SUMMARY: Progress in high-throughput genomic technologies has led to the development of a variety of resources that link genes to functional information contained in the biomedical literature. However, tools attempting to link small molecules to normal and diseased physiology and published data relevant to biologists and clinical investigators, are still lacking. With metabolomics rapidly emerging as a new omics field, the task of annotating small molecule metabolites becomes highly relevant. Our tool Metab2MeSH uses a statistical approach to reliably and automatically annotate compounds with concepts defined in Medical Subject Headings, and the National Library of Medicine's controlled vocabulary for biomedical concepts. These annotations provide links from compounds to biomedical literature and complement existing resources such as PubChem and the Human Metabolome Database.

SUMMARY: GLay provides Cytoscape users an assorted collection of versatile community structure algorithms and graph layout functions for network clustering and structured visualization. High performance is achieved by dynamically linking highly optimized C functions to the Cytoscape JAVA program, which makes GLay especially suitable for decomposition, display and exploratory analysis of large biological networks. AVAILABILITY: http://brainarray.mbni.med.umich.edu/glay/.

UNLABELLED: The MiMI molecular interaction repository integrates data from multiple sources, resolves interactions to standard gene names and symbols, links to annotation data from GO, MeSH and PubMed and normalizes the descriptions of interaction type. Here, we describe a Cytoscape plugin that retrieves interaction and annotation data from MiMI and links out to multiple data sources and tools. Community annotation of the interactome is supported. AVAILABILITY: MiMI plugin v3.0.1 can be installed from within Cytoscape 2.6 using the Cytoscape plugin manager in 'Network and Attribute I/0' category. The plugin is also preloaded when Cytoscape is launched using Java WebStart at http://mimi.ncibi.org by querying a gene and clicking 'View in MiMI Plugin for Cytoscape' link.

UNLABELLED: SciMiner is a web-based literature mining and functional analysis tool that identifies genes and proteins using a context specific analysis of MEDLINE abstracts and full texts. SciMiner accepts a free text query (PubMed Entrez search) or a list of PubMed identifiers as input. SciMiner uses both regular expression patterns and dictionaries of gene symbols and names compiled from multiple sources. Ambiguous acronyms are resolved by a scoring scheme based on the co-occurrence of acronyms and corresponding description terms, which incorporates optional user-defined filters. Functional enrichment analyses are used to identify highly relevant targets (genes and proteins), GO (Gene Ontology) terms, MeSH (Medical Subject Headings) terms, pathways and protein-protein interaction networks by comparing identified targets from one search result with those from other searches or to the full HGNC [HUGO (Human Genome Organization) Gene Nomenclature Committee] gene set. The performance of gene/protein name identification was evaluated using the BioCreAtIvE (Critical Assessment of Information Extraction systems in Biology) version 2 (Year 2006) Gene Normalization Task as a gold standard. SciMiner achieved 87.1% recall, 71.3% precision and 75.8% F-measure. SciMiner's literature mining performance coupled with functional enrichment analyses provides an efficient platform for retrieval and summary of rich biological information from corpora of users' interests. AVAILABILITY: http://jdrf.neurology.med.umich.edu/SciMiner/. A server version of the SciMiner is also available for download and enables users to utilize their institution's journal subscriptions. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: MiSearch is an adaptive biomedical literature search tool that ranks citations based on a statistical model for the likelihood that a user will choose to view them. Citation selections are automatically acquired during browsing and used to dynamically update a likelihood model that includes authorship, journal and PubMed indexing information. The user can optionally elect to include or exclude specific features and vary the importance of timeliness in the ranking. AVAILABILITY: http://misearch.ncibi.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Cytoscape enhanced search plugin (ESP) enables searching complex biological networks on multiple attribute fields using logical operators and wildcards. Queries use an intuitive syntax and simple search line interface. ESP is implemented as a Cytoscape plugin and complements existing search functions in the Cytoscape network visualization and analysis software, allowing users to easily identify nodes, edges and subgraphs of interest, even for very large networks. Availabiity: http://chianti.ucsd.edu/cyto_web/plugins/ CONTACT: [email protected].

