Bioinformatics - current issue

Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data

Choi, H., Nesvizhskii, A. I., Ghosh, D., Qin, Z. S. — 2009-07-02

Motivation: Chromatin immunoprecipitation (ChIP) experiments followed by array hybridization, or ChIP-chip, is a powerful approach for identifying transcription factor binding sites (TFBS) and has been widely used. Recently, massively parallel sequencing coupled with ChIP experiments (ChIP-seq) has been increasingly used as an alternative to ChIP-chip, offering cost-effective genome-wide coverage and resolution up to a single base pair. For many well-studied TFs, both ChIP-seq and ChIP-chip experiments have been applied and their data are publicly available. Previous analyses have revealed substantial technology-specific binding signals despite strong correlation between the two sets of results. Therefore, it is of interest to see whether the two data sources can be combined to enhance the detection of TFBS.

Results: In this work, hierarchical hidden Markov model (HHMM) is proposed for combining data from ChIP-seq and ChIP-chip. In HHMM, inference results from individual HMMs in ChIP-seq and ChIP-chip experiments are summarized by a higher level HMM. Simulation studies show the advantage of HHMM when data from both technologies co-exist. Analysis of two well-studied TFs, NRSF and CCCTC-binding factor (CTCF), also suggests that HHMM yields improved TFBS identification in comparison to analyses using individual data sources or a simple merger of the two.

Availability: Source code for the software ChIPmeta is freely available for download at http://www.umich.edu/~hwchoi/HHMMsoftware.zip, implemented in C and supported on linux.

Contact: ghoshd@psu.edu; qin@umich.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

SOrt-ITEMS: Sequence orthology based approach for improved taxonomic estimation of metagenomic sequences

Monzoorul Haque, M., Ghosh, T. S., Komanduri, D., Mande, S. S. — 2009-07-02

Motivation:One of the first steps in metagenomic analysis is the assignment of reads/contigs obtained from various sequencing technologies to their correct taxonomic bins. Similarity-based binning methods assign a read to a taxon/clade, based on the pattern of significant BLAST hits generated against sequence databases. Existing methods, which use bit-score as the sole parameter to ascertain the significance of BLAST hits, have limited specificity and accuracy of binning. A new binning algorithm, called SOrt-ITEMS is introduced, which addresses these limitations. The method uses alignment parameters besides the bit score to first identify an appropriate taxonomic level where the read can be assigned. An orthology-based approach is subsequently used by the method for the final assignment.

Results:The performance of SOrt-ITEMS has been validated with reads simulating sequences from 454 and Sanger sequencing technologies. In addition, the taxonomic composition of the Sargasso Sea data set has been analyzed using SOrt-ITEMS. SOrt-ITEMS shows improved specificity and accuracy of assignments especially in simulated scenarios, wherein sequences corresponding to the source organism of the reads are absent in the reference database.

Availability:SOrt-ITEMS software is available for download from: http://metagenomics.atc.tcs.com/binning/SOrt-ITEMS. No license is needed for academic and nonprofit use.

Contact: sharmila@atc.tcs.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Data structures and compression algorithms for genomic sequence data

Brandon, M. C., Wallace, D. C., Baldi, P. — 2009-07-02

Motivation: The continuing exponential accumulation of full genome data, including full diploid human genomes, creates new challenges not only for understanding genomic structure, function and evolution, but also for the storage, navigation and privacy of genomic data. Here, we develop data structures and algorithms for the efficient storage of genomic and other sequence data that may also facilitate querying and protecting the data.

Results: The general idea is to encode only the differences between a genome sequence and a reference sequence, using absolute or relative coordinates for the location of the differences. These locations and the corresponding differential variants can be encoded into binary strings using various entropy coding methods, from fixed codes such as Golomb and Elias codes, to variables codes, such as Huffman codes. We demonstrate the approach and various tradeoffs using highly variables human mitochondrial genome sequences as a testbed. With only a partial level of optimization, 3615 genome sequences occupying 56 MB in GenBank are compressed down to only 167 KB, achieving a 345-fold compression rate, using the revised Cambridge Reference Sequence as the reference sequence. Using the consensus sequence as the reference sequence, the data can be stored using only 133 KB, corresponding to a 433-fold level of compression, roughly a 23% improvement. Extensions to nuclear genomes and high-throughput sequencing data are discussed.

