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At least 505 records · Page 28Linked to original sources
PubMed2006

AUGUSTUS at EGASP: using EST, protein and genomic alignments for improved gene prediction in the human genome.

BACKGROUND: A large number of gene prediction programs for the human genome exist. These annotation tools use a variety of methods and data sources. In the recent ENCODE genome annotation assessment project (EGASP), some of the most commonly used and recently developed gene-prediction programs were systematically evaluated and compared on test data from the human genome. AUGUSTUS was among the tools that were tested in this project. RESULTS: AUGUSTUS can be used as an ab initio program, that is, as a program that uses only one single genomic sequence as input information. In addition, it is able to combine information from the genomic sequence under study with external hints from various sources of information. For EGASP, we used genomic sequence alignments as well as alignments to expressed sequence tags (ESTs) and protein sequences as additional sources of information. Within the category of ab initio programs AUGUSTUS predicted significantly more genes correctly than any other ab initio program. At the same time it predicted the smallest number of false positive genes and the smallest number of false positive exons among all ab initio programs. The accuracy of AUGUSTUS could be further improved when additional extrinsic data, such as alignments to EST, protein and/or genomic sequences, was taken into account. CONCLUSION: AUGUSTUS turned out to be the most accurate ab initio gene finder among the tested tools. Moreover it is very flexible because it can take information from several sources simultaneously into consideration.

Amino Acid Sequence↗
PubMed1998

GAIA: framework annotation of genomic sequence.

As increasing amounts of genomic sequence from many organisms become available, and as DNA sequences become a primary reagent in biologic investigations, the role of annotation as a prospective guide for laboratory experiments will expand rapidly. Here we describe a process of high-throughput, reliable annotation, called framework annotation, which is designed to provide a foundation for initial biologic characterization of previously unexamined sequence. To examine this concept in practice, we have constructed Genome Annotation and Information Analysis (GAIA), a prototype software architecture that implements several elements important for framework annotation. The center of GAIA consists of an annotation database and the associated data management subsystem that forms the software bus along which other components communicate. The schema for this database defines three principal concepts: (1) Entries, consisting of sequence and associated historical data; (2) Features, comprising information of biologic interest; and (3) Experiments, describing the evidence that supports Features. The database permits tracking of annotation results over time, as well as assessment of the reliability of particular results. New framework annotation is produced by CARTA, a set of autonomous sensors that perform automatic analyses and assert results into the annotation database. These results are available via a Web-based query interface that uses graphical Java applets as well as text-based HTML pages to display data at different levels of resolution and permit interactive exploration of annotation. We present results for initial application of framework annotation to a set of test sequences, demonstrating its effectiveness in providing a starting point for biologic investigation, and discuss ways in which the current prototype can be improved. The prototype is available for public use and comment at http://www.cbil.upenn.edu/gaia.

Amino Acid Sequence↗
PubMed2003

Integrating biological databases.

Recent years have seen an explosion in the amount of available biological data. More and more genomes are being sequenced and annotated, and protein and gene interaction data are accumulating. Biological databases have been invaluable for managing these data and for making them accessible. Depending on the data that they contain, the databases fulfil different functions. But, although they are architecturally similar, so far their integration has proved problematic.

Animals↗
PubMed2002

Transitive functional annotation by shortest-path analysis of gene expression data.

Current methods for the functional analysis of microarray gene expression data make the implicit assumption that genes with similar expression profiles have similar functions in cells. However, among genes involved in the same biological pathway, not all gene pairs show high expression similarity. Here, we propose that transitive expression similarity among genes can be used as an important attribute to link genes of the same biological pathway. Based on large-scale yeast microarray expression data, we use the shortest-path analysis to identify transitive genes between two given genes from the same biological process. We find that not only functionally related genes with correlated expression profiles are identified but also those without. In the latter case, we compare our method to hierarchical clustering, and show that our method can reveal functional relationships among genes in a more precise manner. Finally, we show that our method can be used to reliably predict the function of unknown genes from known genes lying on the same shortest path. We assigned functions for 146 yeast genes that are considered as unknown by the Saccharomyces Genome Database and by the Yeast Proteome Database. These genes constitute around 5% of the unknown yeast ORFome.

Cell Nucleus↗
PubMed2025

Whole blood transcriptome profile identifies motor neurone disease RNA biomarker signatures.

