BACKGROUND: The falling cost of next-generation sequencing technology has allowed deep sequencing across related species and of individuals within species. Whole genome assemblies from these data remain high time- and resource-consuming computational tasks, particularly if best solutions are sought using different assembly strategies and parameter sets. However, in many cases, the underlying research questions are not genome-wide but rather target specific genes or sets of genes. We describe a novel assembly tool, SRAssembler, that efficiently assembles only contigs containing potential homologs of a gene or protein query, thus enabling gene-specific genome studies over large numbers of short read samples. RESULTS: We demonstrate the functionality of SRAssembler with examples largely drawn from plant genomics. The workflow implements a recursive strategy by which relevant reads are successively pulled from the input sets based on overlapping significant matches, resulting in virtual chromosome walking. The typical workflow behavior is illustrated with assembly of simulated reads. Applications to real data show that SRAssembler produces homologous contigs of equivalent quality to whole genome assemblies. Settings can be chosen to not only assemble presumed orthologs but also paralogous gene loci in distinct contigs. A key application is assembly of the same locus in many individuals from population genome data, which provides assessment of structural variation beyond what can be inferred from read mapping to a reference genome alone. SRAssembler can be used on modest computing resources or used in parallel on high performance computing clusters (most easily by invoking a dedicated Singularity image). CONCLUSIONS: SRAssembler offers an efficient tool to complement whole genome assembly software. It can be used to solve gene-specific research questions based on large genomic read samples from multiple sources and would be an expedient choice when whole genome assembly from the reads is either not feasible, too costly, or unnecessary. The program can also aid decision making on the depth of sequencing in an ongoing novel genome sequencing project or with respect to ultimate whole genome assembly strategies.

BACKGROUND: Accurate gene structure annotation is a fundamental but somewhat elusive goal of genome projects, as witnessed by the fact that (model) genomes typically undergo several cycles of re-annotation. In many cases, it is not only different versions of annotations that need to be compared but also different sources of annotation of the same genome, derived from distinct gene prediction workflows. Such comparisons are of interest to annotation providers, prediction software developers, and end-users, who all need to assess what is common and what is different among distinct annotation sources. We developed ParsEval, a software application for pairwise comparison of sets of gene structure annotations. ParsEval calculates several statistics that highlight the similarities and differences between the two sets of annotations provided. These statistics are presented in an aggregate summary report, with additional details provided as individual reports specific to non-overlapping, gene-model-centric genomic loci. Genome browser styled graphics embedded in these reports help visualize the genomic context of the annotations. Output from ParsEval is both easily read and parsed, enabling systematic identification of problematic gene models for subsequent focused analysis. RESULTS: ParsEval is capable of analyzing annotations for large eukaryotic genomes on typical desktop or laptop hardware. In comparison to existing methods, ParsEval exhibits a considerable performance improvement, both in terms of runtime and memory consumption. Reports from ParsEval can provide relevant biological insights into the gene structure annotations being compared. CONCLUSIONS: Implemented in C, ParsEval provides the quickest and most feature-rich solution for genome annotation comparison to date. The source code is freely available (under an ISC license) at http://parseval.sourceforge.net/.

Many in silico investigations in bioinformatics require access to multiple, distributed data sources and analytic tools. The requisite data sources may include large public data repositories, community databases, and project databases for use in domain-specific research. Different data sources frequently utilize distinct query languages and return results in unique formats, and therefore researchers must either rely upon a small number of primary data sources or become familiar with multiple query languages and formats. Similarly, the associated analytic tools often require specific input formats and produce unique outputs which make it difficult to utilize the output from one tool as input to another. The BioExtract Server (http://bioextract.org) is a Web-based data integration application designed to consolidate, analyze, and serve data from heterogeneous biomolecular databases in the form of a mash-up. The basic operations of the BioExtract Server allow researchers, via their Web browsers, to specify data sources, flexibly query data sources, apply analytic tools, download result sets, and store query results for later reuse. As a researcher works with the system, their "steps" are saved in the background. At any time, these steps can be preserved long-term as a workflow simply by providing a workflow name and description.

The residence of spliceosomal introns within protein-coding genes can fluctuate over time, with genes gaining, losing or conserving introns in a complex process that is not entirely understood. One approach for studying intron evolution is to compare introns with respect to position and type within closely related genes. Here, we describe new, freely available software called Common Introns Within Orthologous Genes (CIWOG), available at http://ciwog.gdcb.iastate.edu/, which detects common introns in protein-coding genes based on position and sequence conservation in the corresponding protein alignments. CIWOG provides dynamic web displays that facilitate detailed intron studies within orthologous genes. User-supplied options control how introns are clustered into sets of common introns. CIWOG also identifies special classes of introns, in particular those with GC- or U12-type donor sites, which enables analyses of these introns in relation to their counterparts in the other genes in orthologous groups. The software is demonstrated with application to a comprehensive study of eight plant transcriptomes. Three specific examples are discussed: intron class conversion from GT- to GC-donor-type introns in monocots, plant U12-type intron conservation and a global analysis of intron evolution across the eight plant species.

