CiteULike: Tag bioinformatics

Evolving research trends in bioinformatics.

Carolina Perez-Iratxeta — 2006-11-21T19:38:15-00:00

Brief Bioinform (31 October 2006)

The cross-disciplinary nature of bioinformatics entails co-evolution with other biomedical disciplines, whereby some bioinformatics applications become popular in certain disciplines and, in turn, these disciplines influence the focus of future bioinformatics development efforts. We observe here that the growth of computational approaches within various biomedical disciplines is not merely a reflection of a general extended usage of computers and the Internet, but due to the production of useful bioinformatics databases and methods for the rest of the biomedical scientific community. We have used the abstracts stored both in the MEDLINE database of biomedical literature and in NIH-funded project grants, to quantify two effects. First, we examine the biomedical literature as a whole and find that the use of computational methods has become increasingly prevalent across biomedical disciplines over the past three decades, while use of databases and the Internet have been rapidly increasing over the past decade. Second, we study the recent trends in the use of bioinformatics topics. We observe that molecular sequence databases are a widely adopted contribution in biomedicine from the field of bioinformatics, and that microarray analysis is one of the major new topics engaged by the bioinformatics community. Via this analysis, we were able to identify areas of rapid growth in the use of informatics to aid in curriculum planning, development of computational infrastructure and strategies for workforce education and funding.

Bayesian methods in bioinformatics and computational systems biology.

Darren J Wilkinson — 2007-04-14T19:31:41-00:00

Brief Bioinform (12 April 2007)

Bayesian methods are valuable, inter alia, whenever there is a need to extract information from data that are uncertain or subject to any kind of error or noise (including measurement error and experimental error, as well as noise or random variation intrinsic to the process of interest). Bayesian methods offer a number of advantages over more conventional statistical techniques that make them particularly appropriate for complex data. It is therefore no surprise that Bayesian methods are becoming more widely used in the fields of genetics, genomics, bioinformatics and computational systems biology, where making sense of complex noisy data is the norm. This review provides an introduction to the growing literature in this area, with particular emphasis on recent developments in Bayesian bioinformatics relevant to computational systems biology.

Multiple structural alignment by secondary structures: algorithm and applications.

O Dror — 2005-02-23T16:17:59-00:00

Protein Sci, Vol. 12, No. 11. (November 2003), pp. 2492-2507.

We present MASS (Multiple Alignment by Secondary Structures), a novel highly efficient method for structural alignment of multiple protein molecules and detection of common structural motifs. MASS is based on a two-level alignment, using both secondary structure and atomic representation. Utilizing secondary structure information aids in filtering out noisy solutions and achieves efficiency and robustness. Currently, only a few methods are available for addressing the multiple structural alignment task. In addition to using secondary structure information, the advantage of MASS as compared to these methods is that it is a combination of several important characteristics: (1) While most existing methods are based on series of pairwise comparisons, and thus might miss optimal global solutions, MASS is truly multiple, considering all the molecules simultaneously; (2) MASS is sequence order-independent and thus capable of detecting nontopological structural motifs; (3) MASS is able to detect not only structural motifs, shared by all input molecules, but also motifs shared only by subsets of the molecules. Here, we show the application of MASS to various protein ensembles. We demonstrate its ability to handle a large number (order of tens) of molecules, to detect nontopological motifs and to find biologically meaningful alignments within nonpredefined subsets of the input. In particular, we show how by using conserved structural motifs, one can guide protein-protein docking, which is a notoriously difficult problem. MASS is freely available at http://bioinfo3d.cs.tau.ac.il/MASS/.

On ontologies for biologists: the Gene Ontology--untangling the web.

M Ashburner — 2005-02-22T16:33:28-00:00

Novartis Found Symp, Vol. 247 (2002)

The mantra of the 'post-genomic' era is 'gene function'. Yet surprisingly little attention has been given to how functional and other information concerning genes is to be captured, made accessible to biologists or structured in a computable form. The aim of the Gene Ontology (GO) Consortium is to provide a framework for both the description and the organisation of such information. The GO Consortium is presently concerned with three structured controlled vocabularies which can be used to describe three discrete biological domains, building structured vocabularies which can be used to describe the molecular function, biological roles and cellular locations of gene products.

A probabilistic view of gene function.

AG Fraser — 2005-02-22T16:31:45-00:00

Nat Genet, Vol. 36, No. 6. (June 2004), pp. 559-564.

Cells are controlled by the complex and dynamic actions of thousands of genes. With the sequencing of many genomes, the key problem has shifted from identifying genes to knowing what the genes do; we need a framework for expressing that knowledge. Even the most rigorous attempts to construct ontological frameworks describing gene function (e.g., the Gene Ontology project) ultimately rely on manual curation and are thus labor-intensive and subjective. But an alternative exists: the field of functional genomics is piecing together networks of gene interactions, and although these data are currently incomplete and error-prone, they provide a glimpse of a new, probabilistic view of gene function. We outline such a framework, which revolves around a statistical description of gene interactions derived from large, systematically compiled data sets. In this probabilistic view, pleiotropy is implicit, all data have errors and the definition of gene function is an iterative process that ultimately converges on the correct functions. The relationships between the genes are defined by the data, not by hand. Even this comprehensive view fails to capture key aspects of gene function, not least their dynamics in time and space, showing that there are limitations to the model that must ultimately be addressed.

A multiple alignment algorithm for metabolic pathway analysis using enzyme hierarchy.

