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PubMed2004

MuTrack: a genome analysis system for large-scale mutagenesis in the mouse.

BACKGROUND: Modern biological research makes possible the comprehensive study and development of heritable mutations in the mouse model at high-throughput. Using techniques spanning genetics, molecular biology, histology, and behavioral science, researchers may examine, with varying degrees of granularity, numerous phenotypic aspects of mutant mouse strains directly pertinent to human disease states. Success of these and other genome-wide endeavors relies on a well-structured bioinformatics core that brings together investigators from widely dispersed institutions and enables them to seamlessly integrate data, observations and discussions. DESCRIPTION: MuTrack was developed as the bioinformatics core for a large mouse phenotype screening effort. It is a comprehensive collection of on-line computational tools and tracks thousands of mutagenized mice from birth through senescence and death. It identifies the physical location of mice during an intensive phenotype screening process at several locations throughout the state of Tennessee and collects raw and processed experimental data from each domain. MuTrack's statistical package allows researchers to access a real-time analysis of mouse pedigrees for aberrant behavior, and subsequent recirculation and retesting. The end result is the classification of potential and actual heritable mutant mouse strains that become immediately available to outside researchers who have expressed interest in the mutant phenotype. CONCLUSION: MuTrack demonstrates the effectiveness of using bioinformatics techniques in data collection, integration and analysis to identify unique result sets that are beyond the capacity of a solitary laboratory. By employing the research expertise of investigators at several institutions for a broad-ranging study, the TMGC has amplified the effectiveness of any one consortium member. The bioinformatics strategy presented here lends future collaborative efforts a template for a comprehensive approach to large-scale analysis.

Animals↗

PubMed2004

A high productivity/low maintenance approach to high-performance computation for biomedicine: four case studies.

The rapid advances in high-throughput biotechnologies such as DNA microarrays and mass spectrometry have generated vast amounts of data ranging from gene expression to proteomics data. The large size and complexity involved in analyzing such data demand a significant amount of computing power. High-performance computation (HPC) is an attractive and increasingly affordable approach to help meet this challenge. There is a spectrum of techniques that can be used to achieve computational speedup with varying degrees of impact in terms of how drastic a change is required to allow the software to run on an HPC platform. This paper describes a high- productivity/low-maintenance (HP/LM) approach to HPC that is based on establishing a collaborative relationship between the bioinformaticist and HPC expert that respects the former's codes and minimizes the latter's efforts. The goal of this approach is to make it easy for bioinformatics researchers to continue to make iterative refinements to their programs, while still being able to take advantage of HPC. The paper describes our experience applying these HP/LM techniques in four bioinformatics case studies: (1) genome-wide sequence comparison using Blast, (2) identification of biomarkers based on statistical analysis of large mass spectrometry data sets, (3) complex genetic analysis involving ordinal phenotypes, (4) large-scale assessment of the effect of possible errors in analyzing microarray data. The case studies illustrate how the HP/LM approach can be applied to a range of representative bioinformatics applications and how the approach can lead to significant speedup of computationally intensive bioinformatics applications, while making only modest modifications to the programs themselves.

Amino Acid Sequence↗

PubMed2025

Benchmarking with synthetic communities provides a baseline for virus-host inferences from Hi-C proximity linking.

Microbiomes influence diverse ecosystems, and viruses increasingly appear to impose key constraints. While viromics has expanded genomic catalogs, host identification for these viruses remains challenging due to the limitations in scaling cultivation-based approaches and the uncertain reliability and relative low resolution of in silico predictions - particularly for understudied viral taxa. Towards this, Hi-C proximity ligation uses sequenced, cross-linked virus and host genomic fragments to infer virus-host linkages and has now been applied in at least 10 studies. However, its accuracy remains unknown. Here we assess Hi-C performance in recovering virus-host interactions using synthetic communities (SynComs) composed of four marine bacterial strains and nine phages with known interactions and then apply optimized bioinformatic protocols to natural soil samples. In SynComs, standard Hi-C sample preparations and analyses showed poor normalized contact score performance (26% specificity, 100% sensitivity, incorrect matches up to class level) that could be dramatically improved by Z-score filtering (Z ≥ 0.5, 99% specificity), though at reduced sensitivity (62% down from 100%). Detection limits were established as reproducibility was poor below minimal phage abundances of 105 PFU/mL. Applying optimized bioinformatic protocols to natural soil samples, we compared virus-host linkages inferred from proximity-ligated Hi-C sequencing with predictions generated by in silico homology-based and machine learning-based bioinformatic approaches. Prior to Z-score thresholding, agreement was relatively high at the phylum to family levels (72%), but not at the genus (43%) or species (15%) levels. Z-score thresholding reduced sensitivity (only 34% of predictions were retained), with only modest improvements in congruence with bioinformatic methods (48% or 18% at genus or species levels, respectively). Regardless, this led to 79 genus-level-congruent virus-host linkages and 293 new ones revealed by Hi-C alone, i.e., providing many new virus-host interactions to explore in already well-studied climate-critical soils. Overall, these findings provide empirical benchmarks and methodological guidelines to improve the accuracy and reliability of Hi-C for virus-host linkage studies in complex microbial communities.

