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PubMed2003

The Gene Ontology Annotation (GOA) project: implementation of GO in SWISS-PROT, TrEMBL, and InterPro.

Gene Ontology Annotation (GOA) is a project run by the European Bioinformatics Institute (EBI) that aims to provide assignments of terms from the Gene Ontology (GO) resource to gene products in a number of its databases (http://www.ebi.ac.uk/GOA). In the first stage of this project, GO assignments have been applied to a data set representing the complete human proteome by a combination of electronic mappings and manual curation. This vocabulary has also been applied to the nonredundant proteome sets for all other completely sequenced organisms as well as to proteins from a wide range of organisms where the proteome is not yet complete.

Computational Biology↗

PubMed2004

A database search for double-strand containing RNAs in Dictyostelium discoideum.

In eukaryotic cells, double-stranded RNA is degraded to 21mers and triggers RNA interference. Using a pattern description language, we have searched the EMBL database for sequences with the potential to form double strands in cis in Dictyostelium discoideum. No extended inverted repeats were found in mRNAs. However, the antisense direction of some mRNAs encoding regulatory or developmentally regulated proteins showed the ability to form double-stranded regions. In EST archives, we found potential double strands derived from a few genes, but these transcripts are not continuously encoded in the genome. Most likely, they represent hybrid molecules of sense and antisense RNAs.

Animals↗

PubMed2003

High-throughput proteomic analysis of human infiltrating ductal carcinoma of the breast.

Large-scale proteomics will play a critical role in the rapid display, identification and validation of new protein targets, and elucidation of the underlying molecular events that are associated with disease development, progression and severity. However, because the proteome of most organisms are significantly more complex than the genome, the comprehensive analysis of protein expression changes will require an analytical effort beyond the capacity of standard laboratory equipment. We describe the first high-throughput proteomic analysis of human breast infiltrating ductal carcinoma (IDCA) using OCT (optimal cutting temperature) embedded biopsies, two-dimensional difference gel electrophoresis (2-D DIGE) technology and a fully automated spot handling workstation. Total proteins from four breast IDCAs (Stage I, IIA, IIB and IIIA) were individually compared to protein from non-neoplastic tissue obtained from a female donor with no personal or family history of breast cancer. We detected differences in protein abundance that ranged from 14.8% in stage I IDCA versus normal, to 30.6% in stage IIB IDCA versus normal. A total of 524 proteins that showed > or = three-fold difference in abundance between IDCA and normal tissue were picked, processed and identified by mass spectrometry. Out of the proteins picked, approximately 80% were unambiguously assigned identities by matrix-assisted laser desorbtion/ionization-time of flight mass spectrometry or liquid chromatography-tandem mass spectrometry in the first pass. Bioinformatics tools were also used to mine databases to determine if the identified proteins are involved in important pathways and/or interact with other proteins. Gelsolin, vinculin, lumican, alpha-1-antitrypsin, heat shock protein-60, cytokeratin-18, transferrin, enolase-1 and beta-actin, showed differential abundance between IDCA and normal tissue, but the trend was not consistent in all samples. Out of the proteins with database hits, only heat shock protein-70 (more abundant) and peroxiredoxin-2 (less abundant) displayed the same trend in all the IDCAs examined. This preliminary study demonstrates quantitative and qualitative differences in protein abundance between breast IDCAs and reveals 2-D DIGE portraits that may be a reflection of the histological and pathological status of breast IDCA.

Adult↗

PubMed2006

Sequence-based protein structure prediction using a reduced state-space hidden Markov model.

This work describes the use of a hidden Markov model (HMM), with a reduced number of states, which simultaneously learns amino acid sequence and secondary structure for proteins of known three-dimensional structure and it is used for two tasks: protein class prediction and fold recognition. The Protein Data Bank and the annotation of the SCOP database are used for training and evaluation of the proposed HMM for a number of protein classes and folds. Results demonstrate that the reduced state-space HMM performs equivalently, or even better in some cases, on classifying proteins than a HMM trained with the amino acid sequence. The major advantage of the proposed approach is that a small number of states is employed and the training algorithm is of low complexity and thus relatively fast.

