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PubMed2004

KARMA: a web server application for comparing and annotating heterogeneous microarray platforms.

We have developed a universal web server application (KARMA) that allows comparison and annotation of user-defined pairs of microarray platforms based on diverse types of genome annotation data (across different species) collected from multiple sources. The application is an effective tool for diverse microarray platforms, including arrays that are provided by (i) the Keck Microarray Resource at Yale, (ii) commercially available Affymetrix GeneChips and spotted arrays and (iii) custom arrays made by individual academics. The tool provides a web interface that allows users to input pairs of test files that represent diverse array platforms for either single or multiple species. The program dynamically identifies analogous DNA fragments spotted or synthesized on multiple microarray platforms based on the following types of information: (i) NCBI-Unigene identifiers, if the platforms being compared are within the same species or (ii) NCBI-Homologene data, if they are cross-species. The single-species comparison is implemented based on set operations: intersection, union and difference. Other forms of retrievable annotation data, including LocusLink, SwissProt and Gene Ontology (GO), are collected from multiple remote sites and stored in an integrated fashion using an Oracle database. The KARMA database, which is updated periodically, is available on line at the following URL: http://ymd.med.yale.edu/karma/cgi-bin/karma.pl.

Gene Expression Profiling↗
PubMed2004

Graph-based iterative Group Analysis enhances microarray interpretation.

BACKGROUND: One of the most time-consuming tasks after performing a gene expression experiment is the biological interpretation of the results by identifying physiologically important associations between the differentially expressed genes. A large part of the relevant functional evidence can be represented in the form of graphs, e.g. metabolic and signaling pathways, protein interaction maps, shared GeneOntology annotations, or literature co-citation relations. Such graphs are easily constructed from available genome annotation data. The problem of biological interpretation can then be described as identifying the subgraphs showing the most significant patterns of gene expression. We applied a graph-based extension of our iterative Group Analysis (iGA) approach to obtain a statistically rigorous identification of the subgraphs of interest in any evidence graph. RESULTS: We validated the Graph-based iterative Group Analysis (GiGA) by applying it to the classic yeast diauxic shift experiment of DeRisi et al., using GeneOntology and metabolic network information. GiGA reliably identified and summarized all the biological processes discussed in the original publication. Visualization of the detected subgraphs allowed the convenient exploration of the results. The method also identified several processes that were not presented in the original paper but are of obvious relevance to the yeast starvation response. CONCLUSIONS: GiGA provides a fast and flexible delimitation of the most interesting areas in a microarray experiment, and leads to a considerable speed-up and improvement of the interpretation process.

Algorithms↗
PubMed2000

Genome sequences and great expectations.

To assess how automatic function assignment will contribute to genome annotation in the next five years, we have performed an analysis of 31 available genome sequences. An emerging pattern is that function can be predicted for almost two-thirds of the 73,500 genes that were analyzed. Despite progress in computational biology, there will always be a great need for large-scale experimental determination of protein function.

Animals↗
PubMed2005

The latest buzz in comparative genomics.

A second species of fruit fly has just been added to the growing list of organisms with complete and annotated genome sequences. The publication of the Drosophila pseudoobscura sequence provides a snapshot of how genomes have changed over tens of millions of years and sets the stage for the analysis of more fly genomes.

Animals↗
PubMed2005

The connexin gene family in mammals.

Unannotated mammalian genome databases (dog, cow, opossum) were searched for candidate connexin genes, using sequences from annotated genomes (man, mouse). All 18 'multi-species' connexin genes, i.e., orthologs of connexin26 , 29/31.3 (duplicated in opossum), 30, 30.2/31.9, 30.3, 31, 31.1, 32, 36, 37, 39/40.1, 40, 43, 45, 44/46, 47, 50, and 57/62 , were found in dog, cow and opossum. Connexin25 and 58 have been considered specific for man, but evident orthologs of connexin25 were found in dog, cow and opossum, and orthologs of connexin58 were found in dog and cow. Moreover, a connexin43 -like sequence (approx. 80% identical to connexin43 ) was found in man, chimpanzee, dog and cow. In the three former species, the sequences were located on the X chromosome. In man, chimpanzee and cow, there were stop codons in all reading frames; these sequences are therefore judged as pseudogenes, called here Cx43pX . In the dog, the sequence contained an open reading frame for a protein of 35.7 kDa (connexin35.7). We suggest that these sequences are orthologs of connexin33 , previously considered as a rodent-specific connexin gene. Thus, connexin25 , 33 and 58 are not species-specific genes. However, the opossum may possess a candidate, connexin39.2 , without obvious orthologs in other mammals. Furthermore, pseudogenes of primate connexin31.3 and opossum connexin35 (one of the two orthologs of primate connexin31.3) were detected. These results suggest that the structure of the mammalian connexin gene family should be revised, especially with regard to the so-called 'species-specific' connexins .

