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At least 127 records · Page 7Linked to original sources

Finding errors in DNA sequences.

An algorithm is described that can detect certain errors within coding regions of DNA sequences. The algorithm is based on the idea that an insertion or deletion error within a coding sequence would interrupt the reading frame and cause the correct translation of a DNA sequence to require one or more frameshifts. If the coding sequence shows similarity to a known protein sequence then such errors can be detected by comparing the conceptual translations of DNA sequences in all six reading frames with every sequence in a protein sequence data base. We have incorporated these ideas into a computer program, called DETECT, that can serve as an aid to the experimentalist who is determining new DNA sequences so that obvious errors may be located and corrected. The program has been tested using raw experimental data and against sequences from the European Molecular Biology Laboratory data base, annotated as containing frameshifts. We have also tested it using unidentified open reading frames that flank known, annotated genes in the GenBank data base. Many potential errors are apparent and in some cases functions can be suggested for the "corrected" versions of these reading frames leading to the identification of new genes. As more sequences are determined the power of this method will increase substantially.

Adenylyl Cyclases↗

AutoFACT: an automatic functional annotation and classification tool.

BACKGROUND: Assignment of function to new molecular sequence data is an essential step in genomics projects. The usual process involves similarity searches of a given sequence against one or more databases, an arduous process for large datasets. RESULTS: We present AutoFACT, a fully automated and customizable annotation tool that assigns biologically informative functions to a sequence. Key features of this tool are that it (1) analyzes nucleotide and protein sequence data; (2) determines the most informative functional description by combining multiple BLAST reports from several user-selected databases; (3) assigns putative metabolic pathways, functional classes, enzyme classes, GeneOntology terms and locus names; and (4) generates output in HTML, text and GFF formats for the user's convenience. We have compared AutoFACT to four well-established annotation pipelines. The error rate of functional annotation is estimated to be only between 1-2%. Comparison of AutoFACT to the traditional top-BLAST-hit annotation method shows that our procedure increases the number of functionally informative annotations by approximately 50%. CONCLUSION: AutoFACT will serve as a useful annotation tool for smaller sequencing groups lacking dedicated bioinformatics staff. It is implemented in PERL and runs on LINUX/UNIX platforms. AutoFACT is available at http://megasun.bch.umontreal.ca/Software/AutoFACT.htm.

Acanthamoeba castellanii↗

The EMBL Nucleotide Sequence Database. Contributing and accessing data.

The European Molecular Biology Laboratory Nucleotide Sequence Database receives sequence and sequence annotation data from genome projects, sequencing centers, individual scientists, and patent offices. Data may be most efficiently submitted to the database using the Internet based submission tool WEBIN or via previously established genome project accounts. Biologist curators will review the data and provide accession numbers within two working days. Non-confidential data are exchanged daily in an international collaboration between EMBL. DDBJ (the DNA Databank of Japan) and GenBank (USA) and may be accessed and retrieved via the Internet with the Sequence Retrieval System (SRS). Sequence database searching algorithms (e.g., Blitz, Fasta, Blast) are available for comparison of query to database sequences.

Amino Acid Sequence↗

The RESID Database of Protein Modifications: 2003 developments.

The RESID Database is a comprehensive collection of annotations and structures for protein pre-, co- and post-translational modifications including amino-terminal, carboxyl-terminal and peptide chain cross-link modifications. The RESID Database includes: systematic and alternate names, atomic formulas and masses, enzyme activities generating the modifications, keywords, literature citations, Gene Ontology cross-references, Protein Information Resource (PIR) and SWISS-PROT protein sequence database feature table annotations, structure diagrams and molecular models. This database is freely accessible on the Internet through the European Bioinformatics Institute at http://srs.ebi.ac.uk/srs6bin/cgi-bin/wgetz?-page+LibInfo+-lib+RESID, through the National Cancer Institute - Frederick Advanced Biomedical Computing Center at http://www.ncifcrf.gov/RESID, or through the Protein Information Resource at http://pir.georgetown.edu/pirwww/dbinfo/resid.html.

Animals↗

cDNA microarray analysis of host-pathogen interactions in a porcine in vitro model for Toxoplasma gondii infection.

