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PubMed2026

Theseus: fast and optimal affine-gap sequence-to-graph alignment.

MOTIVATION: Sequence-to-graph alignment is a central problem in bioinformatics, with applications in multiple sequence alignment (MSA) and pangenome analysis, among others. However, current algorithms for optimal affine-gap alignment impose high memory and computational requirements, limiting their scalability to aligning long sequences to complex graphs. Practical solutions partially address this problem using heuristic strategies that ultimately trade off optimality for speed. RESULTS: This work presents Theseus, a novel, fast, and optimal affine-gap sequence-to-graph alignment algorithm. Theseus leverages similarities between genomic sequences to accelerate the alignment computation and reduces the overall memory requirements without compromising optimality. To that end, Theseus processes only a subset of the dynamic programming cells, using a sparse-data strategy that enables efficient sequence-to-graph alignment. Moreover, our algorithm supports optimal affine-gap alignment on arbitrary directed graphs, including those with cycles. We evaluate Theseus on two key problems: MSA and pangenome read mapping. For MSA, we compare it against SPOA, abPOA, and POASTA. Theseus is 1.6× to 17.6× faster than POASTA, and 7.3× faster, on average, than SPOA, both optimal aligners. Compared with abPOA, Theseus ensures optimality and scales to the largest problems. For pangenome read mapping, we benchmark Theseus against the alignment stage of the mapping tool vg map, along with the alignment kernels of SPOA, abPOA, and POASTA. Theseus outperforms the other methods, showing a 1.9× to 16.9× speedup on short reads. Moreover, Theseus is 1.5× to 36.3× faster than vg when aligning against synthetic cyclic graphs. AVAILABILITY AND IMPLEMENTATION: Theseus code and documentation are publicly available at https://github.com/albertjimenezbl/theseus-lib.

Algorithms↗

PubMed2000

MIPS: a database for genomes and protein sequences.

The Munich Information Center for Protein Sequences (MIPS-GSF), Martinsried, near Munich, Germany, continues its longstanding tradition to develop and maintain high quality curated genome databases. In addition, efforts have been intensified to cover the wealth of complete genome sequences in a systematic, comprehensive form. Bioinformatics, supporting national as well as European sequencing and functional analysis projects, has resulted in several up-to-date genome-oriented databases. This report describes growing databases reflecting the progress of sequencing the Arabidopsis thaliana (MATDB) and Neurospora crassa genomes (MNCDB), the yeast genome database (MYGD) extended by functional analysis data, the database of annotated human EST-clusters (HIB) and the database of the complete cDNA sequences from the DHGP (German Human Genome Project). It also contains information on the up-to-date database of complete genomes (PEDANT), the classification of protein sequences (ProtFam) and the collection of protein sequence data within the framework of the PIR-International Protein Sequence Database. These databases can be accessed through the MIPS WWW server (http://www. mips.biochem.mpg.de).

Arabidopsis↗

PubMed2003

High frequency of alternative splicing of human genes participating in the HIV-1 life cycle: a model using TSG101, betaTrCP, PPIA, INI1, NAF1, and PML.

Alternative splicing may generate splice forms with different biologic roles or missing protein domains implicated in the interaction with HIV-1. To address this issue, 6 human genes were investigated-tumor suppressor gene 101 (TSG101), beta-transducin repeats containing protein (betaTrCP), peptidyl-proly cis-trans isomerase, cyclophilin A (PPIA), integrase interactor 1 protein (INI1), Nef-associated factor 1 (NAF1), and promyelacytic leukemia (PML)-involved in the viral life cycle and HIV-1 pathogenesis. All 6 genes presented alternative splicing, and a combined bioinformatic and reverse transcription polymerase chain reaction (RT-PCR) analysis identified 27 new variants for a total of 53 splice forms (an average of 9 variants per gene). The predicted frequency of the various splice forms based on expressed sequence tags (EST) analysis corresponded to the semiquantitative findings on RT-PCR analysis for the cell culture systems and for native CD4 cells investigated. Interindividual variation in the frequencies of various splice forms in CD4 T cells from blood donors was observed for INI1. Cell type-specific variation of splice pattern was observed for NAF1. Eight splice forms lacked or modified motifs implicated in the interaction with HIV-1, underscoring the potential interest of assessing alternative splicing when investigating viral cell biology and pathogenesis.