Animals↗
PubMed2006

Pairagon+N-SCAN_EST: a model-based gene annotation pipeline.

BACKGROUND: This paper describes Pairagon+N-SCAN_EST, a gene annotation pipeline that uses only native alignments. For each expressed sequence it chooses the best genomic alignment. Systems like ENSEMBL and ExoGean rely on trans alignments, in which expressed sequences are aligned to the genomic loci of putative homologs. Trans alignments contain a high proportion of mismatches, gaps, and/or apparently unspliceable introns, compared to alignments of cDNA sequences to their native loci. The Pairagon+N-SCAN_EST pipeline's first stage is Pairagon, a cDNA-to-genome alignment program based on a PairHMM probability model. This model relies on prior knowledge, such as the fact that introns must begin with GT, GC, or AT and end with AG or AC. It produces very precise alignments of high quality cDNA sequences. In the genomic regions between Pairagon's cDNA alignments, the pipeline combines EST alignments with de novo gene prediction by using N-SCAN_EST. N-SCAN_EST is based on a generalized HMM probability model augmented with a phylogenetic conservation model and EST alignments. It can predict complete transcripts by extending or merging EST alignments, but it can also predict genes in regions without EST alignments. Because they are based on probability models, both Pairagon and N-SCAN_EST can be trained automatically for new genomes and data sets. RESULTS: On the ENCODE regions of the human genome, Pairagon+N-SCAN_EST was as accurate as any other system tested in the EGASP assessment, including ENSEMBL and ExoGean. CONCLUSION: With sufficient mRNA/EST evidence, genome annotation without trans alignments can compete successfully with systems like ENSEMBL and ExoGean, which use trans alignments.

Base Sequence↗
PubMed2003

SLAM web server for comparative gene finding and alignment.

SLAM is a program that simultaneously aligns and annotates pairs of homologous sequences. The SLAM web server integrates SLAM with repeat masking tools and the AVID alignment program to allow for rapid alignment and gene prediction in user submitted sequences. Along with annotations and alignments for the submitted sequences, users obtain a list of predicted conserved non-coding sequences (and their associated alignments). The web site also links to whole genome annotations of the human, mouse and rat genomes produced with the SLAM program. The server can be accessed at http://bio.math.berkeley.edu/slam.

Algorithms↗
PubMed2003

Gene discovery in the hamster: a comparative genomics approach for gene annotation by sequencing of hamster testis cDNAs.

BACKGROUND: Complete genome annotation will likely be achieved through a combination of computer-based analysis of available genome sequences combined with direct experimental characterization of expressed regions of individual genomes. We have utilized a comparative genomics approach involving the sequencing of randomly selected hamster testis cDNAs to begin to identify genes not previously annotated on the human, mouse, rat and Fugu (pufferfish) genomes. RESULTS: 735 distinct sequences were analyzed for their relatedness to known sequences in public databases. Eight of these sequences were derived from previously unidentified genes and expression of these genes in testis was confirmed by Northern blotting. The genomic locations of each sequence were mapped in human, mouse, rat and pufferfish, where applicable, and the structure of their cognate genes was derived using computer-based predictions, genomic comparisons and analysis of uncharacterized cDNA sequences from human and macaque. CONCLUSION: The use of a comparative genomics approach resulted in the identification of eight cDNAs that correspond to previously uncharacterized genes in the human genome. The proteins encoded by these genes included a new member of the kinesin superfamily, a SET/MYND-domain protein, and six proteins for which no specific function could be predicted. Each gene was expressed primarily in testis, suggesting that they may play roles in the development and/or function of testicular cells.