Y Tohsato — 2005-02-22T16:27:02-00:00

Proc Int Conf Intell Syst Mol Biol, Vol. 8 (2000), pp. 376-383.

In many of the chemical reactions in living cells, enzymes act as catalysts in the conversion of certain compounds (substrates) into other compounds (products). Comparative analyses of the metabolic pathways formed by such reactions give important information on their evolution and on pharmacological targets (Dandekar et al. 1999). Each of the enzymes that constitute a pathway is classified according to the EC (Enzyme Commission) numbering system, which consists of four sets of numbers that categorize the type of the chemical reaction catalyzed. In this study, we consider that reaction similarities can be expressed by the similarities between EC numbers of the respective enzymes. Therefore, in order to find a common pattern among pathways, it is desirable to be able to use the functional hierarchy of EC numbers to express the reaction similarities. In this paper, we propose a multiple alignment algorithm utilizing information content that is extended to symbols having a hierarchical structure. The effectiveness of our method is demonstrated by applying the method to pathway analyses of sugar, DNA and amino acid metabolisms.

The gene ontology categorizer.

CA Joslyn — 2005-02-22T16:25:29-00:00

Bioinformatics, Vol. 20 Suppl 1 (4 August 2004)

SUMMARY: The Gene Ontology Categorizer, developed jointly by the Los Alamos National Laboratory and Procter & Gamble Corp., provides a capability for the categorization task in the Gene Ontology (GO): given a list of genes of interest, what are the best nodes of the GO to summarize or categorize that list? The motivating question is from a drug discovery process, where after some gene expression analysis experiment, we wish to understand the overall effect of some cell treatment or condition by identifying 'where' in the GO the differentially expressed genes fall: 'clustered' together in one place? in two places? uniformly spread throughout the GO? 'high', or 'low'? In order to address this need, we view bio-ontologies more as combinatorially structured databases than facilities for logical inference, and draw on the discrete mathematics of finite partially ordered sets (posets) to develop data representation and algorithms appropriate for the GO. In doing so, we have laid the foundations for a general set of methods to address not just the categorization task, but also other tasks (e.g. distances in ontologies and ontology merger and exchange) in both the GO and other bio-ontologies (such as the Enzyme Commission database or the MEdical Subject Headings) cast as hierarchically structured taxonomic knowledge systems.

EzCatDB: the Enzyme Catalytic-mechanism Database.

N Nagano — 2005-02-22T16:21:54-00:00

Nucleic Acids Res, Vol. 33 Database Issue (1 January 2005)

The EzCatDB (Enzyme Catalytic-mechanism Database) specifically includes catalytic mechanisms of enzymes in terms of sequences and tertiary structures of enzymes, and proposed catalytic mechanisms, along with ligand structures. The EzCatDB groups enzyme data in the Protein Data Bank (PDB) and the SWISS-PROT database with identical domain compositions, Enzyme Commission (EC) numbers and catalytic mechanisms. The EzCatDB can be queried by the type of catalytic residue, name and type of ligand molecule that interacts with an enzyme as a cofactor, substrate or product. It can provide literature information, other database codes and EC numbers. The EzCatDB provides ligand annotation for enzymes in the PDB as well as literature information on structure and catalytic mechanisms. Furthermore, the EzCatDB also provides a hierarchic classification of catalytic mechanisms. This classification incorporates catalytic mechanisms and active-site structures of enzymes as well as basic reactions and reactive parts of ligand molecules. The EzCatDB is available at http://mbs.cbrc.jp/EzCatDB/.

The ENZYME data bank.

A Bairoch — 2005-02-22T16:20:06-00:00

Nucleic Acids Res, Vol. 22, No. 17. (September 1994), pp. 3626-3627.

The ENZYME data bank is a repository of information relative to the nomenclature of enzymes. It is primarily based on the recommendations of the Nomenclature Committee of the International Union of Biochemistry and Molecular Biology (IUBMB) and it contains the following data for each type of characterized enzyme for which an EC (Enzyme Commission) number has been provided: EC number Recommended name Alternative names (if any) Catalytic activity Cofactors (if any) Pointers to the SWISS-PROT protein sequence entrie(s) that correspond to the enzyme (if any) Pointers to human disease(s) associated with a deficiency of the enzyme (if any).

PromH: Promoters identification using orthologous genomic sequences.

VV Solovyev — 2005-02-17T18:19:19-00:00

Nucleic Acids Res, Vol. 31, No. 13. (1 July 2003), pp. 3540-3545.

Accurate prediction of promoters is fundamental for understanding gene expression patterns, cell specificity and development. In the studies of conserved features of regulatory regions of orthologous genes, it was observed that major promoter functional components such as transcription start points, TATA-boxes and regulatory motifs, are significantly more conservative than the sequences around them (70-100% compared with 30-50%). To improve promoter identification accuracy, we employed these findings in a new program, PromH, created by extending the TSSW program feature set. PromH uses linear discriminant functions that take into account conservation features and nucleotide sequences of promoter regions in pairs of orthologous genes. The program was tested on two sets of pairs of orthologous, mostly human and rodent, sequences with known transcription start sites (TSS), annotated to have TATA (21 genes, 11 orthologous pairs) and TATA-less (38 genes, 19 pairs) promoters, respectively. The program correctly predicted TSS for all 21 genes of the first set with a median deviation of 2 bp from true site location. Only for two genes, was there significant (46 and 105 bp) discrepancy between predicted and annotated TSS positions. For 38 TATA-less promoters from the second set, TSS was predicted for 27 genes, in 14 cases within 10 bp distance from annotated TSS, and in 21 cases--within 100 bp distance. Despite more discrepancies between predicted and annotated TSS for genes from the second set, these results are consistent with observations of much higher occurrence of multiple TSS in TATA-less promoters. In any case, our results show that PromH identifies TSS positions significantly more accurately than any other published promoter prediction method. The PromH program is available at http://www.softberry.com/berry.phtml?topic=promh.

