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PubMed2002

Comparative ab initio prediction of gene structures using pair HMMs.

We present a novel comparative method for the ab initio prediction of protein coding genes in eukaryotic genomes. The method simultaneously predicts the gene structures of two un-annotated input DNA sequences which are homologous to each other and retrieves the subsequences which are conserved between the two DNA sequences. It is capable of predicting partial, complete and multiple genes and can align pairs of genes which differ by events of exon-fusion or exon-splitting. The method employs a probabilistic pair hidden Markov model. We generate annotations using our model with two different algorithms: the Viterbi algorithm in its linear memory implementation and a new heuristic algorithm, called the stepping stone, for which both memory and time requirements scale linearly with the sequence length. We have implemented the model in a computer program called DOUBLESCAN. In this article, we introduce the method and confirm the validity of the approach on a test set of 80 pairs of orthologous DNA sequences from mouse and human. More information can be found at: http://www.sanger.ac.uk/Software/analysis/doublescan/

Algorithms↗
PubMed2003

Extracting active pathways from gene expression data.

MOTIVATION: A promising way to make sense out of gene expression profiles is to relate them to the activity of metabolic and signalling pathways. Each pathway usually involves many genes, such as enzymes, which can themselves participate in many pathways. The set of all known pathways can therefore be represented by a complex network of genes. Searching for regularities in the set of gene expression profiles with respect to the topology of this gene network is a way to automatically extract active pathways and their associated patterns of activity. METHOD: We present a method to perform this task, which consists in encoding both the gene network and the set of profiles into two kernel functions, and performing a regularized form of canonical correlation analysis between the two kernels. RESULTS: When applied to publicly available expression data the method is able to extract biologically relevant expression patterns, as well as pathways with related activity.

Algorithms↗
PubMed2003

A computational pipeline for protein structure prediction and analysis at genome scale.

MOTIVATION: Experimental techniques alone cannot keep up with the production rate of protein sequences, while computational techniques for protein structure predictions have matured to such a level to provide reliable structural characterization of proteins at large scale. Integration of multiple computational tools for protein structure prediction can complement experimental techniques. RESULTS: We present an automated pipeline for protein structure prediction. The centerpiece of the pipeline is our threading-based protein structure prediction system PROSPECT. The pipeline consists of a dozen tools for identification of protein domains and signal peptide, protein triage to determine the protein type (membrane or globular), protein fold recognition, generation of atomic structural models, prediction result validation, etc. Different processing and prediction branches are determined automatically by a prediction pipeline manager based on identified characteristics of the protein. The pipeline has been implemented to run in a heterogeneous computational environment as a client/server system with a web interface. Genome-scale applications on Caenorhabditis elegans, Pyrococcus furiosus and three cyanobacterial genomes are presented. AVAILABILITY: The pipeline is available at http://compbio.ornl.gov/proteinpipeline/

Algorithms↗
PubMed2006

Combination of text-mining algorithms increases the performance.

MOTIVATION: Recently, several information extraction systems have been developed to retrieve relevant information out of biomedical text. However, these methods represent individual efforts. In this paper, we show that by combining different algorithms and their outcome, the results improve significantly. For this reason, CONAN has been created, a system which combines different programs and their outcome. Its methods include tagging of gene/protein names, finding interaction and mutation data, tagging of biological concepts and linking to MeSH and Gene Ontology terms. RESULTS: In this paper, we will present data that show that combining different text-mining algorithms significantly improves the results. Not only is CONAN a full-scale approach that will ultimately cover all of PubMed/MEDLINE, we also show that this universality has no effect on quality: our system performs as well as or better than existing systems. AVAILABILITY: The LDD corpus presented is available by request to the author. The system will be available shortly. For information and updates on CONAN please visit http://www.cs.uu.nl/people/rainer/conan.html.

Abstracting and Indexing↗
PubMed2006

HiRes--a tool for comprehensive assessment and interpretation of metabolomic data.

