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PubMed2006

FlyRNAi: the Drosophila RNAi screening center database.

RNA interference (RNAi) has become a powerful tool for genetic screening in Drosophila. At the Drosophila RNAi Screening Center (DRSC), we are using a library of over 21,000 double-stranded RNAs targeting known and predicted genes in Drosophila. This library is available for the use of visiting scientists wishing to perform full-genome RNAi screens. The data generated from these screens are collected in the DRSC database (http://flyRNAi.org/cgi-bin/RNAi_screens.pl) in a flexible format for the convenience of the scientist and for archiving data. The long-term goal of this database is to provide annotations for as many of the uncharacterized genes in Drosophila as possible. Data from published screens are available to the public through a highly configurable interface that allows detailed examination of the data and provides access to a number of other databases and bioinformatics tools.

Animals↗
PubMed2006

Protein complex compositions predicted by structural similarity.

Proteins function through interactions with other molecules. Thus, the network of physical interactions among proteins is of great interest to both experimental and computational biologists. Here we present structure-based predictions of 3387 binary and 1234 higher order protein complexes in Saccharomyces cerevisiae involving 924 and 195 proteins, respectively. To generate candidate complexes, comparative models of individual proteins were built and combined together using complexes of known structure as templates. These candidate complexes were then assessed using a statistical potential, derived from binary domain interfaces in PIBASE (http://salilab.org/pibase). The statistical potential discriminated a benchmark set of 100 interface structures from a set of sequence-randomized negative examples with a false positive rate of 3% and a true positive rate of 97%. Moreover, the predicted complexes were also filtered using functional annotation and sub-cellular localization data. The ability of the method to select the correct binding mode among alternates is demonstrated for three camelid VHH domain-porcine alpha-amylase interactions. We also highlight the prediction of co-complexed domain superfamilies that are not present in template complexes. Through integration with MODBASE, the application of the method to proteomes that are less well characterized than that of S.cerevisiae will contribute to expansion of the structural and functional coverage of protein interaction space. The predicted complexes are deposited in MODBASE (http://salilab.org/modbase).

Algorithms↗
PubMed2006

FireDB--a database of functionally important residues from proteins of known structure.

The FireDB database is a databank for functional information relating to proteins with known structures. It contains the most comprehensive and detailed repository of known functionally important residues, bringing together both ligand binding and catalytic residues in one site. The platform integrates biologically relevant data filtered from the close atomic contacts in Protein Data Bank crystal structures and reliably annotated catalytic residues from the Catalytic Site Atlas. The interface allows users to make queries by protein, ligand or keyword. Relevant biologically important residues are displayed in a simple and easy to read manner that allows users to assess binding site similarity across homologous proteins. Binding site residue variations can also be viewed with molecular visualization tools. The database is available at http://firedb.bioinfo.cnio.es.

Amino Acids↗
PubMed2001

The contribution of exon-skipping events on chromosome 22 to protein coding diversity.

Completion of the human genome sequence provides evidence for a gene count with lower bound 30,000-40,000. Significant protein complexity may derive in part from multiple transcript isoforms. Recent EST based studies have revealed that alternate transcription, including alternative splicing, polyadenylation and transcription start sites, occurs within at least 30-40% of human genes. Transcript form surveys have yet to integrate the genomic context, expression, frequency, and contribution to protein diversity of isoform variation. We determine here the degree to which protein coding diversity may be influenced by alternate expression of transcripts by exhaustive manual confirmation of genome sequence annotation, and comparison to available transcript data to accurately associate skipped exon isoforms with genomic sequence. Relative expression levels of transcripts are estimated from EST database representation. The rigorous in silico method accurately identifies exon skipping using verified genome sequence. 545 genes have been studied in this first hand-curated assessment of exon skipping on chromosome 22. Combining manual assessment with software screening of exon boundaries provides a highly accurate and internally consistent indication of skipping frequency. 57 of 62 exon skipping events occur in the protein coding regions of 52 genes. A single gene, (FBXO7) expresses an exon repetition. 59% of highly represented multi-exon genes are likely to express exon-skipped isoforms in ratios that vary from 1:1 to 1:>100. The proportion of all transcripts corresponding to multi-exon genes that exhibit an exon skip is estimated to be 5%.