Algorithms↗

PubMed2001

The PACRAT system: an extensible WWW-based system for correlated sequence retrieval, storage and analysis.

With PACRAT (Patterns, Analyses, Correlations. Remote Archive Testbed) we present an online database solution to the problem of accessing high-confidence sequences with specific relationships to classes of genes, such as upstream intergenic regions attached to tRNA genes. In addition the software contains a data warehousing and analysis-facilitating suite to streamline the process of analyzing the collected data. An unexpected additional benefit of the system is that it also provides easy access to sequences of lower confidence, and may be of assistance in such things as resolving ORF-call conflicts in genomic annotation projects.

Computational Biology↗

PubMed2001

Automated construction of high-density comparative maps between rat, human, and mouse.

Animal models have been used primarily as surrogates for humans, having similar disease-based phenotypes. Genomic organization also tends to be conserved between species, leading to the generation of comparative genome maps. The emergence of radiation hybrid (RH) maps, coupled with the large numbers of available Expressed Sequence Tags (ESTs), has revolutionized the way comparative maps can be built. We used publicly available rat, mouse, and human data to identify genes and ESTs with interspecies sequence identity (homology), identified their UniGene relationships, and incorporated their RH map positions to build integrated comparative maps with >2100 homologous UniGenes mapped in more than one species (approximately 6% of all mammalian genes). The generation of these maps is iterative and labor intensive; therefore, we developed a series of computer tools (not described here) based on our algorithm that identifies anchors between species and produces printable and on-line clickable comparative maps that link to a wide variety of useful tools and databases. The maps were constructed using sequence-based comparisons, thus creating "hooks" for further sequence-based annotation of human, mouse, and rat sequences. Currently, this map enables investigators to link the physiology of the rat with the genetics of the mouse and the clinical significance of the human.

Algorithms↗

PubMed2004

Integrated web service for improving alignment quality based on segments comparison.

BACKGROUND: Defining blocks forming the global protein structure on the basis of local structural regularity is a very fruitful idea, extensively used in description, and prediction of structure from only sequence information. Over many years the secondary structure elements were used as available building blocks with great success. Specially prepared sets of possible structural motifs can be used to describe similarity between very distant, non-homologous proteins. The reason for utilizing the structural information in the description of proteins is straightforward. Structural comparison is able to detect approximately twice as many distant relationships as sequence comparison at the same error rate. RESULTS: Here we provide a new fragment library for Local Structure Segment (LSS) prediction called FRAGlib which is integrated with a previously described segment alignment algorithm SEA. A joined FRAGlib/SEA server provides easy access to both algorithms, allowing a one stop alignment service using a novel approach to protein sequence alignment based on a network matching approach. The FRAGlib used as secondary structure prediction achieves only 73% accuracy in Q3 measure, but when combined with the SEA alignment, it achieves a significant improvement in pairwise sequence alignment quality, as compared to previous SEA implementation and other public alignment algorithms. The FRAGlib algorithm takes approximately 2 min. to search over FRAGlib database for a typical query protein with 500 residues. The SEA service align two typical proteins within circa approximately 5 min. All supplementary materials (detailed results of all the benchmarks, the list of test proteins and the whole fragments library) are available for download on-line at http://ffas.ljcrf.edu/darman/results/. CONCLUSIONS: The joined FRAGlib/SEA server will be a valuable tool both for molecular biologists working on protein sequence analysis and for bioinformaticians developing computational methods of structure prediction and alignment of proteins.

Computational Biology↗

PubMed2005

Systematic analysis of human kinase genes: a large number of genes and alternative splicing events result in functional and structural diversity.

