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PubMed2006

The pitfalls of proteomics experiments without the correct use of bioinformatics tools.

The elucidation of the entire genomic sequence of various organisms, from viruses to complex metazoans, most recently man, is undoubtedly the greatest triumph of molecular biology since the discovery of the DNA double helix. Over the past two decades, the focus of molecular biology has gradually moved from genomes to proteomes, the intention being to discover the functions of the genes themselves. The postgenomic era stimulated the development of new techniques (e.g. 2-DE and MS) and bioinformatics tools to identify the functions, reactions, interactions and location of the gene products in tissues and/or cells of living organisms. Both 2-DE and MS have been very successfully employed to identify proteins involved in biological phenomena (e.g. immunity, cancer, host-parasite interactions, etc.), although recently, several papers have emphasised the pitfalls of 2-DE experiments, especially in relation to experimental design, poor statistical treatment and the high rate of 'false positive' results with regard to protein identification. In the light of these perceived problems, we review the advantages and misuses of bioinformatics tools - from realisation of 2-DE gels to the identification of candidate protein spots - and suggest some useful avenues to improve the quality of 2-DE experiments. In addition, we present key steps which, in our view, need to be to taken into consideration during such analyses. Lastly, we present novel biological entities named 'interactomes', and the bioinformatics tools developed to analyse the large protein-protein interaction networks they form, along with several new perspectives of the field.

Amino Acid Sequence↗

PubMed2006

Metabolomic and bioinformatic analyses in asphyxiated neonates.

OBJECTIVES: We tested the application of bioinformatic algorithms in studying the metabolomic profiles of neonatal urine samples with clinical evidence of severe asphyxia at birth and subsequent neurodevelopmental handicap. DESIGN AND METHODS: The clinical outcomes of 256 newborns that required direct admission to neonatal intensive care unit for respiratory support or did not require direct admission were studied. Urinary metabolite profiles were measured by high throughput mass spectrometry and analyzed by bioinformatic methods. RESULTS: We found a positive relationship between suppressed biochemical networks involved in macromolecular synthesis and birth asphyxia associated with significant neonatal oxidative stress and morbidity. The metabolomic discriminators between good neonatal outcome and poor neonatal outcome were established using hierarchical clustering analysis. Concentrations of eight urinary organic acids in distinct biochemical pathways were elevated and significantly associated with the prognosis of neurodevelopmental handicap with high sensitivity and specificity: ethylmalonate, 3-hydroxy-3-methylglutarate, 2-hydroxy-glutarate and 2-oxo-glutarate were associated with good neonatal outcome, whereas glutarate, methylmalonate, 3-hydroxy-butyrate and orotate were associated with poor outcome. CONCLUSIONS: The data demonstrated the potential application of bioinformatics methods in this metabolomic study and proved its clinical relevance.

Asphyxia Neonatorum↗

PubMed2006

Molecular profiling techniques and bioinformatics in cancer research.

AIMS: Our aim was to describe the commonly used molecular profiling techniques in cancer research, to examine their limitations and to discuss the challenges of bioinformatics. METHODS: A literature search was performed using the PubMed database to identify publications relevant to this review. Citations from these articles were also examined to yield further relevant publications. RESULTS: We describe the use of DNA microarrays, comparative genomic hybridisation, tissue microarrays and digital differential display. The limitations of these technologies, their contribution to cancer research and the challenges of bioinformatics are also discussed. CONCLUSIONS: Although these high throughput technologies each have their own limitations they are rapidly developing and contributing significantly to our understanding of cancer genetics. They have also led to the emergence of bioinformatics as a rapidly developing and vital field.

Biomarkers, Tumor↗

PubMed2004

Bioinformatics integration and agent technology.

Vast amounts of life sciences data are scattered around the world in the form of a variety of heterogeneous data sources. The need to be able to co-relate relevant information is fundamental to increase the overall knowledge and understanding of a specific subject. Bioinformaticians aspire to find ways to integrate biological data sources for this purpose and system integration is a very important research topic. The purpose of this paper is to provide an overview of important integration issues that should be considered when designing a bioinformatics integration system. The currently prevailing approach for integration is presented with examples of bioinformatics information systems together with their main characteristics. Here, we introduce agent technology and we argue why it provides an appropriate solution for designing bioinformatics integration systems.

Algorithms↗

PubMed2004

Constrained binding site diversity within families of transcription factors enhances pattern discovery bioinformatics.