Alternative Splicing↗
PubMed2002

The generic genome browser: a building block for a model organism system database.

The Generic Model Organism System Database Project (GMOD) seeks to develop reusable software components for model organism system databases. In this paper we describe the Generic Genome Browser (GBrowse), a Web-based application for displaying genomic annotations and other features. For the end user, features of the browser include the ability to scroll and zoom through arbitrary regions of a genome, to enter a region of the genome by searching for a landmark or performing a full text search of all features, and the ability to enable and disable tracks and change their relative order and appearance. The user can upload private annotations to view them in the context of the public ones, and publish those annotations to the community. For the data provider, features of the browser software include reliance on readily available open source components, simple installation, flexible configuration, and easy integration with other components of a model organism system Web site. GBrowse is freely available under an open source license. The software, its documentation, and support are available at http://www.gmod.org.

Animals↗
PubMed2002

Predicting protein cellular localization using a domain projection method.

We investigate the co-occurrence of domain families in eukaryotic proteins to predict protein cellular localization. Approximately half (300) of SMART domains form a "small-world network", linked by no more than seven degrees of separation. Projection of the domains onto two-dimensional space reveals three clusters that correspond to cellular compartments containing secreted, cytoplasmic, and nuclear proteins. The projection method takes into account the existence of "bridging" domains, that is, instances where two domains might not occur with each other but frequently co-occur with a third domain; in such circumstances the domains are neighbors in the projection. While the majority of domains are specific to a compartment ("locale"), and hence may be used to localize any protein that contains such a domain, a small subset of domains either are present in multiple locales or occur in transmembrane proteins. Comparison with previously annotated proteins shows that SMART domain data used with this approach can predict, with 92% accuracy, the localizations of 23% of eukaryotic proteins. The coverage and accuracy will increase with improvements in domain database coverage. This method is complementary to approaches that use amino-acid composition or identify sorting sequences; these methods may be combined to further enhance prediction accuracy.

Animals↗
PubMed2005

Learning yeast gene functions from heterogeneous sources of data using hybrid weighted Bayesian networks.

We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene functions is crucial for understanding many fundamental biological mechanisms such as regulatory pathways, cell cycles and diseases. Our major goal is to accurately infer functions of putative genes or ORFs (Open Reading Frames) from existing databases using computational methods. However, this task is intrinsically difficult since the underlying biological processes represent complex interactions of multiple entities. Therefore many functional links would be missing when only one or two source of data is used in the prediction. Our hypothesis is that integrating evidence from multiple and complementary sources could significantly improve the prediction accuracy. In this paper, our experimental results not only suggest that the above hypothesis is valid, but also provide guidelines for using the WNB system for data collection, training and predictions. The combined training data sets contain information from gene annotations, gene expressions, clustering outputs, keyword annotations and sequence homology from public databases. The current system is trained and tested on the genes of budding yeast Saccharomyces cerevisiae. Our WNB model can also be used to analyze the contribution of each source of information toward the prediction performance through the weight training process. The contribution analysis could potentially lead to significant scientific discovery by facilitating the interpretation and understanding of the complex relationships between biological entities.

Artificial Intelligence↗
PubMed2006

Joint learning of gene functions--a Bayesian network model approach.

In this paper, we develop a machine learning system for determining gene functions from heterogeneous data sources using a Weighted Naive Bayesian network (WNB). The knowledge of gene functions is crucial for understanding many fundamental biological mechanisms such as regulatory pathways, cell cycles and diseases. Our major goal is to accurately infer functions of putative genes or Open Reading Frames (ORFs) from existing databases using computational methods. However, this task is intrinsically difficult since the underlying biological processes represent complex interactions of multiple entities. Therefore, many functional links would be missing when only one or two sources of data are used in the prediction. Our hypothesis is that integrating evidence from multiple and complementary sources could significantly improve the prediction accuracy. In this paper, our experimental results not only suggest that the above hypothesis is valid, but also provide guidelines for using the WNB system for data collection, training and predictions. The combined training data sets contain information from gene annotations, gene expressions, clustering outputs, keyword annotations, and sequence homology from public databases. The current system is trained and tested on the genes of budding yeast Saccharomyces cerevisiae. Our WNB model can also be used to analyze the contribution of each source of information toward the prediction performance through the weight training process. The contribution analysis could potentially lead to significant scientific discovery by facilitating the interpretation and understanding of the complex relationships between biological entities.

