexed using 4,538,278 concepts from 29 dictionaries, with 1 638 986 records utilized in KBs. Concepts used are normalized whenever possible. Most of the KBs focus on a particular type of microbial activity, such as production of biocatalysts or nutraceuticals. Others are focused on specific categories of microorganisms, e.g. streptomyces or cyanobacteria. KBs are all structured in a uniform manner and have a standardized user interface. Information exploration is enabled through various searches. Users can explore statistically most significant concepts or pairs of concepts, generate hypotheses, create interactive networks of associated concepts and export results. We believe DESM will be a useful complement to the existing resources to benefit microbiology and biotechnology research.

SummaryRadiation therapy is among the most effective and widely used modalities of cancer therapy in current clinical practice. With the advent of new high throughput genomic technologies and the continuous inflow of transcriptomic data, there has been a paradigm shift in the landscape of radiation oncology. In this era of personalized radiation medicine, genomic datasets hold great promise to investigate novel biomarkers predictive of radiation response. In this regard, the number of available gene expression based signatures built under oxic and hypoxic conditions is getting larger. This poses two main questions in the field, namely, i) how reliable are these signatures when applied across a compendium of datasets in different model systems; and ii) is there redundancy of gene signatures. To address these fundamental radiobiologic questions, we curated a database of gene expression signatures predictive of radiation response under oxic and hypoxic conditions. RadiationGeneSigDB has a collection of 11 oxic and 24 hypoxic signatures with the standardized gene list as a gene symbol, Entrez gene ID, and its function. We present the utility of this database through three case studies: i) comparing breast cancer oxic signatures in cell line data vs. patient data; ii) comparing the similarity of head and neck cancer hypoxia signatures in clinical tumor data; and iii) gaining an understanding of hypoxia-associated miRNA. This valuable, curated repertoire of published gene expression signatures provides a motivating example for how to search for similarities in radiation response for tumors arising from different tissues across model systems under oxic and hypoxic conditions, and how a well-curated set of gene signatures can be used to generate novel hypotheses about the functions of non-coding RNA.Availability and implementationRadiationGeneSigDB is implemented in R. The source code of this package and signatures can be downloaded from the GitHub: https://github.com/vmsatya/RadiationGeneSigDB

Background Advancement in molecular biology research has allowed the measurement of multiomics data points from a single tumor biopsy sample in a reasonable time frame for making significant clinical decisions. There have been tremendous advances from the bioinformatics and systems biology perspective to analyse integrated multi‐scale analysis of the data will prove invaluable for a combined systems‐level understanding of the important biological network processes contributing to the initiation, progression and management of cancer. Cancer networks have greater challenge due to the aberrant functions of hundreds of genes that are translated to altered protein function within each cancer cell, to understand this complexity. Network-based methods have been used to analyse the complexity molecular interactions in the cell and can contribute to prediction of candidate genes responsible for cancer. Methods HI-II- 14 is the largest experimentally determined binary protein – protein interaction map is used for the study. These interactions were mapped to NCBI Entrez gene ID and excluded self-loop and redundant interactions. The known human breast cancer genes were obtained from the Sanger Cancer Gene Census which is a comprehensive catalogue of genes implicated in breast cancer and from Uniprot database. In total, we obtained 913 genes from both for analyses. The genes degree of connections and the centrality in the protein -protein networks were considered. The genome-wide mutation datasets of breast cancer were downloaded from TCGA as maf files. For each gene, we obtained the mutation frequency for each gene is calculated which is defined as the number of mutated samples divided by the total number of samples. The frequency of the top 100 ranked genes and the bottom 100 ranked genes were compared by Wilcox rank sum test. Results In this study, HI-II-14 human protein-protein interactome network is applied to prediction of novel breast cancer genes. Conclusions Our study suggests integrating interactome networks with multiomics datasets could provide novel insights into breast cancer-associated genes and underlying molecular mechanisms. Legal entity responsible for the study M. Peer Mohammed. Funding Has not received any funding. Disclosure The author has declared no conflicts of interest.

The BioCreative VI Track IV (mining protein interactions and mutations for precision medicine) challenge was organized in 2017 with the goal of applying biomedical text mining methods to support advancements in precision medicine approaches. As part of the challenge, a new dataset was introduced for the purpose of building a supervised relation extraction model capable of taking a test article and returning a list of interacting protein pairs identified by their Entrez Gene IDs. Specifically, such pairs represent proteins participating in a binary protein-protein interaction relation where the interaction is additionally affected by a genetic mutation-referred to as a PPIm relation. In this study, we explore an end-to-end approach for PPIm relation extraction by deploying a three-component pipeline involving deep learning-based named-entity recognition and relation classification models along with a knowledge-based approach for gene normalization. We propose several recall-focused improvements to our original challenge entry that placed second when matching on Entrez Gene ID (exact matching) and on HomoloGene ID. On exact matching, the improved system achieved new competitive test results of 37.78% micro-F1 with a precision of 38.22% and recall of 37.34% that corresponds to an improvement from the prior best system by approximately three micro-F1 points. When matching on HomoloGene IDs, we report similarly competitive test results at 46.17% micro-F1 with a precision and recall of 46.67 and 45.59%, respectively, corresponding to an improvement of more than eight micro-F1 points over the prior best result. The code for our deep learning system is made publicly available at https://github.com/bionlproc/biocppi_extraction.

Aim: We release the Janssen Toxicogenomics database. This rat liver gene-expression database was generated using Codelink microarrays, and has been used over the past years within Janssen to derive signatures for multiple end points and to classify proprietary compounds. Materials & methods: The release consists of gene-expression responses to 124 compounds, selected to give a broad coverage of liver-active compounds. A selection of the compounds were also analyzed on Affymetrix microarrays. Results: The release includes results of an in-house reannotation pipeline to Entrez gene annotations, to classify probes into different confidence classes. High confidence unambiguously annotated probes were used to create gene-level data which served as starting point for cross-platform comparisons. Connectivity map-based similarity methods show excellent agreement between Codelink and Affymetrix runs of the same samples. We also compared our dataset with the Japanese Toxicogenomics Project and observed reasonable agreement, especially for compounds with stronger gene signatures. We describe an R-package containing the gene-level data and show how it can be used for expression-based similarity searches. Conclusion: Comparing the same biological samples run on the Affymetrix and the Codelink platform, good correspondence is observed using connectivity mapping approaches. As expected, this correspondence is smaller when the data are compared with an independent dataset such as TG-GATE. We hope that this collection of gene-expression profiles will be incorporated in toxicogenomics pipelines of users.