Alternative Splicing↗

PubMed2006

A gzip-based algorithm to identify bacterial families by 16S rRNA.

AIMS: Microbial family identification of 16S rDNA sequences by applying a strategy based on algorithms for data compression. METHODS AND RESULTS: Perl scripts were developed to analyse similarities in microbial sequences, based on a gzip data compression technique. For each bacterial family (n=196) a 16S rRNA reference file was constructed to compare new queries looking at compression performance. An online user-friendly bioinformatics tool was built up to attribute a bacterial family to a 16S rRNA sequence. It was successfully applied to recognize different bacterial families, including Legionellaceae, Bacillaceae, Enterobacteriaceae, Acetobacteriaceae and Rhizobiaceae. The percentage of positive identifications is higher than 95% for fragments over 450 bp. CONCLUSIONS: A new bioinformatics approach has been developed to assign a taxonomic classification to a 16SrDNA sequence. An online tool provides quick and easy sequence attribution. The general principle can be applied to other genes of taxonomic interest. SIGNIFICANCE AND IMPACT OF THE STUDY: Availability of simple bioinformatics tools can support the development of molecular-based analysis and classification of bacteria, especially for environmental or uncultured strains.

Algorithms↗

PubMed2026

Imaging and genomics in stroke.

Imaging after ischemic and hemorrhagic stroke may allow measurement of key phenotypes of injury and recovery for which targeted therapies are still lacking. Such imaging endophenotypes provide quantifiable and heritable biomarkers that can represent mechanistic aspects of disease processes better than clinical measures. Artificial intelligence is allowing extraction of these imaging biomarkers in large cohorts, which can be paired with genomic and other omics data. This will allow the evaluation of what genetic and other biologic variations impact stroke injury and recovery. Integration of these analyses with bioinformatics tools (such as Mendelian randomization and multi-trait analysis) could further dissect how stroke complications overlap with other biologic processes and how they may be causally linked to risk factors. Further work is required to confirm the translational impact of these methods in elucidating mechanisms and drug targets for stroke. However, global collaborations are accelerating analyses on large multi-ethnic stroke cohorts, with availability of imaging data facilitated by federally-funded repositories such as the Imaging Repository for the Cerebrovascular Disease Knowledge Portal (iCDKP).

Humans↗

PubMed2006

BBP: Brucella genome annotation with literature mining and curation.

BACKGROUND: Brucella species are Gram-negative, facultative intracellular bacteria that cause brucellosis in humans and animals. Sequences of four Brucella genomes have been published, and various Brucella gene and genome data and analysis resources exist. A web gateway to integrate these resources will greatly facilitate Brucella research. Brucella genome data in current databases is largely derived from computational analysis without experimental validation typically found in peer-reviewed publications. It is partially due to the lack of a literature mining and curation system able to efficiently incorporate the large amount of literature data into genome annotation. It is further hypothesized that literature-based Brucella gene annotation would increase understanding of complicated Brucella pathogenesis mechanisms. RESULTS: The Brucella Bioinformatics Portal (BBP) is developed to integrate existing Brucella genome data and analysis tools with literature mining and curation. The BBP InterBru database and Brucella Genome Browser allow users to search and analyze genes of 4 currently available Brucella genomes and link to more than 20 existing databases and analysis programs. Brucella literature publications in PubMed are extracted and can be searched by a TextPresso-powered natural language processing method, a MeSH browser, a keywords search, and an automatic literature update service. To efficiently annotate Brucella genes using the large amount of literature publications, a literature mining and curation system coined Limix is developed to integrate computational literature mining methods with a PubSearch-powered manual curation and management system. The Limix system is used to quickly find and confirm 107 Brucella gene mutations including 75 genes shown to be essential for Brucella virulence. The 75 genes are further clustered using COG. In addition, 62 Brucella genetic interactions are extracted from literature publications. These results make possible more comprehensive investigation of Brucella pathogenesis. Other BBP features include publication email alert service, Brucella researchers' contact database, and discussion forum. CONCLUSION: BBP is a gateway for Brucella researchers to search, analyze, and curate Brucella genome data originated from public databases and literature. Brucella gene mutations and genetic interactions are annotated using Limix leading to better understanding of Brucella pathogenesis.

