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Generating data for databases--the peptide repertoire of HLA molecules.

During the past few years, a huge amount of information about HLA-presented peptides has been compiled: several thousand naturally processed ligands of such cell surface receptors are already known. Nevertheless, our knowledge covers only a minute proportion of the total peptide repertoire. The overall amount of different peptides presented by one given HLA class I molecule lies between 1000 and 10000 individual sequences per cell. There is, however, no HLA molecule of which more than 100 ligands have been published so far. The situation is further complicated by the fact that different cells present different sets of peptides by the same HLA molecules, a feature that provides great hope for immunotherapy. We have been analysing HLA-presented peptides for many years for three reasons. First, the basic rules of peptide presentation (the 'peptide motifs') had to be established. Second, the listing of individual peptides presented by HLA molecules is steadily continuing, although a comprehensive catalogue of all possible HLA-presented peptides is utopical in our days. Third, quantitative differences in the presentation of individual HLA ligands provide information about the dynamic state of the host cells. Comprehensive information about HLA-presented peptides enables accurate epitope prediction and provides a basis for diagnostic assessment and therapeutic intervention.

Allergy and Immunology↗

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↗

A systematic review of large scale and heterogeneous gene array data in heart failure.

Microarray analysis has become a widely available tool for the generation of gene expression data on a genomic scale. Since the studies with similar protocols are growing, it has become necessary to systematically revise the large body of literature to decipher the gene expression data. In this review, we analyzed and critically discussed the database presented from 14 published studies that showed the gene expression profile in heart failure (HF) using microarray as a primary tool. After comparing the diverse database from these studies, we explain the protein translational, matri-cellular, immunological and fibrosis-related mechanisms in HF. In addition to previously annotated genes, we analyzed two differentially expressed expressed sequence tags (ESTs) (KIAA0152 and Suppressor of G(Two) allele of the suppressor of kinetochore protein-1, SGT1) in HF and showed how bio-informatic analysis of ESTs can lead to the identification of novel pathways active in HF. We have also discussed the new publicly accessible tools that link the gene expression data to gene ontogeny (GO) and functionality. Finally, we have systematically revised the chromosomal localization of the genes that are specifically up-regulated in HF. We have thus spotted chromosome 1, 2, 11 and 12 as the chromosomal hotspots of HF. This methodical approach will simplify the existing concepts on the evolution and progression of HF and lead us toward the development of newer diagnostic and therapeutic tools. Although modeled to HF, this approach should be of broader scientific interest to elaborate multiple genes and complex pathways.

Cell Cycle Proteins↗

Comprehensive comparisons of the current human, mouse, and rat RefSeq, Ensembl, EST, and FANTOM3 datasets: identification of new human genes with specific tissue expression profile.

Our understanding of functional genetic elements in the genomes is continuously growing and new entries are entered in various databases on a regular basis. We have here merged the genetic elements in RefSeq, Ensembl, FANTOM3, HINV, and NCBI:s ESTdb using the genome assemblies in order to achieve a comprehensive picture of the current status of the identity and gene number in human, mouse, and rat. The number of human protein coding genes has not increased (25,043) while the increased sequencing of mouse transcripts has provided the considerably higher number of protein coding genes (31,578) in mouse. The results indicate large discrepancies between the datasets, as considerable numbers of unique transcripts can be found in each dataset. Despite the high number of ncRNA (38,129 in mouse) there are also almost 20,000 EST clusters in both mouse and humans with more than one EST that do not overlap any transcript suggesting that several new genetic elements are still to be found. We also demonstrated presence of new genes by identifying new human ones that have specific tissue profiles, using RT-PCR on rat tissues.

Animals↗

Down's syndrome-associated single minded gene as a novel tumor marker.

The Cancer Genome Anatomy Project (CGAP) database has thousands of Expressed Sequence Tags encompassing both known and novel genes. Bioinformatics of the CGAP database led to the prediction that Single Minded Gene (sim2) could be specific to colon tumors. The sim2 gene is located in a minimum region of the chromosome 21 often implicated in trisomia called Down's Syndrome Critical Region. To date, the sim proteins have not been shown to be involved in cancer. Intrigued by the possible association of a Down's syndrome-related gene to solid tumors, efforts were undertaken to validate the expression specificity. The sim2 isoform (sim2-short-form, sim2-s) expression was seen in carcinomas of colon, pancreas and prostate, but not in corresponding normal tissues. Stage-specific expression of the sim2-s protein was seen in normal matched paraffin sections of the colon tumors. In a matched set of tissues of Benign Prostatic Hyperplasia (BPH) and prostate carcinomas, sim2-s expression was detected in the BPH. The expression specificity of sim2-s in select solid tumors offers both diagnostic and therapeutic potential and warrants additional study.

