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David J States

Publications and source records attributed to David J States.

13 recordsLinked to original sources
PubMed2006

SAGA: a subgraph matching tool for biological graphs.

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.

Algorithms↗
PubMed2006

Novel gene and gene model detection using a whole genome open reading frame analysis in proteomics.

BACKGROUND: Defining the location of genes and the precise nature of gene products remains a fundamental challenge in genome annotation. Interrogating tandem mass spectrometry data using genomic sequence provides an unbiased method to identify novel translation products. A six-frame translation of the entire human genome was used as the query database to search for novel blood proteins in the data from the Human Proteome Organization Plasma Proteome Project. Because this target database is orders of magnitude larger than the databases traditionally employed in tandem mass spectra analysis, careful attention to significance testing is required. Confidence of identification is assessed using our previously described Poisson statistic, which estimates the significance of multi-peptide identifications incorporating the length of the matching sequence, number of spectra searched and size of the target sequence database. RESULTS: Applying a false discovery rate threshold of 0.05, we identified 282 significant open reading frames, each containing two or more peptide matches. There were 627 novel peptides associated with these open reading frames that mapped to a unique genomic coordinate placed within the start/stop points of previously annotated genes. These peptides matched 1,110 distinct tandem MS spectra. Peptides fell into four categories based upon where their genomic coordinates placed them relative to annotated exons within the parent gene. CONCLUSION: This work provides evidence for novel alternative splice variants in many previously annotated genes. These findings suggest that annotation of the genome is not yet complete and that proteomics has the potential to further add to our understanding of gene structures.

Alternative Splicing↗
PubMed2006

Challenges in deriving high-confidence protein identifications from data gathered by a HUPO plasma proteome collaborative study.

The Human Proteome Organization (HUPO) recently completed the first large-scale collaborative study to characterize the human serum and plasma proteomes. The study was carried out in different locations and used diverse methods and instruments to compare and integrate tandem mass spectrometry (MS/MS) data on aliquots of pooled serum and plasma from healthy subjects. Liquid chromatography (LC)-MS/MS data sets from 18 laboratories were matched to the International Protein Index database, and an initial integration exercise resulted in 9,504 proteins identified with one or more peptides, and 3,020 proteins identified with two or more peptides. This article uses a rigorous statistical approach to take into account the length of coding regions in genes, and multiple hypothesis-testing techniques. On this basis, we now present a reduced set of 889 proteins identified with a confidence level of at least 95%. We also discuss the importance of such an integrated analysis in providing an accurate representation of a proteome as well as the value such data sets contain for the high-confidence identification of protein matches to novel exons, some of which may be localized in alternatively spliced forms of known plasma proteins and some in previously nonannotated gene sequences.

Blood Proteins↗
PubMed2005

Profiles of growth hormone (GH)-regulated genes reveal time-dependent responses and identify a mechanism for regulation of activating transcription factor 3 by GH.

In examination of mechanisms regulating metabolic responses to growth hormone (GH), microarray analysis identified 561 probe sets showing time-dependent patterns of expression in GH-treated 3T3-F442A adipocytes. Biological functions significantly over-represented among GH-regulated genes include regulators of transcription at early times, and lipid biosynthesis, cholesterol biosynthesis, and mediators of immune responses at later times (48 h). One novel GH-induced gene encodes activating transcription factor 3 (ATF3). Atf3 mRNA expression and promoter activity were stimulated by GH. Genes for ATF3 and growth arrest and DNA damage-inducible gene 45 gamma (GADD45gamma) showed similar time-dependent patterns of responses to GH, suggesting similar regulatory mechanisms. A conserved sequence in the promoters of the Atf3 and Gadd45gamma genes contains a CCAAT/enhancer-binding protein (C/EBP) site previously observed in the Gadd45gamma promoter, suggesting a novel corresponding C/EBP site in the Atf3 promoter. C/EBPbeta was found to bind to the predicted Atf3 C/EBP site, and C/EBPbeta enhanced the activation of the wild-type Atf3 promoter. Mutation of the predicted Atf3 C/EBP site disrupted Atf3 promoter activation not only by C/EBPbeta but also by GH. These findings suggest that GH regulates transcription of Atf3 through a mechanism utilizing factors, such as C/EBPbeta, which bind to a novel C/EBP site.