Tools and resources for identifying protein families, domains and motifs.

NJ Mulder — 2005-02-17T18:18:21-00:00

Genome Biol, Vol. 3, No. 1. (2002)

With the large influx of raw sequence data from genome sequencing projects, there is a need for reliable automatic methods for protein sequence analysis and classification. The most useful tools use various methods for identifying motifs or domains found in previously characterized protein families. This article reviews the tools and resources available on the web for identifying signatures within proteins and discusses how they may be used in the analysis of new or unknown protein sequences.

A novel method for prokaryotic promoter prediction based on DNA stability.

A Kanhere — 2005-02-17T18:17:12-00:00

BMC Bioinformatics, Vol. 6, No. 1. (5 January 2005)

BACKGROUND: In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. This work presents a novel prokaryotic promoter prediction method based on DNA stability. RESULTS: The promoter region is less stable and hence more prone to melting as compared to other genomic regions. Our analysis shows that a method of promoter prediction based on the differences in the stability of DNA sequences in the promoter and non-promoter region works much better compared to existing prokaryotic promoter prediction programs, which are based on sequence motif searches. At present the method works optimally for genomes such as that of Escherichia coli, which have near 50 % G+C composition and also performs satisfactorily in case of other prokaryotic promoters. CONCLUSIONS: Our analysis clearly shows that the change in stability of DNA seems to provide a much better clue than usual sequence motifs, such as Pribnow box and -35 sequence, for differentiating promoter region from non-promoter regions. To a certain extent, it is more general and is likely to be applicable across organisms. Hence incorporation of such features in addition to the signature motifs can greatly improve the presently available promoter prediction programs.

Prediction of prokaryotic promoters based on prediction of transcriptional units.

JC Lin — 2005-02-17T18:16:10-00:00

Sheng Wu Hua Xue Yu Sheng Wu Wu Li Xue Bao (Shanghai), Vol. 35, No. 4. (April 2003), pp. 317-324.

Identification of promoters is very important in understanding gene regulating relationships in an organism, and computational identification of promoters has been a long standing problem in computational biology. A new method was presented to predict promoter regions in prokaryotic organism. The method predicted transcription unit (TU) first and the TU was divided into singlet that contains only one single gene in a TU, and operon that contains more than one gene. Based on these predicted TUs, promoter was predicted for each TU using hidden Markov model including explicit state duration density. Both predicted TUs and promoters were satisfying.

ExPASy: The proteomics server for in-depth protein knowledge and analysis.

E Gasteiger — 2005-02-17T18:13:48-00:00

Nucleic Acids Res, Vol. 31, No. 13. (1 July 2003), pp. 3784-3788.

The ExPASy (the Expert Protein Analysis System) World Wide Web server (http://www.expasy.org), is provided as a service to the life science community by a multidisciplinary team at the Swiss Institute of Bioinformatics (SIB). It provides access to a variety of databases and analytical tools dedicated to proteins and proteomics. ExPASy databases include SWISS-PROT and TrEMBL, SWISS-2DPAGE, PROSITE, ENZYME and the SWISS-MODEL repository. Analysis tools are available for specific tasks relevant to proteomics, similarity searches, pattern and profile searches, post-translational modification prediction, topology prediction, primary, secondary and tertiary structure analysis and sequence alignment. These databases and tools are tightly interlinked: a special emphasis is placed on integration of database entries with related resources developed at the SIB and elsewhere, and the proteomics tools have been designed to read the annotations in SWISS-PROT in order to enhance their predictions. ExPASy started to operate in 1993, as the first WWW server in the field of life sciences. In addition to the main site in Switzerland, seven mirror sites in different continents currently serve the user community.

University bioinformatics programs on the rise.

RJ Zauhar — 2005-02-17T18:10:57-00:00

Nat Biotechnol, Vol. 19, No. 3. (March 2001), pp. 285-286.

Single nucleotide polymorphisms (SNPs) that map to gaps in the human SNP map.

C Tsui — 2005-02-17T18:07:12-00:00

Nucleic Acids Res, Vol. 31, No. 16. (15 August 2003), pp. 4910-4916.

An international effort is underway to generate a comprehensive haplotype map (HapMap) of the human genome represented by an estimated 300,000 to 1 million 'tag' single nucleotide polymorphisms (SNPs). Our analysis indicates that the current human SNP map is not sufficiently dense to support the HapMap project. For example, 24.6% of the genome currently lacks SNPs at the minimal density and spacing that would be required to construct even a conservative tag SNP map containing 300,000 SNPs. In an effort to improve the human SNP map, we identified 140,696 additional SNP candidates using a new bioinformatics pipeline. Over 51,000 of these SNPs mapped to the largest gaps in the human SNP map, leading to significant improvements in these regions. Our SNPs will be immediately useful for the HapMap project, and will allow for the inclusion of many additional genomic intervals in the final HapMap. Nevertheless, our results also indicate that additional SNP discovery projects will be required both to define the haplotype architecture of the human genome and to construct comprehensive tag SNP maps that will be useful for genetic linkage studies in humans.