UNLABELLED: The increasing role of metabolomics in system biology is driving the development of tools for comprehensive analysis of high-resolution NMR spectral datasets. This task is quite challenging since unlike the datasets resulting from other 'omics', a substantial preprocessing of the data is needed to allow successful identification of spectral patterns associated with relevant biological variability. HiRes is a unique stand-alone software tool that combines standard NMR spectral processing functionalities with techniques for multi-spectral dataset analysis, such as principal component analysis and non-negative matrix factorization. In addition, HiRes contains extensive abilities for data cleansing, such as baseline correction, solvent peak suppression, removal of frequency shifts owing to experimental conditions as well as auxiliary information management. Integration of these components together with multivariate analytical procedures makes HiRes very capable of addressing the challenges for assessment and interpretation of large metabolomic datasets, greatly simplifying this otherwise lengthy and difficult process and assuring optimal information retrieval. AVAILABILITY: HiRes is freely available for research purposes at http://hatch.cpmc.columbia.edu/highresmrs.html

Algorithms↗
PubMed2005

Context-dependent codon bias and messenger RNA longevity in the yeast transcriptome.

Context-dependent codon bias and its relationship with messenger RNA (mRNA) longevity was examined in 4,648 mRNA transcripts of the Saccharomyces cerevisiae transcriptome for which mRNA half-lives have been empirically determined. Surprisingly, rare codon usage (codons used <13 times per 1,000 codons in the genome) increased with mRNA half-life. However, it is shown that this pattern was not due to preference for rare codon use within codon families containing both rare and nonrare codons. Rather, the pattern was due to an increase in the frequency of amino acids encoded solely by rare codons, and a decrease in the frequency of amino acids never encoded by rare codons, with mRNA half-life. When standardized by open reading frame length, the use of consecutive rare codons was also positively correlated with mRNA half-life. There was negative correlation between the usage of synonymous A|T dinucleotides spanning codon boundaries and mRNA half-life, despite the fact that the frequency of AT dinucleotide usage overall, and AT dinucleotide usage at other codon position contexts (e.g., 1-2, 2-3, or 3|1 total), was not correlated with mRNA half-life. The use of A|T dinucleotides at synonymous dicodon boundaries could potentially allow for more efficient 3'-5' degradation by endonucleolytic cleavage.

Codon↗
PubMed2002

Dictionary-driven protein annotation.

Computational methods seeking to automatically determine the properties (functional, structural, physicochemical, etc.) of a protein directly from the sequence have long been the focus of numerous research groups. With the advent of advanced sequencing methods and systems, the number of amino acid sequences that are being deposited in the public databases has been increasing steadily. This has in turn generated a renewed demand for automated approaches that can annotate individual sequences and complete genomes quickly, exhaustively and objectively. In this paper, we present one such approach that is centered around and exploits the Bio-Dictionary, a collection of amino acid patterns that completely covers the natural sequence space and can capture functional and structural signals that have been reused during evolution, within and across protein families. Our annotation approach also makes use of a weighted, position-specific scoring scheme that is unaffected by the over-representation of well-conserved proteins and protein fragments in the databases used. For a given query sequence, the method permits one to determine, in a single pass, the following: local and global similarities between the query and any protein already present in a public database; the likeness of the query to all available archaeal/ bacterial/eukaryotic/viral sequences in the database as a function of amino acid position within the query; the character of secondary structure of the query as a function of amino acid position within the query; the cytoplasmic, transmembrane or extracellular behavior of the query; the nature and position of binding domains, active sites, post-translationally modified sites, signal peptides, etc. In terms of performance, the proposed method is exhaustive, objective and allows for the rapid annotation of individual sequences and full genomes. Annotation examples are presented and discussed in Results, including individual queries and complete genomes that were released publicly after we built the Bio-Dictionary that is used in our experiments. Finally, we have computed the annotations of more than 70 complete genomes and made them available on the World Wide Web at http://cbcsrv.watson.ibm.com/Annotations/.

Algorithms↗
PubMed2006

Database resources of the National Center for Biotechnology Information.

