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The Eukaryotic Promoter Database (EPD): recent developments.

The Eukaryotic Promoter Database (EPD) is an annotated non-redundant collection of eukaryotic POL II promoters, for which the transcription start site has been determined experimentally. Access to promoter sequences is provided by pointers to positions in nucleotide sequence entries. The annotation part of an entry includes description of the initiation site mapping data, cross-references to other databases, and bibliographic references. EPD is structured in a way that facilitates dynamic extraction of biologically meaningful promoter subsets for comparative sequence analysis. Recent efforts have focused on exhaustive cross-referencing to the EMBL nucleotide sequence database, and on the improvement of the WWW-based user interfaces and data retrieval mechanisms. EPD can be accessed at http://www.epd.isb-sib.ch

Algorithms↗

The Gene Ontology (GO) project in 2006.

The Gene Ontology (GO) project (http://www.geneontology.org) develops and uses a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see http://song.sourceforge.net/). The GO Consortium continues to improve to the vocabulary content, reflecting the impact of several novel mechanisms of incorporating community input. A growing number of model organism databases and genome annotation groups contribute annotation sets using GO terms to GO's public repository. Updates to the AmiGO browser have improved access to contributed genome annotations. As the GO project continues to grow, the use of the GO vocabularies is becoming more varied as well as more widespread. The GO project provides an ontological annotation system that enables biologists to infer knowledge from large amounts of data.

Database Management Systems↗

TcruziDB: an integrated, post-genomics community resource for Trypanosoma cruzi.

TcruziDB (http://TcruziDB.org) is an integrated post-genomics database for the parasitic organism, Trypanosoma cruzi, the causative agent of Chagas' disease. TcruziDB was established in 2003 as a flat-file database with tools for mining the unannotated sequence reads and preliminary contig assemblies emerging from the Tri-Tryp genome consortium (TIGR/SBRI/Karolinska). Today, TcruziDB houses the recently published assembled genomic contigs and annotation provided by the genome consortium in a relational database supported by the Genomics Unified Schema (GUS) architecture. The combination of an annotated genome and a relational architecture has facilitated the integration of genomic data with expression data (proteomic and EST) and permitted the construction of automated analysis pipelines. TcruziDB has accepted, and will continue to accept the deposition of genomic and functional genomic datasets contributed by the research community.

Animals↗

The UCSC Archaeal Genome Browser.

As more archaeal genomes are sequenced, effective research and analysis tools are needed to integrate the diverse information available for any given locus. The feature-rich UCSC Genome Browser, created originally to annotate the human genome, can be applied to any sequenced organism. We have created a UCSC Archaeal Genome Browser, available at http://archaea.ucsc.edu/, currently with 26 archaeal genomes. It displays G/C content, gene and operon annotation from multiple sources, sequence motifs (promoters and Shine-Dalgarno), microarray data, multi-genome alignments and protein conservation across phylogenetic and habitat categories. We encourage submission of new experimental and bioinformatic analysis from contributors. The purpose of this tool is to aid biological discovery and facilitate greater collaboration within the archaeal research community.

Archaeal Proteins↗

MINT: the Molecular INTeraction database.

The Molecular INTeraction database (MINT, http://mint.bio.uniroma2.it/mint/) aims at storing, in a structured format, information about molecular interactions (MIs) by extracting experimental details from work published in peer-reviewed journals. At present the MINT team focuses the curation work on physical interactions between proteins. Genetic or computationally inferred interactions are not included in the database. Over the past four years MINT has undergone extensive revision. The new version of MINT is based on a completely remodeled database structure, which offers more efficient data exploration and analysis, and is characterized by entries with a richer annotation. Over the past few years the number of curated physical interactions has soared to over 95 000. The whole dataset can be freely accessed online in both interactive and batch modes through web-based interfaces and an FTP server. MINT now includes, as an integrated addition, HomoMINT, a database of interactions between human proteins inferred from experiments with ortholog proteins in model organisms (http://mint.bio.uniroma2.it/mint/).

Animals↗

Gene expression changes induced in the testis by transplacental exposure to high and low doses of 17{alpha}-ethynyl estradiol, genistein, or bisphenol A.