Adenoma↗

GARSA: genomic analysis resources for sequence annotation.

SUMMARY: Growth of genome data and analysis possibilities have brought new levels of difficulty for scientists to understand, integrate and deal with all this ever-increasing information. In this scenario, GARSA has been conceived aiming to facilitate the tasks of integrating, analyzing and presenting genomic information from several bioinformatics tools and genomic databases, in a flexible way. GARSA is a user-friendly web-based system designed to analyze genomic data in the context of a pipeline. EST and GGS data can be analyzed using the system since it accepts (1) chromatograms, (2) download of sequences from GenBank, (3) Fasta files stored locally or (4) a combination of all three. Quality evaluation of chromatograms, vector removing and clusterization are easily performed as part of the pipeline. A number of local and customizable Blast and CDD analyses can be performed as well as Interpro, complemented with phylogeny analyses. GARSA is being used for the analyses of Trypanosoma vivax (GSS and EST), Trypanosoma rangeli (GSS, EST and ORESTES), Bothrops jararaca (EST), Piaractus mesopotamicus (EST) and Lutzomyia longipalpis (EST). AVAILABILITY: The GARSA system is freely available under GPL license (http://www.biowebdb.org/garsa/). For download requests visit http://www.biowebdb.org/garsa/ or contact Dr Alberto Dávila.

Animals↗

Nonribosomal peptide synthetase genes in the genome of Fusarium graminearum, causative agent of wheat head blight.

Fungal nonribosomal peptide synthetases (NRPSs) are responsible for the biosynthesis of numerous metabolites which serve as virulence factors in several plant-pathogen interactions. The aim of our work was to investigate the diversity of these genes in a Fusarium graminearum sequence database using bioinformatic techniques. Our search identified 15 NRPS sequences, among which two were found to be closely related to peptide synthetases of various fungi taking part in ferrichrome biosynthesis. Another peptide synthetase gene was similar to that identified in Aspergillus oryzae which is possibly responsible for the biosynthesis of fusarinine, an extracellular iron-chelating siderophore. To our knowledge, this is the first report on the identification of a putative NRPS gene possibly responsible for the biosynthesis of fusarinine-type siderophores. The other NRPSs were found to be related to peptide synthetases taking part in the biosynthesis of various peptides in other fungi. Transcription factors carrying ankyrin repeats were observed in the vicinity of four of the identified peptide synthetase genes. Additionally, NRPS related genes similar to putative long-chain fatty acid CoA ligases, acyl CoA ligases, ABC transport proteins, a highly conserved putative transmembrane protein of Aspergillus nidulans, and alpha-aminoadipate reductases have also been identified. Further studies are in progress to clarify the role of some of the identified NRPS genes in plant pathogenesis.

Amino Acid Sequence↗

Hidden Markov models for detecting remote protein homologies.

MOTIVATION: A new hidden Markov model method (SAM-T98) for finding remote homologs of protein sequences is described and evaluated. The method begins with a single target sequence and iteratively builds a hidden Markov model (HMM) from the sequence and homologs found using the HMM for database search. SAM-T98 is also used to construct model libraries automatically from sequences in structural databases. METHODS: We evaluate the SAM-T98 method with four datasets. Three of the test sets are fold-recognition tests, where the correct answers are determined by structural similarity. The fourth uses a curated database. The method is compared against WU-BLASTP and against DOUBLE-BLAST, a two-step method similar to ISS, but using BLAST instead of FASTA. RESULTS: SAM-T98 had the fewest errors in all tests-dramatically so for the fold-recognition tests. At the minimum-error point on the SCOP (Structural Classification of Proteins)-domains test, SAM-T98 got 880 true positives and 68 false positives, DOUBLE-BLAST got 533 true positives with 71 false positives, and WU-BLASTP got 353 true positives with 24 false positives. The method is optimized to recognize superfamilies, and would require parameter adjustment to be used to find family or fold relationships. One key to the performance of the HMM method is a new score-normalization technique that compares the score to the score with a reversed model rather than to a uniform null model. AVAILABILITY: A World Wide Web server, as well as information on obtaining the Sequence Alignment and Modeling (SAM) software suite, can be found at http://www.cse.ucsc.edu/research/compbi o/ CONTACT: karplus@cse.ucsc.edu; http://www.cse.ucsc.edu/karplus

Algorithms↗

Genome-wide assembly and analysis of alternative transcripts in mouse.