BACKGROUND: Translation initiation site (TIS) identification is an important aspect of the gene annotation process, requisite for the accurate delineation of protein sequences from transcript data. We have developed the MetWAMer package for TIS prediction in eukaryotic open reading frames of non-viral origin. MetWAMer can be used as a stand-alone, third-party tool for post-processing gene structure annotations generated by external computational programs and/or pipelines, or directly integrated into gene structure prediction software implementations. RESULTS: MetWAMer currently implements five distinct methods for TIS prediction, the most accurate of which is a routine that combines weighted, signal-based translation initiation site scores and the contrast in coding potential of sequences flanking TISs using a perceptron. Also, our program implements clustering capabilities through use of the k-medoids algorithm, thereby enabling cluster-specific TIS parameter utilization. In practice, our static weight array matrix-based indexing method for parameter set lookup can be used with good results in data sets exhibiting moderate levels of 5'-complete coverage. CONCLUSION: We demonstrate that improvements in statistically-based models for TIS prediction can be achieved by taking the class of each potential start-methionine into account pending certain testing conditions, and that our perceptron-based model is suitable for the TIS identification task. MetWAMer represents a well-documented, extensible, and freely available software system that can be readily re-trained for differing target applications and/or extended with existing and novel TIS prediction methods, to support further research efforts in this area.

BACKGROUND: Whole genome shotgun sequencing produces increasingly higher coverage of a genome with random sequence reads. Progressive whole genome assembly and eventual finishing sequencing is a process that typically takes several years for large eukaryotic genomes. In the interim, all sequence reads of public sequencing projects are made available in repositories such as the NCBI Trace Archive. For a particular locus, sequencing coverage may be high enough early on to produce a reliable local genome assembly. We have developed software, Tracembler, that facilitates in silico chromosome walking by recursively assembling reads of a selected species from the NCBI Trace Archive starting with reads that significantly match sequence seeds supplied by the user. RESULTS: Tracembler takes one or multiple DNA or protein sequence(s) as input to the NCBI Trace Archive BLAST engine to identify matching sequence reads from a species of interest. The BLAST searches are carried out recursively such that BLAST matching sequences identified in previous rounds of searches are used as new queries in subsequent rounds of BLAST searches. The recursive BLAST search stops when either no more new matching sequences are found, a given maximal number of queries is exhausted, or a specified maximum number of rounds of recursion is reached. All the BLAST matching sequences are then assembled into contigs based on significant sequence overlaps using the CAP3 program. We demonstrate the validity of the concept and software implementation with an example of successfully recovering a full-length Chrm2 gene as well as its upstream and downstream genomic regions from Rattus norvegicus reads. In a second example, a query with two adjacent Medicago truncatula genes as seeds resulted in a contig that likely identifies the microsyntenic homologous soybean locus. CONCLUSION: Tracembler streamlines the process of recursive database searches, sequence assembly, and gene identification in resulting contigs in attempts to identify homologous loci of genes of interest in species with emerging whole genome shotgun reads. A web server hosting Tracembler is provided at http://www.plantgdb.org/tool/tracembler/, and the software is also freely available from the authors for local installations.

MOTIVATION: The recent efforts of various sequence projects to sequence deeply into various phylogenies provide great resources for comparative sequence analysis. A generic and portable tool is essential for scientists to visualize and analyze sequence comparisons. RESULTS: We have developed SynBrowse, a synteny browser for visualizing and analyzing genome alignments both within and between species. It is intended to help scientists study macrosynteny, microsynteny and homologous genes between sequences. It can also aid with the identification of uncharacterized genes, putative regulatory elements and novel structural features of a species. SynBrowse is a GBrowse (the Generic Genome Browser) family software tool that runs on top of the open source BioPerl modules. It consists of two components: a web-based front end and a set of relational database back ends. Each database stores pre-computed alignments from a focus sequence to reference sequences in addition to the genome annotations of the focus sequence. The user interface lets end users select a key comparative alignment type and search for syntenic blocks between two sequences and zoom in to view the relationships among the corresponding genome annotations in detail. SynBrowse is portable with simple installation, flexible configuration, convenient data input and easy integration with other components of a model organism system. AVAILABILITY: The software is available at http://www.gmod.org CONTACT: [email protected]