The purpose of this study was to determine (1) the transcriptional program elicited by exposure to three estrogen receptor (ER) agonists: 17 alpha-ethynyl estradiol (EE), genistein (Ges), and bisphenol A (BPA) during fetal development of the rat testis and epididymis; and (2) whether very low dosages of estrogens (evaluated over five orders of magnitude of dosage) produce unexpected changes in gene expression (i.e., a non-monotonic dose-response curve). In three independently conducted experiments, Sprague-Dawley rats were dosed (sc) with 0.001-10 microg EE/kg/day, 0.001-100 mg Ges/kg/day, or 0.002-400 mg BPA/kg/day. While morphological changes in the developing reproductive system were not observed, the gene expression profile of target tissues were modified in a dose-responsive manner. Independent dose-response analyses of the three studies identified 59 genes that are significantly modified by EE, 23 genes by Ges, and 15 genes by BPA (out of 8740), by at least 1.5 fold (up- or down-regulated). Even more genes were observed to be significantly changed when only the high dose is compared with all lower doses: 141, 46, and 67 genes, respectively. Global analyses aimed at detecting genes consistently modified by all of the chemicals identified 50 genes whose expression changed in the same direction across the three chemicals. The dose-response curve for gene expression changes was monotonic for each chemical, with both the number of genes significantly changed and the magnitude of change, for each gene, decreasing with decreasing dose. Using the available annotation of the gene expression changes induced by ER-agonist, our data suggest that a variety of cellular pathways are affected by estrogen exposure. These results indicate that gene expression data are diagnostic of mode of action and, if they are evaluated in the context of traditional toxicological end-points, can be used to elucidate dose-response characteristics.

Animals↗

Transcriptomic and proteomic analyses of the pMOL30-encoded copper resistance in Cupriavidus metallidurans strain CH34.

The four replicons of Cupriavidus metallidurans CH34 (the genome sequence was provided by the US Department of Energy-University of California Joint Genome Institute) contain two gene clusters putatively encoding periplasmic resistance to copper, with an arrangement of genes resembling that of the copSRABCD locus on the 2.1 Mb megaplasmid (MPL) of Ralstonia solanacearum, a closely related plant pathogen. One of the copSRABCD clusters was located on the 2.6 Mb MPL, while the second was found on the pMOL30 (234 kb) plasmid as part of a larger group of genes involved in copper resistance, spanning 17 857 bp in total. In this region, 19 ORFs (copVTMKNSRABCDIJGFLQHE) were identified based on the sequencing of a fragment cloned in an IncW vector, on the preliminary annotation by the Joint Genome Institute, and by using transcriptomic and proteomic data. When introduced into plasmid-cured derivatives of C. metallidurans CH34, the cop locus was able to restore the wild-type MIC, albeit with a biphasic survival curve, with respect to applied Cu(II) concentration. Quantitative-PCR data showed that the 19 ORFs were induced from 2- to 1159-fold when cells were challenged with elevated Cu(II) concentrations. Microarray data showed that the genes that were most induced after a Cu(II) challenge of 0.1 mM belonged to the pMOL30 cop cluster. Megaplasmidic cop genes were also induced, but at a much lower level, with the exception of the highly expressed MPL copD. Proteomic data allowed direct observation on two-dimensional gel electrophoresis, and via mass spectrometry, of pMOL30 CopK, CopR, CopS, CopA, CopB and CopC proteins. Individual cop gene expression depended on both the Cu(II) concentration and the exposure time, suggesting a sequential scheme in the resistance process, involving genes such as copK and copT in an initial phase, while other genes, such as copH, seem to be involved in a late response phase. A concentration of 0.4 mM Cu(II) was the highest to induce maximal expression of most cop genes.

Amino Acid Sequence↗

Exome-wide evidence of compound heterozygous effects across common phenotypes in the UK Biobank.