3T3 Cells↗
PubMed2005

A functional annotation of subproteomes in human plasma.

The data collected by Human Proteome Organization's Plasma Proteome Pilot project phase was analyzed by members of our working group. Accordingly, a functional annotation of the human plasma proteome was carried out. Here, we report the findings of our analyses. First, bioinformatic analyses were undertaken to determine the likely sources of plasma proteins and to develop a protein interaction network of proteins identified in this project. Second, annotation of these proteins was performed in the context of functional subproteomes involved in the coagulation pathway, the mononuclear phagocytic system, the inflammation pathway, the cardiovascular system, and the liver; as well as the subset of proteins associated with DNA binding activities. Our analyses contributed to the Plasma Proteome Database (http://www.plasmaproteomedatabase.org), an annotated database of plasma proteins identified by HPPP as well as from other published studies. In addition, we address several methodological considerations including the selective enrichment of post-translationally modified proteins by the use of multi-lectin chromatography as well as the use of peptidomic techniques to characterize the low molecular weight proteins in plasma. Furthermore, we have performed additional analyses of peptide identification data to annotate cleavage of signal peptides, sites of intra-membrane proteolysis and post-translational modifications. The HPPP-organized, multi-laboratory effort, as described herein, resulted in much synergy and was essential to the success of this project.

Blood Coagulation↗
PubMed2005

Data management and preliminary data analysis in the pilot phase of the HUPO Plasma Proteome Project.

The pilot phase of the HUPO Plasma Proteome Project (PPP) is an international collaboration to catalog the protein composition of human blood plasma and serum by analyzing standardized aliquots of reference serum and plasma specimens using a variety of experimental techniques. Data management for this project included collection, integration, analysis, and dissemination of findings from participating organizations world-wide. Accomplishing this task required a communication and coordination infrastructure specific enough to support meaningful integration of results from all participants, but flexible enough to react to changing requirements and new insights gained during the course of the project and to allow participants with varying informatics capabilities to contribute. Challenges included integrating heterogeneous data, reducing redundant information to minimal identification sets, and data annotation. Our data integration workflow assembles a minimal and representative set of protein identifications, which account for the contributed data. It accommodates incomplete concordance of results from different laboratories, ambiguity and redundancy in contributed identifications, and redundancy in the protein sequence databases. Recommendations of the PPP for future large-scale proteomics endeavors are described.

Algorithms↗
PubMed2005

Overview of the HUPO Plasma Proteome Project: results from the pilot phase with 35 collaborating laboratories and multiple analytical groups, generating a core dataset of 3020 proteins and a publicly-available database.

HUPO initiated the Plasma Proteome Project (PPP) in 2002. Its pilot phase has (1) evaluated advantages and limitations of many depletion, fractionation, and MS technology platforms; (2) compared PPP reference specimens of human serum and EDTA, heparin, and citrate-anti-coagulated plasma; and (3) created a publicly-available knowledge base (www.bioinformatics.med.umich.edu/hupo/ppp; www.ebi.ac.uk/pride). Thirty-five participating laboratories in 13 countries submitted datasets. Working groups addressed (a) specimen stability and protein concentrations; (b) protein identifications from 18 MS/MS datasets; (c) independent analyses from raw MS-MS spectra; (d) search engine performance, subproteome analyses, and biological insights; (e) antibody arrays; and (f) direct MS/SELDI analyses. MS-MS datasets had 15 710 different International Protein Index (IPI) protein IDs; our integration algorithm applied to multiple matches of peptide sequences yielded 9504 IPI proteins identified with one or more peptides and 3020 proteins identified with two or more peptides (the Core Dataset). These proteins have been characterized with Gene Ontology, InterPro, Novartis Atlas, OMIM, and immunoassay-based concentration determinations. The database permits examination of many other subsets, such as 1274 proteins identified with three or more peptides. Reverse protein to DNA matching identified proteins for 118 previously unidentified ORFs. We recommend use of plasma instead of serum, with EDTA (or citrate) for anticoagulation. To improve resolution, sensitivity and reproducibility of peptide identifications and protein matches, we recommend combinations of depletion, fractionation, and MS/MS technologies, with explicit criteria for evaluation of spectra, use of search algorithms, and integration of homologous protein matches. This Special Issue of PROTEOMICS presents papers integral to the collaborative analysis plus many reports of supplementary work on various aspects of the PPP workplan. These PPP results on complexity, dynamic range, incomplete sampling, false-positive matches, and integration of diverse datasets for plasma and serum proteins lay a foundation for development and validation of circulating protein biomarkers in health and disease.