Algorithms↗

PubMed2006

Structural characterization of pollen allergens.

The molecular characterization of allergens has accelerated significantly since the widespread implementation of modern analytical methods. The combination of gene cloning and heterologous protein expression has generated an extensive array of allergens that is available for comparative analysis, as well as clinical applications. Several internet-accessible allergen databases integrate the accumulated information from biomedical research and clinical practice. Innovations in classical biophysical methods, such as mass spectrometry, X-ray crystallography, and nuclear magnetic resonance spectroscopy, have rendered complex biological macromolecules amenable to detailed structural analysis. The modern scientific era has realized the synthesis of bioinformatics, molecular biology, biochemistry, biophysics, and immunology, and given us the means needed to decipher the remaining mysteries of allergies. This article addresses how the synergism of modern scientific techniques has hastened our understanding of allergies, how these techniques are applied in the identification and characterization of allergens, and how these methods assist the rational development of clinical tools for allergy diagnosis and treatment.

Allergens↗

PubMed2003

Managing core resources for genomics and proteomics.

Recent years have seen an explosive growth in biological data, which is often not published in a conventional sense but rather deposited in a database. This trend and the need for computational analyses of the data make databases essential tools for biological research. Data from a variety of sources, covering a wide range of biological information, are stored in different, often quite specialized, databases. The provision of such databases as useful resources for the scientific community is a demanding task since the data not only have to be stored in a consistent way, but also have to be easily accessible and highly integrated with other databases. Furthermore, it is necessary to provide users with effective tools to search the databases and to analyze the data. At the European Bioinformatics Institute (EBI), we develop and maintain a number of biological databases and provide a variety of bioinformatics tools to facilitate database and similarity searches and data analysis. In this review, we will provide examples of the core resources maintained at the EBI and summarize important issues of database management of such resources.

Computational Biology↗

PubMed1998

Structural-functional bioinformatics: knowledge-based NMR interpretation.

This paper describes a knowledge-based approach to a problem of structural-functional bioinformatics, specifically the determination of protein structure through the automated analysis of NMR data. Highly successful results in carrying out sequence-specific assignments of residues from multidimensional NMR datasets has led us to automation of NOE dataset interpretation and a design for integrating these results with other protein structure and function analysis programs.

Computational Biology↗

PubMed2002

[The active site of human glucocerebrosidase: structural predictions and experimental validations].

Gaucher disease is a lysosomal storage disorder caused by a deficiency in glucocerebrosidase which cleaves the beta-glucosidic linkage of glucosylceramide, a normal intermediate in glycolipid metabolism. Glucocerebrosidase belongs to the clan GH-A of glycoside hydrolases, a large group of enzymes which function with retention of the anomeric configuration at the hydrolysis site. Accurate three-dimensional (3D) structure data for glucocerebrosidase should help to better understand the molecular bases of Gaucher disease. As such 3D structure data were not available, we used the two-dimensional hydrophobic cluster analysis (HCA) method to make structure predictions for the catalytic domains of clan GH-A glycoside hydrolases. We found that all the enzymes of clan GH-A may share a similar catalytic domain consisting of an (alpha/beta)8 barrel with the critical acid/base and nucleophile residues located at the C-terminal ends of strands beta 4 and beta 7, respectively. In the case of glucocerebrosidase, Glu 235 was predicted to be the putative acid/base catalyst whereas the nucleophile was located at Glu 340. Next, in order to obtain experimental evidence supporting these HCA-based predictions, we used retroviral vectors to express, in murine null cells, E235A and E340A mutant proteins, in which alanine residues unable to participate in the enzymatic reaction replace the presumed critical glutamic acid residues. Both mutants were found to be catalytically inactive although they were correctly folded/processed and sorted to the lysosome. Thus, Glu 235 and Glu 340 do indeed play key roles in the active site of human glucocerebrosidase as predicted by the HCA analysis. In a broader perspective, our work points out that bioinformatics approaches may be highly useful for generating structure-function predictions based on sequence-structure interrelationships, especially in the context of a rapid increase in protein sequence information through genome sequencing.

Amino Acid Sequence↗

PubMed2003

[Cloning of the full length cDNA for a novel leukemia relapse-associated candidate gene LRP15].