Blood-based biomarkers for motor neuron disease are needed for better diagnosis, progression prediction, and clinical trial monitoring. We used whole blood-derived total RNA and performed whole transcriptome analysis to compare the gene expression profiles in (motor neurone disease) MND patients to the control subjects. We compared 42 MND patients to 42 aged and sex-matched healthy controls and described the whole transcriptome profile characteristic for MND. In addition to the formal differential analysis, we performed functional annotation of the genomics data and identified the molecular pathways that are differentially regulated in MND patients. We identified 12,972 genes differentially expressed in the blood of MND patients compared to age and sex-matched controls. Functional genomic annotation identified activation of the pathways related to neurodegeneration, RNA transcription, RNA splicing and extracellular matrix reorganisation. Blood-based whole transcriptomic analysis can reliably differentiate MND patients from controls and can provide useful information for the clinical management of the disease and clinical trials.

Humans↗
PubMed2026

needLR: long-read structural variant annotation with population-scale frequency estimation.

SUMMARY: We present needLR, a structural variant (SV) annotation tool that can be used for filtering and prioritization of candidate pathogenic SVs from long-read sequencing data using population allele frequencies, annotations for genomic context, and gene-phenotype associations. When using population data from 500 presumably healthy individuals to evaluate nine test cases with known pathogenic SVs, needLR assigned allele frequencies to over 97.5% of all detected SVs and reduced the average number of novel genic SVs to 121 per case while retaining all known pathogenic variants. AVAILABILITY AND IMPLEMENTATION: needLR is implemented in bash with dependencies including Truvari v4.2.2, BEDTools v2.31.1, and BCFtools v1.19. Source code, documentation, and pre-computed population allele frequency data are freely available at https://github.com/jgust1/needLR under an MIT license and archived on Zenodo at https://zenodo.org/records/19463479.

Software↗
PubMed2006

Annotation of unknown yeast ORFs by correlation analysis of microarray data and extensive literature searches.

Changes in the expression of genes were used to elucidate the metabolic pathways and regulatory mechanisms that respond to environmental or genetic modifications. Results from previously published chemostat datasets were merged with novel data generated in the present study. ORFs displaying significant changes in expression that correlated with those of other ORFs were analysed using GO mapping tools and supplemented by literature information. The strategy developed was used to propose annotations for ORFs of unknown function. The following ORFs were assigned functions as a result of this study: YMR090w, YGL157w, YGR243w, YLR327c, YER121w, YFR017c, YGR067c, YKL187c, YGR236c (SPG1), YMR107w (SPG4), YMR206w, YER067w, YJL103c, YNL175C (NOP13) YJL200C, YDL070C (FMP16) and YGR173W.

Algorithms↗
PubMed2003

The UCSC Genome Browser Database.

The University of California Santa Cruz (UCSC) Genome Browser Database is an up to date source for genome sequence data integrated with a large collection of related annotations. The database is optimized to support fast interactive performance with the web-based UCSC Genome Browser, a tool built on top of the database for rapid visualization and querying of the data at many levels. The annotations for a given genome are displayed in the browser as a series of tracks aligned with the genomic sequence. Sequence data and annotations may also be viewed in a text-based tabular format or downloaded as tab-delimited flat files. The Genome Browser Database, browsing tools and downloadable data files can all be found on the UCSC Genome Bioinformatics website (http://genome.ucsc.edu), which also contains links to documentation and related technical information.

Animals↗
PubMed2006

SwissRegulon: a database of genome-wide annotations of regulatory sites.

SwissRegulon (http://www.swissregulon.unibas.ch) is a database containing genome-wide annotations of regulatory sites in the intergenic regions of genomes. The regulatory site annotations are produced using a number of recently developed algorithms that operate on multiple alignments of orthologous intergenic regions from related genomes in combination with, whenever available, known sites from the literature, and ChIP-on-chip binding data. Currently SwissRegulon contains annotations for yeast and 17 prokaryotic genomes. The database provides information about the sequence, location, orientation, posterior probability and, whenever available, binding factor of each annotated site. To enable easy viewing of the regulatory site annotations in the context of other features annotated on the genomes, the sites are displayed using the GBrowse genome browser interface and can be queried based on any annotated genomic feature. The database can also be queried for regulons, i.e. sites bound by a common factor.

Algorithms↗
PubMed2025

The UTRs of Leishmania donovani vary in length and are enriched in potential regulatory structures.