Advantages and limitations of microarray technology in human cancer.

G Russo — 2005-02-17T17:41:57-00:00

Oncogene, Vol. 22, No. 42. (29 September 2003), pp. 6497-6507.

Cancer is a highly variable disease with multiple heterogeneous genetic and epigenetic changes. Functional studies are essential to understanding the complexity and polymorphisms of cancer. The final deciphering of the complete human genome, together with the improvement of high throughput technologies, is causing a fundamental transformation in cancer research. Microarray is a new powerful tool for studying the molecular basis of interactions on a scale that is impossible using conventional analysis. This technique makes it possible to examine the expression of thousands of genes simultaneously. This technology promises to lead to improvements in developing rational approaches to therapy as well as to improvements in cancer diagnosis and prognosis, assuring its entry into clinical practice in specialist centers and hospitals within the next few years. Predicting who will develop cancer and how this disease will behave and respond to therapy after diagnosis will be one of the potential benefits of this technology within the next decade. In this review, we highlight some of the recent developments and results in microarray technology in cancer research, discuss potentially problematic areas associated with it, describe the eventual use of microarray technology for clinical applications and comment on future trends and issues.

Candidate gene approach for pharmacogenetic studies.

HZ Ring — 2005-02-17T17:39:02-00:00

Pharmacogenomics, Vol. 3, No. 1. (January 2002), pp. 47-56.

Genetic diversity in the form of single nucleotide DNA polymorphisms (SNPs) contributes to variable disease susceptibility and drug response. The candidate gene approach has been widely used to identify the genetic basis for pharmacogenetic traits and becomes increasingly more powerful with the recent advances in genomic technologies. High-throughput sequencing and SNP genotyping technologies allow the study of thousands of candidate genes and the identification of those involved in drug efficacy and toxicity. Expression-based genomic technologies such as DNA microarrays and proteomics also facilitate the understanding of important biological and pharmacological pathways, thus identifying more candidate genes for SNP studies. Candidate gene-based pharmacogenetic studies will lead to improved drug development, improved clinical trial design and therapeutics tailored to individual genotypes.

Experiments using microarray technology: limitations and standard operating procedures.

T Forster — 2005-02-17T17:35:56-00:00

J Endocrinol, Vol. 178, No. 2. (August 2003), pp. 195-204.

Microarrays are a powerful method for the global analysis of gene or protein content and expression, opening up new horizons in molecular and physiological systems. This review focuses on the critical aspects of acquiring meaningful data for analysis following fluorescence-based target hybridisation to arrays. Although microarray technology is adaptable to the analysis of a range of biomolecules (DNA, RNA, protein, carbohydrates and lipids), the scheme presented here is applicable primarily to customised DNA arrays fabricated using long oligomer or cDNA probes. Rather than provide a comprehensive review of microarray technology and analysis techniques, both of which are large and complex areas, the aim of this paper is to provide a restricted overview, highlighting salient features to provide initial guidance in terms of pitfalls in planning and executing array projects. We outline standard operating procedures, which help streamline the analysis of microarray data resulting from a diversity of array formats and biological systems. We hope that this overview will provide practical initial guidance for those embarking on microarray studies.

Gene selection in microarray data: the elephant, the blind men and our algorithms.

G Stolovitzky — 2005-02-17T17:35:31-00:00

Curr Opin Struct Biol, Vol. 13, No. 3. (June 2003), pp. 370-376.

Gene expression array data provide shadows of intricate cellular processes. Learning how to make the most of the information present in expression arrays has become a discipline in itself. In recent years, there has been an explosion of methods that analyze gene expression arrays to produce long lists of genes that express differentially in distinct cellular states. These lists will have to be organized, and the algorithms that produced them combined, if we wish to piece together the rich cellular structures probed by this high-throughput technology. Researchers will have to understand the benefits and limitations of the many existing methods to produce the combination of algorithms that best suits their gene expression experiments.

Post-analysis follow-up and validation of microarray experiments.

RF Chuaqui — 2005-02-17T17:33:51-00:00

Nat Genet, Vol. 32 Suppl (December 2002), pp. 509-514.

Measurement of gene-expression profiles using microarray technology is becoming increasingly popular among the biomedical research community. Although there has been great progress in this field, investigators are still confronted with a difficult question after completing their experiments: how to validate the large data sets that are generated? This review summarizes current approaches to verifying global expression results, discusses the caveats that must be considered, and describes some methods that are being developed to address outstanding problems.

Diagnostic and prognostic prediction using gene expression profiles in high-dimensional microarray data.

R Simon — 2005-02-17T17:33:10-00:00

Br J Cancer, Vol. 89, No. 9. (3 November 2003), pp. 1599-1604.

DNA microarrays are a potentially powerful technology for improving diagnostic classification, treatment selection and therapeutics development. There are, however, many potential pitfalls in the use of microarrays that result in false leads and erroneous conclusions. This paper provides a review of the key features to be observed in developing diagnostic and prognostic classification systems based on gene expression profiling and some of the pitfalls to be aware of in reading reports of microarray-based studies.

Navigating gene expression using microarrays--a technology review.

A Schulze — 2005-02-17T17:32:17-00:00

Nat Cell Biol, Vol. 3, No. 8. (August 2001)

Parallel quantification of large numbers of messenger RNA transcripts using microarray technology promises to provide detailed insight into cellular processes involved in the regulation of gene expression. This should allow new understanding of signalling networks that operate in the cell and of the molecular basis and classification of disease. But can the technology deliver such far-reaching promises?