As the amount of genome information increases rapidly, there is a correspondingly greater need for methods that provide accurate and automated annotation of gene function. For example, many high-throughput technologies--e.g., next-generation sequencing--are being used today to generate lists of genes associated with specific conditions. However, their functional interpretation remains a challenge and many tools exist trying to characterize the function of gene-lists. Such systems rely typically in enrichment analysis and aim to give a quick insight into the underlying biology by presenting it in a form of a summary-report. While the load of annotation may be alleviated by such computational approaches, the main challenge in modern annotation remains to develop a systems form of analysis in which a pipeline can effectively analyze gene-lists quickly and identify aggregated annotations through computerized resources. In this article we survey some of the many such tools and methods that have been developed to automatically interpret the biological functions underlying gene-lists. We overview current functional annotation aspects from the perspective of their epistemology (i.e., the underlying theories used to organize information about gene function into a body of verified and documented knowledge) and find that most of the currently used functional annotation methods fall broadly into one of two categories: they are based either on 'known' formally-structured ontology annotations created by 'experts' (e.g., the GO terms used to describe the function of Entrez Gene entries), or--perhaps more adventurously--on annotations inferred from literature (e.g., many text-mining methods use computer-aided reasoning to acquire knowledge represented in natural languages). Overall however, deriving detailed and accurate insight from such gene lists remains a challenging task, and improved methods are called for. In particular, future methods need to (1) provide more holistic insight into the underlying molecular systems; (2) provide better follow-up experimental testing and treatment options, and (3) better manage gene lists derived from organisms that are not well-studied. We discuss some promising approaches that may help achieve these advances, especially the use of extended dictionaries of biomedical concepts and molecular mechanisms, as well as greater use of annotation benchmarks.

Background Data extraction and integration methods are becoming essential to effectively access and take advantage of the huge amounts of heterogeneous genomics and clinical data increasingly available. In this work, we focus on The Cancer Genome Atlas, a comprehensive archive of tumoral data containing the results of high-throughout experiments, mainly Next Generation Sequencing, for more than 30 cancer types. Results We propose TCGA2BED a software tool to search and retrieve TCGA data, and convert them in the structured BED format for their seamless use and integration. Additionally, it supports the conversion in CSV, GTF, JSON, and XML standard formats. Furthermore, TCGA2BED extends TCGA data with information extracted from other genomic databases (i.e., NCBI Entrez Gene, HGNC, UCSC, and miRBase). We also provide and maintain an automatically updated data repository with publicly available Copy Number Variation, DNA-methylation, DNA-seq, miRNA-seq, and RNA-seq (V1,V2) experimental data of TCGA converted into the BED format, and their associated clinical and biospecimen meta data in attribute-value text format. Conclusions The availability of the valuable TCGA data in BED format reduces the time spent in taking advantage of them: it is possible to efficiently and effectively deal with huge amounts of cancer genomic data integratively, and to search, retrieve and extend them with additional information. The BED format facilitates the investigators allowing several knowledge discovery analyses on all tumor types in TCGA with the final aim of understanding pathological mechanisms and aiding cancer treatments. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-1419-5) contains supplementary material, which is available to authorized users.

The main databases devoted stricto sensu to cancer cytogenetics are the "Mitelman Database of Chromosome Aberrations and Gene Fusions in Cancer" ( http://cgap.nci.nih.gov/Chromosomes/Mitelman ), the "Atlas of Genetics and Cytogenetics in Oncology and Haematology" ( http://atlasgeneticsoncology.org ), and COSMIC ( http://cancer.sanger.ac.uk/cosmic ).However, being a complex multistep process, cancer cytogenetics are broadened to "cytogenomics," with complementary resources on: general databases (nucleic acid and protein sequences databases; cartography browsers: GenBank, RefSeq, UCSC, Ensembl, UniProtKB, and Entrez Gene), cancer genomic portals associated with recent international integrated programs, such as TCGA or ICGC, other fusion genes databases, array CGH databases, copy number variation databases, and mutation databases. Other resources such as the International System for Human Cytogenomic Nomenclature (ISCN), the International Classification of Diseases for Oncology (ICD-O), and the Human Gene Nomenclature Database (HGNC) allow a common language.Data within the scientific/medical community should be freely available. However, most of the institutional stakeholders are now gradually disengaging, and well-known databases are forced to beg or to disappear (which may happen!).

A great deal of information on the molecular genetics and biochemistry of model organisms has been reported in the scientific literature. However, this data is typically described in free text form and is not readily amenable to computational analyses. To this end, the BioGRID database systematically curates the biomedical literature for genetic and protein interaction data. This data is provided in a standardized computationally tractable format and includes structured annotation of experimental evidence. BioGRID curation necessarily involves substantial human effort by expert curators who must read each publication to extract the relevant information. Computational text-mining methods offer the potential to augment and accelerate manual curation. To facilitate the development of practical text-mining strategies, a new challenge was organized in BioCreative V for the BioC task, the collaborative Biocurator Assistant Task. This was a non-competitive, cooperative task in which the participants worked together to build BioC-compatible modules into an integrated pipeline to assist BioGRID curators. As an integral part of this task, a test collection of full text articles was developed that contained both biological entity annotations (gene/protein and organism/species) and molecular interaction annotations (protein–protein and genetic interactions (PPIs and GIs)). This collection, which we call the BioC-BioGRID corpus, was annotated by four BioGRID curators over three rounds of annotation and contains 120 full text articles curated in a dataset representing two major model organisms, namely budding yeast and human. The BioC-BioGRID corpus contains annotations for 6409 mentions of genes and their Entrez Gene IDs, 186 mentions of organism names and their NCBI Taxonomy IDs, 1867 mentions of PPIs and 701 annotations of PPI experimental evidence statements, 856 mentions of GIs and 399 annotations of GI evidence statements. The purpose, characteristics and possible future uses of the BioC-BioGRID corpus are detailed in this report. Database URL: http://bioc.sourceforge.net/BioC-BioGRID.html

The significantly related diseases of sequences could play an important role in understanding the functions of these sequences. In this paper, we introduced BLAT2DOLite, an online system for annotating human genes and diseases and identifying the significant relationships between sequences and diseases. Currently, BLAT2DOLite integrates Entrez Gene database and Disease Ontology Lite (DOLite), which contain loci of gene and relationships between genes and diseases. It utilizes hypergeometric test to calculate P-values between genes and diseases of DOLite. The system can be accessed from: http://123.59.132.21:8080/BLAT2DOLite. The corresponding web service is described in: http://123.59.132.21:8080/BLAT2DOLite/BLAT2DOLiteIDMappingPort?wsdl.