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI's Web site. NCBI resources include Entrez, the Entrez Programming Utilities, My NCBI, PubMed, PubMed Central, Entrez Gene, the NCBI Taxonomy Browser, BLAST, BLAST Link(BLink), Electronic PCR, OrfFinder, Spidey, Splign, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosomes, Entrez Genome, Genome Project and related tools, the Trace and Assembly Archives, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs), Viral Genotyping Tools, Influenza Viral Resources, HIV-1/Human Protein Interaction Database, Gene Expression Omnibus (GEO), Entrez Probe, GENSAT, Online Mendelian Inheritance in Man (OMIM), Online Mendelian Inheritance in Animals (OMIA), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD), the Conserved Domain Architecture Retrieval Tool (CDART) and the PubChem suite of small molecule databases. Augmenting many of the Web applications are custom implementations of the BLAST program optimized to search specialized data sets. These resources can be accessed through the NCBI home page at www.ncbi.nlm.nih.gov.

Animals↗
PubMed2004

Filtering remote homologues using predicted structural information.

Finding homologues for a given protein plays a major role in predicting the protein's structure and function. However, it is still difficult to find remote homologues with low sequence similarity, even with advanced sequence search methods. We propose a simple filtering method that uses predicted structural information, pertaining to secondary structures and solvent accessibilities. It filters the more promising homologues from the many candidate proteins obtained by PSI-BLAST with a less stringent threshold E-value. The final decision is made by a simple linear discrimination method, considering the E-value of PSI-BLAST and the statistical significance scores of structural matches. An in-house neural network program is used for the prediction of secondary structures and solvent accessibilities for both the query and library proteins. The performance of our filtering method was evaluated by the cross-validation method, using the SCOP superfamily relationship as the correct standard. Coverage-reliability plots show that our filtering method clearly improves the performance of PSI-BLAST. The secondary structure improves PSI-BLAST better than the solvent accessibilities, but the combination of these two features with PSI-BLAST leads to the best result. The advantage of our method is its easy implementation with fewer parameters to be tuned and faster computation. We also discuss its performance with predicted and observed secondary structures.

Algorithms↗
PubMed2004

Tyrosine phosphorylation generates multiple isoforms of the mitochondrial capsule protein, phospholipid hydroperoxide glutathione peroxidase (PHGPx), during hamster sperm capacitation.

Sperm capacitation is a maturation process, occurring in the female reproductive tract, that produces fertilization-competent spermatozoa. Protein tyrosine phosphorylation represents an important event in capacitation. The present study demonstrates the capacitation-dependent tyrosine-phosphorylation of phospholipid hydroperoxide glutathione peroxidase (PHGPx), the disulfide cross-linked, major structural protein of the sperm mitochondrial capsule. Immunofluorescence microscopy using an antiphosphotyrosine monoclonal antibody (anti-pY20) demonstrated the presence of capacitation-associated tyrosine phosphorylated proteins in the flagellum of hamster spermatozoa. Among the tyrosine-phosphorylated polypeptides (M(r) 19,000- 99,000), a 19-kDa polypeptide was the only one that can be solubilized completely by Triton X-100-dithiothreitol (DTT). The 19-kDa polypeptide was purified by anion-exchange chromatography and by immunoaffinity chromatography. Proteomic identification of the 19-kDa polypeptide by nano-electrospray tandem mass spectrometry yielded six peptides that matched the National Center for Biotechnology Information (NCBI) database sequences of bovine PHGPx. Indirect immunofluorescence localized PHGPx to the midpiece of the flagellum and the immunoblot analysis demonstrated its DTT-dependent release from purified flagella. DTT extracts of noncapacitated spermatozoa exhibited a charge train of four major PHGPx isoforms (pIs 7.5- 9.0) by two-dimensional PAGE, whereas capacitated spermatozoa revealed the generation of new acidic PHGPx isoforms with isoelectric points ranging between pH 6.0-7.0 and 4.0-5.0, indicating that it is posttranslationally modified during capacitation. These data suggest that the tyrosine-phosphorylation of PHGPx may represent an important event in the signaling pathway(s) associated with capacitation and could potentially affect mitochondrial function.

Amino Acid Sequence↗
PubMed2003

Cloning, sequencing and heterologous expression of the medermycin biosynthetic gene cluster of Streptomyces sp. AM-7161: towards comparative analysis of the benzoisochromanequinone gene clusters.