Algorithms↗
PubMed2004

Wnt pathway curation using automated natural language processing: combining statistical methods with partial and full parse for knowledge extraction.

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: dstates@umich.edu SUPPLEMENTARY INFORMATION: http://stateslab.bioinformatics.med.umich.edu/software.html.

Animals↗
PubMed2003

Computationally identifying novel NF-kappa B-regulated immune genes in the human genome.

Identifying novel NF-kappa B-regulated immune genes in the human genome is important to our understanding of immune mechanisms and immune diseases. We fit logistic regression models to the promoters of 62 known NF-kappa B-regulated immune genes, to find patterns of transcription factor binding in the promoters of genes with known immune function. Using these patterns, we scanned the promoters of additional genes to find matches to the patterns, selected those with NF-kappa B binding sites conserved in the mouse or fly, and then confirmed them as NF-kappa B-regulated immune genes based on expression data. Among 6440 previously identified promoters in the human genome, we found 28 predicted immune gene promoters, 19 of which regulate genes with known function, allowing us to calculate specificity of 93%-100% for the method. We calculated sensitivity of 42% when searching the 62 known immune gene promoters. We found nine novel NF-kappa B-regulated immune genes which are consistent with available SAGE data. Our method of predicting gene function, based on characteristic patterns of transcription factor binding, evolutionary conservation, and expression studies, would be applicable to finding genes with other functions.

Animals↗
PubMed2002

Comparison of whole genome assemblies of the human genome.

A fundamental problem in the human genome project is uncovering the correct assembly of the human genome. Many studies, including transcriptional analysis, SNP detection and characterization, gene finding and EST clustering, use genome assemblies as templates so it is important to determine the consistency among the various whole genome assemblies. A comparison of the order and orientation of the GenBank entries used to construct the NCBI and UCSC Goldenpath assemblies was made. In addition, a sequence level comparison was performed using MULTI, an efficient database search tool developed to make whole genome comparisons possible. The resulting comparisons show significant discrepancies in the sequence as well as in the order and orientation of GenBank entries used in constructing the NCBI and UCSC assemblies.

Chromosomes, Human↗
PubMed2002

Consensus promoter identification in the human genome utilizing expressed gene markers and gene modeling.

Deciphering the human genome includes locating the promoters that initiate transcription and identifying the exons of genes. Many promoter prediction programs have been proposed, but when they are applied to extended regions of the genome, most of their predictions are false-positives. The extensive collection of gene transcript sequences is an important new source of information, which has not been used previously in promoter predictions. Our approach is to enhance the specificity of predictions by restricting the genomic regions that are searched using gene transcript alignments as anchors in the genome for gene modeling. We developed a consensus promoter prediction method combining previously developed algorithms with the GENSCAN gene modeling program. Our method, CONPRO (CONsensus PROmoter), identifies promoters with very high confidence, and the predicted promoters are guaranteed to be associated with genes. On our test data set, the method correctly detects promoters for approximately half of all human genes (37%-71%), and most predictions are true promoters (85%-90%). Applying our method to the human genome and human genes from the Unigene data set, we find the promoters for 13,744 genes. Of these, 6440 are genes with a functionally cloned mRNA, and 7304 are novel genes for which only expressed sequence tags (ESTs) are available. Candidate promoters for many novel genes will be a useful resource in elucidating complex biological response mechanisms.

5' Untranslated Regions↗