To build a mouse gene index with the most comprehensive coverage of alternative transcription/splicing (ATS), we developed an algorithm and a fully automated computational pipeline for transcript assembly from expressed sequences aligned to the genome. We identified 191,946 genomic loci, which included 27,497 protein-coding genes and 11,906 additional gene candidates (e.g., nonprotein-coding, but multiexon). Comparison of the resulting gene index with TIGR, UniGene, DoTS, and ESTGenes databases revealed that it had a greater number of transcripts, a greater average number of exons and introns with proper splicing sites per gene, and longer ORFs. The 27,497 protein-coding genes had 77,138 transcripts, i.e., 2.8 transcripts per gene on average. Close examination of transcripts led to a combinatorial table of 23 types of ATS units, only nine of which were previously described, i.e., 14 types of alternative splicing, seven types of alternative starts, and two types of alternative termination. The 47%, 18%, and 14% of 20,323 multiexon protein-coding genes with proper splice sites had alternative splicings, alternative starts, and alternative terminations, respectively. The gene index with the comprehensive ATS will provide a useful platform for analyzing the nature and mechanism of ATS, as well as for designing the accurate exon-based DNA microarrays. The sequence data from this study have been submitted to GenBank under accession numbers: CK329321-CK334090; CF891695-CF906652; CF906741-CF916750; CK334091-CK347104; CK387035-CK393993; CN660032-CN690720; CN690721-CN725493.

Algorithms↗

A bioinformatic approach to the identification of candidate genes for the development of new cancer diagnostics.

A multivariate analysis of the National Cancer Institute gene expression database is reported here. The soft independent modelling of a class analogy approach achieved cell line classification according to histological origin. With the PCA method, based on the expression of 9605 genes and ESTs, classification of colon, leukaemia, renal, melanoma and CNS cells could be performed, but not of lung, breast and ovarian cells. Another multivariate procedure, called partial least squares discriminant analysis (PLS-DA), provides bioinformatic clues for the selection of a limited number of gene transcripts most effective in discriminating different tumoral histotypes. Among them it is possible to identify candidates in the development of new diagnostic tests for cancer detection and unknown genes deserving high priority in further studies. In particular, melan-A, acid phosphatase 5, dopachrome tautomerase, S100-beta and acid ceramidase were found to be among the most important genes for melanoma. The potential of the present bioinformatic approach is exemplified by its ability to identify differentiation and diagnostic markers already in use in clinical settings, such as protein S-100, a prognostic parameter in patients with metastatic melanoma and a screening marker for melanoma metastasis.

Biomarkers, Tumor↗

YPD, PombePD and WormPD: model organism volumes of the BioKnowledge library, an integrated resource for protein information.

The BioKnowledge Library is a relational database and web site (http://www.proteome.com) composed of protein-specific information collected from the scientific literature. Each Protein Report on the web site summarizes and displays published information about a single protein, including its biochemical function, role in the cell and in the whole organism, localization, mutant phenotype and genetic interactions, regulation, domains and motifs, interactions with other proteins and other relevant data. This report describes four species-specific volumes of the BioKnowledge Library, concerned with the model organisms Saccharomyces cerevisiae (YPD), Schizosaccharomyces pombe (PombePD) and Caenorhabditis elegans (WormPD), and with the fungal pathogen Candida albicans (CalPD). Protein Reports of each species are unified in format, easily searchable and extensively cross-referenced between species. The relevance of these comprehensively curated resources to analysis of proteins in other species is discussed, and is illustrated by a survey of model organism proteins that have similarity to human proteins involved in disease.

Animals↗

Predicting gene ontology functions from ProDom and CDD protein domains.