Exome-sequencing association studies have successfully linked rare protein-coding variation to risk of thousands of diseases. However, the relationship between rare deleterious compound heterozygous (CH) variation and their phenotypic impact has not been fully investigated. Here, we leverage advances in statistical phasing to accurately phase rare variants (MAF ~ 0.001%) in exome sequencing data from 175,587 UK Biobank (UKBB) participants, which we then systematically annotate to identify putatively deleterious CH coding variation. We show that 6.5% of individuals carry such damaging variants in the CH state, with 90% of variants occurring at MAF < 0.34%. Using a logistic mixed model framework, systematically accounting for relatedness, polygenic risk, nearby common variants, and rare variant burden, we investigate recessive effects in common complex diseases. We find six exome-wide significant () and 17 nominally significant () gene-trait associations. Among these, only four would have been identified without accounting for CH variation in the gene. We further incorporate age-at-diagnosis information from primary care electronic health records, to show that genetic phase influences lifetime risk of disease across 20 gene-trait combinations (FDR < 5%). Using a permutation approach, we find evidence for genetic phase contributing to disease susceptibility for a collection of gene-trait pairs, including FLG-asthma () and USH2A-visual impairment (). Taken together, we demonstrate the utility of phasing large-scale genetic sequencing cohorts for robust identification of the phenome-wide consequences of compound heterozygosity.

Preprint↗

Predicting gene function from patterns of annotation.

The Gene Ontology (GO) Consortium has produced a controlled vocabulary for annotation of gene function that is used in many organism-specific gene annotation databases. This allows the prediction of gene function based on patterns of annotation. For example, if annotations for two attributes tend to occur together in a database, then a gene holding one attribute is likely to hold the other as well. We modeled the relationships among GO attributes with decision trees and Bayesian networks, using the annotations in the Saccharomyces Genome Database (SGD) and in FlyBase as training data. We tested the models using cross-validation, and we manually assessed 100 gene-attribute associations that were predicted by the models but that were not present in the SGD or FlyBase databases. Of the 100 manually assessed associations, 41 were judged to be true, and another 42 were judged to be plausible.

Animals↗

Signal peptide prediction based on analysis of experimentally verified cleavage sites.

A number of computational tools are available for detecting signal peptides, but their abilities to locate the signal peptide cleavage sites vary significantly and are often less than satisfactory. We characterized a set of 270 secreted recombinant human proteins by automated Edman analysis and used the verified cleavage sites to evaluate the success rate of a number of computational prediction programs. An examination of the frequency of amino acid in the N-terminal region of the data set showed a preference of proline and glutamine but a bias against tyrosine. The data set was compared to the SWISS-PROT database and revealed a high percentage of discrepancies with cleavage site annotations that were computationally generated. The best program for predicting signal sequences was found to be SignalP 2.0-NN with an accuracy of 78.1% for cleavage site recognition. The new data set can be utilized for refining prediction algorithms, and we have built an improved version of profile hidden Markov model for signal peptides based on the new data.

Amino Acid Motifs↗

Re-annotation of genome microbial coding-sequences: finding new genes and inaccurately annotated genes.

BACKGROUND: Analysis of any newly sequenced bacterial genome starts with the identification of protein-coding genes. Despite the accumulation of multiple complete genome sequences, which provide useful comparisons with close relatives among other organisms during the annotation process, accurate gene prediction remains quite difficult. A major reason for this situation is that genes are tightly packed in prokaryotes, resulting in frequent overlap. Thus, detection of translation initiation sites and/or selection of the correct coding regions remain difficult unless appropriate biological knowledge (about the structure of a gene) is imbedded in the approach. RESULTS: We have developed a new program that automatically identifies biologically significant candidate genes in a bacterial genome. Twenty-six complete prokaryotic genomes were analyzed using this tool, and the accuracy of gene finding was assessed by comparison with existing annotations. This analysis revealed that, despite the enormous effort of genome program annotators, a small but not negligible number of genes annotated within the framework of sequencing projects are likely to be partially inaccurate or plainly wrong. Moreover, the analysis of several putative new genes shows that, as expected, many short genes have escaped annotation. In most cases, these new genes revealed frameshifts that could be either artifacts or genuine frameshifts. Some entirely unexpected new genes have also been identified. This allowed us to get a more complete picture of prokaryotic genomes. The results of this procedure are progressively integrated into the SWISS-PROT reference databank. CONCLUSIONS: The results described in the present study show that our procedure is very satisfactory in terms of gene finding accuracy. Except in few cases, discrepancies between our results and annotations provided by individual authors can be accounted for by the nature of each annotation process or by specific characteristics of some genomes. This stresses that close cooperation between scientists, regular update and curation of the findings in databases are clearly required to reduce the level of errors in genome annotation (and hence in reducing the unfortunate spreading of errors through centralized data libraries).

