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At least 37 records · Page 2Linked to original sources
PubMed2005

Pseudomonas aeruginosa Genome Database and PseudoCAP: facilitating community-based, continually updated, genome annotation.

Using the Pseudomonas aeruginosa Genome Project as a test case, we have developed a database and submission system to facilitate a community-based approach to continually updated genome annotation (http://www.pseudomonas.com). Researchers submit proposed annotation updates through one of three web-based form options which are then subjected to review, and if accepted, entered into both the database and log file of updates with author acknowledgement. In addition, a coordinator continually reviews literature for suitable updates, as we have found such reviews to be the most efficient. Both the annotations database and updates-log database have Boolean search capability with the ability to sort results and download all data or search results as tab-delimited files. To complement this peer-reviewed genome annotation, we also provide a linked GBrowse view which displays alternate annotations. Additional tools and analyses are also integrated, including PseudoCyc, and knockout mutant information. We propose that this database system, with its focus on facilitating flexible queries of the data and providing access to both peer-reviewed annotations as well as alternate annotation information, may be a suitable model for other genome projects wishing to use a continually updated, community-based annotation approach. The source code is freely available under GNU General Public Licence.

DNA, Bacterial↗
PubMed2004

Proteogenomic mapping as a complementary method to perform genome annotation.

The accelerated rate of genomic sequencing has led to an abundance of completely sequenced genomes. Annotation of the open reading frames (ORFs) (i.e., gene prediction) in these genomes is an important task and is most often performed computationally based on features in the nucleic acid sequence. Using recent advances in proteomics, we set out to predict the set of ORFs for an organism based principally on expressed protein-based evidence. Using a novel search strategy, we mapped peptides detected in a whole-cell lysate of Mycoplasma pneumoniae onto a genomic scaffold and extended these "hits" into ORFs bound by traditional genetic signals to generate a "proteogenomic map". We were able to generate an ORF model for M. pneumoniae strain FH using proteomic data with a high correlation to models based on sequence features. Ultimately, we detected over 81% of the genomically predicted ORFs in M. pneumoniae strain M129 (the originally sequenced strain). We were also able to detect several new ORFs not originally predicted by genomic methods, various N-terminal extensions, and some evidence that would suggest that certain predicted ORFs are bogus. Some of these differences may be a result of the strain analyzed but demonstrate the robustness of protein analysis across closely related genomes. This technique is a cost-effective means to add value to genome annotation, and a prerequisite for proteome quantitation and in vivo interaction measures.

Chromosome Mapping↗
PubMed2000

Genome annotation assessment in Drosophila melanogaster.

Computational methods for automated genome annotation are critical to our community's ability to make full use of the large volume of genomic sequence being generated and released. To explore the accuracy of these automated feature prediction tools in the genomes of higher organisms, we evaluated their performance on a large, well-characterized sequence contig from the Adh region of Drosophila melanogaster. This experiment, known as the Genome Annotation Assessment Project (GASP), was launched in May 1999. Twelve groups, applying state-of-the-art tools, contributed predictions for features including gene structure, protein homologies, promoter sites, and repeat elements. We evaluated these predictions using two standards, one based on previously unreleased high-quality full-length cDNA sequences and a second based on the set of annotations generated as part of an in-depth study of the region by a group of Drosophila experts. Although these standard sets only approximate the unknown distribution of features in this region, we believe that when taken in context the results of an evaluation based on them are meaningful. The results were presented as a tutorial at the conference on Intelligent Systems in Molecular Biology (ISMB-99) in August 1999. Over 95% of the coding nucleotides in the region were correctly identified by the majority of the gene finders, and the correct intron/exon structures were predicted for >40% of the genes. Homology-based annotation techniques recognized and associated functions with almost half of the genes in the region; the remainder were only identified by the ab initio techniques. This experiment also presents the first assessment of promoter prediction techniques for a significant number of genes in a large contiguous region. We discovered that the promoter predictors' high false-positive rates make their predictions difficult to use. Integrating gene finding and cDNA/EST alignments with promoter predictions decreases the number of false-positive classifications but discovers less than one-third of the promoters in the region. We believe that by establishing standards for evaluating genomic annotations and by assessing the performance of existing automated genome annotation tools, this experiment establishes a baseline that contributes to the value of ongoing large-scale annotation projects and should guide further research in genome informatics.

Alcohol Dehydrogenase↗
PubMed2005

Large-scale prokaryotic gene prediction and comparison to genome annotation.

