MOTIVATION: Computational identification of promoters is notoriously difficult as human genes often have unique promoter sequences that provide regulation of transcription and interaction with transcription initiation complex. While there are many attempts to develop computational promoter identification methods, we have no reliable tool to analyze long genomic sequences. RESULTS: In this work, we further develop our deep learning approach that was relatively successful to discriminate short promoter and non-promoter sequences. Instead of focusing on the classification accuracy, in this work we predict the exact positions of the transcription start site inside the genomic sequences testing every possible location. We studied human promoters to find effective regions for discrimination and built corresponding deep learning models. These models use adaptively constructed negative set, which iteratively improves the model's discriminative ability. Our method significantly outperforms the previously developed promoter prediction programs by considerably reducing the number of false-positive predictions. We have achieved error-per-1000-bp rate of 0.02 and have 0.31 errors per correct prediction, which is significantly better than the results of other human promoter predictors. AVAILABILITY AND IMPLEMENTATION: The developed method is available as a web server at http://www.cbrc.kaust.edu.sa/PromID/.

MOTIVATION: Next-generation sequencing generates large amounts of data affected by errors in the form of substitutions, insertions or deletions of bases. Error correction based on the high-coverage information, typically improves de novo assembly. Most existing tools can correct substitution errors only; some support insertions and deletions, but accuracy in many cases is low. RESULTS: We present Karect, a novel error correction technique based on multiple alignment. Our approach supports substitution, insertion and deletion errors. It can handle non-uniform coverage as well as moderately covered areas of the sequenced genome. Experiments with data from Illumina, 454 FLX and Ion Torrent sequencing machines demonstrate that Karect is more accurate than previous methods, both in terms of correcting individual-bases errors (up to 10% increase in accuracy gain) and post de novo assembly quality (up to 10% increase in NGA50). We also introduce an improved framework for evaluating the quality of error correction. AVAILABILITY AND IMPLEMENTATION: Karect is available at: http://aminallam.github.io/karect. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: Gene transcription is mostly conducted through interactions of various transcription factors and their binding sites on DNA (regulatory elements, REs). Today, we are still far from understanding the real regulatory content of promoter regions. Computer methods for identification of REs remain a widely used tool for studying and understanding transcriptional regulation mechanisms. The Nsite, NsiteH and NsiteM programs perform searches for statistically significant (non-random) motifs of known human, animal and plant one-box and composite REs in a single genomic sequence, in a pair of aligned homologous sequences and in a set of functionally related sequences, respectively. AVAILABILITY AND IMPLEMENTATION: Pre-compiled executables built under commonly used operating systems are available for download by visiting http://www.molquest.kaust.edu.sa and http://www.softberry.com. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

UNLABELLED: In this paper we propose a new method for recognition of prokaryotic promoter regions with startpoints of transcription. The method is based on Sequence Alignment Kernel, a function reflecting the quantitative measure of match between two sequences. This kernel function is further used in Dual SVM, which performs the recognition. Several recognition methods have been trained and tested on positive data set, consisting of 669 sigma70-promoter regions with known transcription startpoints of Escherichia coli and two negative data sets of 709 examples each, taken from coding and non-coding regions of the same genome. The results show that our method performs well and achieves 16.5% average error rate on positive & coding negative data and 18.6% average error rate on positive & non-coding negative data. AVAILABILITY: The demo version of our method is accessible from our website http://mendel.cs.rhul.ac.uk/