To clone the full length cDNA of a novel leukemia relapse-associated candidate gene (LRP15), the human ESTs (Expression Sequence Tags) fragments obtained from electronic hybridization were assembled by a 1.8 kb DNA probe, which was only methylated in relapsed leukemia. Then the primers were designed for rapid amplification of cDNA end (RACE). Bioinformatic data of High Throughout Genomic Sequences (HTGS) and Serial Analysis of Gene Expression (SAGE) were used for chromosome localization and tissue expression analysis. The results showed that the full-length cDNA of the novel gene had an open reading frame of 780 bp encoding a 259 amino acid protein of unkown functions. LRP15 gene expressed in many different tissues was localized in chromosome 3p24. It is concluded that RACE is an effective method to clone novel genes and LRP15 may be a leukemia relapse-associated candidate gene playing an important role in carcinogenesis.

Amino Acid Sequence↗

PubMed2004

Development of novel vaccines using DNA shuffling and screening strategies.

DNA shuffling and screening technologies recombine and evolve genes in vitro to rapidly obtain molecules with improved biological activity and fitness. In this way, genes from related strains are bred like plants or livestock and their successive progeny are selected. These technologies have also been called molecular breeding-directed molecular evolution. Recent developments in bioinformatics-assisted computer programs have facilitated the design, synthesis and analysis of DNA shuffled libraries of chimeric molecules. New applications in vaccine development are among the key features of DNA shuffling and screening technologies because genes from several strains or antigenic variants of pathogens can be recombined to create novel molecules capable of inducing immune responses that protect against infections by multiple strains of pathogens. In addition, molecules such as co-stimulatory molecules and cytokines have been evolved to have improved T-cell proliferation and cytokine production compared with the wild-type human molecules. These molecules can be used to immunomodulate vaccine responsiveness and have multiple applications in infectious diseases, cancer, allergy and autoimmunity. Moreover, DNA shuffling and screening technologies can facilitate process development of vaccine manufacturing through increased expression of recombinant polypeptides and viruses. Therefore, DNA shuffling and screening technologies can overcome some of the challenges that vaccine development currently faces.

Animals↗

PubMed2004

Using prior knowledge to improve genetic network reconstruction from microarray data.

The use of Bayesian Network methods to recover transcriptional regulatory networks from static microarray data is an active area of bioinformatics research. However, early work in this area lacked realistic analysis of the effects of data set size on learning performance and ignored the potentially immense benefits of using prior biological knowledge. More recent work which has utilized such information has tended to focus on qualitative descriptions of the results. In this paper, we construct a detailed, realistic model for glucose homeostasis and use this model to generate static, synthetic gene expression data. We then use a Bayesian Network method to reconstruct this genetic network from the synthetic microarray data utilizing various amounts and types of prior knowledge. By quantitatively analyzing the effects of data set size and the incorporation of different types of prior biological knowledge on our ability to reconstruct the original network, we show that characteristic portions of genetic networks can be reconstructed from microarray data. Incorporating prior knowledge into the learning scheme greatly reduces the data required, allowing these reverse engineering techniques to be used to learn regulatory interactions from microarray data sets of realistic size.

Bayes Theorem↗

PubMed2006

[Preparation of the cDNA microarray on the differential expressed cDNA of senescence-accelerated mouse's hippocampus].

Alzheimer' s disease (AD) is the most common form of dementia in the elderly. AD is an invariably fatal neurodegenerative disorder with no effective treatment. Senescence-accelerated mouse prone 8 (SAMP8) is a model for studying age-related cognitive impairments and also is a good model to study brain aging and one of mouse model of AD. The technique of cDNA microarray can monitor the expression levels of thousands of genes simultaneously and can be used to study AD with the character of multi-mechanism, multi-targets and multi-pathway. In order to disclose the mechanism of AD and find the drug targets of AD, cDNA microarray containing 3136 cDNAs amplified from the suppression subtracted cDNA library of hippocampus of SAMP8 and SAMR1 was prepared with 16 blocks and 14 x 14 pins, the housekeeping gene beta-actin and G3PDH as inner conference. The background of this microarray was low and unanimous, and dots divided evenly. The conditions of hybridization and washing were optimized during the hybridization of probe and target molecule. After the data of hybridization analysis, the differential expressed cDNAs were sequenced and analyzed by the bioinformatics, and some of genes were quantified by the real time RT-PCR and the reliability of this cDNA microarray were validated. This cDNA microarray may be the good means to select the differential expressed genes and disclose the molecular mechanism of SAMP8's brain aging and AD.