BACKGROUND: Protein kinases are a well defined family of proteins, characterized by the presence of a common kinase catalytic domain and playing a significant role in many important cellular processes, such as proliferation, maintenance of cell shape, apoptosis. In many members of the family, additional non-kinase domains contribute further specialization, resulting in subcellular localization, protein binding and regulation of activity, among others. About 500 genes encode members of the kinase family in the human genome, and although many of them represent well known genes, a larger number of genes code for proteins of more recent identification, or for unknown proteins identified as kinase only after computational studies. RESULTS: A systematic in silico study performed on the human genome, led to the identification of 5 genes, on chromosome 1, 11, 13, 15 and 16 respectively, and 1 pseudogene on chromosome X; some of these genes are reported as kinases from NCBI but are absent in other databases, such as KinBase. Comparative analysis of 483 gene regions and subsequent computational analysis, aimed at identifying unannotated exons, indicates that a large number of kinase may code for alternately spliced forms or be incorrectly annotated. An InterProScan automated analysis was performed to study domain distribution and combination in the various families. At the same time, other structural features were also added to the annotation process, including the putative presence of transmembrane alpha helices, and the cystein propensity to participate into a disulfide bridge. CONCLUSION: The predicted human kinome was extended by identifying both additional genes and potential splice variants, resulting in a varied panorama where functionality may be searched at the gene and protein level. Structural analysis of kinase proteins domains as defined in multiple sources together with transmembrane alpha helices and signal peptide prediction provides hints to function assignment. The results of the human kinome analysis are collected in the KinWeb database, available for browsing and searching over the internet, where all results from the comparative analysis and the gene structure annotation are made available, alongside the domain information. Kinases may be searched by domain combinations and the relative genes may be viewed in a graphic browser at various level of magnification up to gene organization on the full chromosome set.

Algorithms↗

PubMed2000

The role of the pathologist as tissue refiner and data miner: the impact of functional genomics on the modern pathology laboratory and the critical roles of pathology informatics and bioinformatics.

This article provides an overview of how functional genomics is likely to impact on the pathology laboratory and highlights how informatics and tissue banking will greatly facilitate the molecular age of medicine. Important aspects of functional genomics in the post-genome era, including the roles of laser capture microdissection, DNA- and complementary DNA-based microarrays, proteomic methods, collaborative human tissue banking, tissue microarrays, and pathobioinformatics in the modern pathology laboratory are discussed. The role of mass spectroscopy in the analysis of RNA, DNA, and protein and its impact on the clinical laboratory, particularly in cost-effectiveness and time savings, are evaluated. This article explores how laboratory information systems (LISs) and the devices that feed them information may need to be modified to adapt to greater volumes of data for the new testing modalities that require understanding sophisticated fluorescence detection methods and image processing. Emerging genomic testing methods and their impact on pathology laboratory testing, especially in the area of molecular classification of neoplasms, are examined. The role of the tissue bank in the modern pathology laboratory as an archive of control normal tissues, as well as subsamples of the spectrum of progressive neoplastic states, is discussed in light of its critical importance to the molecular classification of cancer. Establishing a database that combines structured reports in pathology LISs and construction of tissue banking information systems will provide a rich resource for pathology departments. The article discusses a hypothetical resource, such as the Shared Tumor Expression Profiler, that would provide access to well-characterized tissue-based research resources for clinicians and researchers. Last, the article emphasizes how LISs can prepare for these changes, and how training pathologists in pathology informatics and bioinformatics (pathobioinformatics) is critical to ensure pathology's overall leadership role in the post-genome era.

Clinical Laboratory Techniques↗

PubMed2002

Calculating the SNP-effective sample size from an alignment.

MOTIVATION: The number of Single Nucleotide Polymorphisms (SNPs) detectable in an alignment is a function of the length and the number of the aligned sequences. The latter is called sample size. However, a typical alignment, for instance obtained as a BLAST-search result of a query sequence against an EST database, does not evenly cover the query sequence. Therefore, it is usually not clear what the actual sample size is. RESULTS: We present a method to calculate the effective sample size, called n(eff), for a given BLAST alignment. This method takes into account that multiple coverage contributes only logarithmically to the SNP yield of a given sequence stretch. We show that the effective sample size n(eff) is usually much smaller than would be expected for a given amount of coverage and illustrate this with two typical examples.