MOTIVATION: The goal of the work was to develop a WWW-oriented computer system providing a maximal integration of informational and software resources on the regulation of gene expression and navigation through them. Rapid growth of the variety and volume of information accumulated in the databases on regulation of gene expression necessarily requires the development of computer systems for automated discovery of the knowledge that can be further used for analysis of regulatory genomic sequences. RESULTS: The GeneExpress system developed includes the following major informational and software modules: (1) Transcription Regulation (TRRD) module, which contains the databases on transcription regulatory regions of eukaryotic genes and TRRD Viewer for data visualization; (2) Site Activity Prediction (ACTIVITY), the module for analysis of functional site activity and its prediction; (3) Site Recognition module, which comprises (a) B-DNA-VIDEO system for detecting the conformational and physicochemical properties of DNA sites significant for their recognition, (b) Consensus and Weight Matrices (ConsFrec) and (c) Transcription Factor Binding Sites Recognition (TFBSR) systems for detecting conservative contextual regions of functional sites and their recognition; (4) Gene Networks (GeneNet), which contains an object-oriented database accumulating the data on gene networks and signal transduction pathways, and the Java-based Viewer for exploration and visualization of the GeneNet information; (5) mRNA Translation (Leader mRNA), designed to analyze structural and contextual properties of mRNA 5'-untranslated regions (5'-UTRs) and predict their translation efficiency; (6) other program modules designed to study the structure-function organization of regulatory genomic sequences and regulatory proteins. AVAILABILITY: GeneExpress is available at http://wwwmgs.bionet.nsc. ru/systems/GeneExpress/ and the links to the mirror site(s) can be found at http://wwwmgs.bionet.nsc.ru/mgs/links/mirrors.html+ ++.

We have developed a computer program POLYAH and an algorithm for the identification of 3'-processing sites of human mRNA precursors. The algorithm is based on a linear discriminant function (LDF) trained to discriminate real poly(A) signal regions from the other regions of human genes possessing the AATAAA sequence which is most likely non-functional. As the parameters of LDF, various significant contextual characteristics of sequences surrounding AATAAA signals were used. An accuracy of method has been estimated on a set of 131 poly(A) regions and 1466 regions of human genes having the AATAAA sequence. When the threshold was set to predict 86% of poly(A) regions correctly, specificity of 51% and correlation coefficient of 0.62 had been achieved. The precision of this approach is better than for the other methods and has been tested on a larger data set. POLYAH can be used through World Wide Web (at Gene-Finder Home page: URL http:@dot.imgen.bcm.tmc.edu:9331/gene-finder/ gf.html) or by sending files with uncharacterized human sequences to the University of Houston or Weizmann Institute of Science e-mail servers.

All current methods of protein secondary structure prediction are based on evaluation of a single residue state. Although the accuracy of the best of them is approximately 60-70%, for reliable prediction of tertiary structure it is more useful to predict an approximate location of alpha-helix and beta-strand segments, especially prolonged ones. We have developed a simple method for protein secondary structure prediction which is oriented on the location of secondary structure segments. The method uses linear discriminant analysis to assign segments of a given amino acid sequence a particular type of secondary structure, by taking into account the amino acid composition of internal parts of segments as well as their terminal and adjacent regions. Four linear discriminant functions were constructed for recognition of short and long alpha-helix and beta-strand segments respectively. These functions combine three characteristics: hydrophobic moment, segment singlet, and pair preferences to an alpha-helix or beta-strand. The last two characteristics are calculated by summing the preference parameters of single residues and pairs of residues located in a segment and its adjacent regions. The final program SSP predicts all possible potential alpha-helices and beta-strands and resolves some possible overlap between them. Overall three-state (alpha, beta, c) prediction gives approximately 65.1% correctly predicted residues on 126 non-homologous proteins using the jackknife test procedure. Analysis of the prediction results shows a high prediction accuracy of long secondary structure segments (approximately 89% of alpha-helices of length > 8 and approximately 71% of beta-strands of length > 6 are correctly located with probability of correct prediction 0.82 and 0.78 respectively.(ABSTRACT TRUNCATED AT 250 WORDS)

A new method for distinguishing among protein families based on the analysis of oligopeptide composition of amino acid sequences is presented. It is assumed that any protein family can be characterized by a set of essential oligopeptides (oligopeptide vocabulary). A simple approach to find such a vocabulary is suggested. It is shown that comparison of the vocabularies can distinguish among different families and the latter from random sequences. This comparison can be successfully made with a small set of frequencies of 25 dipeptides (or tripeptides). No preliminary alignment is necessary. It is established that characteristic peptides are located in the regions of functional value, as shown for GTP-binding domains of the translation elongation factors. It is demonstrated that this method is reasonably efficient for localizing functional domains in the amino acid sequences. The average error of prediction does not exceed three or four amino acid residues as shown for several functional domains.