Medermycin is a Streptomyces aromatic C-glycoside antibiotic classified in the benzoisochromanequinones (BIQs), which presents several interesting biosynthetic problems concerning polyketide synthase (PKS), post-PKS tailoring and deoxysugar pathways. The biosynthetic gene cluster for medermycin (the med cluster) was cloned from Streptomyces sp. AM-7161. Completeness of the clone was proved by the heterologous expression of a cosmid carrying the entire med cluster in Streptomyces coelicolor CH999 to produce medermycin. The DNA sequence of the cosmid (36 202 bp) revealed 34 complete ORFs, with an incomplete ORF at either end. Functional assignment of the deduced products was made for PKS and biosynthetically related enzymes, tailoring steps including strereochemical control, oxidation, angolosamine pathway, C-glycosylation, and regulation. The med cluster was estimated to be about 30 kb long, covering 29 ORFs. An unusual characteristic of the cluster is the disconnected organization of the minimal PKS genes: med-ORF23 encoding the acyl carrier protein is 20 kb apart from med-ORF1 and med-ORF2 for the two ketosynthase components. Secondly, the six genes (med-ORF14, 15, 16, 17, 18 and 20) for the biosynthesis of the deoxysugar, angolosamine, are all contiguous. Finally, the finding of a glycosyltransferase gene, med-ORF8, suggests a possible involvement of conventional C-glycosylation in medermycin biosynthesis. Comparison among the three complete BIQ gene clusters - med and those for actinorhodin (act) and granaticin (gra) - revealed some common genes whose deduced functions are unavailable from database searches (the 'unknowns'). An example is med-ORF5, a homologue of actVI-ORF3 and gra-ORF18, which was highlighted by a recent proteomic analysis of S. coelicolor A3(2).

Amino Acid Sequence↗
PubMed2003

Impact of alternative initiation, splicing, and termination on the diversity of the mRNA transcripts encoded by the mouse transcriptome.

We analyzed the FANTOM2 clone set of 60,770 RIKEN full-length mouse cDNA sequences and 44,122 public mRNA sequences. We developed a new computational procedure to identify and classify the forms of splice variation evident in this data set and organized the results into a publicly accessible database that can be used for future expression array construction, structural genomics, and analyses of the mechanism and regulation of alternative splicing. Statistical analysis shows that at least 41% and possibly as much as 60% of multiexon genes in mouse have multiple splice forms. Of the transcription units with multiple splice forms, 49% contain transcripts in which the apparent use of an alternative transcription start (stop) is accompanied by alternative splicing of the initial (terminal) exon. This implies that alternative transcription may frequently induce alternative splicing. The fact that 73% of all exons with splice variation fall within the annotated coding region indicates that most splice variation is likely to affect the protein form. Finally, we compared the set of constitutive (present in all transcripts) exons with the set of cryptic (present only in some transcripts) exons and found statistically significant differences in their length distributions, the nucleotide distributions around their splice junctions, and the frequencies of occurrence of several short sequence motifs.

Alternative Splicing↗
PubMed2003

Subsystem identification through dimensionality reduction of large-scale gene expression data.

The availability of parallel, high-throughput biological experiments that simultaneously monitor thousands of cellular observables provides an opportunity for investigating cellular behavior in a highly quantitative manner at multiple levels of resolution. One challenge to more fully exploit new experimental advances is the need to develop algorithms to provide an analysis at each of the relevant levels of detail. Here, the data analysis method non-negative matrix factorization (NMF) has been applied to the analysis of gene array experiments. Whereas current algorithms identify relationships on the basis of large-scale similarity between expression patterns, NMF is a recently developed machine learning technique capable of recognizing similarity between subportions of the data corresponding to localized features in expression space. A large data set consisting of 300 genome-wide expression measurements of yeast was used as sample data to illustrate the performance of the new approach. Local features detected are shown to map well to functional cellular subsystems. Functional relationships predicted by the new analysis are compared with those predicted using standard approaches; validation using bioinformatic databases suggests predictions using the new approach may be up to twice as accurate as some conventional approaches.