Benchmarking↗

PubMed2005

Accurate prediction of the functional significance of single nucleotide polymorphisms and mutations in the ABCA1 gene.

The human genome contains an estimated 100,000 to 300,000 DNA variants that alter an amino acid in an encoded protein. However, our ability to predict which of these variants are functionally significant is limited. We used a bioinformatics approach to define the functional significance of genetic variation in the ABCA1 gene, a cholesterol transporter crucial for the metabolism of high density lipoprotein cholesterol. To predict the functional consequence of each coding single nucleotide polymorphism and mutation in this gene, we calculated a substitution position-specific evolutionary conservation score for each variant, which considers site-specific variation among evolutionarily related proteins. To test the bioinformatics predictions experimentally, we evaluated the biochemical consequence of these sequence variants by examining the ability of cell lines stably transfected with the ABCA1 alleles to elicit cholesterol efflux. Our bioinformatics approach correctly predicted the functional impact of greater than 94% of the naturally occurring variants we assessed. The bioinformatics predictions were significantly correlated with the degree of functional impairment of ABCA1 mutations (r2 = 0.62, p = 0.0008). These results have allowed us to define the impact of genetic variation on ABCA1 function and to suggest that the in silico evolutionary approach we used may be a useful tool in general for predicting the effects of DNA variation on gene function. In addition, our data suggest that considering patterns of positive selection, along with patterns of negative selection such as evolutionary conservation, may improve our ability to predict the functional effects of amino acid variation.

ATP Binding Cassette Transporter 1↗

PubMed2006

Information management for the study of allergies.

Microarrays and other large-scale screening technologies produce quantities of increasingly complex allergy data. These data link molecular and clinical measurements and observations and provide fertile ground for improving our understanding of the processes involved in allergic reactions. Information technology is employed in gathering, storage, retrieval and analysis of these data. The increasing proportion of allergy data are generated from genomics and proteomics approaches. The major activity focuses on characterization of allergens including IgE reactivity, structural properties, and mapping of IgE and T-cell epitopes. Because of the complexity of allergy data, their utilization requires bioinformatics approaches. Allergen data are stored in the general and specialist databases. At least a dozen of important allergen databases and data repositories have been developed to date. These data are analysed using general and specialist bioinformatics tools. The major applications of bioinformatics include support for allergen characterization, assessment of allergenicity, and identification of allergic cross-reactivity. These applications in turn support the development of vaccines and therapies for allergic disease. In this article we review allergen databases and tools for the analysis of allergens, and discuss the new directions in the field supported by large scale screening involving genomics, proteomics, and bioinformatics support.

Allergens↗

PubMed2001

High-throughput interpretation of pathways and biology.

The genomics revolution is opening many diverse pathways in science. In the midst of the tidal wave of information being generated by the new "omics" disciplines, bioinformatics is meeting the challenge to decipher and organize the data mountain. The endeavor to integrate diverse biological data into a common vocabulary will be an essential part of the "biotechnological age" leading to a new understanding of complex biological systems. This potential promises to revolutionize medicine as knowledge is translated into novel therapies. The Integrated Bioinformatics meeting held in Zurich January 2426, 2001, provided an insight into the cutting edge of bioinformatics and its central role in the genomics revolution. The evolution of bioinformatics will act as a guide in 21st century biological sciences.

Journal Article↗

PubMed2006

Design and implementation of a library-based information service in molecular biology and genetics at the University of Pittsburgh.