A heuristic algorithm for associating Gene Ontology (GO) defined molecular functions to protein domains as listed in the ProDom and CDD databases is described. The algorithm generates rules for function-domain associations based on the intersection of functions assigned to gene products by the GO consortium that contain ProDom and/or CDD domains at varying levels of sequence similarity. The hierarchical nature of GO molecular functions is incorporated into rule generation. Manual review of a subset of the rules generated indicates an accuracy rate of 87% for ProDom rules and 84% for CDD rules. The utility of these associations is that novel sequences can be assigned a putative function if sufficient similarity exists to a ProDom or CDD domain for which one or more GO functions has been associated. Although functional assignments are increasingly being made for gene products from model organisms, it is likely that the needs of investigators will continue to outpace the efforts of curators, particularly for nonmodel organisms. A comparison with other methods in terms of coverage and agreement was performed, indicating the utility of the approach. The domain-function associations and function assignments are available from our website http://www.cbil.upenn.edu/GO.

Algorithms↗

The PEPR GeneChip data warehouse, and implementation of a dynamic time series query tool (SGQT) with graphical interface.

Publicly accessible DNA databases (genome browsers) are rapidly accelerating post-genomic research (see http://www.genome.ucsc.edu/), with integrated genomic DNA, gene structure, EST/ splicing and cross-species ortholog data. DNA databases have relatively low dimensionality; the genome is a linear code that anchors all associated data. In contrast, RNA expression and protein databases need to be able to handle very high dimensional data, with time, tissue, cell type and genes, as interrelated variables. The high dimensionality of microarray expression profile data, and the lack of a standard experimental platform have complicated the development of web-accessible databases and analytical tools. We have designed and implemented a public resource of expression profile data containing 1024 human, mouse and rat Affymetrix GeneChip expression profiles, generated in the same laboratory, and subject to the same quality and procedural controls (Public Expression Profiling Resource; PEPR). Our Oracle-based PEPR data warehouse includes a novel time series query analysis tool (SGQT), enabling dynamic generation of graphs and spreadsheets showing the action of any transcript of interest over time. In this report, we demonstrate the utility of this tool using a 27 time point, in vivo muscle regeneration series. This data warehouse and associated analysis tools provides access to multidimensional microarray data through web-based interfaces, both for download of all types of raw data for independent analysis, and also for straightforward gene-based queries. Planned implementations of PEPR will include web-based remote entry of projects adhering to quality control and standard operating procedure (QC/SOP) criteria, and automated output of alternative probe set algorithms for each project (see http://microarray.cnmcresearch.org/pgadatatable.asp).

Algorithms↗

Detection and characterization of fungal-specific proteins in Saccharomyces cerevisiae.

In this study, I searched for fungal-specific proteins in the genome of the budding yeast Saccharomyces cerevisiae, inferred from a comparison of amino acid sequences. I used the GTOP (Genomes to Protein structures and functions) database of the DDBJ (DNA Data Bank of Japan), which consists of 21 genomes from Archaea, 203 genomes from Bacteria, and 50 genomes from Eucarya (including 18 fungal genomes). Among 5,874 proteins of S. cerevisiae, 1,551 have homologs only in Eucarya, and 504 of the 1,551 have homologs only in fungi. To find fungal-specific proteins, homologs of the homologs have been searched repeatedly. As a result, 132 of the 504 are characterized as fungal-specific proteins. The genes encoding the 132 fungal-specific proteins are not included in the list of essential genes for viability in the S. cerevisiae genome deletion project. Among the 132 proteins, 99 are S. cerevisiae-specific, and no protein that is distributed among 10 or more of the 18 fungal species exists. In addition, most of the fungal-specific proteins are very small and functionally unknown. My results show that the fungal-specific proteins have short evolutionary histories, suggesting that S. cerevisiae produces novel proteins and that ancestral fungi also produced small proteins most of which have disappeared or have been combined with other proteins during fungal evolution.

Amino Acids↗

More than 1,000 putative new human signalling proteins revealed by EST data mining.

Cloning procedures aided by homology searches of EST databases have accelerated the pace of discovery of new genes, but EST database searching remains an involved and onerous task. More than 1.6 million human EST sequences have been deposited in public databases, making it difficult to identify ESTs that represent new genes. Compounding the problems of scale are difficulties in detection associated with a high sequencing error rate and low sequence similarity between distant homologues. We have developed a new method, coupling BLAST-based searches with a domain identification protocol, that filters candidate homologues. Application of this method in a large-scale analysis of 100 signalling domain families has led to the identification of ESTs representing more than 1,000 novel human signalling genes. The 4,206 publicly available ESTs representing these genes are a valuable resource for rapid cloning of novel human signalling proteins. For example, we were able to identify ESTs of at least 106 new small GTPases, of which 6 are likely to belong to new subfamilies. In some cases, further analyses of genomic DNA led to the discovery of previously unidentified full-length protein sequences. This is exemplified by the in silico cloning (prediction of a gene product sequence using only genomic and EST sequence data) of a new type of GTPase with two catalytic domains.