Availability: Data are publicly available from GenBank, the HapMap web site, and the MITOMAP database. Supplementary materials with additional results, statistics, and software implementations are available from http://mammag.web.uci.edu/bin/view/Mitowiki/ProjectDNACompression.

Contact: pfbaldi@ics.uci.edu

ESG: extended similarity group method for automated protein function prediction

Chitale, M., Hawkins, T., Park, C., Kihara, D. — 2009-07-02

Motivation: Importance of accurate automatic protein function prediction is ever increasing in the face of a large number of newly sequenced genomes and proteomics data that are awaiting biological interpretation. Conventional methods have focused on high sequence similarity-based annotation transfer which relies on the concept of homology. However, many cases have been reported that simple transfer of function from top hits of a homology search causes erroneous annotation. New methods are required to handle the sequence similarity in a more robust way to combine together signals from strongly and weakly similar proteins for effectively predicting function for unknown proteins with high reliability.

Results: We present the extended similarity group (ESG) method, which performs iterative sequence database searches and annotates a query sequence with Gene Ontology terms. Each annotation is assigned with probability based on its relative similarity score with the multiple-level neighbors in the protein similarity graph. We will depict how the statistical framework of ESG improves the prediction accuracy by iteratively taking into account the neighborhood of query protein in the sequence similarity space. ESG outperforms conventional PSI-BLAST and the protein function prediction (PFP) algorithm. It is found that the iterative search is effective in capturing multiple-domains in a query protein, enabling accurately predicting several functions which originate from different domains.

Availability: ESG web server is available for automated protein function prediction at http://dragon.bio.purdue.edu/ESG/

Contact: cspark@cau.ac.kr; dkihara@purdue.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Efficient computation of all perfect repeats in genomic sequences of up to half a gigabyte, with a case study on the human genome

Becher, V., Deymonnaz, A., Heiber, P. — 2009-07-02

Motivation: There is a significant ongoing research to identify the number and types of repetitive DNA sequences. As more genomes are sequenced, efficiency and scalability in computational tools become mandatory. Existing tools fail to find distant repeats because they cannot accommodate whole chromosomes, but segments. Also, a quantitative framework for repetitive elements inside a genome or across genomes is still missing.

Results: We present a new efficient algorithm and its implementation as a software tool to compute all perfect repeats in inputs of up to 500 million nucleotide bases, possibly containing many genomes. Our algorithm is based on a suffix array construction and a novel procedure to extract all perfect repeats in the entire input, that can be arbitrarily distant, and with no bound on the repeat length. We tested the software on the Homo sapiens DNA genome NCBI 36.49. We computed all perfect repeats of at least 40 bases occurring in any two chromosomes with exact matching. We found that each H.sapiens chromosome shares ~10% of its full sequence with every other human chromosome, distributed more or less evenly among the chromosome surfaces. We give statistics including a quantification of repeats by diversity, length and number of occurrences. We compared the computed repeats against all biological repeats currently obtainable from Ensembl enlarged with the output of the dust program and all elements identified by TRF and RepeatMasker (ftp://ftp.ebi.ac.uk/pub/databases/ensembl/jherrero/.repeats/all_repeats.txt.bz2). We report novel repeats as well as new occurrences of repeats matching with known biological elements.

Availability: The source code, results and visualization of some statistics are accessible from http://kapow.dc.uba.ar/patterns/

Contact: vbecher@dc.uba.ar

Fast and accurate short read alignment with Burrows-Wheeler transform

Li, H., Durbin, R. — 2009-07-02

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals.

Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ~10–20x faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package.

Availability: http://maq.sourceforge.net

Contact: rd@sanger.ac.uk

pGenTHREADER and pDomTHREADER: new methods for improved protein fold recognition and superfamily discrimination

Lobley, A., Sadowski, M. I., Jones, D. T. — 2009-07-02

Motivation: Generation of structural models and recognition of homologous relationships for unannotated protein sequences are fundamental problems in bioinformatics. Improving the sensitivity and selectivity of methods designed for these two tasks therefore has downstream benefits for many other bioinformatics applications.

Results: We describe the latest implementation of the GenTHREADER method for structure prediction on a genomic scale. The method combines profile–profile alignments with secondary-structure specific gap-penalties, classic pair- and solvation potentials using a linear combination optimized with a regression SVM model. We find this combination significantly improves both detection of useful templates and accuracy of sequence-structure alignments relative to other competitive approaches. We further present a second implementation of the protocol designed for the task of discriminating superfamilies from one another. This method, pDomTHREADER, is the first to incorporate both sequence and structural data directly in this task and improves sensitivity and selectivity over the standard version of pGenTHREADER and three other standard methods for remote homology detection.