Toxoplasma gondii induces the expression of proinflammatory cytokines, reorganizes organelles, scavenges nutrients, and inhibits apoptosis in infected host cells. We used a cDNA microarray of 420 annotated porcine expressed sequence tags to analyze the molecular basis of these changes at eight time points over a 72-hour period in porcine kidney epithelial (PK13) cells infected with T. gondii. A total of 401 genes with Cy3 and Cy5 spot intensities of >/=500 were selected for analysis, of which 263 (65.6%) were induced >/=2-fold (expression ratio, >/=2.0; P </= 0.05 [t test]) over at least one time point and 48 (12%) were significantly down-regulated. At least 12 functional categories of genes were modulated (up- or down-regulated) by T. gondii. The majority of induced genes were clustered as transcription, signal transduction, host immune response, nutrient metabolism, and apoptosis related. The expression of selected genes altered by T. gondii was validated by quantitative real-time reverse transcription-PCR. These results suggest that significant changes in gene expression occur in response to T. gondii infection in PK13 cells, facilitating further analysis of host-pathogen interactions in toxoplasmosis in a secondary host.

Animals↗

TCRspec: A Recognition Interface-Informed Multimodal Method for TCR-pMHC Specificity Prediction.

Specific recognition between T-cell receptors (TCRs) and peptide-major histocompatibility complexes (pMHCs) is central to adaptive immunity, yet accurate prediction of TCR-pMHC specificity remains challenging. Existing models mainly rely on sequence features or isolated molecular structures, limiting their ability to capture interface-level determinants within the ternary recognition complex. Here, we constructed the multimodal TCR-pMHC ternary complex (MM-TCR) data set, integrating paired TCR-pMHC sequences, V/J gene annotations, and modeled TCR-pMHC complex structures refined by short molecular dynamics-based relaxation. Based on MM-TCR, we developed TCRspec, an interpretable multimodal framework combining sequence embeddings, gene-usage features, and complex-level structural representations. Under a stringent CD-HIT TCR-cluster-disjoint split, TCRspec achieved an average AUROC of 0.896 and AUPRC of 0.882 across seven antigen-specific test data sets, outperforming representative baseline models. Cross-validation and ablation analyses confirmed the contribution of ternary complex structural information and MD-refined structures. In independent OOD peptide-TCR systems, TCRspec retained discriminative performance and identified model-inferred peptide positions associated with TCR recognition, providing a structure-informed framework for TCR specificity prediction.

Receptors, Antigen, T-Cell↗

PROMPT: a protein mapping and comparison tool.

BACKGROUND: Comparison of large protein datasets has become a standard task in bioinformatics. Typically researchers wish to know whether one group of proteins is significantly enriched in certain annotation attributes or sequence properties compared to another group, and whether this enrichment is statistically significant. In order to conduct such comparisons it is often required to integrate molecular sequence data and experimental information from disparate incompatible sources. While many specialized programs exist for comparisons of this kind in individual problem domains, such as expression data analysis, no generic software solution capable of addressing a wide spectrum of routine tasks in comparative proteomics is currently available. RESULTS: PROMPT is a comprehensive bioinformatics software environment which enables the user to compare arbitrary protein sequence sets, revealing statistically significant differences in their annotation features. It allows automatic retrieval and integration of data from a multitude of molecular biological databases as well as from a custom XML format. Similarity-based mapping of sequence IDs makes it possible to link experimental information obtained from different sources despite discrepancies in gene identifiers and minor sequence variation. PROMPT provides a full set of statistical procedures to address the following four use cases: i) comparison of the frequencies of categorical annotations between two sets, ii) enrichment of nominal features in one set with respect to another one, iii) comparison of numeric distributions, and iv) correlation of numeric variables. Analysis results can be visualized in the form of plots and spreadsheets and exported in various formats, including Microsoft Excel. CONCLUSION: PROMPT is a versatile, platform-independent, easily expandable, stand-alone application designed to be a practical workhorse in analysing and mining protein sequences and associated annotation. The availability of the Java Application Programming Interface and scripting capabilities on one hand, and the intuitive Graphical User Interface with context-sensitive help system on the other, make it equally accessible to professional bioinformaticians and biologically-oriented users. PROMPT is freely available for academic users from http://webclu.bio.wzw.tum.de/prompt/.