SETTING: In summer 2002, the Health Sciences Library System (HSLS) at the University of Pittsburgh initiated an information service in molecular biology and genetics to assist researchers with identifying and utilizing bioinformatics tools. PROGRAM COMPONENTS: This novel information service comprises hands-on training workshops and consultation on the use of bioinformatics tools. The HSLS also provides an electronic portal and networked access to public and commercial molecular biology databases and software packages. EVALUATION MECHANISMS: Researcher feedback gathered during the first three years of workshops and individual consultation indicate that the information service is meeting user needs. NEXT STEPS/FUTURE DIRECTIONS: The service's workshop offerings will expand to include emerging bioinformatics topics. A frequently asked questions database is also being developed to reuse advice on complex bioinformatics questions.

Computational Biology↗

PubMed2025

A genome-wide approach for the discovery of novel repeat expansion disorders in the Undiagnosed Diseases Network cohort.

PURPOSE: The Undiagnosed Diseases Network is a National Institutes of Health funded research study that aims to solve a broad clinical spectrum of challenging rare disease cases. Participants receive care from multiple clinical specialists, who collaborate to perform deep phenotyping and state-of-the-art multiomics analyses. As bioinformatics of short-read sequencing has matured, the discovery of repeat expansion disorders (REDs) is accelerating. REDs comprise approximately 60 characterized disorders, which exhibit a broad spectrum of phenotypes. Thus, a largely unbiased genome-wide approach in a phenotypically diverse sample will add to the diagnostic depth, explore the limits of short-read genome analysis, and establish novel candidate RED loci. METHODS: Here, we present a genome-wide analysis of repeat expansions conducted on 1018 genomes from the Undiagnosed Diseases Network. By leveraging 2 distinct bioinformatics tools, ExpansionHunter Denovo and STRling, we showed that repeat expansions can be accurately detected in short-read genomes. RESULTS: We demonstrated that a genotype-first approach can diagnose atypical cases of known REDs and provide valuable clinical insights. We present clinical details on participants with expansions in ATXN7, DMPK, FMR1, GLS, HTT, RFC1, AFF3, and MARCH6. Importantly, we highlight 2 cases of juvenile Huntington disease that were discovered through our analysis. Finally, we present a list of novel candidate short tandem repeats (TR) that could potentially be pathogenic if expanded. CONCLUSION: Importantly, our approach showcases the bioinformatic advancements in genome analysis for RED detection and highlights its practical applications.

Humans↗

PubMed2026

Community-driven advances in computational mass spectrometry: The perspective of EuBIC-MS members.

Advances in data acquisition, artificial intelligence, and integrative bioinformatics are driving the rapid evolution of computational mass spectrometry, and in turn, transforming modern proteomics, metabolomics, and lipidomics. These developments have greatly increased the scale and complexity of mass spectrometry data, underscoring the importance of evolving accurate, transparent, efficient and reproducible data processing workflows. Addressing these challenges requires collaborative innovation that brings together expertise in software engineering, statistics, and biology. The European Bioinformatics Community for Mass Spectrometry (EuBIC-MS), an initiative of the European Proteomics Association (EuPA), fosters a culture of open, community-driven development through its biennial Developers Meetings and Winter Schools. This commentary summarizes the scientific background and outcomes of the EuBIC-MS Developers Meeting 2025, which took place in Novacella, Italy. Three keynote presentations highlighted major frontiers in the field: deep proteome and phosphoproteome profiling, text mining for protein-protein interaction extraction, and scalable proteomics for AI-driven drug discovery. Seven community-selected hackathons addressed emerging challenges such as single-cell proteomics data analysis, FAIR metadata extraction, deep learning frameworks, R-Python interoperability, and DIA validation. Together, these efforts demonstrate the potential for scientific and technical innovation to arise from open collaboration, and highlight how community-driven initiatives can accelerate progress in computational mass spectrometry. SIGNIFICANCE: Modern proteomics increasingly depends on computational advances to translate complex, high-dimensional data into biological knowledge. The EuBIC-MS Developers Meeting 2025 exemplifies how community-driven collaboration can directly accelerate this process by bringing together experts from bioinformatics, statistics, and experimental proteomics to co-develop open, interoperable, and reproducible analytical tools. By fostering shared software frameworks, transparent benchmarking, and collaborative problem solving, the EuBIC-MS community helps ensure that technological innovation translates into reliable biological insights. This collaborative model strengthens the foundation for quantitative, system-level understanding of proteomes and establishes a sustainable path for integrating artificial intelligence and next-generation data acquisition into routine biological discovery. This commentary shows some current highlights in the field of computational mass spectrometry and community-based approaches undertaken during the most recent Developers Meeting to solve these challenges. The approaches discussed and initiated during the meeting - ranging from deep proteome profiling and phosphosite mapping to text mining, single-cell data analysis, and FAIR metadata extraction - address key bottlenecks that currently limit the biological interpretability and comparability of proteomics data.