Artificial Intelligence↗
PubMed2005

GENETAG: a tagged corpus for gene/protein named entity recognition.

BACKGROUND: Named entity recognition (NER) is an important first step for text mining the biomedical literature. Evaluating the performance of biomedical NER systems is impossible without a standardized test corpus. The annotation of such a corpus for gene/protein name NER is a difficult process due to the complexity of gene/protein names. We describe the construction and annotation of GENETAG, a corpus of 20K MEDLINE sentences for gene/protein NER. 15K GENETAG sentences were used for the BioCreAtIvE Task 1A Competition. RESULTS: To ensure heterogeneity of the corpus, MEDLINE sentences were first scored for term similarity to documents with known gene names, and 10K high- and 10K low-scoring sentences were chosen at random. The original 20K sentences were run through a gene/protein name tagger, and the results were modified manually to reflect a wide definition of gene/protein names subject to a specificity constraint, a rule that required the tagged entities to refer to specific entities. Each sentence in GENETAG was annotated with acceptable alternatives to the gene/protein names it contained, allowing for partial matching with semantic constraints. Semantic constraints are rules requiring the tagged entity to contain its true meaning in the sentence context. Application of these constraints results in a more meaningful measure of the performance of an NER system than unrestricted partial matching. CONCLUSION: The annotation of GENETAG required intricate manual judgments by annotators which hindered tagging consistency. The data were pre-segmented into words, to provide indices supporting comparison of system responses to the "gold standard". However, character-based indices would have been more robust than word-based indices. GENETAG Train, Test and Round1 data and ancillary programs are freely available at ftp://ftp.ncbi.nlm.nih.gov/pub/tanabe/GENETAG.tar.gz. A newer version of GENETAG-05, will be released later this year.

Animals↗
PubMed2003

Identification of conserved regulatory elements by comparative genome analysis.

BACKGROUND: For genes that have been successfully delineated within the human genome sequence, most regulatory sequences remain to be elucidated. The annotation and interpretation process requires additional data resources and significant improvements in computational methods for the detection of regulatory regions. One approach of growing popularity is based on the preferential conservation of functional sequences over the course of evolution by selective pressure, termed 'phylogenetic footprinting'. Mutations are more likely to be disruptive if they appear in functional sites, resulting in a measurable difference in evolution rates between functional and non-functional genomic segments. RESULTS: We have devised a flexible suite of methods for the identification and visualization of conserved transcription-factor-binding sites. The system reports those putative transcription-factor-binding sites that are both situated in conserved regions and located as pairs of sites in equivalent positions in alignments between two orthologous sequences. An underlying collection of metazoan transcription-factor-binding profiles was assembled to facilitate the study. This approach results in a significant improvement in the detection of transcription-factor-binding sites because of an increased signal-to-noise ratio, as demonstrated with two sets of promoter sequences. The method is implemented as a graphical web application, ConSite, which is at the disposal of the scientific community at http://www.phylofoot.org/. CONCLUSIONS: Phylogenetic footprinting dramatically improves the predictive selectivity of bioinformatic approaches to the analysis of promoter sequences. ConSite delivers unparalleled performance using a novel database of high-quality binding models for metazoan transcription factors. With a dynamic interface, this bioinformatics tool provides broad access to promoter analysis with phylogenetic footprinting.

Algorithms↗
PubMed1995

New developments in EPG3 software.

New developments in EPG3 software are described including: a three-dimensional display of the electrodes on the palate in the form of a wire frame representation; a facility for annotating EPG3 files; and a new data management system. Recent methods of EPG data reduction are also described, including numerical indices for representing tongue-palate contact profiles. A new clinical version of the Reading EPG system, EPG4, is introduced.