Fourteen skull base chordoma specimens and three normal specimens were microdissected from paraffin-embedded tissue. Pools of RNA from highly enriched preparations of these cell types were subjected to expression profiling using whole-transcriptome shotgun sequencing. Using strict criteria, 294 differentially expressed transcripts were found, with 28 % upregulated and 72 % downregulated. The transcripts were annotated using NCBI Entrez Gene and computationally analyzed with the Ingenuity Pathway Analysis program. From these significantly changed expressions, the analysis identified 222 cancer-related transcripts. These 294 differentially expressed genes and non-coding RNA transcripts provide here a set to specifically define skull base chordomas and to identify novel and potentially important targets for diagnosis, prognosis, and therapy of this cancer. Significance Genomic profiling to subtype skull base chordoma reveals potential candidates for specific biomarkers, with validation by IHC for selected candidates. The highly expressed developmental genes T, LMX1A, ZIC4, LHX4, and HOXA1 may be potential drivers of this disease.

Late onset Alzheimer's disease (AD) and Parkinson's disease (PD) are mostly "sporadic" age-related neurodegenerative disorders, but with a clear genetic component. However, their genetic architecture is complex and heterogeneous, largely remaining a conundrum, with only a handful of well-established genetic risk factors consistently associated with these diseases. It is possible that numerous, yet undiscovered, AD and PD related genes might exist. We focused on the 'gene' as a mediator to find new potential genes that might have a relationship with both disorders using bio-literature mining techniques. Based on Entrez Gene, we extracted the genes and directional gene-gene relation in the entire MEDLINE records and then constructed a directional gene-gene network. We identified common genes associated with two different but related diseases by performing shortest path analysis on the network. With our approach, we were able to identify and map already known genes that have a direct relationship with PD and AD. In addition, we identified 7 genes previously unknown to be a bridge between these two disorders. We confirmed 4 genes, ROS1, FMN1, ATP8A2, and SNORD12C, by biomedical literature and further checked 3 genes, ERVK-10, PRS, and C7orf49, that might have a high possibility to be related with both diseases. Additional experiments were performed to demonstrate the effectiveness of our proposed method. Comparing to the co-occurrence approach, our approach detected 25% more candidate genes and verified 10% more genes that have the relationship between both diseases than the co-occurrence approach did.

Increasing evidences indicated that function annotation of human genome in molecular level and phenotype level is very important for systematic analysis of genes. In this study, we presented a framework named Gene2Function to annotate Gene Reference into Functions (GeneRIFs), in which each functional description of GeneRIFs could be annotated by a text mining tool Open Biomedical Annotator (OBA), and each Entrez gene could be mapped to Human Genome Organisation Gene Nomenclature Committee (HGNC) gene symbol. After annotating all the records about human genes of GeneRIFs, 288,869 associations between 13,148 mRNAs and 7,182 terms, 9,496 associations between 948 microRNAs and 533 terms, and 901 associations between 139 long noncoding RNAs (lncRNAs) and 297 terms were obtained as a comprehensive annotation resource of human genome. High consistency of term frequency of individual gene (Pearson correlation = 0.6401,p=2.2e-16) and gene frequency of individual term (Pearson correlation = 0.1298,p=3.686e-14) in GeneRIFs and GOA shows our annotation resource is very reliable.

Motivation: Although the amount of data in biology is rapidly increasing, critical information for understanding biological events like phosphorylation or gene expression remains locked in the biomedical literature. Most current text mining (TM) approaches to extract information about biological events are focused on either limited-scale studies and/or abstracts, with data extracted lacking context and rarely available to support further research. Results: Here we present BioContext, an integrated TM system which extracts, extends and integrates results from a number of tools performing entity recognition, biomolecular event extraction and contextualization. Application of our system to 10.9 million MEDLINE abstracts and 234 000 open-access full-text articles from PubMed Central yielded over 36 million mentions representing 11.4 million distinct events. Event participants included over 290 000 distinct genes/proteins that are mentioned more than 80 million times and linked where possible to Entrez Gene identifiers. Over a third of events contain contextual information such as the anatomical location of the event occurrence or whether the event is reported as negated or speculative. Availability: The BioContext pipeline is available for download (under the BSD license) at http://www.biocontext.org, along with the extracted data which is also available for online browsing. Contact: martin.gerner@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

PurposeGene term variation is a shortcoming in text‐mining applications based on biomedical literature‐based knowledge discovery. The purpose of this paper is to propose a technique for normalizing gene names in biomedical literature.Design/methodology/approachUnder this proposal, the normalized forms can be characterized as a unique gene symbol, defined as the official symbol or normalized name. The unification method involves five stages: collection of the gene term, using the resources provided by the Entrez Gene database; encoding of gene‐naming terms in a table or binary matrix; design of a parametrized finite‐state graph (P‐FSG); automatic generation of a dictionary; and matching based on dictionary look‐up to transform the gene mentions into the corresponding unified form.FindingsThe findings show that the approach yields a high percentage of recall. Precision is only moderately high, basically due to ambiguity problems between gene‐naming terms and words and abbreviations in general English.Research limitations/implicationsThe major limitation of this study is that biomedical abstracts were analyzed instead of full‐text documents. The number of under‐normalization and over‐normalization errors is reduced considerably by limiting the realm of application to biomedical abstracts in a well‐defined domain.Practical implicationsThe system can be used for practical tasks in biomedical literature mining. Normalized gene terms can be used as input to literature‐based gene clustering algorithms, for identifying hidden gene‐to‐disease, gene‐to‐gene and gene‐to‐literature relationships.Originality/valueFew systems for gene term variation handling have been developed to date. The technique described performs gene name normalization by dictionary look‐up.

Motivation: The scientific literature contains a wealth of information about biological systems. Manual curation lacks the scalability to extract this information due to the ever-increasing numbers of papers being published. The development and application of text mining technologies has been proposed as a way of dealing with this problem. However, the inter-species ambiguity of the genomic nomenclature makes mapping of gene mentions identified in text to their corresponding Entrez gene identifiers an extremely difficult task. We propose a novel method, which transforms a MEDLINE record into a mixture of adjacency matrices; by performing a random walkover the resulting graph, we can perform multi-class supervised classification allowing the assignment of taxonomy identifiers to individual gene mentions. The ability to achieve good performance at this task has a direct impact on the performance of normalizing gene mentions to Entrez gene identifiers. Such graph mixtures add flexibility and allow us to generate probabilistic classification schemes that naturally reflect the uncertainties inherent, even in literature-derived data. Results: Our method performs well in terms of both micro- and macro-averaged performance, achieving micro-F1 of 0.76 and macro-F1 of 0.36 on the publicly available DECA corpus. Re-curation of the DECA corpus was performed, with our method achieving 0.88 micro-F1 and 0.51 macro−F1. Our method improves over standard classification techniques [such as support vector machines (SVMs)] in a number of ways: flexibility, interpretability and its resistance to the effects of class bias in the training data. Good performance is achieved without the need for computationally expensive parse tree generation or ‘bag of words classification’. Contact: m.stumpf@imperial.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.