Leishmania spp. regulate gene expression largely post-transcriptionally, yet untranslated regions (UTRs) remain poorly delineated. We generated high-quality genome and transcriptome datasets for Leishmania donovani strain 1S2D (Ld1S) by combining PacBio HiFi de novo assembly with Oxford Nanopore direct RNA sequencing of promastigotes and axenic amastigotes. The genome assembly consists of 65 scaffolds totaling ~33.3 Mb. Structural comparisons to LdBPK282A1 revealed numerous rearrangements, including some reshuffling genes among polycistronic transcription units and validated by polycistronic reads from RNA sequencing. Promastigote and amastigote RNA sequencing produced 469,010 and 46,729 monocistronic reads containing a spliced-leader and a polyA tail sequences, defining 8,479 transcripts and supporting 7,415 of the 7,969 annotated protein coding genes, as well as 604 putative long non-coding RNAs. We annotated UTRs for 4,921 genes and observed that putative RNA G-quadruplexes were markedly enriched in UTRs. We also noted that 31.9% and 11.5% were expressed into multiple isoforms in promastigotes and amastigotes, respectively. Collectively, these data provide a comprehensive annotation of L. donovani genes and their UTRs and reveal widespread and stage-specific UTR length polymorphisms, and, overall, points to an important role of 3' UTR in post-transcriptional regulation in L. donovani.

Journal Article↗
PubMed2006

Annotating proteins by mining protein interaction networks.

MOTIVATION: In general, most accurate gene/protein annotations are provided by curators. Despite having lesser evidence strengths, it is inevitable to use computational methods for fast and a priori discovery of protein function annotations. This paper considers the problem of assigning Gene Ontology (GO) annotations to partially annotated or newly discovered proteins. RESULTS: We present a data mining technique that computes the probabilistic relationships between GO annotations of proteins on protein-protein interaction data, and assigns highly correlated GO terms of annotated proteins to non-annotated proteins in the target set. In comparison with other techniques, probabilistic suffix tree and correlation mining techniques produce the highest prediction accuracy of 81% precision with the recall at 45%. AVAILABILITY: Code is available upon request. Results and used materials are available online at http://kirac.case.edu/PROTAN.

Amino Acid Sequence↗
PubMed2006

Using hidden Markov models and observed evolution to annotate viral genomes.

MOTIVATION: ssRNA (single stranded) viral genomes are generally constrained in length and utilize overlapping reading frames to maximally exploit the coding potential within the genome length restrictions. This overlapping coding phenomenon leads to complex evolutionary constraints operating on the genome. In regions which code for more than one protein, silent mutations in one reading frame generally have a protein coding effect in another. To maximize coding flexibility in all reading frames, overlapping regions are often compositionally biased towards amino acids which are 6-fold degenerate with respect to the 64 codon alphabet. Previous methodologies have used this fact in an ad hoc manner to look for overlapping genes by motif matching. In this paper differentiated nucleotide compositional patterns in overlapping regions are incorporated into a probabilistic hidden Markov model (HMM) framework which is used to annotate ssRNA viral genomes. This work focuses on single sequence annotation and applies an HMM framework to ssRNA viral annotation. A description of how the HMM is parameterized, whilst annotating within a missing data framework is given. A Phylogenetic HMM (Phylo-HMM) extension, as applied to 14 aligned HIV2 sequences is also presented. This evolutionary extension serves as an illustration of the potential of the Phylo-HMM framework for ssRNA viral genomic annotation. RESULTS: The single sequence annotation procedure (SSA) is applied to 14 different strains of the HIV2 virus. Further results on alternative ssRNA viral genomes are presented to illustrate more generally the performance of the method. The results of the SSA method are encouraging however there is still room for improvement, and since there is overwhelming evidence to indicate that comparative methods can improve coding sequence (CDS) annotation, the SSA method is extended to a Phylo-HMM to incorporate evolutionary information. The Phylo-HMM extension is applied to the same set of 14 HIV2 sequences which are pre-aligned. The performance improvement that results from including the evolutionary information in the analysis is illustrated.

Algorithms↗
PubMedDate not supplied

Historical background: Why is it important to improve automated particle selection methods?