MOTIVATION: The vast majority of introns in protein-coding genes of higher eukaryotes have a GT dinucleotide at their 5'-terminus and an AG dinucleotide at their 3' end. About 1-2% of introns are non-canonical, with the most abundant subtype of non-canonical introns being characterized by GC and AG dinucleotides at their 5'- and 3'-termini, respectively. Most current gene prediction software, whether based on ab initio or spliced alignment approaches, does not include explicit models for non-canonical introns or may exclude their prediction altogether. With present amounts of genome and transcript data, it is now possible to apply statistical methodology to non-canonical splice site prediction. We pursued one such approach and describe the training and implementation of GC-donor splice site models for Arabidopsis and rice, with the goal of exploring whether specific modeling of non-canonical introns can enhance gene structure prediction accuracy. RESULTS: Our results indicate that the incorporation of non-canonical splice site models yields dramatic improvements in annotating genes containing GC-AG and AT-AC non-canonical introns. Comparison of models shows differences between monocot and dicot species, but also suggests GC intron-specific biases independent of taxonomic clade. We also present evidence that GC-AG introns occur preferentially in genes with atypically high exon counts. AVAILABILITY: Source code for the updated versions of GeneSeqer and SplicePredictor (distributed with the GeneSeqer code) isavailable at http://bioinformatics.iastate.edu/bioinformatics2go/gs/download.html. Web servers for Arabidopsis, rice and other plant species are accessible at http://www.plantgdb.org/PlantGDB-cgi/GeneSeqer/AtGDBgs.cgi, http://www.plantgdb.org/PlantGDB-cgi/GeneSeqer/OsGDBgs.cgi and http://www.plantgdb.org/PlantGDB-cgi/GeneSeqer/PlantGDBgs.cgi, respectively. A SplicePredictor web server is available at http://bioinformatics.iastate.edu/cgi-bin/sp.cgi. Software to generate training data and parameterizations for Bayesian splice site models is available at http://gremlin1.gdcb.iastate.edu/~volker/SB05B/BSSM4GSQ/

MOTIVATION: Accurate gene structure annotation is a challenging computational problem in genomics. The best results are achieved with spliced alignment of full-length cDNAs or multiple expressed sequence tags (ESTs) with sufficient overlap to cover the entire gene. For most species, cDNA and EST collections are far from comprehensive. We sought to overcome this bottleneck by exploring the possibility of using combined EST resources from fairly diverged species that still share a common gene space. Previous spliced alignment tools were found inadequate for this task because they rely on very high sequence similarity between the ESTs and the genomic DNA. RESULTS: We have developed a computer program, GeneSeqer, which is capable of aligning thousands of ESTs with a long genomic sequence in a reasonable amount of time. The algorithm is uniquely designed to tolerate a high percentage of mismatches and insertions or deletions in the EST relative to the genomic template. This feature allows use of non-cognate ESTs for gene structure prediction, including ESTs derived from duplicated genes and homologous genes from related species. The increased gene prediction sensitivity results in part from novel splice site prediction models that are also available as a stand-alone splice site prediction tool. We assessed GeneSeqer performance relative to a standard Arabidopsis thaliana gene set and demonstrate its utility for plant genome annotation. In particular, we propose that this method provides a timely tool for the annotation of the rice genome, using abundant ESTs from other cereals and plants. AVAILABILITY: The source code is available for download at http://bioinformatics.iastate.edu/bioinformatics2go/gs/download.html. Web servers for Arabidopsis and other plant species are accessible at http://www.plantgdb.org/cgi-bin/AtGeneSeqer.cgi and http://www.plantgdb.org/cgi-bin/GeneSeqer.cgi, respectively. For non-plant species, use http://bioinformatics.iastate.edu/cgi-bin/gs.cgi. The splice site prediction tool (SplicePredictor) is distributed with the GeneSeqer code. A SplicePredictor web server is available at http://bioinformatics.iastate.edu/cgi-bin/sp.cgi

Expressed sequence tags, abbreviated as ESTs, are DNA molecules experimentally derived from expressed portions of genes. Clustering of ESTs is essential for gene recognition and for understanding important genetic variations such as those resulting in diseases. We present the algorithmic foundations and implementation of PaCE, a parallel software system we developed for large-scale EST clustering. The novel features of our approach include 1) design of space-efficient algorithms to limit the space required to linear in the size of the input data set, 2) a combination of algorithmic techniques to reduce the total work without sacrificing the quality of EST clustering, and 3) use of parallel processing to reduce runtime and facilitate clustering of large data sets. Using a combination of these techniques, we report the clustering of 327,632 rat ESTs in 47 minutes, and 420,694 Triticum aestivum ESTs in 3 hours and 15 minutes, using a 60-processor IBM xSeries cluster. These problems are well beyond the capabilities of state-of-the-art sequential software. We also present thorough experimental evaluation of our software including quality assessment using benchmark Arabidopsis EST data.

