Making sense of EST sequences by CLOBBing them

BACKGROUND: Expressed sequence tags (ESTs) are single pass reads from randomly selected cDNA clones. They provide a highly cost-effective method to access and identify expressed genes. However, they are often prone to sequencing errors and typically define incomplete transcripts. To increase the amount of information obtainable from ESTs and reduce sequencing errors, it is necessary to cluster ESTs into groups sharing significant sequence similarity. RESULTS: As part of our ongoing EST programs investigating 'orphan' genomes, we have developed a clustering algorithm, CLOBB (Cluster on the basis of BLAST similarity) to identify and cluster ESTs. CLOBB may be used incrementally, preserving original cluster designations. It tracks cluster-specific events such as merging, identifies 'superclusters' of related clusters and avoids the expansion of chimeric clusters. Based on the Perl scripting language, CLOBB is highly portable relying only on a local installation of NCBI's freely available BLAST executable and can be usefully applied to > 95 % of the current EST datasets. Analysis of the Danio rerio EST dataset demonstrates that CLOBB compares favourably with two less portable systems, UniGene and TIGR Gene Indices. CONCLUSIONS: CLOBB provides a highly portable EST clustering solution and is freely downloaded from: http://www.nematodes.org/CLOB

ESTpass: a web-based server for processing and annotating expressed sequence tag (EST) sequences

We present a web-based server, called ESTpass, for processing and annotating sequence data from expressed sequence tag (EST) projects. ESTpass accepts a FASTA-formatted EST file and its quality file as inputs, and it then executes a back-end EST analysis pipeline consisting of three consecutive steps. The first is cleansing the input EST sequences. The second is clustering and assembling the cleansed EST sequences using d2_cluster and CAP3 programs and producing putative transcripts. From the CAP3 output, ESTpass detects chimeric EST sequences which are confirmed through comparison with the nr database. The last step is annotating the putative transcript sequences using RefSeq, InterPro, GO and KEGG gene databases according to user-specified options. The major advantages of ESTpass are the integration of cleansing and annotating processes, rigorous chimeric EST detection, exhaustive annotation, and email reporting to inform the user about the progress and to send the analysis results. The ESTpass results include three reports (summary, cleansing and annotation) and download function, as well as graphic statistics. They can be retrieved and downloaded using a standard web browser. The server is available at http://estpass.kobic.re.kr/

ParPEST: a pipeline for EST data analysis based on parallel computing

BACKGROUND: Expressed Sequence Tags (ESTs) are short and error-prone DNA sequences generated from the 5' and 3' ends of randomly selected cDNA clones. They provide an important resource for comparative and functional genomic studies and, moreover, represent a reliable information for the annotation of genomic sequences. Because of the advances in biotechnologies, ESTs are daily determined in the form of large datasets. Therefore, suitable and efficient bioinformatic approaches are necessary to organize data related information content for further investigations. RESULTS: We implemented ParPEST (Parallel Processing of ESTs), a pipeline based on parallel computing for EST analysis. The results are organized in a suitable data warehouse to provide a starting point to mine expressed sequence datasets. The collected information is useful for investigations on data quality and on data information content, enriched also by a preliminary functional annotation. CONCLUSION: The pipeline presented here has been developed to perform an exhaustive and reliable analysis on EST data and to provide a curated set of information based on a relational database. Moreover, it is designed to reduce execution time of the specific steps required for a complete analysis using distributed processes and parallelized software. It is conceived to run on low requiring hardware components, to fulfill increasing demand, typical of the data used, and scalability at affordable costs