MOTIVATION: Cell lines are used extensively in biomedical research, but the nomenclature describing cell lines has not been standardized. The problems are both linguistic and experimental. Many ambiguous cell line names appear in the published literature. Users of the same cell line may refer to it in different ways, and cell lines may mutate or become contaminated without the knowledge of the user. As a first step towards rationalizing this nomenclature, we created a cell line knowledgebase (CLKB) with a well-structured collection of names and descriptive data for cell lines cultured in vitro. The objectives of this work are: (i) to assist users in extracting useful information from biomedical text and (ii) to highlight the importance of standardizing cell line names in biomedical research. This CLKB contains a broad collection of cell line names compiled from ATCC, Hyper CLDB and MeSH. In addition to names, the knowledgebase specifies relationships between cell lines. We analyze the use of cell line names in biomedical text. Issues include ambiguous names, polymorphisms in the use of names and the fact that some cell line names are also common English words. Linguistic patterns associated with the occurrence of cell line names are analyzed. Applying these patterns to find additional cell line names in the literature identifies only a small number of additional names. Annotation of microarray gene expression studies is used as a test case. The CLKB facilitates data exploration and comparison of different cell lines in support of clinical and experimental research. AVAILABILITY: The web ontology file for this cell line collection can be downloaded at http://www.stateslab.org/data/celllineOntology/cellline.zip.

Abstract The old Asian legend about the blind men and the elephant comes to mind when looking at how different authors of scientific papers describe a piece of related prior work. It turns out that different citations to the same paper often focus on different aspects of that paper and that neither provides a full description of its full set of contributions. In this article, we will describe our investigation of this phenomenon. We studied citation summaries in the context of research papers in the biomedical domain. A citation summary is the set of citing sentences for a given article and can be used as a surrogate for the actual article in a variety of scenarios. It contains information that was deemed by peers to be important. Our study shows that citation summaries overlap to some extent with the abstracts of the papers and that they also differ from them in that they focus on different aspects of these papers than do the abstracts. In addition to this, co‐cited articles (which are pairs of articles cited by another article) tend to be similar. We show results based on a lexical similarity metric called cohesion to justify our claims.

MOTIVATION: With the rapid increase in the availability of biological graph datasets, there is a growing need for effective and efficient graph querying methods. Due to the noisy and incomplete characteristics of these datasets, exact graph matching methods have limited use and approximate graph matching methods are required. Unfortunately, existing graph matching methods are too restrictive as they only allow exact or near exact graph matching. This paper presents a novel approximate graph matching technique called SAGA. This technique employs a flexible model for computing graph similarity, which allows for node gaps, node mismatches and graph structural differences. SAGA employs an indexing technique that allows it to efficiently evaluate queries even against large graph datasets. RESULTS: SAGA has been used to query biological pathways and literature datasets, which has revealed interesting similarities between distinct pathways that cannot be found by existing methods. These matches associate seemingly unrelated biological processes, connect studies in different sub-areas of biomedical research and thus pose hypotheses for new discoveries. SAGA is also orders of magnitude faster than existing methods. AVAILABILITY: SAGA can be accessed freely via the web at http://www.eecs.umich.edu/saga. Binaries are also freely available at this website.