Summary A systems genetics approach combining pathway analysis of quantitative trait loci (QTL) and gene expression information has provided strong evidence for common pathways associated with genetic resistance to internal parasites. Gene data, collected from published QTL regions in sheep, cattle, mice, rats and humans, and microarray data from sheep, were converted to human Entrez Gene IDs and compared to the KEGG pathway database. Selection of pathways from QTL data was based on a selection index that ensured that the selected pathways were in all species and the majority of the projects overall and within species. Pathways with either up- and down-regulated genes, primarily up-regulated genes or primarily down-regulated genes, were selected from gene expression data. After comparing the data sets independently, the pathways from each data set were compared and the common set of pathways and genes was identified. Comparisons within data sets identified 21 pathways from QTL data and 66 pathways from gene expression data. Both selected sets were enriched with pathways involved in immune functions, disease and cell responses to signals. The analysis identified 14 pathways that were common between QTL and gene expression data, and four directly associated with IFNγ or MHCII, with 31 common genes, including three MHCII genes. In conclusion, a systems genetics approach combining data from multiple QTL and gene expression projects led to the discovery of common pathways associated with genetic resistance to internal parasites. This systems genetics approach may prove significant for the discovery of candidate genes for many other multifactorial, economically important traits.

Entrez Gene (www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gene) is NCBI's database for gene-specific information. It does not include all known or predicted genes; instead Entrez Gene focuses on the genomes that have been completely sequenced, that have an active research community to contribute gene-specific information, or that are scheduled for intense sequence analysis. The content of Entrez Gene represents the result of curation and automated integration of data from NCBI's Reference Sequence project (RefSeq), from collaborating model organism databases, and from many other databases available from NCBI. Records are assigned unique, stable and tracked integers as identifiers. The content (nomenclature, map location, gene products and their attributes, markers, phenotypes, and links to citations, sequences, variation details, maps, expression, homologs, protein domains and external databases) is updated as new information becomes available. Entrez Gene is a step forward from NCBI's LocusLink, with both a major increase in taxonomic scope and improved access through the many tools associated with NCBI Entrez.

Expression microarrays including the Affymetrix GeneChip Porcine Genome Arrays are valuable tools for studying genes and functional networks relevant for the expression of complex traits and the responsiveness of the organism to various treatments. An updated annotation and, for the first time, localization on the porcine physical genome map of »Affymetrix GeneChip Porcine Genome Array probe sets« was made through a workflow of 3 pipelines of comparisions addressing various NCBI (National Center for Biotechnology Information) and EnsEMBL (Ensembl project) databases. »BLAST« (Basic Local Alignment Search Tool) comparisons of Affymetrix probe set consensus sequences with the EnsEMBL Sscrofa 9 cDNA database provided 23 799 probe sets with hits. After annotation 19 730 gene symbols were obtained using the data management system BioMart. Comparison of the Affymetrix probe set consensus sequences with the porcine genome sequence (EnsEMBL Sscrofa 9 LatestGP database) revealed 23 298 probe sets with BLAST hits. In the third pipeline in addition to EnsEMBL Sscrofa 9 cDNA and genomic sequence databases also human, mouse and pig NCBI reference sequence RNA databases were interrogated in an integrated approach where also a threshold of bit score >50 or >90 % identity over >100 bp was applied in order to filter questionable annotations and localizations. Gene symbols and gene names were queried from HGNC (human genome organization (HUGO) gene nomenclature committee), EASE (EASE: the Expression Analysis Systematic Explorer) and Entrez Gene revealing 20 269 annotated probe sets. 20 467 probe sets were in silico mapped addressing various sources: EnsEMBL Sscrofa 9 LatestGP, pre-EnsEMBL Sscrofa 8.52 LatestGP, NCBI pig reference sequence RNA and genomic databases and PigQTLdb (Pig Quantitative Trait Locus [QTL] database). Using the new annotation and localization data in functional genomics studies will facilitate improving the understanding of the control of quantitative traits in pigs.

Genetic databases contain a variety of annotation errors that often go unnoticed due to the large size of modern genetic data sets. Interpretation of these data sets requires bioinformatics tools that may contribute to this problem. While providing gene symbol annotations for identifiers (IDs) such as microarray probe set, RefSeq, GenBank, and Entrez Gene is seemingly trivial, the accuracy is fundamental to any subsequent conclusions. We examine gene symbol annotations and results from three commercial pathway analysis software (PAS) packages: Ingenuity Pathways Analysis, GeneGO, and Pathway Studio. We compare gene symbol annotations and canonical pathway results over time and among different input ID types. We find that PAS results can be affected by variation in gene symbol annotations across software releases and the input ID type analyzed. As a result, we offer suggestions for using commercial PAS and reporting microarray results to improve research quality. We propose a wiki type website to facilitate communication of bioinformatics software problems within the scientific community.

We developed an identifier mapping application for bio-informatics research in Java programming language. It is easy to use and provides many usability functionalities that are expected as essentials for a professional appli-cation. It supports three widely used mapping services and can convert many ids from one source database in-to many target databases at once. Id mapping across service providers is possible by remapping the resultant ids. Because it adheres to the NetBeans platform archi-tecture, it can be incorporated into other NetBeans plat-form applications as an id mapping provider without adaption or modification.Availability: You can access the application at http://ne-on. gachon.ac.kr/idmapper.html.Keywords: ID mapping, java, netbeans, rich client, web services Introduction “-omics” style experiments, such as sequencing, micro-array, and proteomics, generate enormous amounts of data (Kim et al., 2006, Kim et al., 2008). To integrate and analyze the data in various respects, multiple tools are used. Each tool supports its own set of identifiers for genes or gene products. Even though there are widely used identifiers, such as Ensembl (Flicek et al., 2008), NCBI RefSeq (Pruitt et al., 2007), UniProt (The Uniprot Consortium, 2008) and HGNC (Povey et al., 2001), there is no universal identifier. When multiple tools and services are used, identifier conversion prob-lems across multiple databases arise frequently. A small research group can not afford its own mapping service and relies on external services. There exist efforts to reconcile identifiers across multiple source databases, such as the David gene ID conversion tool (Huang et al., 2008), MatchMiner (Bussey et al., 2003), IDconverter (Alibes et al., 2007), Onto- translate (Khatri et al., 2006), PICR (Cote et al., 2007), Synergizer (Brriz et al., 2008), and Biomart (Smedley et al., 2009). Services that are provided in the form of a web page can not be incorporated into other applications easily. Only a few services provide API access for batch map-ping or incorporation into other applications. The Protein Identifier Cross-Reference (PICR) service is a web application that provides interactive and pro-grammatic (SOAP and REST) access to a mapping algo-rithm that uses the UniProt Archive (UniParc) as a data warehouse to offer protein cross-references. The Syner-gizer is a service for translating between sets of bio-logical identifiers. It can, for example, translate Ensembl Gene IDs to Entrez Gene IDs, or IPI IDs to HGNC gene symbols, and much more. The Synergizer works via a web interface (for users who are not programmers) or through a web service (for programmatic access). BioMart is a query-oriented data management system, developed jointly by the Ontario Institute for Cancer Research (OICR) and the European Bioinformatics Insti-tute (EBI). It can be accessed via web services and can be adapted for identifier mapping. BridgeDb, though not released publically yet, is an-other attempt at an id mapping framework for bio-informatics applications. BridgeDb lets one add the fol-lowing capabilities quickly and easily: translate identi-fiers from one system to another, search references by id or symbol, and link out to online information for an identifier. Applications, such as the PathVisio pathway analysis tool, WikiPathways, CyThesaurus Cytoscape plug-in, and the NetworkMerge Cytoscape plug-in, are utilizing functionalities provided by BridgeDB.

