ABSTRACT
Scorpion toxins are common experimental tools for studies of biochemical and pharmacological properties of ion channels. The number of functionally annotated scorpion toxins is steadily growing, but the number of identified toxin sequences is increasing at much faster pace. With an estimated 100,000 different variants, bioinformatic analysis of scorpion toxins is becoming a necessary tool for their systematic functional analysis. Here, we report a bioinformatics-driven system involving scorpion toxin structural classification, functional annotation, database technology, sequence comparison, nearest neighbour analysis, and decision rules which produces highly accurate predictions of scorpion toxin functional properties.
Subject(s)
Scorpion Venoms/chemistry , Scorpion Venoms/toxicity , Scorpions/chemistry , Scorpions/physiology , Sequence Analysis, Protein/methods , Algorithms , Animals , Predictive Value of Tests , Scorpion Venoms/classification , Species Specificity , Structure-Activity RelationshipABSTRACT
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/).
Subject(s)
Database Management Systems , Databases, Protein , Information Storage and Retrieval/methods , Neurotoxins/chemistry , Neurotoxins/classification , Snake Venoms/chemistry , Snake Venoms/classification , Animals , Databases, Bibliographic , Documentation/methods , Natural Language Processing , Sequence Alignment , Sequence Analysis, Protein , Sequence Homology, Amino Acid , Snakes , Systems IntegrationABSTRACT
We present an advanced system for recognition of gene starts in mammalian genomes. The system makes predictions of gene start location by combining information about CpG islands, transcription start sites (TSSs), and signals downstream of the predicted TSSs. The system aims at predicting a region that contains the gene start or is in its proximity. Evaluation on human chromosomes 4, 21, and 22 resulted in Se of over 65% and in a ppv of approximately 78%. The system makes on average one prediction per 177000 nucleotides on the human genome, as judged by the results on chromosome 21. Comparison of abilities to predict TSS with the two other systems on human chromosomes 4, 21, and 22 reveals that our system has superior accuracy and overall provides the most confident predictions.
Subject(s)
Software , Transcription Initiation Site , Transcription, Genetic , Algorithms , Chromosome Mapping/methods , Chromosomes, Human, Pair 21/genetics , Chromosomes, Human, Pair 22/genetics , Chromosomes, Human, Pair 4/genetics , Databases, Genetic , Genome, Human , Humans , Predictive Value of Tests , Transcription, Genetic/geneticsABSTRACT
Recognition of gene starts is a difficult and yet unsolved problem. We present a program, Dragon Gene Start Finder (DGSF), which assesses the gene start in mammalian genomes and predicts a region which should overlap with the first exon of the gene or be in its proximity. The program has been rigorously tested on human chromosomes 4, 21 and 22, and in a strand specific search achieves an overall sensitivity of approximately 65% and a positive predictive value of approximately 78%. The sensitivity for the CpG-island related promoters is >88%. DGSF is free for academic and non-profit users at http://sdmc.lit.org.sg/promoter/dragonGSF1_0/genestart.htm; the download version of the program integrated within the TRANSPLORER package can be obtained from Biobase GmbH, at http://www.biobase.de/.
Subject(s)
5' Flanking Region , Sequence Analysis, DNA/methods , Software , Transcription Initiation Site , Algorithms , Chromosomes, Human , CpG Islands , Humans , Internet , Promoter Regions, GeneticABSTRACT
This paper introduces a new computer system for recognition of functional transcription start sites (TSSs) in RNA polymerase II promoter regions of vertebrates. This system allows scanning complete vertebrate genomes for promoters with significantly reduced number of false positive predictions. It can be used in the context of gene finding through its recognition of the 5' end of genes. The implemented recognition model uses a composite-hierarchical approach, artificial intelligence, statistics, and signal processing techniques. It also exploits the separation of promoter sequences into those that are C+G-rich or C+G-poor. The system was evaluated on a large and diverse human sequence-set and exhibited several times higher accuracy than several publicly available TSS-finding programs. Results obtained using human chromosome 22 data showed even greater specificity than the evaluation set results. The system has been implemented in the Dragon Promoter Finder package, which can be accessed at http://sdmc.krdl.org.sg:8080/promoter/.
Subject(s)
Computer Simulation , Promoter Regions, Genetic , RNA Polymerase II/genetics , Transcription Initiation Site , Animals , Base Composition , Chromosomes, Human, Pair 22 , Humans , RNA Polymerase II/metabolism , Sequence Analysis, DNA/methodsABSTRACT
Dragon Promoter Finder (DPF) locates RNA polymerase II promoters in DNA sequences of vertebrates by predicting Transcription Start Site (TSS) positions. DPF's algorithm uses sensors for three functional regions (promoters, exons and introns) and an Artificial Neural Network (ANN). Results on a large and diverse evaluation set indicate that DPF exhibits a superior predicting ability for TSS location compared to three other promoter-finding programs.