Contact: d.jones@cs.ucl.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Literature-based priors for gene regulatory networks

Steele, E., Tucker, A., Hoen, P.A.C. t, Schuemie, M.J. — 2009-07-02

Motivation: The use of prior knowledge to improve gene regulatory network modelling has often been proposed. In this article we present the first research on the massive incorporation of prior knowledge from literature for Bayesian network learning of gene networks. As the publication rate of scientific papers grows, updating online databases, which have been proposed as potential prior knowledge in past research, becomes increasingly challenging. The novelty of our approach lies in the use of gene-pair association scores that describe the overlap in the contexts in which the genes are mentioned, generated from a large database of scientific literature, harnessing the information contained in a huge number of documents into a simple, clear format.

Results: We present a method to transform such literature-based gene association scores to network prior probabilities, and apply it to learn gene sub-networks for yeast, Escherichia coli and Human organisms. We also investigate the effect of weighting the influence of the prior knowledge. Our findings show that literature-based priors can improve both the number of true regulatory interactions present in the network and the accuracy of expression value prediction on genes, in comparison to a network learnt solely from expression data. Networks learnt with priors also show an improved biological interpretation, with identified subnetworks that coincide with known biological pathways.

Contact: emma.steele@brunel.ac.uk

Supplementary information: Supplementary data are available at Bioinformatics online.

Gradient lasso for Cox proportional hazards model

Sohn, I., Kim, J., Jung, S.-H., Park, C. — 2009-07-02

Motivation: There has been an increasing interest in expressing a survival phenotype (e.g. time to cancer recurrence or death) or its distribution in terms of a subset of the expression data of a subset of genes. Due to high dimensionality of gene expression data, however, there is a serious problem of collinearity in fitting a prediction model, e.g. Cox's proportional hazards model. To avoid the collinearity problem, several methods based on penalized Cox proportional hazards models have been proposed. However, those methods suffer from severe computational problems, such as slow or even failed convergence, because of high-dimensional matrix inversions required for model fitting. We propose to implement the penalized Cox regression with a lasso penalty via the gradient lasso algorithm that yields faster convergence to the global optimum than do other algorithms. Moreover the gradient lasso algorithm is guaranteed to converge to the optimum under mild regularity conditions. Hence, our gradient lasso algorithm can be a useful tool in developing a prediction model based on high-dimensional covariates including gene expression data.

Results: Results from simulation studies showed that the prediction model by gradient lasso recovers the prognostic genes. Also results from diffuse large B-cell lymphoma datasets and Norway/Stanford breast cancer dataset indicate that our method is very competitive compared with popular existing methods by Park and Hastie and Goeman in its computational time, prediction and selectivity.

Availability: R package glcoxph is available at http://datamining.dongguk.ac.kr/R/glcoxph.

Contact: park463@uos.ac.kr

Relating periodicity of nucleosome organization and gene regulation

Wan, J., Lin, J., Zack, D. J., Qian, J. — 2009-07-02

Motivation: The relationship between nucleosome positioning and gene regulation is fundamental yet complex. Previous studies on genomic nucleosome positions have revealed a correlation between nucleosome occupancy on promoters and gene expression levels. Many of these studies focused on individual nucleosomes, especially those proximal to transcription start sites. To study the collective effect of multiple nucleosomes on the gene expression, we developed a mathematical approach based on autocorrelation to relate genomic nucleosome organization to gene regulation.

Results: We found that nucleosome organization in gene promoters can be well described by autocorrelation transformation. Some promoters show obvious periods in their nucleosome organization, while others have no clear periodicity. The genes with periodic nucleosome organization in promoters tend to be lower expressed than the genes without periodic nucleosome organization. These suggest that regular organization of nucleosomes plays a critical role in gene regulation. To quantitatively associate nucleosome organization and gene expression, we predicted gene expression solely based on nucleosome status and found that nucleosome status accounts for ~25% of the observed gene expression variability. Furthermore, we explored the underlying forces that maintain the periodicity in nucleosome organization, namely intrinsic (i.e. DNA sequence) and extrinsic forces (i.e. chromatin remodeling factors). We found that the extrinsic factors play a critical role in maintaining the periodic nucleosome organization.