Hyperlink Management System (HMS) is a system for automatically updating and maintaining hyperlinks among major public databases in the field of life science. We daily create corresponding tables of data IDs of major databases for human genes and proteins, and provide a CGI-program that returns correct and up-to-date URLs for showing data of various databases that correspond to user-specified IDs. The HMS can deal with various IDs: accession numbers of International Nucleotide Sequence Databases, HUGO Gene Symbols and IDs of UniProt, PDB, H-InvDB and others, and it can return URLs of various databases: H-InvDB, HUGO Gene Nomenclature Committee Database, NCBI Entrez Gene, UniProt, PDB and others. For example, 23 297 pages of Locus view of H-InvDB are reachable by using HUGO Gene Symbols through the HMS. Not only the CGI-program, the HMS provides a Web page for finding and opening URLs of these databases. Although hyperlinking is an effective way of relating biological data among different databases, updating hyperlinks has been a laborious work. The HMS fully automates the job, enabling maintenance-free hyperlinks. We also developed the ID Converter System (ICS) for simply converting data IDs by using corresponding tables in the HMS. The HMS and ICS are freely available at http://biodb.jp/.

Genetic association studies and linkage analyses using single nucleotide polymorphisms (SNPs) are rapidly increasing in number, and the results are important for evaluating the utility of SNPs in the biomedical sciences. Although many SNP databases have been established, there is no database focusing on published SNPs, where the research results of scientific investigations are available. To enhance the utilization of such SNP data, we have developed the MedRefSNP database (http://www.medclue.com/medrefsnp) to provide integrated information about SNPs collected from the PubMed and OMIM databases. The RefSNP identifiers are automatically identified and are linked to various information sources such as the dbSNP, the HapMap database, the Entrez Gene database, the UCSC genome browser, the CGAP Pathway Searcher, and genetic association databases. And, each SNP is checked to determine whether the PolyDoms, SNPs3D or PolyPhen databases predicts that the SNP affects the phenotype of the protein encoded by the gene carrying the SNP. Also, neighboring SNPs showing strong linkage disequilibrium (LD) with published SNPs are included, using HapMap data. Currently, 36199 unique SNPs (including 31368 neighboring SNPs) collected from 25906 PubMed abstracts and 590 OMIM records are stored along with 2491 human genes related to 466 molecular pathways. The MedRefSNP database will help researchers to review previously investigated results more efficiently, and will expand knowledge by using the genomic and functional contexts of the SNPs.

The 'Human Immunodeficiency Virus Type 1 (HIV-1), Human Protein Interaction Database', available through the National Library of Medicine at www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions, was created to catalog all interactions between HIV-1 and human proteins published in the peer-reviewed literature. The database serves the scientific community exploring the discovery of novel HIV vaccine candidates and therapeutic targets. To facilitate this discovery approach, the following information for each HIV-1 human protein interaction is provided and can be retrieved without restriction by web-based downloads and ftp protocols: Reference Sequence (RefSeq) protein accession numbers, Entrez Gene identification numbers, brief descriptions of the interactions, searchable keywords for interactions and PubMed identification numbers (PMIDs) of journal articles describing the interactions. Currently, 2589 unique HIV-1 to human protein interactions and 5135 brief descriptions of the interactions, with a total of 14,312 PMID references to the original articles reporting the interactions, are stored in this growing database. In addition, all protein-protein interactions documented in the database are integrated into Entrez Gene records and listed in the 'HIV-1 protein interactions' section of Entrez Gene reports. The database is also tightly linked to other databases through Entrez Gene, enabling users to search for an abundance of information related to HIV pathogenesis and replication.

The expanding number of members in the various human heat shock protein (HSP) families and the inconsistencies in their nomenclature have often led to confusion. Here, we propose new guidelines for the nomenclature of the human HSP families, HSPH (HSP110), HSPC (HSP90), HSPA (HSP70), DNAJ (HSP40), and HSPB (small HSP) as well as for the human chaperonin families HSPD/E (HSP60/HSP10) and CCT (TRiC). The nomenclature is based largely on the more consistent nomenclature assigned by the HUGO Gene Nomenclature Committee and used in the National Center of Biotechnology Information Entrez Gene database for the heat shock genes. In addition to this nomenclature, we provide a list of the human Entrez Gene IDs and the corresponding Entrez Gene IDs for the mouse orthologs.

Biomedical research has identified many human genes and various knowledge about them. The National Cancer Institute Thesaurus (NCIT) represents such knowledge as concepts and roles (relationships). Due to the rapid advances in this field, it is to be expected that the NCIT’s Gene hierarchy will contain role errors. A comparative methodology to audit the Gene hierarchy with the use of the National Center for Biotechnology Information’s (NCBI’s) Entrez Gene database is presented. The two knowledge sources are accessed via a pair of Web crawlers to ensure up-to-date data. Our algorithms then compare the knowledge gathered from each, identify discrepancies that represent probable errors, and suggest corrective actions. The primary focus is on two kinds of gene-roles: (1) the chromosomal locations of genes, and (2) the biological processes in which genes play a role. Regarding chromosomal locations, the discrepancies revealed are striking and systematic, suggesting a structurally common origin. In regard to the biological processes, difficulties arise because genes frequently play roles in multiple processes, and processes may have many designations (such as synonymous terms). Our algorithms make use of the roles defined in the NCIT Biological Process hierarchy to uncover many probable gene-role errors in the NCIT. These results show that automated comparative auditing is a promising technique that can identify a large number of probable errors and corrections for them in a terminological genomic knowledge repository, thus facilitating its overall maintenance.