The use and analysis of microarray data.

A Butte — 2005-02-17T17:31:05-00:00

Nat Rev Drug Discov, Vol. 1, No. 12. (December 2002), pp. 951-960.

Functional genomics is the study of gene function through the parallel expression measurements of genomes, most commonly using the technologies of microarrays and serial analysis of gene expression. Microarray usage in drug discovery is expanding, and its applications include basic research and target discovery, biomarker determination, pharmacology, toxicogenomics, target selectivity, development of prognostic tests and disease-subclass determination. This article reviews the different ways to analyse large sets of microarray data, including the questions that can be asked and the challenges in interpreting the measurements.

Microbial gene identification using interpolated Markov models.

SL Salzberg — 2005-02-14T17:49:04-00:00

Nucleic Acids Res, Vol. 26, No. 2. (15 January 1998), pp. 544-548.

This paper describes a new system, GLIMMER, for finding genes in microbial genomes. In a series of tests on Haemophilus influenzae , Helicobacter pylori and other complete microbial genomes, this system has proven to be very accurate at locating virtually all the genes in these sequences, outperforming previous methods. A conservative estimate based on experiments on H.pylori and H. influenzae is that the system finds >97% of all genes. GLIMMER uses interpolated Markov models (IMMs) as a framework for capturing dependencies between nearby nucleotides in a DNA sequence. An IMM-based method makes predictions based on a variable context; i.e., a variable-length oligomer in a DNA sequence. The context used by GLIMMER changes depending on the local composition of the sequence. As a result, GLIMMER is more flexible and more powerful than fixed-order Markov methods, which have previously been the primary content-based technique for finding genes in microbial DNA.

tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence.

TM Lowe — 2005-02-14T17:47:25-00:00

Nucleic Acids Res, Vol. 25, No. 5. (1 March 1997), pp. 955-964.

We describe a program, tRNAscan-SE, which identifies 99-100% of transfer RNA genes in DNA sequence while giving less than one false positive per 15 gigabases. Two previously described tRNA detection programs are used as fast, first-pass prefilters to identify candidate tRNAs, which are then analyzed by a highly selective tRNA covariance model. This work represents a practical application of RNA covariance models, which are general, probabilistic secondary structure profiles based on stochastic context-free grammars. tRNAscan-SE searches at approximately 30 000 bp/s. Additional extensions to tRNAscan-SE detect unusual tRNA homologues such as selenocysteine tRNAs, tRNA-derived repetitive elements and tRNA pseudogenes.

The PROSITE database, its status in 2002.

L Falquet — 2005-02-14T17:46:25-00:00

Nucleic Acids Res, Vol. 30, No. 1. (1 January 2002), pp. 235-238.

PROSITE [Bairoch and Bucher (1994) Nucleic Acids Res., 22, 3583-3589; Hofmann et al. (1999) Nucleic Acids Res., 27, 215-219] is a method of identifying the functions of uncharacterized proteins translated from genomic or cDNA sequences. The PROSITE database (http://www.expasy.org/prosite/) consists of biologically significant patterns and profiles designed in such a way that with appropriate computational tools it can rapidly and reliably help to determine to which known family of proteins (if any) a new sequence belongs, or which known domain(s) it contains.

Recent improvements to the PROSITE database.

N Hulo — 2005-02-14T17:44:19-00:00

Nucleic Acids Res, Vol. 32, No. Database issue. (1 January 2004)

The PROSITE database consists of a large collection of biologically meaningful signatures that are described as patterns or profiles. Each signature is linked to documentation that provides useful biological information on the protein family, domain or functional site identified by the signature. The PROSITE web page has been redesigned and several tools have been implemented to help the user discover new conserved regions in their own proteins and to visualize domain arrangements. We also introduced the facility to search PDB with a PROSITE entry or a user's pattern and visualize matched positions on 3D structures. The latest version of PROSITE (release 18.17 of November 30, 2003) contains 1676 entries. The database is accessible at http://www.expasy.org/prosite/.

Automatic annotation of protein motif function with Gene Ontology terms.

X Lu — 2005-02-14T17:37:05-00:00

BMC Bioinformatics, Vol. 5, No. 1. (2 September 2004)

BACKGROUND: Conserved protein sequence motifs are short stretches of amino acid sequence patterns that potentially encode the function of proteins. Several sequence pattern searching algorithms and programs exist foridentifying candidate protein motifs at the whole genome level. However, a much needed and important task is to determine the functions of the newly identified protein motifs. The Gene Ontology (GO) project is an endeavor to annotate the function of genes or protein sequences with terms from a dynamic, controlled vocabulary and these annotations serve well as a knowledge base. RESULTS: This paper presents methods to mine the GO knowledge base and use the association between the GO terms assigned to a sequence and the motifs matched by the same sequence as evidence for predicting the functions of novel protein motifs automatically. The task of assigning GO terms to protein motifs is viewed as both a binary classification and information retrieval problem, where PROSITE motifs are used as samples for mode training and functional prediction. The mutual information of a motif and aGO term association is found to be a very useful feature. We take advantage of the known motifs to train a logistic regression classifier, which allows us to combine mutual information with other frequency-based features and obtain a probability of correct association. The trained logistic regression model has intuitively meaningful and logically plausible parameter values, and performs very well empirically according to our evaluation criteria. CONCLUSIONS: In this research, different methods for automatic annotation of protein motifs have been investigated. Empirical result demonstrated that the methods have a great potential for detecting and augmenting information about the functions of newly discovered candidate protein motifs.