Electrodiagnosis↗
PubMed1999

An effective approach for analyzing "prefinished" genomic sequence data.

Ongoing efforts to sequence the human genome are already generating large amounts of data, with substantial increases anticipated over the next few years. In most cases, a shotgun sequencing strategy is being used, which rapidly yields most of the primary sequence in incompletely assembled sequence contigs ("prefinished" sequence) and more slowly produces the final, completely assembled sequence ("finished" sequence). Thus, in general, prefinished sequence is produced in excess of finished sequence, and this trend is certain to continue and even accelerate over the next few years. Even at a prefinished stage, genomic sequence represents a rich source of important biological information that is of great interest to many investigators. However, analyzing such data is a challenging and daunting task, both because of its sheer volume and because it can change on a day-by-day basis. To facilitate the discovery and characterization of genes and other important elements within prefinished sequence, we have developed an analytical strategy and system that uses readily available software tools in new combinations. Implementation of this strategy for the analysis of prefinished sequence data from human chromosome 7 has demonstrated that this is a convenient, inexpensive, and extensible solution to the problem of analyzing the large amounts of preliminary data being produced by large-scale sequencing efforts. Our approach is accessible to any investigator who wishes to assimilate additional information about particular sequence data en route to developing richer annotations of a finished sequence.

Algorithms↗
PubMed2000

Analysis of expressed sequence tags from Brassica rapa L. ssp. pekinensis.

Non-redundant expressed sequence tags (ESTs) were generated from six different organs at various developmental stages of Chinese cabbage, Brassica rapa L. ssp. pekinensis. Of the 1,295 ESTs, 915 (71%) showed significantly high homology in nucleotide or deduced amino acid sequences with other sequences deposited in databases, while 380 did not show similarity to any sequences. Briefly, 598 ESTs matched with proteins of identified biological function, 177 with hypothetical proteins or non-annotated Arabidopsis genome sequences, and 140 with other ESTs. About 82% of the top-scored matching sequences were from Arabidopsis or Brassica, but overall 558 (43%) ESTs matched with Arabidopsis ESTs at the nucleotide sequence level. This observation strongly supports the idea that gene-expression profiles of Chinese cabbage differ from that of Arabidopsis, despite their genome structures being similar to each other. Moreover, sequence analyses of 21 Brassica ESTs revealed that their primary structure is different from those of corresponding annotated sequences of Arabidopsis genes. Our data suggest that direct prediction of Brassica gene expression pattern based on the information from Arabidopsis genome research has some limitations. Thus, information obtained from the Brassica EST study is useful not only for understanding of unique developmental processes of the plant, but also for the study of Arabidopsis genome structure.

Arabidopsis↗
PubMed2003

Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project.

BACKGROUND: Seattle Biomedical Research Institute (SBRI) as part of the Leishmania Genome Network (LGN) is sequencing chromosomes of the trypanosomatid protozoan species Leishmania major. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with ARTEMIS, an annotation platform with rich and user-friendly interfaces. RESULTS: Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (GLIMMER, TESTCODE and GENESCAN) into the ARTEMIS sequence viewer and annotation tool. Comparison of these methods, along with the CODONUSAGE algorithm built into ARTEMIS, shows the importance of combining methods to more accurately annotate the L. major genomic sequence. CONCLUSION: An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the Leishmania genome project where there is large proportion of novel genes requiring manual annotation.

Algorithms↗
PubMed2025

Deep learning-based annotation of plant abiotic stress resistance genes for crops.

The declining costs of DNA sequencing have expanded genomic data, crucial for understanding plant abiotic stress responses and crop improvement. However, accurate gene annotation remains challenging. To address this limitation, we propose the PASRGA, a deep learning approach that leverages transfer learning and contrastive learning to annotate genes related to drought, salt, cold, and UV resistance. PASRGA achieves high F1-scores, area under the receiver operating characteristic (AUROC), area under the precision-recall curve (AUPRC), and Matthews correlation coefficient (MCC) in annotating stress resistance genes, significantly outperforming the general protein annotation model CLEAN, the plant phosphatase gene annotation model PF-NET, the top-ranked model in the CAFA5 challenge NetGO 4.0, and four traditional machine learning methods. Its effectiveness was further validated with a salt stress treatment experiment in Eutrema salsugineum. To facilitate crop breeding practices, we utilized PASRGA to annotate the genomes of 17 major crops. To improve accessibility and utility, we incorporated both manually curated and PASRGA-predicted gene data, together with the PASRGA tool, into the PlantASRG database (https://bioinfor.nefu.edu.cn/PlantASRG/). This comprehensive resource aims to support crop breeding initiatives and ensure food security.