Computational Biology↗

Structural proteomics: a tool for genome annotation.

In any newly sequenced genome, 30% to 50% of genes encode proteins with unknown molecular or cellular function. Fortunately, structural genomics is emerging as a powerful approach of functional annotation. Because of recent developments in high-throughput technologies, ongoing structural genomics projects are generating new structures at an unprecedented rate. In the past year, structural studies have identified many new structural motifs involved in enzymatic catalysis or in binding ligands or other macromolecules (DNA, RNA, protein). The efficiency by which function is deduced from structure can be further improved by the integration of structure with bioinformatics and other experimental approaches, such as screening for enzymatic activity or ligand binding.

DNA↗

From immunogenetics to immunomics: functional prospecting of genes and transcripts.

Human and mouse genome and transcriptome projects have expanded the field of 'immunogenetics' beyond the traditional study of the genetics and evolution of MHC, TCR and Ig loci into the new interdisciplinary area of 'immunomics'. Immunomics is the study of the molecular functions associated with all immune-related coding and non-coding mRNA transcripts. To unravel the function, regulation and diversity of the immunome requires that we identify and correctly categorize all immune-related transcripts. The importance of intercalated genes, antisense transcripts and non-coding RNAs and their potential role in regulation of immune development and function are only just starting to be appreciated. To better understand immune function and regulation, transcriptome projects (e.g. Functional Annotation of the Mouse, FANTOM), that focus on sequencing full-length transcripts from multiple tissue sources, ideally should include specific immune cells (e.g. T cell, B cells, macrophages, dendritic cells) at various states of development, in activated and unactivated states and in different disease contexts. Progress in deciphering immune regulatory networks will require the cooperative efforts of immunologists, immunogeneticists, molecular biologists and bioinformaticians. Although primary sequence analysis remains useful for annotation of new transcripts it is less useful for identifying novel functions of known transcripts in a new context (protein interaction network or pathway). The most efficient approach to mine useful information from the vast a priori knowledge contained in biological databases and the scientific literature, is to use a combination of computational and expert-driven knowledge discovery strategies. This paper will illustrate the challenges posed in attempts to functionally infer transcriptional regulation and interaction of immune-related genes from text and sequence-based data sources.

Alternative Splicing↗

Automatic discovery of sub-molecular sequence domains in multi-aligned sequences: a dynamic programming algorithm for multiple alignment segmentation.

Automatic identification of sub-structures in multi-aligned sequences is of great importance for effective and objective structural/functional domain annotation, phylogenetic treeing and other molecular analyses. We present a segmentation algorithm that optimally partitions a given multi-alignment into a set of potentially biologically significant blocks, or segments. This algorithm applies dynamic programming and progressive optimization to the statistical profile of a multi-alignment in order to optimally demarcate relatively homogenous sub-regions. Using this algorithm, a large multi-alignment of eukaryotic 16S rRNA was analyzed. Three types of sequence patterns were identified automatically and efficiently: shared conserved domain; shared variable motif; and rare signature sequence. Results were consistent with the patterns identified through independent phylogenetic and structural approaches. This algorithm facilitates the automation of sequence-based molecular structural and evolutionary analyses through statistical modeling and high performance computation.

Algorithms↗

Screening the receptorome yields validated molecular targets for drug discovery.

With the recently completed sequencing and annotation of the human genome, it has become clear that a significant portion of the genome encodes signal-transducing molecules including receptors, protein kinases, ion channels, transporters and coupling proteins. This review focuses on membrane-localized receptors, which represent the largest single group of signal-transducing molecules. Indeed, one can estimate that nearly 10% of the human genome encodes membrane-localized receptors (e.g. G-protein coupled receptors, ligand-gated ion channels and transporters). We have defined that portion of the human genome that encodes 'receptors' the receptorome. In this article, we will demonstrate how the massively parallel screening of the receptorome provides a facile and under-utilized screening platform for drug discovery. Using case studies, we will show how receptorome-based screening elucidates the mechanisms responsible for serious side-effects of both approved and investigational medications. Additionally, we will provide evidence that receptorome-based screening provides insights into novel therapeutic indications of approved medications and serves to validate targets for therapeutic drug discovery.