Mass Spectrometry↗

PubMed2004

Diagnostic protein discovery using proteolytic peptide targeting and identification.

Plasma protein profiling with mass spectrometry is currently being evaluated as a diagnostic tool for cancer and other diseases. These experiments consist of three steps: plasma protein fractionation, analysis with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS), and comparisons of the MALDI profiles to develop diagnostic fingerprints using bioinformatic techniques. While preliminary results appear promising in small sample groups, the method is limited by the sensitivity of MALDI-MS for intact proteins, the limited mass range of MALDI-MS, and difficulties associated with isolating individual proteins for identification to validate the diagnostic fingerprint. Here we present an alternative and improved method directed toward diagnostic protein discovery, which incorporates proteolytic peptide profiling, bioinformatic targeting of ion signals, and MALDI tandem mass spectrometry (MS/MS) peptide sequencing, rather than fingerprinting. Pancreatic cancer patients, pancreatitis patients, and controls are used as the model system. Profiling peptides after enzymatic digestion improves sensitivity and extends the accessible protein molecular weight range when compared to intact protein profiling. The first step is to extract and fractionate the proteins from plasma. Each fraction is digested with trypsin and subsequently analyzed by MALDI-MS. Rather than using bioinformatic analysis as a pattern-matching technique, peptides are targeted based on the disease to control peak intensity ratios measured in the averages of all mass spectra in each group and t-tests of the intensity of each individual peak. The targeted peptide ion signals are subsequently identified using MALDI-MS/MS in quadrupole-TOF and tandem-TOF instruments. This study found not only the proteins targeted and identified by a previous protein profiling experiment, but also detected additional proteins. These initial results are consistent with the known biology of pancreatic cancer or pancreatitis, but are not specific to those diseases.

Adult↗

PubMed2005

A global view of CK2 function and regulation.

The wealth of biochemical, molecular, genetic, genomic, and bioinformatic resources available in S. cerevisiae make it an excellent system to explore the global role of CK2 in a model organism. Traditional biochemical and genetic studies have revealed that CK2 is required for cell viability, cell cycle progression, cell polarity, ion homeostasis, and other functions, and have identified a number of potential physiological substrates of the enzyme. Data mining of available bioinformatic resources indicates that (1) there are likely to be hundreds of CK2 targets in this organism, (2) the majority of predicted CK2 substrates are involved in various aspects of global gene expression, (3) CK2 is present in several nuclear protein complexes predicted to have a role in chromatin structure and remodeling, transcription, or RNA metabolism, and (4) CK2 is localized predominantly in the nucleus. These bioinformatic results suggest that the observed phenotypic consequences of CK2 depletion may lie downstream of primary defects in chromatin organization and/or global gene expression. Further progress in defining the physiological role of CK2 will almost certainly require a better understanding of the mechanism of regulation of the enzyme. Beginning with the crystal structure of the human CK2 holoenzyme, we present a molecular model of filamentous CK2 that is consistent with earlier proposals that filamentous CK2 represents an inactive form of the enzyme. The potential role of filamentous CK2 in regulation in vivo is discussed.

Casein Kinase II↗

PubMed2006

A comparative study of cells in inflammation, EAE and MS using biomedical literature data mining.

Biomedical literature and database annotations, available in electronic forms, contain a vast amount of knowledge resulting from global research. Users, attempting to utilize the current state-of-the-art research results are frequently overwhelmed by the volume of such information, making it difficult and time-consuming to locate the relevant knowledge. Literature mining, data mining, and domain specific knowledge integration techniques can be effectively used to provide a user-centric view of the information in a real-world biological problem setting. Bioinformatics tools that are based on real-world problems can provide varying levels of information content, bridging the gap between biomedical and bioinformatics research. We have developed a user-centric bioinformatics research tool, called BioMap, that can provide a customized, adaptive view of the information and knowledge space. BioMap was validated by using inflammatory diseases as a problem domain to identify and elucidate the associations among cells and cellular components involved in multiple sclerosis (MS) and its animal model, experimental allergic encephalomyelitis (EAE). The BioMap system was able to demonstrate the associations between cells directly excavated from biomedical literature for inflammation, EAE and MS. These association graphs followed the scale-free network behavior (average gamma = 2.1) that are commonly found in biological networks.