Computational Biology↗

MiCoViTo: a tool for gene-centric comparison and visualization of yeast transcriptome states.

BACKGROUND: Information obtained by DNA microarray technology gives a rough snapshot of the transcriptome state, i.e., the expression level of all the genes expressed in a cell population at any given time. One of the challenging questions raised by the tremendous amount of microarray data is to identify groups of co-regulated genes and to understand their role in cell functions. RESULTS: MiCoViTo (Microarray Comparison Visualization Tool) is a set of biologists' tools for exploring, comparing and visualizing changes in the yeast transcriptome by a gene-centric approach. A relational database includes data linked to genome expression and graphical output makes it easy to visualize clusters of co-expressed genes in the context of available biological information. To this aim, upload of personal data is possible and microarray data from fifty publications dedicated to S. cerevisiae are provided on-line. A web interface guides the biologist during the usage of this tool and is freely accessible at http://www.transcriptome.ens.fr/micovito/. CONCLUSIONS: MiCoViTo offers an easy-to-read picture of local transcriptional changes connected to current biological knowledge. This should help biologists to mine yeast microarray data and better understand the underlying biology. We plan to add functional annotations from other organisms. That would allow inter-species comparison of transcriptomes via orthology tables.

Cluster Analysis↗

GeneRank: using search engine technology for the analysis of microarray experiments.

BACKGROUND: Interpretation of simple microarray experiments is usually based on the fold-change of gene expression between a reference and a "treated" sample where the treatment can be of many types from drug exposure to genetic variation. Interpretation of the results usually combines lists of differentially expressed genes with previous knowledge about their biological function. Here we evaluate a method--based on the PageRank algorithm employed by the popular search engine Google--that tries to automate some of this procedure to generate prioritized gene lists by exploiting biological background information. RESULTS: GeneRank is an intuitive modification of PageRank that maintains many of its mathematical properties. It combines gene expression information with a network structure derived from gene annotations (gene ontologies) or expression profile correlations. Using both simulated and real data we find that the algorithm offers an improved ranking of genes compared to pure expression change rankings. CONCLUSION: Our modification of the PageRank algorithm provides an alternative method of evaluating microarray experimental results which combines prior knowledge about the underlying network. GeneRank offers an improvement compared to assessing the importance of a gene based on its experimentally observed fold-change alone and may be used as a basis for further analytical developments.

Algorithms↗

A scale of functional divergence for yeast duplicated genes revealed from analysis of the protein-protein interaction network.

BACKGROUND: Studying the evolution of the function of duplicated genes usually implies an estimation of the extent of functional conservation/divergence between duplicates from comparison of actual sequences. This only reveals the possible molecular function of genes without taking into account their cellular function(s). We took into consideration this latter dimension of gene function to approach the functional evolution of duplicated genes by analyzing the protein-protein interaction network in which their products are involved. For this, we derived a functional classification of the proteins using PRODISTIN, a bioinformatics method allowing comparison of protein function. Our work focused on the duplicated yeast genes, remnants of an ancient whole-genome duplication. RESULTS: Starting from 4,143 interactions, we analyzed 41 duplicated protein pairs with the PRODISTIN method. We showed that duplicated pairs behaved differently in the classification with respect to their interactors. The different observed behaviors allowed us to propose a functional scale of conservation/divergence for the duplicated genes, based on interaction data. By comparing our results to the functional information carried by GO annotations and sequence comparisons, we showed that the interaction network analysis reveals functional subtleties, which are not discernible by other means. Finally, we interpreted our results in terms of evolutionary scenarios. CONCLUSIONS: Our analysis might provide a new way to analyse the functional evolution of duplicated genes and constitutes the first attempt of protein function evolutionary comparisons based on protein-protein interactions.

Computational Biology↗

Putative function and prognostic molecular marker of mast cells in colorectal cancer.