TIGRFAMs: a protein family resource for the functional identification of proteins.

DH Haft — 2005-02-14T17:34:26-00:00

Nucleic Acids Res, Vol. 29, No. 1. (1 January 2001), pp. 41-43.

TIGRFAMs is a collection of protein families featuring curated multiple sequence alignments, hidden Markov models and associated information designed to support the automated functional identification of proteins by sequence homology. We introduce the term 'equivalog' to describe members of a set of homologous proteins that are conserved with respect to function since their last common ancestor. Related proteins are grouped into equivalog families where possible, and otherwise into protein families with other hierarchically defined homology types. TIGRFAMs currently contains over 800 protein families, available for searching or downloading at www.tigr.org/TIGRFAMs. Classification by equivalog family, where achievable, complements classification by orthology, superfamily, domain or motif. It provides the information best suited for automatic assignment of specific functions to proteins from large-scale genome sequencing projects.

The Pfam protein families database.

A Bateman — 2005-02-14T17:25:22-00:00

Nucleic Acids Res, Vol. 30, No. 1. (1 January 2002), pp. 276-280.

Pfam is a large collection of protein multiple sequence alignments and profile hidden Markov models. Pfam is available on the World Wide Web in the UK at http://www.sanger.ac.uk/Software/Pfam/, in Sweden at http://www.cgb.ki.se/Pfam/, in France at http://pfam.jouy.inra.fr/ and in the US at http://pfam.wustl.edu/. The latest version (6.6) of Pfam contains 3071 families, which match 69% of proteins in SWISS-PROT 39 and TrEMBL 14. Structural data, where available, have been utilised to ensure that Pfam families correspond with structural domains, and to improve domain-based annotation. Predictions of non-domain regions are now also included. In addition to secondary structure, Pfam multiple sequence alignments now contain active site residue mark-up. New search tools, including taxonomy search and domain query, greatly add to the functionality and usability of the Pfam resource.

Improved microbial gene identification with GLIMMER.

AL Delcher — 2005-02-14T17:22:42-00:00

Nucleic Acids Res, Vol. 27, No. 23. (1 December 1999), pp. 4636-4641.

The GLIMMER system for microbial gene identification finds approximately 97-98% of all genes in a genome when compared with published annotation. This paper reports on two new results: (i) significant technical improvements to GLIMMER that improve its accuracy still further, and (ii) a comprehensive evaluation that demonstrates that the accuracy of the system is likely to be higher than previously recognized. A significant proportion of the genes missed by the system appear to be hypothetical proteins whose existence is only supported by the predictions of other programs. When the analysis is restricted to genes that have significant homology to genes in other organisms, GLIMMER misses <1% of known genes.

The Pfam protein families database.

A Bateman — 2004-12-14T10:42:03-00:00

Nucleic Acids Res, Vol. 32 Database issue (1 January 2004)

Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations include modelling of discontinuous domains allowing Pfam domain definitions to be closer to those found in structure databases. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://Pfam.cgb.ki.se/).

Functional genomics and proteomics--the role of nuclear medicine.

U Haberkorn — 2005-08-18T18:10:41-00:00

Eur J Nucl Med Mol Imaging, Vol. 29, No. 1. (January 2002), pp. 115-132.

Now that the sequencing of the human genome has been completed, the basic challenges are finding the genes, locating their coding regions and predicting their functions. This will result in a new understanding of human biology as well as in the design of new molecular structures as potential novel diagnostic or drug discovery targets. The assessment of gene function may be performed using the tools of the genome program. These tools represent high-throughput methods used to evaluate changes in the expression of many or all genes of an organism at the same time in order to investigate genetic pathways for normal development and disease. This will lead to a shift in the scientific paradigm: In the pre-proteomics era, functional assignments were derived from hypothesis-driven experiments designed to understand specific cellular processes. The new tools describe proteins on a proteome-wide scale, thereby creating a new way of doing cell research which results in the determination of three-dimensional protein structures and the description of protein networks. These descriptions may then be used for the design of new hypotheses and experiments in the traditional physiological, biochemical and pharmacological sense. The evaluation of genetically manipulated animals or newly designed biomolecules will require a thorough understanding of physiology, biochemistry and pharmacology and the experimental approaches will involve many new technologies, including in vivo imaging with single-photon emission tomography and positron emission tomography. Nuclear medicine procedures may be applied for the determination of gene function and regulation using established and new tracers or using in vivo reporter genes such as enzymes, receptors, antigens or transporters. Pharmacogenomics will identify new surrogate markers for therapy monitoring which may represent potential new tracers for imaging. Also, drug distribution studies for new therapeutic biomolecules are needed, at least during preclinical stages of drug development. Finally, new biomolecules will be developed by bioengineering methods which may be used for isotope-based diagnosis and treatment of disease.

Bioinformatics and genomic medicine.