Diverse computational and experimental efforts are required to elucidate the control circuitry regulating the transcription of human genes. The fusion of gene-specific promoter analyses with large microarray studies and bioinformatics advances has produced optimism that significant progress can be made in unravelling this complex network. Within bioinformatics, past emphasis for improved pattern discovery has been placed upon "phylogenetic footprinting", the identification of sequences conserved over moderate periods of evolution (e.g. human and mouse comparisons). We introduce a new direction in bioinformatics based on the constraints imposed by the structures of DNA-binding proteins. For most structurally related families of transcription factors, there are clear similarities in the sequences of the sites to which they bind. On the basis of this observation, we construct familial binding profiles for well-characterized transcription factor families. The profiles are shown to classify correctly the structural class of mediating transcription factors for novel motifs in 88% of cases. By incorporating the familial profiles into pattern discovery procedures, we demonstrate that functional binding sites can be found in genomic sequences of dramatically greater length than is possible otherwise. Thus, incorporating familial models can overcome the signal-to-noise challenge that has hindered the transition from microarray data to regulatory control sequences for human genes. Biochemically motivated constraints upon sequence diversity of binding sites will complement the genetically motivated constraints imposed in "phylogenetic footprinting" algorithms.

Algorithms↗

PubMed2004

Bioinformatics: harvesting information for plant and crop science.

Bioinformatics is an integral aspect of plant and crop science research. Developments in data management and analytical software are reviewed with an emphasis on applications in functional genomics. This includes information resources for Arabidopsis and crop species, and tools available for analysis and visualisation of comparative genomic data. Approaches used to explore relationships between plant genes and expressed sequences are compared, including use of ontologies. The impact of bioinformatics in forward and reverse genetics is described, together with the potential from data mining. The role of bioinformatics is explored in the wider context of plant and crop science.

Algorithms↗

PubMed2005

Bioinformatics toolbox for narrowing rodent quantitative trait loci.

Quantitative trait locus (QTL) analysis is a powerful method for localizing disease genes, but identifying the causal gene remains difficult. Rodent models of disease facilitate QTL gene identification, and causal genes underlying rodent QTL are often associated with the corresponding human diseases. Recently developed bioinformatics methods, including comparative genomics, combined cross analysis, interval-specific and genome-wide haplotype analysis, followed by sequence and expression analysis, each facilitated by public databases, provide new tools for narrowing rodent QTLs. Here we discuss each tool, illustrate its application and generate a bioinformatics strategy for narrowing QTLs. Combining these bioinformatics tools with classical experimental methods should accelerate QTL gene identification.

Animals↗

PubMed2006

A combined bioinformatics and chemoinformatics approach for the development of new antiparasitic drugs.

A modern concept for the development of novel antiparasitic drugs is the combination of bioinformatics and chemoinformatics approaches. This covers, for example, the identification of target proteins serving as molecular points of attack for parasiticides--the idea is that, owing to some essential role, inhibition of a target protein should eradicate the parasite. To prevent toxicity problems for vertebrate host organisms, it is advantageous that these proteins show significant differences from their vertebrate counterparts. In the present work, we identified potential target proteins in parasitic nematodes (Ascaris suum, Brugia malayi, and Haemonchus contortus) and arthropods (Boophilus microplus and Rhipicephalus appendiculatus) using bioinformatic sequence comparison methods on expressed sequence tags. Interesting target proteins (e.g., S-adenosyl-l-methionine synthetase) were characterized in detail by subjecting them to in-depth bioinformatic analysis. S-Adenosyl-l-methionine synthetase was also used to elucidate chemoinformatics approaches like homology modeling and docking, which represent appropriate methods for generating valuable data for the development of new drug candidates.

Amino Acid Sequence↗

PubMed1996

Information services of the European Bioinformatics Institute.

The scope of the EBI is focused on providing better services to the scientific community. Technological advancements in the hardware area provide EBI with means of producing data much faster than before, and with greater accuracy since there is now a better technical ability to produce more exhaustive searches through larger indices. Hand in hand with the technological developments, research and development work is continuing on better indexing systems and more efficient ways of establishing and maintaining the future databases. The existing links of communication between EBI and the user community are exploited to study the needs of the scientific community, to provide better services, and to enhance the quality of databases by interpreting user feedback and updates. A very important goal is to enhance the awareness of the scientific (and, maybe even more, the nonscientific) public of the importance of the modern field of bioinformatics and to introduce special meetings and courses, in which more specific subjects will be studied in depth. Another aspect of this goal is to help in constructing special bioinformatics programs in university faculties. In such programs, in contrast to the existing layout, students will pursue studies in a combined environment that provides basic training in biology and in computation. Currently, one of the main problems in the field is that scientists are either biologists, who are self-educated in the field of computers and programming, or computer scientists without sufficient knowledge of biology. It is hoped that a combined program will provide a high level of education in both fields of interest at the appropriate ratios. Building an efficient and friendly interface between the EBI and the user community is the basis for any future development. This aim is achieved by using the most modern server systems while continuously researching newer and better systems and interfaces. This task can never be complete without involvement of the user community by providing feedback to any of EBI's services. A better bioinformatics community is a necessity for any future development of the biological research aiming at a better society.