Contact: jiang.qian@jhmi.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Seeing the forest for the trees: using the Gene Ontology to restructure hierarchical clustering

Dotan-Cohen, D., Kasif, S., Melkman, A. A. — 2009-07-02

Motivation: There is a growing interest in improving the cluster analysis of expression data by incorporating into it prior knowledge, such as the Gene Ontology (GO) annotations of genes, in order to improve the biological relevance of the clusters that are subjected to subsequent scrutiny. The structure of the GO is another source of background knowledge that can be exploited through the use of semantic similarity.

Results: We propose here a novel algorithm that integrates semantic similarities (derived from the ontology structure) into the procedure of deriving clusters from the dendrogram constructed during expression-based hierarchical clustering. Our approach can handle the multiple annotations, from different levels of the GO hierarchy, which most genes have. Moreover, it treats annotated and unannotated genes in a uniform manner. Consequently, the clusters obtained by our algorithm are characterized by significantly enriched annotations. In both cross-validation tests and when using an external index such as protein–protein interactions, our algorithm performs better than previous approaches. When applied to human cancer expression data, our algorithm identifies, among others, clusters of genes related to immune response and glucose metabolism. These clusters are also supported by protein–protein interaction data.

Contact: dotna@cs.bgu.ac.il

Supplementary information: Supplementary data are available at Bioinformatics online.

On the inference of spatial structure from population genetics data

Guillot, G. — 2009-07-02

Motivation: In a series of recent papers, Tess, a computer program based on the concept of hidden Markov random field, has been proposed to infer the number and locations of panmictic population units from the genotypes and spatial locations of these individuals. The method seems to be of broad appeal as it is conceptually much simpler than other competing methods and it has been reported by its authors to be fast and accurate. However, this methodology is not grounded in a formal statistical inference method and seems to rely to a large extent on arbitrary choices regarding the parameters used. The present article is an investigation of the accuracy of this method and an attempt to assess whether recent results reported on the basis of this method are genuine features of the genetic process or artefacts of the method.

Method: I analyse simulated data consisting of populations at Hardy–Weinberg and linkage equilibrium and also data simulated under a scenario of isolation-by-distance at mutation–migration–drift equilibrium. Arabidopsis thaliana data previously analysed with this method are also reconsidered.

Results: Using the Tess program under the no-admixture model to analyse data consisting of several genuine HWLE populations with individuals of pure ancestries leads to highly inaccurate results; Using the Tess program under the admixture model to analyse data consisting of a continuous isolation-by-distance population leads to the inference of spurious HWLE populations whose number and features depend on the parameters used. Results previously reported about the A.thaliana using Tess seem to a large extent to be artefacts of the statistical methodology used. The findings go beyond population clustering models and can be an help to design more efficient algorithms based on graphs.

Availability: The data analysed in the present article are available from http://folk.uio.no/gillesg/Bioinformatics-HMRF

Contact: gilles.guillot@bio.uio.no

Supplementary information: Supplementary data are available at Bioinformatics online.

Comment on 'On the inference of spatial structure from population genetics data'

Durand, E., Chen, C., Francois, O. — 2009-07-02

Contact: Olivier.francois@imag.fr

Response to comment on 'On the inference of spatial structure from population genetics data'

Guillot, G. — 2009-07-02

Estimating the posterior probability that genome-wide association findings are true or false

Bukszar, J., McClay, J. L., van den Oord, E. J. C. G. — 2009-07-02

Motivation: A limitation of current methods used to declare significance in genome-wide association studies (GWAS) is that they do not provide clear information about the probability that GWAS findings are true of false. This lack of information increases the chance of false discoveries and may result in real effects being missed.

Results: We propose a method to estimate the posterior probability that a marker has (no) effect given its test statistic value, also called the local false discovery rate (FDR), in the GWAS. A critical step involves the estimation the parameters of the distribution of the true alternative tests. For this, we derived and implemented the real maximum likelihood function, which turned out to provide us with significantly more accurate estimates than the widely used mixture model likelihood. Actual GWAS data are used to illustrate properties of the posterior probability estimates empirically. In addition to evaluating individual markers, a variety of applications are conceivable. For instance, posterior probability estimates can be used to control the FDR more precisely than Benjamini–Hochberg procedure.

Availability: The codes are freely downloadable from the web site http://www.people.vcu.edu/~jbukszar.