Aging↗

PubMed2004

Nanopore cheminformatics.

A cheminformatics method is described for classification, and biophysical examination, of individual molecules. A novel molecular detector is used--one based on current blockade measurements through a nanometer-scale ion channel (alpha-hemolysin). Classification results are described for blockades caused by DNA molecules in the alpha-hemolysin nanopore detector, with signal analysis and pattern recognition performed using a combination of methods from bioinformatics and machine learning. Due to the size of the alpha-hemolysin protein channel, the blockade events report on one DNA molecule at a time, which enables a variety of reproducible, single-molecule biophysical experiments. To capture the full sensitivity of the nanopore detector's blockade signal, Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of each captured DNA molecule. Support Vector Machines (SVMs) that employ novel kernel designs were then used as discriminators. With SVM training performed off-line, and economical HMM processing on-line, blockade classification was possible during capture. HMMs were also used in conjunction with a time-domain finite state automaton (off-line) for feature discovery and kinetics analysis. Analysis of the DNA data indicates a variety of binding (DNA-protein), fraying, and conformational shifts that are consistent with data obtained from thermodynamic analyses (melting curves), X-ray crystallography, and NMR studies. The software tools are designed for analysis of generic blockades in ionic channels, including those in other biological pore-forming toxins, other biological channels in general, and semiconductor-based channels.

Base Sequence↗

PubMed2006

Partial least squares: a versatile tool for the analysis of high-dimensional genomic data.

Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as, e.g. tumor classification from transcriptome data, identification of relevant genes, survival analysis and modeling of gene networks and transcription factor activities.

Animals↗

PubMed2004

Protein profiling in respiratory disease: techniques and impact.

Multifactorial diseases such as respiratory disease call for a global analysis of such disorders. Recent advances in protein profiling techniques may allow for early diagnosis of respiratory disease, which is crucial for intervention and treatment. In order to reduce false-positive rates, clinical diagnosis requires a high degree of sensitivity and specificity to be an effective screening tool. Protein profiles identified by ProteinChip (Ciphergen Biosystems) technology coupled with mass spectrometry affords a global analysis of clinical samples and is beginning to reach acceptable levels of sensitivity and specificity. Combining the profile with another diagnostic tool enhances the effectiveness of protein profiles to classify disease. Although current efforts have centered on serum protein profiling, the local environment of the lung may be better reflected in proteins of bronchoalveolar lavage or sputum. Identification of biomarkers of disease by protein profiling analyses may lead to an understanding of the mechanisms of this disease and contribute to the discovery of new therapeutics for the prevention and treatment of disease. Advancing these analyses are techniques such as ProteinChip mass spectrometry, laser capture microdissection, tissue microarrays and fluorescently labeled antibody bead arrays, which enable the direct global analysis of complex mixtures. Effective high-throughput and ease of use of clinical testing will arrive with improvements in bioinformatics and decreases in instrumentation costs.

Antibodies↗

PubMed2006

bioNMF: a versatile tool for non-negative matrix factorization in biology.

BACKGROUND: In the Bioinformatics field, a great deal of interest has been given to Non-negative matrix factorization technique (NMF), due to its capability of providing new insights and relevant information about the complex latent relationships in experimental data sets. This method, and some of its variants, has been successfully applied to gene expression, sequence analysis, functional characterization of genes and text mining. Even if the interest on this technique by the bioinformatics community has been increased during the last few years, there are not many available simple standalone tools to specifically perform these types of data analysis in an integrated environment. RESULTS: In this work we propose a versatile and user-friendly tool that implements the NMF methodology in different analysis contexts to support some of the most important reported applications of this new methodology. This includes clustering and biclustering gene expression data, protein sequence analysis, text mining of biomedical literature and sample classification using gene expression. The tool, which is named bioNMF, also contains a user-friendly graphical interface to explore results in an interactive manner and facilitate in this way the exploratory data analysis process. CONCLUSION: bioNMF is a standalone versatile application which does not require any special installation or libraries. It can be used for most of the multiple applications proposed in the bioinformatics field or to support new research using this method. This tool is publicly available at http://www.dacya.ucm.es/apascual/bioNMF.

Algorithms↗