Amino Acid Sequence↗

PubMedDate not supplied

Bioinformatics for the genomic sciences and towards systems biology. Japanese activities in the post-genome era.

The knowledge gleaned from genome sequencing and post-genome analyses is having a very significant impact on a whole range of life sciences and their applications. 'Genome-wide analysis' is a good keyword to represent this tendency. Thanks to innovations in high-throughput measurement technologies and information technologies, genome-wide analysis is becoming available in a broad range of research fields from DNA sequences, gene and protein expressions, protein structures and interactions, to pathways or networks analysis. In fact, the number of research targets has increased by more than two orders in recent years and we should change drastically the attitude to research activities. The scope and speed of research activities are expanding and the field of bioinformatics is playing an important role. In parallel with the data-driven research approach that focuses on speedy handling and analyzing of the huge amount of data, a new approach is gradually gaining power. This is a 'model-driven research' approach, that incorporates biological modeling in its research framework. Computational simulations of biological processes play a pivotal role. By modeling and simulating, this approach aims at predicting and even designing the dynamic behaviors of complex biological systems, which is expected to make rapid progress in life science researches and lead to meaningful applications to various fields such as health care, food supply and improvement of environment. Genomic sciences are now advancing as great frontiers of research and applications in the 21st century. This article starts with surveying the general progress of bioinformatics (Section 1), and describes Japanese activities in bioinformatics (Section 2). In Section 3, I will introduce recent developments in Systems Biology which I think will become more important in the future.

Animals↗

PubMed2002

Biological data becomes computer literate: new advances in bioinformatics.

Bioinformatics is an art and science concerned with the use of computing in biological research areas such as genomics, transcriptomics, proteomics, genetics, and evolution. This review paints a broad picture of bioinformatics, drawing examples from genomic sequencing and microarray analysis. I highlight the role of bioinformatics at multiple points along the path from high-tech data generation to biological discovery.

Computational Biology↗

PubMed2003

Bioinformatics in the post-sequence era.

In the past decade, bioinformatics has become an integral part of research and development in the biomedical sciences. Bioinformatics now has an essential role both in deciphering genomic, transcriptomic and proteomic data generated by high-throughput experimental technologies and in organizing information gathered from traditional biology. Sequence-based methods of analyzing individual genes or proteins have been elaborated and expanded, and methods have been developed for analyzing large numbers of genes or proteins simultaneously, such as in the identification of clusters of related genes and networks of interacting proteins. With the complete genome sequences for an increasing number of organisms at hand, bioinformatics is beginning to provide both conceptual bases and practical methods for detecting systemic functional behaviors of the cell and the organism.

Computational Biology↗

PubMed2004

Bioinformatics for the 'bench biologist': how to find regulatory regions in genomic DNA.

The combination of bioinformatic and biological approaches constitutes a powerful method for identifying gene regulatory elements. High-quality genome sequences are available in public databases for several vertebrate species. Comparative cross-species sequence analysis of these genomes shows considerable conservation of noncoding sequences in DNA. Biological analyses show that an unexpectedly high number of the conserved sequences correspond to functional cis-regulatory regions that influence gene transcription. Because research biologists are often unfamiliar with the bioinformatic resources at their disposal, this commentary discusses how to integrate biological and bioinformatic methods in the discovery of gene regulatory regions and includes a tutorial on widely available comparative genomics programs.

Animals↗

PubMed2003

Bioinformatics goes back to the future.

The need to turn raw data into knowledge has led the bioinformatics field to focus increasingly on the manipulation of information. By drawing parallels with both cryptography and artificial intelligence, we can develop an understanding of the changes that are occurring in bioinformatics, and how these changes are likely to influence the bioinformatics job market.

Artificial Intelligence↗

PubMed2001

Hickam 2000: the maturation of, and linkages between, medical informatics and bioinformatics.