Animals↗

PubMed2004

Genomic biomarkers for cancer assessment: implementation challenges for laboratory practice.

Genomic biomarkers are an emerging class of laboratory tests, which present special implementation challenges for clinical laboratory services, compared to conventional laboratory tests. These challenges, which include analytical, bioinformatics, bioethical, interpretation and commercialization issues, represent real obstacles to widespread implementation of these tests. Technical challenges include the capacity to detect and identify many different kinds of markers for different diseases in a short time period, capacity to identify simultaneously gene rearrangements, amplification, inhibition, deletions and replications. Bioinformatics challenges include rapid analysis of genomic data, as well as the cross reference to other genomic data, and to other laboratory tests. Bioethical issues relate to consent to retain and use genetic data, which may be obtained inadvertently during analysis for genomic markers. Interpretation challenges include observations that the particular genomic markers may not be independent variables, as other undetected genomic alterations could invalidate or alter genomic marker interpretation. Further, as early experience with predictive genetic markers for cancer has shown, proprietary commercial interests may conflict with public health values of identifying genomic markers in subject populations. Based on our 10 years of experience with genomic biomarkers, important implementation strategies for genomic markers include development of:Standard high throughput analyzers capable of detecting any alteration of any genomic variant at any time. Bioinformatics analysis online, coupled to stored patient data. Laboratory service framework that preserves confidentiality but integrates genomic data with other laboratory tests. Laboratory service framework, which links consents, genomic analysis, reports to both specimen and data repositories. Overall, the laboratory service challenges for genomic markers are to manage very large analytical sets and very large data sets in finite time with responsible interpretation, all within finite funding. To meet these challenges, implementation strategies beyond the one disease, one diagnosis, one genomic marker concept must begin now.

Biomarkers, Tumor↗

PubMed2005

Structural relatedness of plant food allergens with specific reference to cross-reactive allergens: an in silico analysis.

BACKGROUND: The body of sequence and structural information on allergens and the sequence analysis of whole plant genomes are facilitating the application of bioinformatic approaches to identifying and defining plant allergens. OBJECTIVE: An in silico approach was used to quantify the distribution of plant food allergen sequences across protein families and to develop and apply a novel means of assessing conserved surface features important for IgE cross-reactivity. METHODS: Plant food allergen sequences were classified into Pfam families on the basis of sequence homology. Contact surface areas of selected proteins were calculated with MOLMOL by using a 1.4-A probe, corrected by removing contributions from IgE inaccessible main chains and side chains forming the ligand binding sites. RESULTS: A set of 129 food allergen sequences were classified into only 20 of 3849 possible Pfam families, with 4 families accounting for more than 65% of food allergens. Structural bioinformatic analysis of conserved exterior main chains and amino acid side chains in cross-reactive homologues of Bet v 1 and nonspecific lipid transfer proteins showed higher levels of similarity than shown by simple sequence comparisons. Thus, 75% of the Mal d 1 surface is likely to bind anti-Bet v 1 antibodies, compared with a sequence identity of approximately 56%. CONCLUSION: Most plant food allergens belong to only 4 structural families, indicating that conserved structures and biological activities may play a role in determining or promoting allergenic properties. Structural bioinformatic analysis shows that conservation of 3-dimensional structure should be included in any assessment of potential IgE cross-reactivity in, for example, novel proteins.

Allergens↗

PubMed2002

Specific mode of interaction between components of model pulmonary surfactants using computer simulations.

Atomistic molecular dynamics simulations and structural bioinformatics tools enable the identification of the exact mode of interaction between model pulmonary surfactant components. Two nanosecond long simulations of the N-terminal region of human surfactant protein-B (SP-B(1-25)) in dipalmitoylphosphatidylcholine (DPPC) and dipalmitoylphosphatidylglycerol (DPPG) monolayers of different lipid surface densities reveal the preferential affinity of SP-B(1-25) for anionic phospholipids. In particular, arginine 12 and lysine 24 interact strongly and with high specificity with the phosphate group of the DPPG lipids, stabilizing the position, the orientation, and the secondary structure of the peptide in the monolayer. The peptide lies at an oblique angle to the interfacial plane, ranging between 47 degrees and 62 degrees, increasing with decreasing lipid surface density. In DPPC monolayers the interaction is largely determined by hydrophobic interactions. The non-specific nature of DPPC-SP-B(1-25) interactions allows for significant flexibility in the topology of the peptide in the lipid matrix. Bioinformatics tools are employed to generalize the simulation results to the sequences of SP-B(1-25) in other organisms. The importance of specific residues, and the role of the largely helical and amphiphilic nature of the peptide in the functionality of SP-B(1-25) are established. The synergy of classical mechanics tools with bioinformatics methods greatly enhances the molecular-level interpretation of pulmonary surfactant action and facilitates the development of design rules for synthetic surfactant analogues.