Contact: jbukszar@vcu.edu

Supplementary information: Supplementary data are available at Bioinformatics online.

Structure discovery in PPI networks using pattern-based network decomposition

Bachman, P., Liu, Y. — 2009-07-02

Motivation: The large, complex networks of interactions between proteins provide a lens through which one can examine the structure and function of biological systems. Previous analyses of these continually growing networks have primarily followed either of two approaches: large-scale statistical analysis of holistic network properties, or small-scale analysis of local topological features. Meanwhile, investigation of meso-scale network structure (above that of individual functional modules, while maintaining the significance of individual proteins) has been hindered by the computational complexity of structural search in networks. Examining protein–protein interaction (PPI) networks at the meso-scale may provide insights into the presence and form of relationships between individual protein complexes and functional modules.

Results: In this article, we present an efficient algorithm for performing sub-graph isomorphism queries on a network and show its computational advantage over previous methods. We also present a novel application of this form of topological search which permits analysis of a network's structure at a scale between that of individual functional modules and that of network-wide properties. This analysis provides support for the presence of hierarchical modularity in the PPI network of Saccharomyces cerevisiae.

Contact: ying.liu@utdallas.edu

Robust synthetic biology design: stochastic game theory approach

Chen, B.-S., Chang, C.-H., Lee, H.-C. — 2009-07-02

Motivation: Synthetic biology is to engineer artificial biological systems to investigate natural biological phenomena and for a variety of applications. However, the development of synthetic gene networks is still difficult and most newly created gene networks are non-functioning due to uncertain initial conditions and disturbances of extra-cellular environments on the host cell. At present, how to design a robust synthetic gene network to work properly under these uncertain factors is the most important topic of synthetic biology.

Results: A robust regulation design is proposed for a stochastic synthetic gene network to achieve the prescribed steady states under these uncertain factors from the minimax regulation perspective. This minimax regulation design problem can be transformed to an equivalent stochastic game problem. Since it is not easy to solve the robust regulation design problem of synthetic gene networks by non-linear stochastic game method directly, the Takagi–Sugeno (T–S) fuzzy model is proposed to approximate the non-linear synthetic gene network via the linear matrix inequality (LMI) technique through the Robust Control Toolbox in Matlab. Finally, an in silico example is given to illustrate the design procedure and to confirm the efficiency and efficacy of the proposed robust gene design method.

Availability: http://www.ee.nthu.edu.tw/bschen/SyntheticBioDesign_supplement.pdf

Contact: bschen@ee.nthu.edu.tw

Supplementary information: Supplementary data are available at Bioinformatics online.

Rahnuma: hypergraph-based tool for metabolic pathway prediction and network comparison

Mithani, A., Preston, G. M., Hein, J. — 2009-07-02

Summary:We present a tool called Rahnuma for prediction and analysis of metabolic pathways and comparison of metabolic networks. Rahnuma represents metabolic networks as hypergraphs and computes all possible pathways between two or more metabolites. It provides an intuitive way to answer biological ques- tions focusing on differences between organisms or the evolution of different species by allowing pathway-based metabolic network comparisons at an organism as well as at a phylogenetic level.

Availability: Rahnuma is available online at http://portal.stats.ox.ac.uk:8080/rahnuma/.

Contact: gail.preston@plants.ox.ac.uk

Supplementary information: Supplementary data are available at the Bioinformatics online.

baobabLUNA: the solution space of sorting by reversals

Braga, M. D. V. — 2009-07-02

Summary: Computing the reversal distance and searching for an optimal sequence of reversals to transform a unichromosomal genome into another are useful algorithmic tools to analyse real evolutionary scenarios. Currently, these problems can be solved by at least two available softwares, the prominent of which are GRAPPA and GRIMM. However, the number of different optimal sequences is usually huge and taking only the distance and/or one example is often insufficient to do a proper analysis. Here, we offer an alternative and present baobabLUNA, a framework that contains an algorithm to give a compact representation of the whole space of solutions for the sorting by reversals problem.

Availability and Implementation: Compiled code implemented in Java is freely available for download at http://pbil.univ-lyon1.fr/software/luna/. Documentation with methodological background, technical aspects, download and setup instructions, interface description and tutorial are available at http://pbil.univ-lyon1.fr/software/luna/doc/luna-doc.pdf.

Contact: mdvbraga@gmail.com

Supplementary information: Supplementary data are available at Bioinformatics online.