I have always been infatuated with computers and convinced of their potential for solving problems in biologic research and clinical care. In the 1960s I thought we could use the computer to predict the shape of macromolecules from their chemical formulas and fundamental physical chemical principles. However, with the computers of the 1960s that was a fantasy. So I focused on the use of computers to manage medical record content and to assist with clinical care. The Electronic Medical Record (EMR) we began developing in 1972 with 33 diabetes patients now carries nearly 300 million separate results for more than 3 million patients. The data include lab and other diagnostic studies, dictated notes, orders, encounter records, radiology images, electrocardiograph tracings, and motion cardiac echoes, and the care provider at Indiana University and Wishard Hospital is accessed 10 million times per year. We have also agitated for standards to make the collection of these data easier. This work has become part of a field called medical informatics. In the meantime, the application of computers to biology has rapidly matured into a field called bioinformatics, and researchers in this field now provide annotated databases for many categories of molecules, programs for "matching" newly discovered genomic sequences with previously studied sequences, and systems for storing and processing massive amounts of genomic and molemic data. They have developed sophisticated methods for predicting the shape of biologic macromolecules and other important insights about biology and evolution. Medical informatics and bioinformatics intersect at many points. The most important intersection is between electronic medical records and the human specimen databases that can link genotype to the phenotype, as needed, to unravel polygenetic disease causality. The National Cancer Institute is embarking on an intriguing effort to use EMRs (phenotype) to link to paraffin blocks (genotype) in pathology laboratories where opportunities for cancer genomic discovery are open. We will participate in this effort and look forward to bending the EMR we developed for clinical use to bioinformatics uses as well.

Clinical Medicine↗

PubMed2005

Statistical signals in bioinformatics.

The Arthur M. Sackler Colloquium of the National Academy of Sciences, "Frontiers in Bioinformatics: Unsolved Problems and Challenges," organized by David Eisenberg, Russ Altman, and myself, was held October 15-17, 2004, to provide a forum for discussing concepts and methods in bioinformatics serving the biological and medical sciences. The deluge of genomic and proteomic data in the last two decades has driven the creation of tools that search and analyze biomolecular sequences and structures. Bioinformatics is highly interdisciplinary, using knowledge from mathematics, statistics, computer science, biology, medicine, physics, chemistry, and engineering.

Animals↗

PubMed2001

Functional assignment of the 20 S proteasome from Trypanosoma brucei using mass spectrometry and new bioinformatics approaches.

As experimental technologies for characterization of proteomes emerge, bioinformatic analysis of the data becomes essential. Separation and identification technologies currently based on two-dimensional gels/mass spectrometry provide the inherent analytical power required. This strategy involves protein spot digestion and accurate mass mapping together with computational interrogation of available data bases for protein functional identification. When either no exact match is found or when the possible matches only partially account for molecular weights actually observed, peptide sequencing by tandem mass spectrometry has emerged as the methodology of choice to provide the basic additional information required. To evaluate the capabilities of bioinformatics methods employed for identifying homologs of a protein of interest, we attempted to identify the major proteins from the 20 S proteasome of Trypanosoma brucei using sequence information determined using mass spectrometry. The results suggest that neither the traditional query engines, BLAST and FASTA, nor specialized software developed for analysis of sequence information obtained by mass spectrometry are able to identify even closely related sequences at statistically significant scores. To address this deficit, new bioinformatics approaches were developed for concomitant use of the multiple fragments of short sequence typically available from methods of tandem mass spectrometry. These approaches rely on the occurrence of congruence across searches of multiple fragments from a single protein. This method resulted in sharply better statistical significance values for correct hits in the data base output relative to that achieved for independent searches using single sequence fragments.

Algorithms↗

PubMed2004

A high-throughput approach for subcellular proteome: identification of rat liver proteins using subcellular fractionation coupled with two-dimensional liquid chromatography tandem mass spectrometry and bioinformatic analysis.

Four fractions from rat liver (a crude mitochondria (CM) and cytosol (C) fraction obtained with differential centrifugation, a purified mitochondrial (PM) fraction obtained with nycodenz density gradient centrifugation, and a total liver (TL) fraction) were analyzed with two-dimensional liquid chromatography tandem mass spectrometry analysis. A total of 564 rat proteins were identified and were bioinformatically annotated according to their physicochemical characteristics and functions. While most extreme alkaline ribosomal proteins were identified in the TL fraction, the C fraction mainly included neutral enzymes and the PM fraction enriched alkaline proteins and proteins with electron transfer activity or oxygen binding activity. Such characteristics were more apparent in proteins identified only in the TL, C, or PM fraction. The Swiss-Prot annotation and the bioinformatic prediction results proved that the C and PM fractions had enriched cytoplasmic or mitochondrial proteins, respectively. Combination usage of subcellular fractionation with two-dimensional liquid chromatography tandem mass spectrometry was proved to be a high-throughput, sensitive, and effective analytical approach for subcellular proteomics research. Using such a strategy, we have constructed the largest proteome database to date for rat liver (564 rat proteins) and its cytosol (222 rat proteins) and mitochondrial fractions (227 rat proteins). Moreover, the 352 proteins with Swiss-Prot subcellular location annotation in the 564 identified proteins were used as an actual subcellular proteome dataset to evaluate the widely used bioinformatics tools such as PSORT, TargetP, TMHMM, and GRAVY.

Animals↗