Although many interactions between HIV-1 and human proteins have been reported in the scientific literature, no publicly accessible source for efficiently reviewing this information was available. Therefore, a project was initiated in an attempt to catalogue all published interactions between HIV-1 and human proteins. HIV-related articles in PubMed were used to develop a database containing names, Entrez GeneIDs, and RefSeq protein accession numbers of interacting proteins. Furthermore, brief descriptions of the interactions, PubMed identification numbers of articles describing the interactions, and keywords for searching the interactions were incorporated. Over 100,000 articles were reviewed, resulting in the identification of 1448 human proteins that interact with HIV-1 comprising 2589 unique HIV-1-to-human protein interactions. Preliminary analysis of the extracted data indicates 32% were direct physical interactions (e.g., binding) and 68% were indirect interactions (e.g., upregulation through activation of signaling pathways). Interestingly, 37% of human proteins in the database were found to interact with more than one HIV-1 protein. For example, the signaling protein mitogen-activated protein kinase 1 has a surprising range of interactions with 10 different HIV-1 proteins. Moreover, large numbers of interactions were published for the HIV-1 regulatory protein Tat and envelope proteins: 30% and 33% of total interactions identified, respectively. The database is accessible at http://www.ncbi.nlm.nih.gov/RefSeq/HIVInteractions/ and is cross-linked to other National Center for Biotechnology Information databases and programs via Entrez Gene. This database represents a unique and continuously updated scientific resource for understanding HIV-1 replication and pathogenesis to assist in accelerating the development of effective therapeutic and vaccine interventions.

This research analyzes the gene relationship according to their annotations. We present here a similar genes discovery system (SGDS), based upon semantic similarity measure of gene ontology (GO) and Entrez gene, to identify groups of similar genes. In order to validate the proposed measure, we analyze the relationships between similarity and expression correlation of pairs of genes. We explore a number of semantic similarity measures and compute the Pearson correlation coefficient. Highly correlated genes exhibit strong similarity in the ontology taxonomies. The results show that our proposed semantic similarity measure outperforms the others and seems better suited for use in GO. We use MAPK homogenous genes group and MAP kinase pathway as benchmarks to tune the parameters in our system for achieving higher accuracy. We applied the SGDS to RON and Lutheran pathways, the results show that it is able to identify a group of similar genes and to predict novel pathways based on a group of candidate genes.

Objectives: This paper illustrates how Semantic Web technologies (especially RDF, OWL, and SPARQL) can support information integration and make it easy to create semantic mashups (semantically integrated resources). In the context of understanding the genetic basis of nicotine dependence, we integrate gene and pathway information and show how three complex biological queries can be answered by the integrated knowledge base. Methods: We use an ontology-driven approach to integrate two gene resources (Entrez Gene and HomoloGene) and three pathway resources (KEGG, Reactome and BioCyc), for five organisms, including humans. We created the Entrez Knowledge Model (EKoM), an information model in OWL for the gene resources, and integrated it with the extant BioPAX ontology designed for pathway resources. The integrated schema is populated with data from the pathway resources, publicly available in BioPAX-compatible format, and gene resources for which a population procedure was created. The SPARQL query language is used to formulate queries over the integrated knowledge base to answer the three biological queries. Results: Simple SPARQL queries could easily identify hub genes, i.e., those genes whose gene products participate in many pathways or interact with many other gene products. The identification of the genes expressed in the brain turned out to be more difficult, due to the lack of a common identification scheme for proteins. Conclusion: Semantic Web technologies provide a valid framework for information integration in the life sciences. Ontology-driven integration represents a flexible, sustainable and extensible solution to the integration of large volumes of information. Additional resources, which enable the creation of mappings between information sources, are required to compensate for heterogeneity across namespaces. Resource page: http://knoesis.wright.edu/research/lifesci/integration/structured_data/JBI-2008/

With the advent of high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly in Korea. However, there is little information on gene relatedness of the Korean patent sequences. The primary aims of this study are two-fold. First, we associated Korean patent applications with genes. Second, we have constructed a database server, named Patome@Korea, to provide the gene-patent map and the Korean patent data containing biological sequences. To associate the sequences with genes, we have received patent sequence data from the Korean Intellectual Property Office (KIPO) and annotated them with RefSeq and Entrez Gene. Through the association analysis, we found that nearly 14.7% of human genes were related to Korean patenting, compared to 25% of human genes in the US patent. We have consolidated the association results and the patent sequence data to a relational database and implemented a web-based user interface to provide search service. The database can be queried using application number, applicant, titles, gene ID/name, and RefSeq number. We also provide web-based BLAST facility to allow users to compare their sequences against patent sequences.

ORegAnno is an open-source, open-access database and literature curation system for community-based annotation of experimentally identified DNA regulatory regions, transcription factor binding sites and regulatory variants. The current release comprises 30 145 records curated from 922 publications and describing regulatory sequences for over 3853 genes and 465 transcription factors from 19 species. A new feature called the 'publication queue' allows users to input relevant papers from scientific literature as targets for annotation. The queue contains 4438 gene regulation papers entered by experts and another 54 351 identified by text-mining methods. Users can enter or 'check out' papers from the queue for manual curation using a series of user-friendly annotation pages. A typical record entry consists of species, sequence type, sequence, target gene, binding factor, experimental outcome and one or more lines of experimental evidence. An evidence ontology was developed to describe and categorize these experiments. Records are cross-referenced to Ensembl or Entrez gene identifiers, PubMed and dbSNP and can be visualized in the Ensembl or UCSC genome browsers. All data are freely available through search pages, XML data dumps or web services at: http://www.oreganno.org.

The Biological General Repository for Interaction Datasets (BioGRID) database (http://www.thebiogrid.org) was developed to house and distribute collections of protein and genetic interactions from major model organism species. BioGRID currently contains over 198 000 interactions from six different species, as derived from both high-throughput studies and conventional focused studies. Through comprehensive curation efforts, BioGRID now includes a virtually complete set of interactions reported to date in the primary literature for both the budding yeast Saccharomyces cerevisiae and the fission yeast Schizosaccharomyces pombe. A number of new features have been added to the BioGRID including an improved user interface to display interactions based on different attributes, a mirror site and a dedicated interaction management system to coordinate curation across different locations. The BioGRID provides interaction data with monthly updates to Saccharomyces Genome Database, Flybase and Entrez Gene. Source code for the BioGRID and the linked Osprey network visualization system is now freely available without restriction.

Thousands of data-, discipline-, and organism-specific databases exist for genes and proteins, making it difficult for researchers to determine which resources to search for the most complete picture. Entrez Gene, a freely accessible online database from the National Center for Biotechnology Information (NCBI) pulls together gene-centered authoritative information from a number of resources, serving as a true "Information Hub." Although not fully comprehensive in nature, many librarians and other bioinformatics support specialists turn first to Entrez Gene when a researcher asks for "everything known about a specific gene or gene product." This article introduces the typical Entrez Gene record, describes the information found in each of its fields, and explains why such information might be of interest to scientists.