Algorithms↗
PubMed2006

TMABoost: an integrated system for comprehensive management of tissue microarray data.

In the last decade, high-throughput technologies such as DNA and tissue microarrays (TMAs) have become a means of large-scale investigation of gene expression, providing a plethora of new biomedical data in a relatively short time. Data collection and organization are critical aspects in this process to ensure the quality and reliability of future data interpretation. In this work, we propose a comprehensive approach to handle TMA data with the aim of supporting and promoting biomarker development. We describe a web-based system for the complete management of tissue microarray data in the field of pathology. The system has been in use since June, 2003. Our approach includes automatic localization and identification of tissue microarray samples, and quantitative image analysis that allows high-throughput screening of TMAs by ensuring nonsubjective measures and novel prognosis associations. In this paper, we present the architecture and the components of this system.

Algorithms↗
PubMed2003

Asymmetric mutation rates at enzyme-inhibitor interfaces: implications for the protein-protein docking problem.

We have carried out a thorough and systematic sequence-structure study on how the pattern of conservation at the interface differs from the noninteracting surface in seven proteases and their inhibitors. As expected, the interface of a protease could be easily distinguished from the noninteracting surface by a concentrated area of conservation. In contrast, there was less distinction to be made between the interface and the noninteracting surface of inhibitors, and in five of the seven cases, a higher proportion of the interface area was variable compared to the rest of the surface. This is likely to cause a problem for binding-site prediction methods that assume the largest cluster of highly conserved residues on the surface of a protein corresponds to the interface. We conclude that such methods would succeed when applied to our protease test cases, but complications could arise with the inhibitors. These results also impact on methods to solve the protein-protein docking problem that use conservation at the interface to provide the location of the two protein binding sites prior to application of the docking algorithm.

Algorithms↗
PubMed2000

The effect of nucleotide bias upon the composition and prediction of transmembrane helices.

Transmembrane helices are the most readily predictable secondary structure components of proteins. They can be predicted to a high degree of accuracy in a variety of ways. Many of these methods compare new sequence data with the sequence characteristics of known transmembrane domains. However, the known transmembrane sequences are not necessarily representative of a particular organism. We attempt to demonstrate that parameters optimized for the known transmembrane domains are far from optimal when predicting transmembrane regions in a given genome. In particular, we have tested the effect of nucleotide bias upon the composition and hence the prediction characteristics of transmembrane helices. Our analysis shows that nucleotide bias of a genome has a strong and predictable influence upon the occurrences of several of the most important hydrophobic amino acids found within transmembrane helices. Thus, we show that nucleotide bias should be taken into account when determining putative transmembrane domains from sequence data.

Amino Acids↗
PubMed2007

TPRpred: a tool for prediction of TPR-, PPR- and SEL1-like repeats from protein sequences.

BACKGROUND: Solenoid repeat proteins of the Tetratrico Peptide Repeat (TPR) family are involved as scaffolds in a broad range of protein-protein interactions. Several resources are available for the prediction of TPRs, however, they often fail to detect divergent repeat units. RESULTS: We have developed TPRpred, a profile-based method which uses a P-value-dependent score offset to include divergent repeat units and which exploits the tendency of repeats to occur in tandem. TPRpred detects not only TPR-like repeats, but also the related Pentatrico Peptide Repeats (PPRs) and SEL1-like repeats. The corresponding profiles were generated through iterative searches, by varying the threshold parameters for inclusion of repeat units into the profiles, and the best profiles were selected based on their performance on proteins of known structure. We benchmarked the performance of TPRpred in detecting TPR-containing proteins and in delineating the individual repeats therein, against currently available resources. CONCLUSION: TPRpred performs significantly better in detecting divergent repeats in TPR-containing proteins, and finds more individual repeats than the existing methods. The web server is available at http://tprpred.tuebingen.mpg.de, and the C++ and Perl sources of TPRpred along with the profiles can be downloaded from ftp://ftp.tuebingen.mpg.de/ebio/protevo/TPRpred/.

Algorithms↗