JH Kim — 2005-08-18T18:08:47-00:00

Genet Med, Vol. 4, No. 6 Suppl. (c 2002)

Bioinformatics is a rapidly emerging field of biomedical research. A flood of large-scale genomic and postgenomic data means that many of the challenges in biomedical research are now challenges in computational science. Clinical informatics has long developed methodologies to improve biomedical research and clinical care by integrating experimental and clinical information systems. The informatics revolution in both bioinformatics and clinical informatics will eventually change the current practice of medicine, including diagnostics, therapeutics, and prognostics. Postgenome informatics, powered by high-throughput technologies and genomic-scale databases, is likely to transform our biomedical understanding forever, in much the same way that biochemistry did a generation ago. This paper describes how these technologies will impact biomedical research and clinical care, emphasizing recent advances in biochip-based functional genomics and proteomics. Basic data preprocessing with normalization and filtering, primary pattern analysis, and machine-learning algorithms are discussed. Use of integrative biochip informatics technologies, including multivariate data projection, gene-metabolic pathway mapping, automated biomolecular annotation, text mining of factual and literature databases, and the integrated management of biomolecular databases, are also discussed.

Intrinsic errors in genome annotation

Damien Devos — 2005-02-08T18:02:47-00:00

Trends in Genetics, Vol. 17, No. 8. (01 August 2001), pp. 429-431.

Genome sequencing is usually followed by routine annotation of protein function based on the assumption that similar sequences will have similar functions. Here, we introduce a simple calculation to estimate the magnitude of any possible annotation errors. We counted the number of discrepancies in the annotation of well-established sets of similar proteins and extrapolated these values to the pairs of similar sequences used for the annotation of different microbial genomes. We conclude that the number of potential errors in the prediction of detailed functions is higher than is usually believed.

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

S Bocs — 2005-02-08T17:49:30-00:00

BMC Bioinformatics, Vol. 3, No. 1. (2002)

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).

The past, present and future of genome-wide re-annotation.

CA Ouzounis — 2005-02-08T17:48:24-00:00

Genome Biol, Vol. 3, No. 2. (2002)

Annotation, the process by which structural or functional information is inferred for genes or proteins, is crucial for obtaining value from genome sequences. We define the process of annotating a previously annotated genome sequence as 're-annotation', and examine the strengths and weaknesses of current manual and automatic genome-wide re-annotation approaches.

Connecting the dots between genes, biochemistry, and disease susceptibility: systems biology modeling in human genetics

Jason Moore — 2005-01-29T12:58:18-00:00

Molecular Genetics and Metabolism, Vol. 84, No. 2. (February 2005), pp. 104-111.

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two or more DNA sequence variations. We review here this approach and then discuss how it can be used to model biochemical and metabolic data in the context of genetic studies of human disease susceptibility.

Overview of commonly used bioinformatics methods and their applications.

IM Kapetanovic — 2005-05-26T18:27:20-00:00

Ann N Y Acad Sci, Vol. 1020 (May 2004), pp. 10-21.

Bioinformatics, in its broad sense, involves application of computer processes to solve biological problems. A wide range of computational tools are needed to effectively and efficiently process large amounts of data being generated as a result of recent technological innovations in biology and medicine. A number of computational tools have been developed or adapted to deal with the experimental riches of complex and multivariate data and transition from data collection to information or knowledge. These include a wide variety of clustering and classification algorithms, including self-organized maps (SOM), artificial neural networks (ANN), support vector machines (SVM), fuzzy logic, and even hyphenated techniques as neuro-fuzzy networks. These bioinformatics tools are being evaluated and applied in various medical areas including early detection, risk assessment, classification, and prognosis of cancer. The goal of these efforts is to develop and identify bioinformatics methods with optimal sensitivity, specificity, and predictive capabilities.

Genome-wide operon prediction in Staphylococcus aureus.

L Wang — 2005-01-25T14:32:47-00:00

Nucleic Acids Res, Vol. 32, No. 12. (2004), pp. 3689-3702.

Identification of operon structure is critical to understanding gene regulation and function, and pathogenesis, and for identifying targets towards the development of new antibiotics in bacteria. Recently, the complete genome sequences of a large number of important human bacterial pathogens have become available for computational analysis, including the major human Gram-positive pathogen Staphylococcus aureus. By annotating the predicted operon structure of the S.aureus genome, we hope to facilitate the exploration of the unique biology of this organism as well as the comparative genomics across a broad range of bacteria. We have integrated several operon prediction methods and developed a consensus approach to score the likelihood of each adjacent gene pair to be co-transcribed. Gene pairs were separated into distinct operons when scores were equal to or below an empirical threshold. Using this approach, we have generated a S.aureus genome map with scores annotated at the intersections of every adjacent gene pair. This approach predicted about 864 monocistronic transcripts and 533 polycistronic operons from the protein-encoding genes in the S.aureus strain Mu50 genome. When compared with a set of experimentally determined S.aureus operons from literature sources, this method successfully predicted at least 91% of gene pairs. At the transcription unit level, this approach correctly identified at least 92% of complete operons in this dataset. This consensus approach has enabled us to predict operons with high accuracy from a genome where limited experimental evidence for operon structure is available.

Having a BLAST with bioinformatics (and avoiding BLASTphemy).

A Pertsemlidis — 2005-01-25T14:28:27-00:00

Genome Biol, Vol. 2, No. 10. (2001)

Searching for similarities between biological sequences is the principal means by which bioinformatics contributes to our understanding of biology. Of the various informatics tools developed to accomplish this task, the most widely used is BLAST, the basic local alignment search tool. This article discusses the principles, workings, applications and potential pitfalls of BLAST, focusing on the implementation developed at the National Center for Biotechnology Information.

Architectures for Java-based bioinformatics applications

Suresh Chalasani — 2004-11-29T09:43:24-00:00

Industrial Management & Data Systems, Vol. 104, No. 7. (1 July 2004), pp. 578-588.