Gene expression patterns can reflect gene regulations in human tissues under normal or pathologic conditions. Gene expression profiling data from studies of primary human disease samples are particularly valuable since these studies often span many years in order to collect patient clinical information and achieve a large sample size. Disease-to-Gene Expression Mapper (DGEM) provides a beneficial community resource to access and analyze these data; it currently includes Affymetrix oligonucleotide array datasets for more than 40 human diseases and 1400 samples. The data are normalized to the same scale and stored in a relational database. A statistical-analysis pipeline was implemented to identify genes abnormally expressed in disease tissues or genes whose expressions are associated with clinical parameters such as cancer patient survival. Data-mining results can be queried through a web-based interface at http://dgem.dhcp.iupui.edu/. The query tool enables dynamic generation of graphs and tables that are further linked to major gene and pathway resources that connect the data to relevant biology, including Entrez Gene and Kyoto Encyclopedia of Genes and Genomes (KEGG). In summary, DGEM provides scientists and physicians a valuable tool to study disease mechanisms, to discover potential disease biomarkers for diagnosis and prognosis, and to identify novel gene targets for drug discovery. The source code is freely available for non-profit use, on request to the authors.

With the advent of automated and high-throughput techniques, the number of patent applications containing biological sequences has been increasing rapidly. However, they have attracted relatively little attention compared to other sequence resources. We have built a database server called Patome, which contains biological sequence data disclosed in patents and published applications, as well as their analysis information. The analysis is divided into two steps. The first is an annotation step in which the disclosed sequences were annotated with RefSeq database. The second is an association step where the sequences were linked to Entrez Gene, OMIM and GO databases, and their results were saved as a gene–patent table. From the analysis, we found that 55% of human genes were associated with patenting. The gene–patent table can be used to identify whether a particular gene or disease is related to patenting. Patome is available at http://www.patome. org/; the information is updated bimonthly.

Models of transcription factor (TF) binding sites provide a basis for a wide spectrum of studies in regulatory genomics, from reconstruction of regulatory networks to functional annotation of transcripts and sequence variants. While TFs may recognize different sequence patterns in different conditions, it is pragmatic to have a single generic model for each particular TF as a baseline for practical applications. Here we present the expanded and enhanced version of HOCOMOCO (http://hocomoco.autosome.ru and http://www.cbrc.kaust.edu.sa/hocomoco10), the collection of models of DNA patterns, recognized by transcription factors. HOCOMOCO now provides position weight matrix (PWM) models for binding sites of 601 human TFs and, in addition, PWMs for 396 mouse TFs. Furthermore, we introduce the largest up to date collection of dinucleotide PWM models for 86 (52) human (mouse) TFs. The update is based on the analysis of massive ChIP-Seq and HT-SELEX datasets, with the validation of the resulting models on in vivo data. To facilitate a practical application, all HOCOMOCO models are linked to gene and protein databases (Entrez Gene, HGNC, UniProt) and accompanied by precomputed score thresholds. Finally, we provide command-line tools for PWM and diPWM threshold estimation and motif finding in nucleotide sequences.

Entrez Gene lists four HPRT1 gene pseudogenes (HPRTP1, HPRTP2, HPRTP3, and HPRTP4) mapping to chromosomes 3, 5, 11q, and 11q, respectively, as originally reported by Patel et al. in 1984 (Patel PI, et al. 1984 Somat Cell Mol Genet 10:483-493). However, the Entrez Gene reports for three of the four pseudogenes (HPRTP1, HPRTP3, and HPRTP4) are currently empty. A BLAST search of both GenBank (Homo sapiens) and the human genome found the chromosome 5 associated HPRTP2 sequence and a single chromosome 11q sequence (HPRTP3 or HPRTP4?). This chromosome 11 sequence had a unique 7.2 kb insert, which may explain why it originally appeared to be two separate pseudogenes. No evidence of a chromosome 3 associated sequence was found; however, a sequence highly homologous to HPRT1 was located on chromosome 4. All of these sequences are intronless processed pseudogenes. Lastly, a sequence highly homologous to HPRT1 exon 8 was found on chromosome 10. This homologous sequence was exactly exon 8 of a gene designated PRTFDC1, for phosphoribosyl transferase domain containing 1. This gene with unknown function is almost completely homologous to HPRT1 in exon structure (except for a 21 bp (seven amino acid) insertion in exon 1) and 68% homologous in amino acid sequence.

The RTPrimerDB (http://medgen.ugent.be/rtprimerdb) project provides a freely accessible data retrieval system and an in silico assay evaluation pipeline for real-time quantitative PCR assays. Over the last year the number of user submitted assays has grown to 3500. Data conveyance from Entrez Gene by establishing an assay-to-gene relationship enables the addition of new primer assays for one of the 1.5 million different genes from 2300 species stored in the system. Easy access to the primer and probe data is possible by using multiple search criteria. Assay reports contain gene information, assay details (such as oligonucleotide sequences, detection chemistry and reaction conditions), publication information, users' experimental evaluation feedback and submitter's contact details. Gene expression assays are extended with a scalable assay viewer that provides detailed information on the alignment of primer and probe sequences on the known transcript variants of a gene, along with Single Nucleotide Polymorphisms (SNP) positions and peptide domain information. Furthermore, an mfold module is implemented to predict the secondary structure of the amplicon sequence, as this has been reported to impact the efficiency of the PCR. RTPrimerDB is also extended with an in silico analysis pipeline to streamline the evaluation of custom designed primer and probe sequences prior to ordering and experimental evaluation. In a secured environment, the pipeline performs automated BLAST specificity searches, mfold secondary structure prediction, SNP or plain sequence error identification, and graphical visualization of the aligned primer and probe sequences on the target gene.

180 BioTechniques Vol. 39, No. 2 (2005) Identifying the genes that are responsible for human genetic disorders with complex inheritance patterns (e.g., multigenic diseases) has proven to be more difficult than anticipated. Indeed, when the mode of inheritance is unknown, classical parametric linkage studies are not relevant. Nonparametric linkage analysis can help approximate the candidate loci, but this type of analysis often leads to the identification of large intervals (10–20 cM) that may contain hundreds of genes, thus rendering the candidate gene approach rather tedious (1). Adding an expression screening may significantly reduce the number of candidate genes. Microarray expression studies of the genes located within such genetic intervals should be performed on relevant tissues (2). The design of a custom microrarray containing probes of all the genes located in the intervals of interest is required to carry out such experiments. Designing such microarrays involves gathering much additional information concerning these genes, in particular, name, transcript accession numbers, or nucleotide sequences. Collecting these data manually is time-consuming and very error-prone. GeneRetriever is a Perl-based data mining tool developed to automate, accelerate, and secure the process of locally retrieving user-chosen comprehensive information about human genes or transcripts located between two genetic markers. As annotation strategies are specific for each database, we implemented a database parameter entry that allows collection of data from either the National Center for Biotechnology Information (NCBI; www.ncbi.nlm.nih. gov) or Ensembl (www.ensembl.org) databases (3,4). Then, several options make it possible to define which data should be included in the returned gene/transcript table. These options are clustered into three parts: (i) genespecific data; (ii) transcript-specific data; and (iii) expression analysis data. Gene-specific options include database (either NCBI or Ensembl) identifier, Hugo Gene Nomenclature Committee symbol (5), gene description, DNA strand (plus or minus), type of gene (known or predicted), cytogenetic localization, summary of functional annotations, Entrez Gene identifier (6), web address of either NCBI or Ensembl gene page, and the number of transcript variants. Additional transcript information is optionally available, such as Ensembl transcript or RefSeq accession numbers. Structural data, including transcript size, number of exons, and size of the longest exon, can also be added to the query. These data may be useful when working with predicted genes; for instance, the relevance of predicted genes composed of one single exon of