MOTIVATION: Prokaryotic genomes are sequenced and annotated at an increasing rate. The methods of annotation vary between sequencing groups. It makes genome comparison difficult and may lead to propagation of errors when questionable assignments are adapted from one genome to another. Genome comparison either on a large or small scale would be facilitated by using a single standard for annotation, which incorporates a transparency of why an open reading frame (ORF) is considered to be a gene. RESULTS: A total of 143 prokaryotic genomes were scored with an updated version of the prokaryotic genefinder EasyGene. Comparison of the GenBank and RefSeq annotations with the EasyGene predictions reveals that in some genomes up to approximately 60% of the genes may have been annotated with a wrong start codon, especially in the GC-rich genomes. The fractional difference between annotated and predicted confirms that too many short genes are annotated in numerous organisms. Furthermore, genes might be missing in the annotation of some of the genomes. We predict 41 of 143 genomes to be over-annotated by >5%, meaning that too many ORFs are annotated as genes. We also predict that 12 of 143 genomes are under-annotated. These results are based on the difference between the number of annotated genes not found by EasyGene and the number of predicted genes that are not annotated in GenBank. We argue that the average performance of our standardized and fully automated method is slightly better than the annotation.

Base Composition↗
PubMed2007

The use of evolutionary biology concepts for genome annotation.

The past decade has seen the completion of numerous whole-genome sequencing projects, began with bacterial genomes and continued with eukaryotic species from different phyla: fungi, plants and animals. Besides, more biological information are produced and are shared thanks to information exchange systems, and more biological concepts, as well as more bioinformatics tools, are available. In this article, we will describe how the evolutionary biology concepts, as well as computer science, are useful for a better understanding of biology in general and genome annotation in particular. The genome annotation process consists of taking the raw DNA produced, for example, by the genome sequencing projects, adding the layers of analysis and interpretation necessary to extract its biological significance and placing it in the context of our understanding of biological processes. Genome annotation is a multistep process falling into two broad categories: structural and functional annotation.

Amino Acid Sequence↗
PubMed2003

Reinvestigation of the Saccharomyces cerevisiae genome annotation by comparison to the genome of a related fungus: Ashbya gossypii.

BACKGROUND: The recently sequenced genome of the filamentous fungus Ashbya gossypii revealed remarkable similarities to that of the budding yeast Saccharomyces cerevisiae both at the level of homology and synteny (conservation of gene order). Thus, it became possible to reinvestigate the S. cerevisiae genome in the syntenic regions leading to an improved annotation. RESULTS: We have identified 23 novel S. cerevisiae open reading frames (ORFs) as syntenic homologs of A. gossypii genes; for all but one, homologs are present in other eukaryotes including humans. Other comparisons identified 13 overlooked introns and suggested 69 potential sequence corrections resulting in ORF extensions or ORF fusions with improved homology to the syntenic A. gossypii homologs. Of the proposed corrections, 25 were tested and confirmed by resequencing. In addition, homologs of nearly 1,000 S. cerevisiae ORFs, presently annotated as hypothetical, were found in A. gossypii at syntenic positions and can therefore be considered as authentic genes. Finally, we suggest that over 400 S. cerevisiae ORFs that overlap other ORFs in S. cerevisiae and for which no homolog can be detected in A. gossypii should be regarded as spurious. CONCLUSIONS: Although, the S. cerevisiae genome is rightly considered as one of the most accurately sequenced and annotated eukaryotic genomes, we have shown that it still benefits substantially from comparison to the completed sequence and syntenic gene map of A. gossypii, an evolutionarily related fungus. This type of approach will strongly support the annotation of more complex genomes such as the human and murine genomes.

Amino Acid Sequence↗
PubMed2025

Efficient evidence-based genome annotation with EviAnn.

For many years, machine learning-based ab initio gene finding approaches have been central components of eukaryotic genome annotation pipelines, and they remain so today. The reliance on these approaches was originally sustained by the high cost and low availability of gene expression data, a primary source of evidence for gene annotation along with protein homology. However, innovations in modern sequencing technologies have revolutionized the acquisition of gene expression data, allowing scientists to rely more heavily on this class of evidence. In addition, proteins found in a multitude of well-annotated genomes represent another invaluable resource for gene annotation. Existing annotation packages often underutilize these data sources, which prompted us to develop EviAnn (Evidence-based Annotator), a novel evidence-based eukaryotic gene annotation system. EviAnn takes a strongly data-driven approach, building the exon-intron structure of genes from transcript alignments or protein-sequence homology rather than from purely ab initio gene finding techniques. We show that when provided with the same input data, EviAnn consistently outperforms current state-of-the-art packages including BRAKER3, MAKER2, and FINDER, while utilizing considerably less computer time. Annotation of a mammalian genome can be completed in less than an hour on a single multi-core server. EviAnn is freely available under an open-source license from https://github.com/alekseyzimin/EviAnn_release and from Bioconda as "eviann".