Antipsychotic Agents↗

Differential gene expression in abdomens of the malaria vector mosquito, Anopheles gambiae, after sugar feeding, blood feeding and Plasmodium berghei infection.

BACKGROUND: Large scale sequencing of cDNA libraries can provide profiles of genes expressed in an organism under defined biological and environmental circumstances. We have analyzed sequences of 4541 Expressed Sequence Tags (ESTs) from 3 different cDNA libraries created from abdomens from Plasmodium infection-susceptible adult female Anopheles gambiae. These libraries were made from sugar fed (S), rat blood fed (RB), and P. berghei-infected (IRB) mosquitoes at 30 hours after the blood meal, when most parasites would be transforming ookinetes or very early oocysts. RESULTS: The S, RB and IRB libraries contained 1727, 1145 and 1669 high quality ESTs, respectively, averaging 455 nucleotides (nt) in length. They assembled into 1975 consensus sequences--567 contigs and 1408 singletons. Functional annotation was performed to annotate probable molecular functions of the gene products and the biological processes in which they function. Genes represented at high frequency in one or more of the libraries were subjected to digital Northern analysis and results on expression of 5 verified by qRT-PCR. CONCLUSION: 13% of the 1965 ESTs showing identity to the A. gambiae genome sequence represent novel genes. These, together with untranslated regions (UTR) present on many of the ESTs, will inform further genome annotation. We have identified 23 genes encoding products likely to be involved in regulating the cellular oxidative environment and 25 insect immunity genes. We also identified 25 genes as being up or down regulated following blood feeding and/or feeding with P. berghei infected blood relative to their expression levels in sugar fed females.

Abdomen↗

Omiga: a PC-based sequence analysis tool.

Computer-based sequence analysis, notation, and manipulation are a necessity for all molecular biologists working with any but the most simple DNA sequences. As sequence data become increasingly available, tools that can be used to manipulate and annotate individual sequences and sequence elements will become an even more vital implement in the molecular biologist's arsenal. The Omiga DNA and Protein Sequence Analysis Software tool, version 2.0 provides an effective and comprehensive tool for the analysis of both nucleic acid and protein sequences that runs on a standard PC available in every molecular biology laboratory. Omiga allows the import of sequences in several common formats. Upon importing sequences and assigning them to various projects, Omiga allows the user to produce, analyze, and edit sequence alignments. Sequences may also be queried for the presence of restriction sites, sequence motifs, and other sequence features, all of which can be added into the notations accompanying each sequence. This newest version of Omiga also allows for sequencing and polymerase chain reaction (PCR) primer prediction, a functionality missing in earlier versions. Finally, Omiga allows rapid searches for putative coding regions, and Basic Local Alignment Search Tool (BLAST) queries against public databases at the National Center for Biotechnology Information (NCBI).

Humans↗

Systematic analysis of snake neurotoxins' functional classification using a data warehousing approach.

MOTIVATION: Sequence annotations, functional and structural data on snake venom neurotoxins (svNTXs) are scattered across multiple databases and literature sources. Sequence annotations and structural data are available in the public molecular databases, while functional data are almost exclusively available in the published articles. There is a need for a specialized svNTXs database that contains NTX entries, which are organized, well annotated and classified in a systematic manner. RESULTS: We have systematically analyzed svNTXs and classified them using structure-function groups based on their structural, functional and phylogenetic properties. Using conserved motifs in each phylogenetic group, we built an intelligent module for the prediction of structural and functional properties of unknown NTXs. We also developed an annotation tool to aid the functional prediction of newly identified NTXs as an additional resource for the venom research community. AVAILABILITY: We created a searchable online database of NTX proteins sequences (http://research.i2r.a-star.edu.sg/Templar/DB/snake_neurotoxin). This database can also be found under Swiss-Prot Toxin Annotation Project website (http://www.expasy.org/sprot/).