Bioinformatics projects are currently under way at numerous universities and in industry. These projects typically involve processing large amounts of biological data and comparison of biological signals or sequences. Much of the existing work in bioinformatics software is based on such languages and platforms as Perl and Unix. This paper, proposes software architectures in Java to support biological applications allowing access of biological data using server-side Java programs on the Internet. The architecture follows the standards of unified modeling language (UML). UML architecture diagrams are presented for the Java-based bioinformatics applications. In addition, an overview of the Bio-Soft project under way at The Biomedical Research Institute (BRI) of the University of Wisconsin-Parkside is provided, which includes research and instructional software for bioinformatics applications.

Incorporating structure to predict microRNA targets.

H Robins — 2005-07-18T22:08:05-00:00

Proc Natl Acad Sci U S A, Vol. 102, No. 11. (15 March 2005), pp. 4006-4009.

MicroRNAs (miRNAs) are a recently discovered set of regulatory genes that constitute up to an estimated 1% of the total number of genes in animal genomes, including Caenorhabditis elegans, Drosophila, mouse, and humans [Lagos-Quintana, M., Rauhut, R., Lendeckel, W. & Tuschl, T. (2001) Science 294, 853-858; Lai, E. C., Tomancak, P., Williams, R. W. & Rubin, G.M. (2003) Genome Biol. 4, R42; Lau, N. C., Lim, L. P., Weinstein, E. G. & Bartel, D. P. (2001) Science 294, 858-862; Lee, R. C. & Ambros, V. (2001) Science 294, 862-8644; and Lee, R. C., Feinbaum, R. L. & Ambros, V. (1993) Cell 115, 787-798]. In animals, miRNAs regulate genes by attenuating protein translation through imperfect base pair binding to 3' UTR sequences of target genes. A major challenge in understanding the regulatory role of miRNAs is to accurately predict regulated targets. We have developed an algorithm for predicting targets that does not rely on evolutionary conservation. As one of the features of this algorithm, we incorporate the folded structure of mRNA. By using Drosophila miRNAs as a test case, we have validated our predictions in 10 of 15 genes tested. One of these validated genes is mad as a target for bantam. Furthermore, our computational and experimental data suggest that miRNAs have fewer targets than previously reported.

The impact of next-generation sequencing technology on genetics

Elaine Mardis — 2008-02-13T12:35:19-00:00

Trends in Genetics, Vol. In Press, Corrected Proof

If one accepts that the fundamental pursuit of genetics is to determine the genotypes that explain phenotypes, the meteoric increase of DNA sequence information applied toward that pursuit has nowhere to go but up. The recent introduction of instruments capable of producing millions of DNA sequence reads in a single run is rapidly changing the landscape of genetics, providing the ability to answer questions with heretofore unimaginable speed. These technologies will provide an inexpensive, genome-wide sequence readout as an endpoint to applications ranging from chromatin immunoprecipitation, mutation mapping and polymorphism discovery to noncoding RNA discovery. Here I survey next-generation sequencing technologies and consider how they can provide a more complete picture of how the genome shapes the organism.

Protein-protein interaction networks and biology—what's the connection?

Luke Hakes — 2008-01-08T18:08:02-00:00

Nature Biotechnology, Vol. 26, No. 1., pp. 69-72.

Computational biology and high-performance computing

David Bader — 2005-02-08T13:28:49-00:00

Commun. ACM, Vol. 47, No. 11. (November 2004), pp. 34-41.

The minimum information required for reporting a molecular interaction experiment (MIMIx)

Sandra Orchard — 2007-08-09T00:39:24-00:00

Nature Biotechnology, Vol. 25, No. 8. (08 August 2007), pp. 894-898.

Characterization and prediction of protein-protein interactions within and between complexes.

Einat Sprinzak — 2006-09-29T13:25:04-00:00

Proc Natl Acad Sci U S A (26 September 2006)

Databases of experimentally determined protein interactions provide information on binary interactions and on involvement in multiprotein complexes. These data are valuable for understanding the general properties of the interaction between proteins as well as for the development of prediction schemes for unknown interactions. Here we analyze experimentally determined protein interactions by measuring various sequence, genomic, transcriptomic, and proteomic attributes of each interacting pair in the yeast Saccharomyces cerevisiae. We find that dividing the data into two groups, one that includes binary interactions within protein complexes (stable) and another that includes binary interactions that are not within complexes (transient), enables better characterization of the interactions by the different attributes and improves the prediction of new interactions. This analysis revealed that most attributes were more indicative in the set of intracomplex interactions. Using this data set for training, we integrated the different attributes by logistic regression and developed a predictive scheme that distinguishes between interacting and noninteracting protein pairs. Analysis of the logistic-regression model showed that one of the strongest contributors to the discrimination between interacting and noninteracting pairs is the presence of distinct pairs of domain signatures that were suggested previously to characterize interacting proteins. The predictive algorithm succeeds in identifying both intracomplex and other interactions (possibly the more stable ones), and its correct identification rate is 2-fold higher than that of large-scale yeast two-hybrid experiments.

Modeling the Cell Division Cycle: cdc2 and Cyclin Interactions

JJ Tyson — 2008-06-10T04:50:03-00:00

Proceedings of the National Academy of Sciences, Vol. 88, No. 16. (15 August 1991), pp. 7328-7332.

10.1073/pnas.88.16.7328