A current trend in single-particle electron microscopy is to compute three-dimensional reconstructions with ever-increasing numbers of particles in the data sets. Since manual--or even semi-automated--selection of particles represents a major bottleneck when the data set exceeds several thousand particles, there is growing interest in developing automatic methods for selecting images of individual particles. Except in special cases, however, it has proven difficult to achieve the degree of efficiency and reliability that would make fully automated particle selection a useful tool. The simplest methods such as cross correlation (i.e., matched filtering) do not perform well enough to be used for fully automated particle selection. Geometric properties (area, perimeter-to-area ratio, etc.) and the integrated "mass" of candidate particles are additional factors that could improve automated particle selection if suitable methods of contouring particles could be developed. Another suggestion is that data be always collected as pairs of images, the first taken at low defocus (to capture information at the highest possible resolution) and the second at very high defocus (to improve the visibility of the particle). Finally, it is emphasized that well-annotated, open-access data sets need to be established in order to encourage the further development and validation of methods for automated particle selection.

Algorithms↗
PubMed2004

Java-based application framework for visualization of gene regulatory region annotations.

MOTIVATION: The genome sequences of several organisms are either complete, or being sequenced. Each genome needs to be integrated with various types of annotations, e.g. locations of genes, promoters and other functional elements such as transcriptional regulatory elements. A robust application framework will be useful for developing web-based applications to visualize various genome annotations. RESULTS: We developed genome data visualization toolkit (GDVTK) as an application framework that consists of a set of data structures and core classes, using Java technology. GDVTK is a sound framework for developing web-based applications to present the gene regulatory region annotations in visual form. The current version of GDVTK consists of eight packages and 38 Java classes that are portable, reusable and extensible for plugging in new data sources and models. We implemented GDVTK for visualization of promoter annotations in Mammalian Promoter Database (MPromDb), a web-based gene-regulatory information server. AVAILABILITY: GDVTK is available under GNU general public license. Source code and software documentation can be found at the URL http://bioinformatics.med.ohio-state.edu/GDVTK.

Computer Graphics↗
PubMed2004

BioBuilder as a database development and functional annotation platform for proteins.

BACKGROUND: The explosion in biological information creates the need for databases that are easy to develop, easy to maintain and can be easily manipulated by annotators who are most likely to be biologists. However, deployment of scalable and extensible databases is not an easy task and generally requires substantial expertise in database development. RESULTS: BioBuilder is a Zope-based software tool that was developed to facilitate intuitive creation of protein databases. Protein data can be entered and annotated through web forms along with the flexibility to add customized annotation features to protein entries. A built-in review system permits a global team of scientists to coordinate their annotation efforts. We have already used BioBuilder to develop Human Protein Reference Database http://www.hprd.org, a comprehensive annotated repository of the human proteome. The data can be exported in the extensible markup language (XML) format, which is rapidly becoming as the standard format for data exchange. CONCLUSIONS: As the proteomic data for several organisms begins to accumulate, BioBuilder will prove to be an invaluable platform for functional annotation and development of customizable protein centric databases. BioBuilder is open source and is available under the terms of LGPL.

Computational Biology↗
PubMed2001

A one-bead, one-stock solution approach to chemical genetics: part 2.

BACKGROUND: Chemical genetics provides a systematic means to study biology using small molecules to effect spatial and temporal control over protein function. As complementary approaches, phenotypic and proteomic screens of structurally diverse and complex small molecules may yield not only interesting individual probes of biological function, but also global information about small molecule collections and the interactions of their members with biological systems. RESULTS: We report a general high-throughput method for converting high-capacity beads into arrayed stock solutions amenable to both phenotypic and proteomic assays. Polystyrene beads from diversity-oriented syntheses were arrayed individually into wells. Bound compounds were cleaved, eluted, and resuspended to generate 'mother plates' of stock solutions. The second phase of development of our technology platform includes optimized cleavage and elution conditions, a novel bead arraying method, and robotic distribution of stock solutions of small molecules into 'daughter plates' for direct use in chemical genetic assays. This library formatting strategy enables what we refer to as annotation screening, in which every member of a library is annotated with biological assay data. This phase was validated by arraying and screening 708 members of an encoded 4320-member library of structurally diverse and complex dihydropyrancarboxamides. CONCLUSIONS: Our 'one-bead, multiple-stock solution' library formatting strategy is a central element of a technology platform aimed at advancing chemical genetics. Annotation screening provides a means for biology to inform chemistry, complementary to the way that chemistry can inform biology in conventional ('investigator-initiated') small molecule screens.

Bromodeoxyuridine↗