Crops, Agricultural↗
PubMed2002

Electronic procedure logs: taking it further.

OBJECTIVE: To create an electronic procedural logbook with enhanced user interactivity and usefulness as an educational resource. DESCRIPTION: From our own work on electronic student logbooks and other studies, it is clear that compliance with complete and valid data entry remains a challenge. Without direct and visible benefit, students are reluctant to spend time logging all their cases. We developed an interactive procedural logbook using Microsoft Embedded Visual Basic and Metrowerks CodeWarrior. Interface design focused on rapid data entry with minimum requirement for text, and field-level automation where possible. We loaded it onto a mixed platform of personal digital assistants (PDAs)--Compaq iPaq Pocket PCs and HandEra 330 PalmOS devices. Our rural residents were supplied with the devices of their choice. Various built-in educational reference resources included: (1) contextual help options about each procedure, which contained diagrams and pictures; (2) diagnostic and fee coding so they could see how poorly some procedures pay; and (3) Quick Tips relevant to each procedure, which can be easily modified by the preceptor. Preceptor evaluations and comments can be entered rapidly. Using built-in database conduits, data are automatically collected from each device on every HotSync with the desktop. Data can then be collated and analyzed using Microsoft Access or via secure Web access. DISCUSSION: Improved compliance has been dramatic-one resident logged 250 procedures in just two months. However, not all residents have been successful in establishing seamless synchronization, and the resulting data loss has caused frustration. The evidence indicates the need to implement central data collection and backup right from the outset. Central data collection provides many advantages. The program director has better information for future applications. Preceptor evaluations are now spread over many interactions and yet can be collated and analyzed. Quick Tips have been very popular-we have been able to collect the tips and redistribute them. Focus-group feedback from the residents has shown that the rich data in the logbook's reference component improved its usefulness and popularity as an educational tool. Choice of device type is important for user acceptance because devotees of one platform are reluctant to switch to another. Cross-platform development does slow the process but is increasingly easy with the latest software design tools, such as AppForge. These new tools have enabled us to explore further improvements in data entry. Digital ink provides the ability to capture annotated diagrams and preceptor signatures. Voice input is built in with these devices, and our software now allows for voice annotation for more detailed commentary by preceptors or student. The compressed digital sound file is collected along with the data and transcribed centrally (on-device voice recognition is not feasible yet). Point-of-care accessibility has been the key attraction of using these devices for logging encounter data. This project demonstrates that it is important to explore all multimodal interactive capabilities to provide a truly rich educational tool.

Electronic Data Processing↗
PubMed2006

Analysis and management of HIV peptide microarray experiments.

OBJECTIVES: To develop a tool for then easy and user-friendly management of peptide microarray experiments and for the use of the results of these experiments for the study the immune response against HIV virus infection in clinical samples. METHODS: Applying bioinformatics and statistics for the analysis of data coming from microarray experiments as well as implementing a MIAME (Minimum Information About a Microarray Experiment) compliant database for managing and annotating experiments, results and samples. RESULTS: We present a new tool for managing not only nucleic acid microarray experiments but also protein microarray experiments. From the analysis of experimental data, we can detect different profiles in the reactivity of the sera with different genotypes. CONCLUSIONS: We have developed a new tool for managing microarray data including clinical annotations for the samples as well as the capability of annotating other microarray formats different to those based on nucleic acids. The use of peptide microarrays and bioinformatics analysis opens a new scope for the characterization of the immune response, and analyzing and identifying the humoral response of viruses with different genotypes.

Computational Biology↗