Journal Article↗
PubMed2000

gff2ps: visualizing genomic annotations.

gff2psis a program for visualizing annotations of genomic sequences. The program takes the annotated features on a genomic sequence in GFF format as input, and produces a visual output in PostScript. While it can be used in a very simple way, it also allows for a great degree of customization through a number of options and/or customization files.

Computational Biology↗
PubMed1999

Genome annotation: which tools do we have for it?

Genome data have to be converted into knowledge to be useful to biologists. Many valuable computational tools have already been developed to help annotation of plant genome sequences, and these may be improved further, for example by identification of more gene regulatory elements. The lack of a standard computer-assisted annotation platform for eukaryotic genomes remains major bottle-neck.

Arabidopsis↗
PubMed2002

Genome annotation techniques: new approaches and challenges.

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

Computational Biology↗
PubMed2005

The Vertebrate Genome Annotation (Vega) database.

The Vertebrate Genome Annotation (Vega) database (http://vega.sanger.ac.uk) has been designed to be a community resource for browsing manual annotation of finished sequences from a variety of vertebrate genomes. Its core database is based on an Ensembl-style schema, extended to incorporate curation-specific metadata. In collaboration with the genome sequencing centres, Vega attempts to present consistent high-quality annotation of the published human chromosome sequences. In addition, it is also possible to view various finished regions from other vertebrates, including mouse and zebrafish. Vega displays only manually annotated gene structures built using transcriptional evidence, which can be examined in the browser. Attempts have been made to standardize the annotation procedure across each vertebrate genome, which should aid comparative analysis of orthologues across the different finished regions.

Animals↗
PubMed2001

Descartes' fly: the geometry of genomic annotation.

The completion of the Drosophila melanogaster genome marks another significant milestone in the growth of sequence information. But it also contributes to the ever-widening gap between sequence information and biological knowledge. One important approach to reducing this gap is theoretical inference through computational technologies. Many computer programs have been designed to annotate genomic sequence information with biologically relevant information. Here, I suggest that all of these methods have a common structure in which the sequence fragments are "coordinated" by some method of description such as Hidden Markov models. The key to the algorithms lies in constructing the most efficient set of coordinates that allow extrapolation and interpolation from existing knowledge. Efficient extrapolation and interpolation are produced if the sequence fragments acquire a natural geometrical structure in the coordinated description. Finding such a coordinate frame is an inductive problem with no algorithmic solution. The greater part of the problem of genomic annotation lies in biological modeling of the data rather than in algorithmic improvements.

Animals↗
PubMed2004

A graphical tool for parasite genome annotation.

A graphical tool to facilitate rapid primary annotation of genomic sequence has been developed. Within a single interface the user can import sequences or database entries, run feature prediction programs and similarity searches, filter results, add additional manually found features and notes, and finally export annotations for database submission. Integrated rule-based feature corroboration and a novel decision support heuristic using ORF orientation, length and base-composition further enhances the efficiency of the annotation process without compromising flexibility. The program has been explicitly tailored to use in protozoan parasite genome projects, but can constitute a useful tool for prokaryote annotation as well. It is successfully being used by our lab in the Trypanosoma cruzi genome project, and can be obtained from the authors upon request.

Animals↗
PubMed1999

Benchmarking PSI-BLAST in genome annotation.

The recognition of remote protein homologies is a major aspect of the structural and functional annotation of newly determined genomes. Here we benchmark the coverage and error rate of genome annotation using the widely used homology-searching program PSI-BLAST (position-specific iterated basic local alignment search tool). This study evaluates the one-to-many success rate for recognition, as often there are several homologues in the database and only one needs to be identified for annotating the sequence. In contrast, previous benchmarks considered one-to-one recognition in which a single query was required to find a particular target. The benchmark constructs a model genome from the full sequences of the structural classification of protein (SCOP) database and searches against a target library of remote homologous domains (<20 % identity). The structural benchmark provides a reliable list of correct and false homology assignments. PSI-BLAST successfully annotated 40 % of the domains in the model genome that had at least one homologue in the target library. This coverage is more than three times that if one-to-one recognition is evaluated (11 % coverage of domains). Although a structural benchmark was used, the results equally apply to just sequence homology searches. Accordingly, structural and sequence assignments were made to the sequences of Mycoplasma genitalium and Mycobacterium tuberculosis (see http://www.bmm.icnet. uk). The extent of missed assignments and of new superfamilies can be estimated for these genomes for both structural and functional annotations.