By combining a bioinformatics tool (FamDBTool) and the expertise of a network of calcium binding proteins specialists (European Calcium Society), we aim to accelerate and to rationalize the curation of the public general biological database such as Swissprot and Entrez Gene with respect to specific protein families. In this paper, we show the feasibility of such rationale in order to set and to curate the human, mouse and rat sets of EF-Hand genes, the EF-Handome.

In 2004, when the protein estimate from the finished human genome was only 24,000, the surprise was compounded as reviewed estimates fell to 19,000 by 2014. However, variability in the total canonical protein counts (i.e. excluding alternative splice forms) of open reading frames (ORFs) in different annotation portals persists. This work assesses these differences and possible causes. A 16-year analysis of Ensembl and UniProtKB/Swiss-Prot shows convergence to a protein number of ~20,000. The former had shown some yo-yoing, but both have now plateaued. Nine major annotation portals, reviewed at the beginning of 2017, gave a spread of counts from 21,819 down to 18,891. The 4-way cross-reference concordance (within UniProt) between Ensembl, Swiss-Prot, Entrez Gene and the Human Gene Nomenclature Committee (HGNC) drops to 18,690, indicating methodological differences in protein definitions and experimental existence support between sources. The Swiss-Prot and neXtProt evidence criteria include mass spectrometry peptide verification and also cross-references for antibody detection from the Human Protein Atlas. Notwithstanding, hundreds of Swiss-Prot entries are classified as non-coding biotypes by HGNC. The only inference that protein numbers might still rise comes from numerous reports of small ORF (smORF) discovery. However, while there have been recent cases of protein verifications from previous miss-annotation of non-coding RNA, very few have passed the Swiss-Prot curation and genome annotation thresholds. The post-genomic era has seen both advances in data generation and improvements in the human reference assembly. Notwithstanding, current numbers, while persistently discordant, show that the earlier yo-yoing has largely ceased. Given the importance to biology and biomedicine of defining the canonical human proteome, the task will need more collaborative inter-source curation combined with broader and deeper experimental confirmation in vivo and in vitro of proteins predicted in silico. The eventual closure could be well be below ~19,000.

A disease molecular signature is a set of biomolecular features that are prognostic of clinical phenotypes and indicative of underlying pathology. It is of great importance to develop computational approaches for finding more relevant molecular signatures. Based upon the hypothesis that various components in a molecular signature are more likely to share similar patterns, we introduced a novel three step network based approach (TSNBA) to identify the molecular signature and key pathological regulators. Protein-protein interaction (PPI) network and ranking algorithm were integrated in the first step to find pathology related proteins with high accuracy. It was followed by the second step to further screen with co-expression patterns for better pathology enrichment. Context likelihood of relatedness (CLR) algorithm was used in the third step to infer gene regulatory networks and identify key transcription regulators. We applied this approach to study IL-1 (interleukin-1) and TNF-alpha (tumor necrosis factor-alpha) stimulated inflammation. TSNBA identified inflammatory signature with high accuracy and outperformed 5 competing methods namely fold change, degree, interconnectivity, neighborhood score and network propagation based approaches. The best molecular signature, with 80% (40/50) confirmed inflammatory genes, was used to predict inflammation related genes. As a result, 8 out of 10 predicted inflammation genes that were not included in the benchmark Entrez Gene database were validated by literature evidence. Furthermore, 23 of the 32 predicted inflammation regulators were validated by literature evidence. The rest 9 were also validated with TF (transcription factor) binding site analysis. In conclusion, we developed an efficient strategy for disease molecular signature finding and key pathological regulator identification.

Graphical abstractDisplay Omitted Knowledge-based functional enrichment for gene prioritization of high throughput data.Automatic ontology generation from MEDLINE.Novel and fully automatic literature-based discovery.Literature ontologies perform better than expert-derived ones. MotivationGene set enrichment analysis (GSEA) annotates gene microarray data with functional information from the biomedical literature to improve gene-disease association prediction. We hypothesize that supplementing GSEA with comprehensive gene function catalogs built automatically using information extracted from the scientific literature will significantly enhance GSEA prediction quality. MethodsGold standard gene sets for breast cancer (BrCa) and colorectal cancer (CRC) were derived from the literature. Two gene function catalogs (CMeSH and CUMLS) were automatically generated. 1. By using Entrez Gene to associate all recorded human genes with PubMed article IDs. 2. Using the genes mentioned in each PubMed article and associating each with the article's MeSH terms (in CMeSH) and extracted UMLS concepts (in CUMLS). Microarray data from the Gene Expression Omnibus for BrCa and CRC was then annotated using CMeSH and CUMLS and for comparison, also with several pre-existing catalogs (C2, C4 and C5 from the Molecular Signatures Database). Ranking was done using, a standard GSEA implementation (GSEA-p). Gene function predictions for enriched array data were evaluated against the gold standard by measuring area under the receiver operating characteristic curve (AUC). ResultsComparison of ranking using the literature enrichment catalogs, the pre-existing catalogs as well as five randomly generated catalogs show the literature derived enrichment catalogs are more effective. The AUC for BrCa using the unenriched gene expression dataset was 0.43, increasing to 0.89 after gene set enrichment with CUMLS. The AUC for CRC using the unenriched gene expression dataset was 0.54, increasing to 0.9 after enrichment with CMeSH. C2 increased AUC (BrCa 0.76, CRC 0.71) but C4 and C5 performed poorly (between 0.35 and 0.5). The randomly generated catalogs also performed poorly, equivalent to random guessing. DiscussionGene set enrichment significantly improved prediction of gene-disease association. Selection of enrichment catalog had a substantial effect on prediction accuracy. The literature based catalogs performed better than the MSigDB catalogs, possibly because they are more recent. Catalogs generated automatically from the literature can be kept up to date. ConclusionPrediction of gene-disease association is a fundamental task in biomedical research. GSEA provides a promising method when using literature-based enrichment catalogs. AvailabilityThe literature based catalogs generated and used in this study are available from http://www2.chi.unsw.edu.au/literature-enrichment.