The MYC genes encode nuclear sequence specific-binding DNA-binding proteins that are pleiotropic regulators of cellular function, and the c-MYC proto-oncogene is deregulated and/or mutated in most human cancers. Experimental studies of MYC binding to the genome are not fully consistent. While many c-MYC recognition sites can be identified in c-MYC responsive genes, other motif matches-even experimentally confirmed sites-are associated with genes showing no c-MYC response. We have developed a computational model that integrates multiple sources of evidence to predict which genes will bind and be regulated by MYC in vivo. First, a Bayesian network classifier is used to predict those c-MYC recognition sites that are most likely to exhibit high-occupancy binding in chromatin immunoprecipitation studies. This classifier incorporates genomic sequence, experimentally determined genomic chromatin acetylation islands, and predicted methylation status from a computational model estimating the likelihood of genomic DNA methylation. We find that the predictions from this classifier are also applicable to other transcription factors, such as cAMP-response element-binding protein, whose binding sites are sensitive to DNA methylation. Second, the MYC binding probability is combined with the gene expression profile data from nine independent microarray datasets in multiple tissues. Finally, we may consider gene function annotations in Gene Ontology to predict the c-MYC targets. We assess the performance of our prediction results by comparing them with the c-myc targets identified in the biomedical literature. In total, we predict 460 likely c-MYC target genes in the human genome, of which 67 have been reported to be both bound and regulated by MYC, 68 are bound by MYC, and another 80 are MYC-regulated. The approach thus successfully identifies many known c-MYC targets and suggests many novel sites. Our findings suggest that to identify c-MYC genomic targets, integration of different data sources helps to improve the accuracy.

The systematic inference of biologically relevant influence networks remains a challenging problem in computational biology. Even though the availability of high-throughput data has enabled us to use probabilistic models to infer the plausible structure of such networks, their true interpretation of the biology of the process is questionable. In this work, we propose a probabilistic network inference methodology, based on the Directed information criterion, which incorporates the biology of transcription within the framework, so as to enable experimentally verifiable inference. We use a publicly available embryonic kidney microarray dataset to demonstrate our results on the regulation of the Gata2/Gata3 genes.

MOTIVATION: Wnt signaling is a very active area of research with highly relevant publications appearing at a rate of more than one per day. Building and maintaining databases describing signal transduction networks is a time-consuming and demanding task that requires careful literature analysis and extensive domain-specific knowledge. For instance, more than 50 factors involved in Wnt signal transduction have been identified as of late 2003. In this work we describe a natural language processing (NLP) system that is able to identify references to biological interaction networks in free text and automatically assembles a protein association and interaction map. RESULTS: A 'gold standard' set of names and assertions was derived by manual scanning of the Wnt genes website (http://www.stanford.edu/~rnusse/wntwindow.html) including 53 interactions involved in Wnt signaling. This system was used to analyze a corpus of peer-reviewed articles related to Wnt signaling including 3369 Pubmed and 1230 full text papers. Names for key Wnt-pathway associated proteins and biological entities are identified using a chi-squared analysis of noun phrases over-represented in the Wnt literature as compared to the general signal transduction literature. Interestingly, we identified several instances where generic terms were used on the website when more specific terms occur in the literature, and one typographic error on the Wnt canonical pathway. Using the named entity list and performing an exhaustive assertion extraction of the corpus, 34 of the 53 interactions in the 'gold standard' Wnt signaling set were successfully identified (64% recall). In addition, the automated extraction found several interactions involving key Wnt-related molecules which were missing or different from those in the canonical diagram, and these were confirmed by manual review of the text. These results suggest that a combination of NLP techniques for information extraction can form a useful first-pass tool for assisting human annotation and maintenance of signal pathway databases. AVAILABILITY: The pipeline software components are freely available on request to the authors. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: http://stateslab.bioinformatics.med.umich.edu/software.html.