Algorithms↗
PubMed2001

Automated structure-based prediction of functional sites in proteins: applications to assessing the validity of inheriting protein function from homology in genome annotation and to protein docking.

A major problem in genome annotation is whether it is valid to transfer the function from a characterised protein to a homologue of unknown activity. Here, we show that one can employ a strategy that uses a structure-based prediction of protein functional sites to assess the reliability of functional inheritance. We have automated and benchmarked a method based on the evolutionary trace approach. Using a multiple sequence alignment, we identified invariant polar residues, which were then mapped onto the protein structure. Spatial clusters of these invariant residues formed the predicted functional site. For 68 of 86 proteins examined, the method yielded information about the observed functional site. This algorithm for functional site prediction was then used to assess the validity of transferring the function between homologues. This procedure was tested on 18 pairs of homologous proteins with unrelated function and 70 pairs of proteins with related function, and was shown to be 94 % accurate. This automated method could be linked to schemes for genome annotation. Finally, we examined the use of functional site prediction in protein-protein and protein-DNA docking. The use of predicted functional sites was shown to filter putative docked complexes with a discrimination similar to that obtained by manually including biological information about active sites or DNA-binding residues.

Algorithms↗
PubMed2004

Whole genome sequence comparisons and "full-length" cDNA sequences: a combined approach to evaluate and improve Arabidopsis genome annotation.

To evaluate the existing annotation of the Arabidopsis genome further, we generated a collection of evolutionary conserved regions (ecores) between Arabidopsis and rice. The ecore analysis provides evidence that the gene catalog of Arabidopsis is not yet complete, and that a number of these annotations require re-examination. To improve the Arabidopsis genome annotation further, we used a novel "full-length" enriched cDNA collection prepared from several tissues. An additional 1931 genes were covered by new "full-length" cDNA sequences, raising the number of annotated genes with a corresponding "full-length" cDNA sequence to about 14,000. Detailed comparisons between these "full-length" cDNA sequences and annotated genes show that this resource is very helpful in determining the correct structure of genes, in particular, those not yet supported by "full-length" cDNAs. In addition, a total of 326 genomic regions not included previously in the Arabidopsis genome annotation were detected by this cDNA resource, providing clues for new gene discovery. Because, as expected, the two data sets only partially overlap, their combination produces very useful information for improving the Arabidopsis genome annotation.

Arabidopsis↗
PubMed2005

The institute for genomic research Osa1 rice genome annotation database.

We have developed a rice (Oryza sativa) genome annotation database (Osa1) that provides structural and functional annotation for this emerging model species. Using the sequence of O. sativa subsp. japonica cv Nipponbare from the International Rice Genome Sequencing Project, pseudomolecules, or virtual contigs, of the 12 rice chromosomes were constructed. Our most recent release, version 3, represents our third build of the pseudomolecules and is composed of 98% finished sequence. Genes were identified using a series of computational methods developed for Arabidopsis (Arabidopsis thaliana) that were modified for use with the rice genome. In release 3 of our annotation, we identified 57,915 genes, of which 14,196 are related to transposable elements. Of these 43,719 non-transposable element-related genes, 18,545 (42.4%) were annotated with a putative function, 5,777 (13.2%) were annotated as encoding an expressed protein with no known function, and the remaining 19,397 (44.4%) were annotated as encoding a hypothetical protein. Multiple splice forms (5,873) were detected for 2,538 genes, resulting in a total of 61,250 gene models in the rice genome. We incorporated experimental evidence into 18,252 gene models to improve the quality of the structural annotation. A series of functional data types has been annotated for the rice genome that includes alignment with genetic markers, assignment of gene ontologies, identification of flanking sequence tags, alignment with homologs from related species, and syntenic mapping with other cereal species. All structural and functional annotation data are available through interactive search and display windows as well as through download of flat files. To integrate the data with other genome projects, the annotation data are available through a Distributed Annotation System and a Genome Browser. All data can be obtained through the project Web pages at http://rice.tigr.org.

Computational Biology↗