Apollo: a community resource for genome annotation editing

Ed, L., Nomi, H., Mark, G., Raymond, C., Suzanna, L. — 2009-07-02

Summary: Apollo is a genome annotation-editing tool with an easy to use graphical interface. It is a component of the GMOD project, with ongoing development driven by the community. Recent additions to the software include support for the generic feature format version 3 (GFF3), continuous transcriptome data, a full Chado database interface, integration with remote services for on-the-fly BLAST and Primer BLAST analyses, graphical interfaces for configuring user preferences and full undo of all edit operations. Apollo's user community continues to grow, including its use as an educational tool for college and high-school students.

Availability: Apollo is a Java application distributed under a free and open source license. Installers for Windows, Linux, Unix, Solaris and Mac OS X are available at http://apollo.berkeleybop.org, and the source code is available from the SourceForge CVS repository at http://gmod.cvs.sourceforge.net/gmod/apollo.

Contact: elee@berkeleybop.org

NTAP: for NimbleGen tiling array ChIP-chip data analysis

He, K., Li, X., Zhou, J., Deng, X.-W., Zhao, H., Luo, J. — 2009-07-02

Summary:NTAP is designed to analyze ChIP-chip data generated by the NimbleGen tiling array platform and to accomplish various pattern recognition tasks that are useful especially for epigenetic studies. The modular design of NTAP makes the data processing highly customizable. Users can either use NTAP to perform the full process of NimbleGen tiling array data analysis, or choose post-processing modules in NTAP to analyze pre-processed epigenetic data generated by other platforms. The output of NTAP can be saved in standard GFF format files and visualized in GBrowse.

Availability and Implementation:The source code of NTAP is freely available at http://ntap.cbi.pku.edu.cn/. It is implemented in Perl and R and can be used on Linux, Mac and Windows platforms.

Contact: ntap@mail.cbi.pku.edu.cn; luojc@pku.edu.cn; hekun78@gmail.com

rtracklayer: an R package for interfacing with genome browsers

Lawrence, M., Gentleman, R., Carey, V. — 2009-07-02

Summary: The rtracklayer package supports the integration of existing genome browsers with experimental data analyses performed in R. The user may (i) transfer annotation tracks to and from a genome browser and (ii) create and manipulate browser views to focus on a particular set of annotations in a specific genomic region. Currently, the UCSC genome browser is supported.

Availability: The package is freely available from http://www.bioconductor.org/. A quick-start vignette is included with the package.

Contact: mflawren@fhcrc.org

MetaTISA: Metagenomic Translation Initiation Site Annotator for improving gene start prediction

Hu, G.-Q., Guo, J.-T., Liu, Y.-C., Zhu, H. — 2009-07-02

Summary: We proposed a tool named MetaTISA with an aim to improve TIS prediction of current gene-finders for metagenomes. The method employs a two-step strategy to predict translation initiation sites (TISs) by first clustering metagenomic fragments into phylogenetic groups and then predicting TISs independently for each group in an unsupervised manner. As evaluated on experimentally verified TISs, MetaTISA greatly improves the accuracies of TIS prediction of current gene-finders.

Availability: The C++ source code is freely available under the GNU GPL license via http://mech.ctb.pku.edu.cn/MetaTISA/.

Contact: hqzhu@pku.edu.cn

Supplementary information: Supplementary data are available at Bioinformatics online.

PESTAS: a web server for EST analysis and sequence mining

2009-07-02

Summary: We have developed a web server for the high-throughput annotation of expressed sequence tags (ESTs) called pipeline for EST analysis service (PESTAS). PESTAS processes entire datasets with an automated pipeline of 13 analytic services, then deposits the data into the MySQL database and transforms it into three kinds of reports: preprocessing, assembling and annotation. All annotated information is provided to the scientist and can be downloaded through a web browser. To get more relevant functional annotation results, a curation function was introduced with which biologists can easily change the best-hit annotation information. We included a gene chip module that detects gene expression differences between libraries by comparing accession number counts from BLAST search results. PESTAS also provides access to the pathway information of KEGG, which is useful for mapping the relationships among networks of annotated enzymes, and is especially valuable for those researchers interested in biological pathways.

Availability: PESTAS is available at http://pestas.kribb.re.kr/

Supplementary information: Supplementary data are available at http://pestas.kribb.re.kr/pestas.jsp.

Contact: odysseus@kribb.re.kr