Amino Acid Sequence↗
PubMed2005

Validation of mRNA/EST-based gene predictions in human Xp11.4 revealed differences to the organization of the orthologous mouse locus.

Careful manual annotation of the human reference sequence provides a solid basis for the identification of disease-associated genes. Toward this end, we focused on a medically relevant 2.6-Mb region of the human chromosome Xp11.4 between markers DXS9851 and DXS9751 and identified 16 transcription units according to the Vertebrate Genome Annotation (Vega) rules. In order to validate these annotations, we performed a comprehensive RT-PCR expression analysis and a human-mouse comparison. This revealed, despite the high overall genomic conservation of the region, remarkable differences of the gene content between human and mouse. Whereas 12 of 16 annotations were confirmed by RT-PCR in human tissues, for only seven genes mouse orthologs could be identified and found to be expressed. This indicates that a comprehensive and experimentally supported annotation effort of the human genome simultaneously highlights regions with striking differences in gene organization to other species and may indicate evolutionary events specific to the human lineage demanding further functional analyses.

Animals↗
PubMed1998

How representative are the known structures of the proteins in a complete genome? A comprehensive structural census.

BACKGROUND: Determining how representative the known structures are of the proteins encoded by a complete genome is important for assessing to what extent our current picture of protein stability and folding is overly influenced by biases in the structure databank (PDB). It is also important for improving database-based methods of structure prediction and genome annotation. RESULTS: The known structures are compared to the proteins encoded by eight complete microbial genomes in terms of simple statistics such as sequence length, composition and secondary structure. The known structures are represented by a collection of nonhomologous domains from the PDB and a smaller list of 'biophysical proteins' on which folding experiments have concentrated. The proteins encoded by the genomes are considered as a whole and divided into various regions, such as known-structure homologue, low complexity (nonglobular), transmembrane or linker. Various tests are performed to assess the significance of the reported differences, in both a practical and a statistical sense. CONCLUSIONS: The proteins encoded by the genomes are significantly different from those in the PDB. Their sequence lengths, which follow an extreme value distribution, are longer than the PDB proteins and much longer than the biophysical proteins. Their composition differs from the PDB proteins in having more Lys, Ile, Asn and Gln and less Cys and Trp. This is true overall and especially for the regions corresponding to soluble proteins of as yet unknown fold. Secondary-structure prediction on these uncharacterized regions indicates that they contain on average more helical structure than the PDB; differences about this mean are small, with yeast having slightly more sheet structure and Haemophilus influenzae and Helicobacter pylori more helical structure. Further information is available through the GeneCensus system at http://bioinfo.mbb.yale.edu/genome.

Animals↗
PubMed2004

CADRE: the Central Aspergillus Data REpository.

CADRE is a public resource for housing and analysing genomic data extracted from species of Aspergillus. It arose to enable maintenance of the complete annotated genomic sequence of Aspergillus fumigatus and to provide tools for searching, analysing and visualizing features of fungal genomes. By implementing CADRE using Ensembl, a framework is in place for storing and comparing several genomes: the resource will thus expand by including other Aspergillus genomes (such as Aspergillus nidulans) as they become available. CADRE is accessible at http://www.cadre. man.ac.uk.

Aspergillus↗
PubMed2004

Insights into the genomic basis of niche specificity of Pseudomonas putida KT2440.