MOTIVATION: Current methods for identifying sequence specific binding sites in DNA sequence using position specific weight matrices are limited in both sensitivity and specificity. Double strand DNA helix exhibits sequence dependent variations in conformation. Interactions between macromolecules result from complementarity of the two tertiary structures. We hypothesize that this conformational variation plays a role in transcription factor binding site recognition, and that the use of this structure information will improve the predictive power of transcription factor binding site models. RESULTS: Conformation models for the sequence dependence of DNA helix distortion have been developed. Using our conformational models, we defined a tertiary structure template for the met operon repressor MetJ binding site. Both naturally occurring sites and precursor binding sites identified through in vitro selection were used as the basis for template definition. The conformational model appears to recognize features of protein binding sites that are distinct from the features recognized by primary sequence based profiles. Combining the conformational model and primary sequence profile yields a hybrid model with improved discriminatory power compared with either the conformational model or sequence profile alone. Using our hybrid model, we searched the E.coli genome. We are able to identify the documented MetJ sites in the promoter regions of metA, metB, metC, metR and metF. In addition, we find several novel loci with characteristics suggesting that they are functional MetJ repressor binding sites. Novel MetJ binding sites are found upstream of the metK gene, as well as upstream of a gene, abc, a gene that encodes for a component of a multifunction transporter which may transport amino acids across the membrane. The false positive rate is significantly lower than the sequence profile method. AVAILABILITY: The programs of implementation of this algorithm are available upon request. The list of crystal structures used for compiling the mean base step parameters of DNA is available by anonymous ftp at http://stateslab.wustl.edu/pub/helix/StructureList.

Double strand DNA exhibits sequence dependent variations in three-dimensional conformation.Crystallographic studies have shown that regulatory proteins bind to DNA and may use these structural effects to indirectly recognize binding sites in a sequence specific manner.In this paper we describe a sequence similarity scoring measure based on the energetics of helix distortion.A set of preferred conformations is compiled from data in the Nucleic Acid Database ( NDB ) for 16 dinucleotide steps.These conformational parameters are combined with elastic force constants derived from solution studies of DNA to compute a log likelihood score for one sequence adopting the preferred conformation of another.

Parametric model fitting of unprocessed sequencing-gel trace data and a least-squares optimization algorithm provide a method for accurately determiniug allele freqnenties of single nucleotide substitutions in a population.The method uses trace data corn two homozygous individuals and from either a heterozygous individual or a mixed population of templates.A parametric model is fit to each of the traces to estimate the amount of each of the four flnorescent.dyes that is present at each site.The parameters estimated from each trace are then normalized to account, for scalar variations due to differences in the amount of sample loaded.The parameters estimated from the trace of the heterozygous individual or from the mixture are viewed as a weighted sum of the parameters estimated from the traces of the homozygous individuals.The weights, or allele freqnenties, are estimated by mhhizing the sum of squared errors between the linear combination of homozygous traces and the mixed trace.Comparison of allele frequencies estimated by our method to known frequencies at polymorphic sites in three pools of CEPH individuals show that our method is accurate.Our method is automatic and much less laborintensive than previous approaches.

MOTIVATION: Searching a protein sequence database for homologs is a powerful tool for discovering the structure and function of a sequence. Two new methods for searching sequence databases have recently been described: Probabilistic Smith-Waterman (PSW), which is based on Hidden Markov models for a single sequence using a standard scoring matrix, and a new version of BLAST (WU-BLAST2), which uses Sum statistics for gapped alignments. RESULTS: This paper compares and contrasts the effectiveness of these methods with three older methods (Smith-Waterman: SSEARCH, FASTA and BLASTP). The analysis indicates that the new methods are useful, and often offer improved accuracy. These tools are compared using a curated (by Bill Pearson) version of the annotated portion of PIR 39. Three different statistical criteria are utilized: equivalence number, minimum errors and the receiver operating characteristic. For complete-length protein query sequences from large families, PSW's accuracy is superior to that of the other methods, but its accuracy is poor when used with partial-length query sequences. False negatives are twice as common as false positives irrespective of the search methods if a family-specific threshold score that minimizes the total number of errors (i.e. the most favorable threshold score possible) is used. Thus, sensitivity, not selectivity, is the major problem. Among the analyzed methods using default parameters, the best accuracy was obtained from SSEARCH and PSW for complete-length proteins, and the two BLAST programs, plus SSEARCH, for partial-length proteins.