Amino Acid Sequence↗

A new criterion to classify globular proteins based on their secondary structure contents.

MOTIVATION: With the enlargement of protein structure databases, it is hoped that a method to classify proteins automatically will be developed. Although the classification criterion proposed by Nakashima et al. ( J. Biochem., 1986, 99, 153-162) was widely used in the literature, it leads to some inconsistencies with the classification databases currently available in the class assignment of protein structures. To improve their work, a new classification criterion is proposed relying on statistical analysis of the secondary structure contents of more than 200 proteins with well-known structural classes. The Fisher linear discriminant algorithm is used to derive the new classification criterion. RESULTS: Three cross-validation tests are performed to evaluate the new criterion. In the jackknife test, of the 210 proteins used to derive the criterion, 206 are correctly classified with an accuracy of 98.10%. Of the 16 proteins of purely intermediate structure (i.e. structures lying near borderlines between two classes) in the first test set, 15 are correctly classified with an accuracy of 93.75%. For the second test set which consists of 200 proteins selected randomly from SCOP, a testing accuracy of 94.00% is obtained. For comparison, the criterion of Nakashima et al. is also used to classify the 210, 16 and 200 proteins, respectively. Consequently, accuracies of 94.76%, 62.50% and 91.50% are obtained, respectively. On average, the accuracy of the new classification criterion is 4% higher than that of Nakashima et al. AVAILABILITY: The program is available on request from the first author. CONTACT: ctzhang@tju.edu.cn

Algorithms↗

A procedure for assessing GO annotation consistency.

MOTIVATION: The Gene Ontology (GO) is widely used to annotate molecular attributes of genes and gene products. Multiple groups undertaking functional annotations of genomes contribute their annotation sets to the GO database resource and these data are subsequently used in comparative functional analysis research. Although GO curators adhere to the same protocols and standards while assigning GO annotations, the specific procedure followed by each annotation group can vary. Since differences in application of annotation standards would dilute the effectiveness of comparative analysis, methods for assessing annotation consistency are essential. The development of methodologies that are broadly applicable for the assessment of GO annotation consistency is an important issue for the comparative genomics community. RESULTS: We have developed a methodology for assessing the consistency of GO annotations provided by different annotation groups. The method is completely general and can be applied to compare any two sets of GO annotations. This is the first attempt to assess cross-species GO annotation consistency. Our method compares annotation sets utilizing the hierarchical structure of the GO to compare GO annotations between orthologous gene pairs. The method produces a report on the annotation consistency and inconsistency for each orthologous pair. We present results obtained by comparing GO annotations for mouse and human gene sets. AVAILABILITY: The complete current MGI_GOA GO annotation consistency report is available online at http://www.spatial.maine.edu/~mdolan/

Animals↗

Efficient methods for filtering and ranking fragments for the prediction of structurally variable regions in proteins.

The prediction of protein 3D structures close to insertions and deletions or, more generally, loop prediction, is still one of the major challenges in homology modeling projects. In this article, we developed ranking criteria and selection filters to improve knowledge-based loop predictions. These criteria were developed and optimized for a test data set containing 678 insertions and deletions. The examples are, in principle, predictable from the used loop database with an RMSD < 1 A and represent realistic modeling situations. Four noncorrelated criteria for the selection of fragments are evaluated. A fast prefilter compares the distance between the anchor groups in the template protein with the stems of the fragments. The RMSD of the anchor groups is used for fitting and ranking of the selected loop candidates. After fitting, repulsive close contacts of loop candidates with the template protein are used for filtering, and fragments with backbone torsion angles, which are unfavorable according to a knowledge-based potential, are eliminated. By the combined application of these filter criteria to the test set, it was possible to increase the percentage of predictions with a global RMSD < 1 A to over 50% among the first five ranks, with average global RMSD values for the first rank candidate that are between 1.3 and 2.2 A for different loop lengths. Compared to other examples described in the literature, our large numbers of test cases are not self-predictions, where loops are placed in a protein after a peptide loop has been cut out, but are attempts to predict structural changes that occur in evolution when a protein is affected by insertions and deletions.

Algorithms↗