Algorithms↗

PubMed2025

TIPP3 and TIPP3-fast: Improved abundance profiling in metagenomics.

We present TIPP3 and TIPP3-fast, new tools for abundance profiling in metagenomic datasets. Like its predecessor, TIPP2, the TIPP3 pipeline uses a maximum likelihood approach to place reads into labeled taxonomies using marker genes, but it achieves superior accuracy to TIPP2 by enabling the use of much larger taxonomies through improved algorithmic techniques. We show that TIPP3 is generally more accurate than leading methods for abundance profiling in two important contexts: when reads come from genomes not already in a public database (i.e., novel genomes) and when reads contain sequencing errors. We also show that TIPP3-fast has slightly lower accuracy than TIPP3, but is also generally more accurate than other leading methods and uses a small fraction of TIPP3's runtime. Additionally, we highlight the potential benefits of restricting abundance profiling methods to those reads that map to marker genes (i.e., using a filtered marker-gene based analysis), which we show typically improves accuracy. TIPP3 is freely available at https://github.com/c5shen/TIPP3.

Metagenomics↗

PubMed2005

Preprocessing of tandem mass spectrometric data based on decision tree classification.

In this study, we present a preprocessing method for quadrupole time-of-flight (Q-TOF) tandem mass spectra to increase the accuracy of database searching for peptide (protein) identification. Based on the natural isotopic information inherent in tandem mass spectra, we construct a decision tree after feature selection to classify the noise and ion peaks in tandem spectra. Furthermore, we recognize overlapping peaks to find the monoisotopic masses of ions for the following identification process. The experimental results show that this preprocessing method increases the search speed and the reliability of peptide identification.

Amino Acid Sequence↗

PubMed2002

An integrated computational pipeline and database to support whole-genome sequence annotation.

We describe here our experience in annotating the Drosophila melanogaster genome sequence, in the course of which we developed several new open-source software tools and a database schema to support large-scale genome annotation. We have developed these into an integrated and reusable software system for whole-genome annotation. The key contributions to overall annotation quality are the marshalling of high-quality sequences for alignments and the design of a system with an adaptable and expandable flexible architecture.

Animals↗

PubMed2004

Structural variant of the intergenic internal ribosome entry site elements in dicistroviruses and computational search for their counterparts.

The intergenic region (IGR) located upstream of the capsid protein gene in dicistroviruses contains an internal ribosome entry site (IRES). Translation initiation mediated by the IRES does not require initiator methionine tRNA. Comparison of the IGRs among dicistroviruses suggested that Taura syndrome virus (TSV) and acute bee paralysis virus have an extra side stem loop in the predicted IRES. We examined whether the side stem is responsible for translation activity mediated by the IGR using constructs with compensatory mutations. In vitro translation analysis showed that TSV has an IGR-IRES that is structurally distinct from those previously described. Because IGR-IRES elements determine the translation initiation site by virtue of their own tertiary structure formation, the discovery of this initiation mechanism suggests the possibility that eukaryotic mRNAs might have more extensive coding regions than previously predicted. To test this hypothesis, we searched full-length cDNA databases and whole genome sequences of eukaryotes using the pattern matching program, Scan For Matches, with parameters that can extract sequences containing secondary structure elements resembling those of IGR-IRES. Our search yielded several sequences, but their predicted secondary structures were suggested to be unstable in comparison to those of dicistroviruses. These results suggest that RNAs structurally similar to dicistroviruses are not common. If some eukaryotic mRNAs are translated independently of an initiator methionine tRNA, their structures are likely to be significantly distinct from those of dicistroviruses.

Computational Biology↗

PubMed2004

PPD - Proteome Profile Database.