UNLABELLED: MyGV is an application to visualize (potentially genome-scale) gene structure annotation and prediction. The output of any external gene prediction program can be easily converted to a generalized format for input into MyGV. The application displays all input simultaneously in graphical representation, with a toggle option for a text-based view. Zooming capabilities allow detailed comparisons for specific genome locations. The tool is particularly helpful for refinement of ab initio predicted gene structures by spliced alignment with cDNA or protein homologs. AVAILABILITY: The program was written in Java and is freely available to non-commercial users by electronic download from http://bioinformatics.iastate.edu/bioinformatics2go/MyGV.

SUMMARY: MuSeqBox is a program to parse BLAST output and store attributes of BLAST hits in tabular form. The user can apply a number of selection criteria to filter out hits with particular attributes. MuSeqBox provides a powerful annotation tool for large sets of query sequences that are simultaneously compared against a database with any of the standard stand-alone or network-client BLAST programs. We discuss such application to the problem of annotation and analysis of EST collections. AVAILABILITY: The program was written in standard C++ and is freely available to noncommercial users by request from the authors. The program is also available over the web at http://bioinformatics.iastate.edu/bioinformatics2go/mb/MuSeqBox.html.

MOTIVATION: Supplementary cDNA or EST evidence is often decisive for discriminating between alternative gene predictions derived from computational sequence inspection by any of a number of requisite programs. Without additional experimental effort, this approach must rely on the occurrence of cognate ESTs for the gene under consideration in available, generally incomplete, EST collections for the given species. In some cases, particular exon assignments can be supported by sequence matching even if the cDNA or EST is produced from non-cognate genomic DNA, including different loci of a gene family or homologous loci from different species. However, marginally significant sequence matching alone can also be misleading. We sought to develop an algorithm that would simultaneously score for predicted intrinsic splice site strength and sequence matching between the genomic DNA template and a related cDNA or EST. In this case, weakly predicted splice sites may be chosen for the optimal scoring spliced alignment on the basis of surrounding sequence matching. Strongly predicted splice sites will enter the optimal spliced alignment even without strong sequence matching. RESULTS: We designed a novel algorithm that produces the optimal spliced alignment of a genomic DNA with a cDNA or EST based on scoring for both sequence matching and intrinsic splice site strength. By example, we demonstrate that this combined approach appears to improve gene prediction accuracy compared with current methods that rely only on either search by content and signal or on sequence similarity. AVAILABILITY: The algorithm is available as a C subroutine and is implemented in the SplicePredictor and GeneSeqer programs. The source code is available via anonymous ftp from ftp. zmdb.iastate.edu. Both programs are also implemented as a Web service at http://gremlin1.zool.iastate.edu/cgi-bin/s p.cgiand http://gremlin1.zool.iastate.edu/cgi-bin/g s.cgi, respectively. CONTACT: [email protected]

MOTIVATION: We developed GeneGenerator because of the need for a tool to predict gene structure without knowing in advance how to score potential exons and introns in order to obtain the best results, pertinent in particular to less well-studied organisms for which suitable training sets are small. GeneGenerator is a very flexible algorithm which for a given genomic sequence generates a number of feasible gene structures satisfying user-defined constraints. The specific implementation described in detail requires minimum scoring for translation start and donor and acceptor splice sites according to previously trained logitlinear models. In addition, potential exons and introns are required to exceed specified minimal lengths and threshold scores for coding or non-coding potential derived as log-likelihood ratios of appropriate Markov sequence models. RESULTS: A database of 46 non-redundant genomic sequences from maize is used for illustration. It is shown that the correct gene structures do not always maximize the considered target function. However, in most cases, the correct or nearly correct structures are found in a small set of high-scoring structures. A critical review of the generated structures sometimes allows the choices to be narrowed by considering additional variables such as predicted splice site strength or local optimality of splice site scores. Summary statistics for prediction accuracy over all 46 maize genes are derived under cross-validation and non-cross-validation training conditions for the Markov sequence models. The algorithm achieved exon sensitivity of 0.81 and specificity of 0.75 on an independent set of 14 novel maize genomic segments. AVAILABILITY: GeneGenerator runs under Borland-Pascal 7.0 using MS-DOS and C on UNIX work stations. The source code is available upon request. CONTACT: [email protected]