A major challenge in microbiology is the elucidation of the genetic and ecophysiological basis of habitat specificity of microbes. Pseudomonas putida is a paradigm of a ubiquitous metabolically versatile soil bacterium. Strain KT2440, a safety strain that has become a laboratory workhorse worldwide, has been recently sequenced and its genome annotated. By drawing on both published information and on original in silico analysis of its genome, we address here the question of what genomic features of KT2440 could explain or are consistent with its ubiquity, metabolic versatility and adaptability. The genome of KT2440 exhibits combinations of features characteristic of terrestrial, rhizosphere and aquatic bacteria, which thrive in either copiotrophic or oligotrophic habitats, and suggests that P. putida has evolved and acquired functions that equip it to thrive in diverse, often inhospitable environments, either free-living, or in close association with plants. The high diversity of protein families encoded by its genome, the large number and variety of small aralogous families, insertion elements, repetitive extragenic palindromic sequences, as well as the mosaic structure of the genome (with many regions of 'atypical' composition) and the multiplicity of mobile elements, reflect a high functional diversity in P. putida and are indicative of its evolutionary trajectory and adaptation to the diverse habitats in which it thrives. The unusual wealth of determinants for high affinity nutrient acquisition systems, mono- and di-oxygenases, oxido-reductases, ferredoxins and cytochromes, dehydrogenases, sulfur metabolism proteins, for efflux pumps and glutathione-S-transfereases, and for the extensive array of extracytoplasmatic function sigma factors, regulators, and stress response systems, constitute the genomic basis for the exceptional nutritional versatility and opportunism of P. putida , its ubiquity in diverse soil, rhizosphere and aquatic systems, and its renowned tolerance of natural and anthropogenic stresses. This metabolic diversity is also the basis of the impressive evolutionary potential of KT2440, and its utility for the experimental design of novel pathways for the catabolism of organic, particularly aromatic, pollutants, and its potential for bioremediation of soils contaminated with such compounds as well as for its application in the production of high-added value compounds.

Adaptation, Physiological↗
PubMed2000

Using GeneWise in the Drosophila annotation experiment.

The GeneWise method for combining gene prediction and homology searches was applied to the 2.9-Mb region from Drosophila melanogaster. The results from the Genome Annotation Assessment Project (GASP) showed that GeneWise provided reasonably accurate gene predictions. Further investigation indicates that many of the incorrect gene predictions from GeneWise were due to transposons with valid protein-coding genes and the remaining cases are pseudogenes or possible annotation oversights.

Animals↗
PubMed2005

Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria.

BACKGROUND: Identification of a bacterial protein's subcellular localization (SCL) is important for genome annotation, function prediction and drug or vaccine target identification. Subcellular fractionation techniques combined with recent proteomics technology permits the identification of large numbers of proteins from distinct bacterial compartments. However, the fractionation of a complex structure like the cell into several subcellular compartments is not a trivial task. Contamination from other compartments may occur, and some proteins may reside in multiple localizations. New computational methods have been reported over the past few years that now permit much more accurate, genome-wide analysis of the SCL of protein sequences deduced from genomes. There is a need to compare such computational methods with laboratory proteomics approaches to identify the most effective current approach for genome-wide localization characterization and annotation. RESULTS: In this study, ten subcellular proteome analyses of bacterial compartments were reviewed. PSORTb version 2.0 was used to computationally predict the localization of proteins reported in these publications, and these computational predictions were then compared to the localizations determined by the proteomics study. By using a combined approach, we were able to identify a number of contaminants and proteins with dual localizations, and were able to more accurately identify membrane subproteomes. Our results allowed us to estimate the precision level of laboratory subproteome studies and we show here that, on average, recent high-precision computational methods such as PSORTb now have a lower error rate than laboratory methods. CONCLUSION: We have performed the first focused comparison of genome-wide proteomic and computational methods for subcellular localization identification, and show that computational methods have now attained a level of precision that is exceeding that of high-throughput laboratory approaches. We note that analysis of all cellular fractions collectively is required to effectively provide localization information from laboratory studies, and we propose an overall approach to genome-wide subcellular localization characterization that capitalizes on the complementary nature of current laboratory and computational methods.

Bacterial Proteins↗
PubMed2004

The use of Open Reading frame ESTs (ORESTES) for analysis of the honey bee transcriptome.

BACKGROUND: The ongoing efforts to sequence the honey bee genome require additional initiatives to define its transcriptome. Towards this end, we employed the Open Reading frame ESTs (ORESTES) strategy to generate profiles for the life cycle of Apis mellifera workers. RESULTS: Of the 5,021 ORESTES, 35.2% matched with previously deposited Apis ESTs. The analysis of the remaining sequences defined a set of putative orthologs whose majority had their best-match hits with Anopheles and Drosophila genes. CAP3 assembly of the Apis ORESTES with the already existing 15,500 Apis ESTs generated 3,408 contigs. BLASTX comparison of these contigs with protein sets of organisms representing distinct phylogenetic clades revealed a total of 1,629 contigs that Apis mellifera shares with different taxa. Most (41%) represent genes that are in common to all taxa, another 21% are shared between metazoans (Bilateria), and 16% are shared only within the Insecta clade. A set of 23 putative genes presented a best match with human genes, many of which encode factors related to cell signaling/signal transduction. 1,779 contigs (52%) did not match any known sequence. Applying a correction factor deduced from a parallel analysis performed with Drosophila melanogaster ORESTES, we estimate that approximately half of these no-match ESTs contigs (22%) should represent Apis-specific genes. CONCLUSIONS: The versatile and cost-efficient ORESTES approach produced minilibraries for honey bee life cycle stages. Such information on central gene regions contributes to genome annotation and also lends itself to cross-transcriptome comparisons to reveal evolutionary trends in insect genomes.