1,2-Dipalmitoylphosphatidylcholine↗

PubMed2001

From genome to vaccine: in silico predictions, ex vivo verification.

Bioinformatics tools enable researchers to move rapidly from genome sequence to vaccine design. EpiMer and EpiMatrix are computer-driven pattern-matching algorithms that identify T cell epitopes. Conservatrix, BlastiMer, and Patent-Blast permit the analysis of protein sequences for highly conserved regions, for homology with other known proteins, and for homology with previously patented epitopes, respectively. Two applications of these tools to epitope-driven vaccine design are described in this review. Using Conservatrix and EpiMatrix, we analyzed more than 10000 HIV-1 sequences and identified peptides that were potentially immunostimulatory and highly conserved across HIV-1 clades. MHC binding assays and CTL assays have been carried out: 50 (69%) of the 72 candidate epitopes bound in assays with cell lines expressing the corresponding MHC molecule; 15 of the 24 B7 peptides (63%) stimulated gamma-interferon release in ELISpot assays. These results lend support to the bioinformatics approach to selecting novel, conserved, HIV-1 CTL epitopes. EpiMatrix was also applied to the entire 'proteome' derived from two Mycobacterium tuberculosis (Mtb) genomes. Using EpiMatrix, BlastiMer, and Patent-Blast, we narrowed the list of putative Mtb epitopes to be tested in vitro from 1600000 to 3000, a 99.8% reduction. The pace of vaccine design will accelerate when these and other bioinformatics tools are systematically applied to whole genomes and used in combination with in vitro methods for screening and confirming epitopes.

AIDS Vaccines↗

PubMed2000

The quest to deduce protein function from sequence: the role of pattern databases.

In the wake of the numerous now-fruitful genome projects, we have witnessed a 'tsunami' of sequence data and with it the birth of the field of bioinformatics. Bioinformatics involves the application of information technology to the management and analysis of biological data. For many of us, this means that databases and their search tools have become an essential part of the research environment. However, the rate of sequence generation and the haphazard proliferation of databases have made it difficult to keep pace with developments, even for the cognoscenti. Moreover, increasing amounts of sequence information do not necessarily equate with an increase in knowledge, and in the panic to automate the route from raw data to biological insight, we may be generating and propagating innumerable errors in our precious databases. In the genome era upon us, researchers want rapid, easy-to-use, reliable tools for functional characterisation of newly determined sequences. For the pharmaceutical industry in particular, the Pandora's box of bioinformatics harbours an information-rich nugget, ripe with potential drug targets and possible new avenues for the development of therapeutic agents. This review outlines the current status of the major pattern databases now used routinely in the analysis of protein sequences. The review is divided into three main sections. In the first, commonly used terms are defined and the methods behind the databases are briefly described; in the second, the structure and content of the principal pattern databases are discussed; and in the final part, several alignment databases, which are frequently confused with pattern databases, are mentioned. For the new-comer, the array of resources, the range of methods behind them and the different tools required to search them can be confusing. The review therefore also briefly mentions a current international endeavour to integrate the diverse databases, which effort should facilitate sequence analysis in the future. This is particularly important for target-discovery programmes, where the challenge is to rationalise the enormous numbers of potential targets generated by sequence database searches. This problem may be addressed, at least in part, by reducing search outputs to the more focused and manageable subsets suggested by searches of integrated groups of family-specific pattern databases.

Amino Acid Motifs↗

PubMed2002

Genome annotation techniques: new approaches and challenges.

As more of the human genome draft sequence is finished, and genomes from other organisms begin to be sequenced, the demand for accurate and reliable genome annotation will increase significantly. To facilitate this industrial-scale genome annotation, automated bioinformatics solutions are increasingly required. As a result, automatic genome annotation systems have become more important in gene discovery within recent years. The design of such large-scale bioinformatics systems is an evolving and dynamic field, based on central cores of bioinformatics software tools and relational databases. Not only must these systems efficiently manage and integrate large volumes of genomic data, but they must also deliver accurate gene predictions and effectively distribute annotation data to the biosciences community.

Computational Biology↗