With the complete sequencing of multiple genomes, there have been extensions in the methods of sequence analysis from single gene/protein-based to analyzing multiple genes and proteins simultaneously. Therefore, there is a demand of user-friendly software tools that will allow mining of these enormous datasets. PPD is a WWW-based database for comparative analysis of protein lengths in completely sequenced prokaryotic and eukaryotic genomes. PPD's core objective is to create protein classification tables based on the lengths of proteins by specifying a set of organisms and parameters. The interface can also generate information on changes in proteins of specific length distributions. This feature is of importance when the user's interest is focused on some evolutionarily related organisms or on organisms with similar or related tissue specificity or life-style. PPD is available at: PPD Home.

Animals↗

PubMed2001

Predicting protein--protein interactions from primary structure.

MOTIVATION: An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. The expectation is that this will provide a fuller appreciation of cellular processes and networks at the protein level, ultimately leading to a better understanding of disease mechanisms and suggesting new means for intervention. This paper addresses the question: can protein-protein interactions be predicted directly from primary structure and associated data? Using a diverse database of known protein interactions, a Support Vector Machine (SVM) learning system was trained to recognize and predict interactions based solely on primary structure and associated physicochemical properties. RESULTS: Inductive accuracy of the trained system, defined here as the percentage of correct protein interaction predictions for previously unseen test sets, averaged 80% for the ensemble of statistical experiments. Future proteomics studies may benefit from this research by proceeding directly from the automated identification of a cell's gene products to prediction of protein interaction pairs.

Artificial Intelligence↗

PubMed2004

Computational prediction of protein-protein interactions.

Eukaryotic proteins typically contain one or more modular domains such as kinases, phosphatases, and phoshopeptide-binding domains, as well as characteristic sequence motifs that direct post-translational modifications such as phosphorylation, or mediate binding to specific modular domains. A computational approach to predict protein interactions on a proteome-wide basis would therefore consist of identifying modular domains and sequence motifs from protein primary sequence data, creating sequence specificity-based algorithms to connect a domain in one protein with a motif in another in "interaction space," and then graphically constructing possible interaction networks. Computational methods for predicting modular domains in proteins have been quite successful, but identifying the short sequence motifs these domains recognize has been more difficult. We are developing improved methods to identify these motifs by combining experimental and computational techniques with databases of sequences and binding information. Scansite is a web-accessible program that predicts interactions between proteins using experimental binding data from peptide library and phage display experiments. This program focuses on domains important in cell signaling, but it can, in principle, be used for other interactions if the domains and binding motifs are known. This chapter describes in detail how to use Scansite to predict the binding partners of an input protein, and how to find all proteins that contain a given sequence motif.

Amino Acid Motifs↗

PubMed2003

IMGT, the international ImMunoGeneTics information system, http://imgt.cines.fr.

IMGT, the international ImMunoGeneTics information system (http://imgt.cines.fr), is a high quality integrated knowledge resource specializing in immunoglobulins (IG), T cell receptors (TR) and major histocompatibility complexes (MHC) and related proteins of the immune system (RPI) of human and other vertebrates, created in 1989 by LIGM at the Université Montpellier II, CNRS, Montpellier, France. IMGT provides a common access to standardized data which include nucleotide and protein sequences, oligonucleotide primers, gene maps, genetic polymorphisms, specificities, and 2D and 3D structures. IMGT includes five databases (IMGT/LIGM-DB, IMGT/3Dstructure-DB, IMGT/MHC-DB, IMGT/PRIMER-DB, IMGT/GENE-DB) Web resources ('IMGT Marie-Paule page') and interactive tools (IMGT/V-QUEST, IMGT/JunctionAnalysis, IMGT/PhyloGene, IMGT/LocusView, IMGT/GeneView, IMGT/GeneSearch, IMGT/StructureQuery). IMGT data are expertly annotated according to the rules of the IMGT Scientific chart based on IMGTONTOLOGY. IMGT tools are particularly useful for the analysis of the IG and TR repertoires in physiological normal and pathological situations. IMGT has important applications in medical research (autoimmune diseases, AIDS, leukaemias, lymphomas, myelomas), biotechnology related to antibody engineering (phage displays, combinatorial libraries) and therapeutic approaches (graft, immunotherapy). IMGT is freely available at http://imgt.cines.fr.

Animals↗