Animals↗

Processing sequence annotation data using the Lua programming language.

The data processing language in a graphical software tool that manages sequence annotation data from genome databases should provide flexible functions for the tasks in molecular biology research. Among currently available languages we adopted the Lua programming language. It fulfills our requirements to perform computational tasks for sequence map layouts, i.e. the handling of data containers, symbolic reference to data, and a simple programming syntax. Upon importing a foreign file, the original data are first decomposed in the Lua language while maintaining the original data schema. The converted data are parsed by the Lua interpreter and the contents are stored in our data warehouse. Then, portions of annotations are selected and arranged into our catalog format to be depicted on the sequence map. Our sequence visualization program was successfully implemented, embedding the Lua language for processing of annotation data and layout script. The program is available at http://staff.aist.go.jp/yutaka.ueno/guppy/.

Computational Biology↗

Annotated expressed sequence tags and cDNA microarrays for studies of brain and behavior in the honey bee.

To accelerate the molecular analysis of behavior in the honey bee (Apis mellifera), we created expressed sequence tag (EST) and cDNA microarray resources for the bee brain. Over 20,000 cDNA clones were partially sequenced from a normalized (and subsequently subtracted) library generated from adult A. mellifera brains. These sequences were processed to identify 15,311 high-quality ESTs representing 8912 putative transcripts. Putative transcripts were functionally annotated (using the Gene Ontology classification system) based on matching gene sequences in Drosophila melanogaster. The brain ESTs represent a broad range of molecular functions and biological processes, with neurobiological classifications particularly well represented. Roughly half of Drosophila genes currently implicated in synaptic transmission and/or behavior are represented in the Apis EST set. Of Apis sequences with open reading frames of at least 450 bp, 24% are highly diverged with no matches to known protein sequences. Additionally, over 100 Apis transcript sequences conserved with other organisms appear to have been lost from the Drosophila genome. DNA microarrays were fabricated with over 7000 EST cDNA clones putatively representing different transcripts. Using probe derived from single bee brain mRNA, microarrays detected gene expression for 90% of Apis cDNAs two standard deviations greater than exogenous control cDNAs. [The sequence data described in this paper have been submitted to Genbank data library under accession nos. BI502708-BI517278. The sequences are also available at http://titan.biotec.uiuc.edu/bee/honeybee_project.htm.]

Animals↗

[DNA arrays: technological aspects and applications].

The Human Genome Project has allowed considerable progress in the construction of physical and genetic maps and the identification of genes involved in human sicknesses. The accelerated accumulation of biological information and knowledge is due in large part to the sequencing projects of other organisms, which in fact paved the way for the Human Genome Project. In parallel, recently developed techniques which take advantage of genomic sequences allow large scale molecular analyses resulting in the functional annotation of many of the proteins represented by these genes. This is the goal of functional genomics. These progresses are at the origin of the present revolution in biomedical research. DNA microarrays are playing a dominant role compared to the other developing technologies since they are relatively easy to make and use and are applicable to numerous scientific inquiries. They allow the simultaneous analysis of several thousands of genes in biological samples from sick or healthy tissues, at the genome or transcriptome level. The data obtained is expected to result in major advances in the health sciences. In addition to an improved understanding of the complex molecular interaction networks of healthy cells and tissues, a more precise genetic characterization of the molecular mechanisms involved in pathology should result in the identification of new therapeutic targets and the development of new medicines. The genetic profiles thus obtained should also permit the definition of new pathologic subclasses not recognizable by traditional clinical factors, as well as new markers for susceptibility to certain illnesses, and new prognostic markers or methods of predicting responses to treatment. In this article, we present the different approaches and potential applications of DNA microarray technology, in particular as applied to cancer research.

Chromosome Mapping↗

The role of protein structure in genomics.

The genome projects produce an enormous amount of sequence data that needs to be annotated in terms of molecular structure and biological function. These tasks have triggered additional initiatives like structural genomics. The intention is to determine as many protein structures as possible, in the most efficient way, and to exploit the solved structures for the assignment of biological function to hypothetical proteins. We discuss the impact of these developments on protein classification, gene function prediction, and protein structure prediction.

Databases, Factual↗