Animals↗
PubMed2002

[A evolutionary approach to identification of orthologous relationship across proteomes].

How to identify the true orthologous and paralogous relationships among protein families is still a key problem in genome annotation and comparative protemics. Here, a evolutionary approach to ascertainment of the orthologous relationships across the genomes is developed. Forty-four cases of protein families are used in the test for the evolutionary approach. Compared with the method of COG (cluster of orthologous groups of proteins), this approach can generally identify the orthologous relationships and accurately predict the genome function.

Animals↗
PubMed2006

Using several pair-wise informant sequences for de novo prediction of alternatively spliced transcripts.

BACKGROUND: As part of the ENCODE Genome Annotation Assessment Project (EGASP), we developed the MARS extension to the Twinscan algorithm. MARS is designed to find human alternatively spliced transcripts that are conserved in only one or a limited number of extant species. MARS is able to use an arbitrary number of informant sequences and predicts a number of alternative transcripts at each gene locus. RESULTS: MARS uses the mouse, rat, dog, opossum, chicken, and frog genome sequences as pairwise informant sources for Twinscan and combines the resulting transcript predictions into genes based on coding (CDS) region overlap. Based on the EGASP assessment, MARS is one of the more accurate dual-genome prediction programs. Compared to the GENCODE annotation, we find that predictive sensitivity increases, while specificity decreases, as more informant species are used. MARS correctly predicts alternatively spliced transcripts for 11 of the 236 multi-exon GENCODE genes that are alternatively spliced in the coding region of their transcripts. For these genes a total of 24 correct transcripts are predicted. CONCLUSION: The MARS algorithm is able to predict alternatively spliced transcripts without the use of expressed sequence information, although the number of loci in which multiple predicted transcripts match multiple alternatively spliced transcripts in the GENCODE annotation is relatively small.

Algorithms↗
PubMed2026

Identification and characterization of G protein-coupled receptors in the nocturnal halictid bee Megalopta genalis.

G protein-coupled receptors (GPCRs) are one of the largest families of membrane proteins in insects, regulating vision, neural signal transduction, and various physiological behaviors. Megalopta genalis exhibits a unique facultatively eusocial lifestyle and possesses adaptations for nocturnal activity; however, its GPCR family has not yet been systematically characterized. In this study, we performed genome-wide identification, phylogenetic analysis, and expression profiling of GPCRs in M. genalis by integrating genomic annotation and transcriptomic analysis. The results showed that a total of 99 GPCRs were identified in the genome of M. genalis, which were classified into four major families. Here, we show that M. genalis has undergone lineage-specific GPCR repertoire remodeling, marked by the expansion of novel orphan receptors and the systematic loss of multiple receptor subtypes, such as the neuropeptide receptors MIP-R and NPFR. Moreover, opsins have formed a diverse array of combinations and non-GPCR odorant receptors have undergone significant expansion via tandem duplication. Together, these features may represent part of the molecular repertoire associated with the adaptation of M. genalis to a nocturnal lifestyle. Furthermore, transcriptomic analysis revealed distinct spatiotemporal expression divergence within each of the Mth/Mthl and Fz GPCR families, suggesting functional specialization across development and adult tissues. This study provides the first systematic identification and initial functional characterization of GPCRs in M. genalis, revealing an evolutionary pattern characterized by the coexistence of contraction and expansion within the GPCR family. These findings lay a foundation for further studies aimed at elucidating the roles of these GPCRs in regulating M. genalis physiology and behavior.

Animals↗