BACKGROUND: The increased demand for markers for colorectal cancer (CRC) highlights the importance of investigating immune cells involved in CRC progression. This study aims to dissect the mast cells in CRC, characterize the role of mast cells in CRC development, coordinate molecular communication between mast cells and malignant cells, and construct and validate a prognostic classification model based on mast cell markers. METHODS: Single-cell transcriptome data of CRC patients were extracted from GSE146771 for cell classification and annotation. The malignant cells were identified by copykat and the communication between mast cells and malignant cells was analyzed by CellChat. Least absolute shrinkage and selection operator (LASSO) regression analysis and Cox regression analysis of mast cell markers were performed in the TCGA-COAD cohort to construct a prognostic classification model. qRT-PCR was performed to detect the mRNA expression of the molecules in the classification model in P815 and MC-9 cells. The co-culture experiment of MC38 and P815 cells&#xa0;were performed in 12-well transwell dish. Wound healing assay and Transwell assay were performed to detect cell migration and invasion. RESULTS: 10,186 high-quality cells in GSE146771 were annotated to 9&#xa0;cell types. Six markers in mast cells (HDC, GATA2, ASAH1, BTBD19, TIMP1, FAM110A) were selected to construct a classification model. The high-risk score defined showed high infiltration of immunosuppressive cells, including endothelial cells, CAFs, Tregs and high angiogenesis and epithelial-mesenchymal transition (EMT) activities. In the model, HDC were abnormally low expressed in P815 cells, while BTBD19, FAM110A, GATA2, ASAH1 and TIMP1 showed excessive expression in P815 cells. Knockdown of GATA2 in the co-culture system of P815 and MC38 cells&#xa0;blocked cell migration and invasion. CONCLUSION: This study identified the cell types within CRC, elaborated the cellular functions of mast cells in CRC development and their molecular communication to coordinate malignant cells, and highlighted the molecular components and biological features that constitute promising prognostic classification model.

Mast Cells↗

Omiga: a PC-based sequence analysis tool.

Computer-based sequence analysis, notation, and manipulation are a necessity for all molecular biologists working with any but the most simple DNA sequences. As sequence data become increasingly available, tools that can be used to manipulate and annotate individual sequences and sequence elements will become an even more vital implement in the molecular biologist's arsenal. The Omiga DNA and Protein Sequence Analysis Software tool, version 2.0 provides an effective and comprehensive tool for the analysis of both nucleic acid and protein sequences that runs on a standard PC available in every molecular biology laboratory. Omiga allows the import of sequences in several common formats. Upon importing sequences and assigning them to various projects, Omiga allows the user to produce, analyze, and edit sequence alignments. Sequences may also be queried for the presence of restriction sites, sequence motifs, and other sequence features, all of which can be added into the notations accompanying each sequence. This newest version of Omiga also allows for sequencing and polymerase chain reaction (PCR) primer prediction, a functionality missing in earlier versions. Finally, Omiga allows rapid searches for putative coding regions, and Basic Local Alignment Search Tool (BLAST) queries against public databases at the National Center for Biotechnology Information (NCBI).

Humans↗

Empirical data on corpus design and usage in biomedical natural language processing.

This paper describes the design of six publicly available biomedical corpora. We then present usage data for the six corpora. We show that corpora that are carefully annotated with respect to structural and linguistic characteristics and that are distributed in standard formats are more widely used than corpora that are not. These findings have implications for the design of the next generation of biomedical corpora.

Computational Biology↗

Beyond the genome: turning data into knowledge.

Since the publication of the draft sequence of the human genome in April 2001 (1,2), the pressure on life scientists to annotate and gain useful knowledge from the millions of As, Gs, Cs and Ts, is ever increasing. Now that the initial hype has passed, the life sciences field demands the technologies and computer power that can produce and analyze data in a high-throughput manner for drug discovery and disease diagnosis. Cambridge Healthtech Institute's Beyond Genome 2001 conference(1) (17-22 June 2001, San Francisco, CA, USA) brought together the latest research in these technologies in a 'tri-conference' that encompassed the 10th Annual Conference of Bioinformatics and Genome Research, the 3rd Annual Conference of In Silico Biology and the 5th Annual Conference of Proteomics.

Journal Article↗