PartiGeneDB—collating partial genomes

Owing to the high costs involved, only 28 eukaryotic genomes have been fully sequenced to date. On the other hand, an increasing number of projects have been initiated to generate survey sequence data for a large number of other eukaryotic organisms. For the most part, these data are poorly organized and difficult to analyse. Here, we present PartiGeneDB (http://www.partigenedb.org), a publicly available database resource, which collates and processes these sequence datasets on a species-specific basis to form non-redundant sets of gene objects—which we term partial genomes. Users may query the database to identify particular genes of interest either on the basis of sequence similarity or via the use of simple text searches for specific patterns of BLAST annotation. Alternatively, users can examine entire partial genome datasets on the basis of relative expression of gene objects or by the use of an interactive Java-based tool (SimiTri), which displays sequence similarity relationships for a large number of sequence objects in a single graphic. PartiGeneDB facilitates regular incremental updates of new sequence datasets associated with both new and exisitng species. PartiGeneDB currently contains the assembled partial genomes derived from 1.83 million sequences associated with 247 different eukaryotes

PEACE: Parallel Environment for Assembly and Clustering of Gene Expression

We present PEACE, a stand-alone tool for high-throughput ab initio clustering of transcript fragment sequences produced by Next Generation or Sanger Sequencing technologies. It is freely available from www.peace-tools.org. Installed and managed through a downloadable user-friendly graphical user interface (GUI), PEACE can process large data sets of transcript fragments of length 50 bases or greater, grouping the fragments by gene associations with a sensitivity comparable to leading clustering tools. Once clustered, the user can employ the GUI's analysis functions, facilitating the easy collection of statistics and allowing them to single out specific clusters for more comprehensive study or assembly. Using a novel minimum spanning tree-based clustering method, PEACE is the equal of leading tools in the literature, with an interface making it accessible to any user. It produces results of quality virtually identical to those of the WCD tool when applied to Sanger sequences, significantly improved results over WCD and TGICL when applied to the products of Next Generation Sequencing Technology and significantly improved results over Cap3 in both cases. In short, PEACE provides an intuitive GUI and a feature-rich, parallel clustering engine that proves to be a valuable addition to the leading cDNA clustering tools

ParPEST: a pipeline for EST data analysis based on parallel computing

Background Expressed Sequence Tags (ESTs) are short and error-prone DNA sequences generated from the 5' and 3' ends of randomly selected cDNA clones. They provide an important resource for comparative and functional genomic studies and, moreover, represent a reliable information for the annotation of genomic sequences. Because of the advances in biotechnologies, ESTs are daily determined in the form of large datasets. Therefore, suitable and efficient bioinformatic approaches are necessary to organize data related information content for further investigations. Results We implemented ParPEST (Parallel Processing of ESTs), a pipeline based on parallel computing for EST analysis. The results are organized in a suitable data warehouse to provide a starting point to mine expressed sequence datasets. The collected information is useful for investigations on data quality and on data information content, enriched also by a preliminary functional annotation. Conclusion The pipeline presented here has been developed to perform an exhaustive and reliable analysis on EST data and to provide a curated set of information based on a relational database. Moreover, it is designed to reduce execution time of the specific steps required for a complete analysis using distributed processes and parallelized software. It is conceived to run on low requiring hardware components, to fulfill increasing demand, typical of the data used, and scalability at affordable cost

ButterflyBase: a platform for lepidopteran genomics

With over 100 000 species and a large community of evolutionary biologists, population ecologists, pest biologists and genome researchers, the Lepidoptera are an important insect group. Genomic resources [expressed sequence tags (ESTs), genome sequence, genetic and physical maps, proteomic and microarray datasets] are growing, but there has up to now been no single access and analysis portal for this group. Here we present ButterflyBase (http://www.butterflybase.org), a unified resource for lepidopteran genomics. A total of 273 077 ESTs from more than 30 different species have been clustered to generate stable unigene sets, and robust protein translations derived from each unigene cluster. Clusters and their protein translations are annotated with BLAST-based similarity, gene ontology (GO), enzyme classification (EC) and Kyoto encyclopaedia of genes and genomes (KEGG) terms, and are also searchable using similarity tools such as BLAST and MS-BLAST. The database supports many needs of the lepidopteran research community, including molecular marker development, orthologue prediction for deep phylogenetics, and detection of rapidly evolving proteins likely involved in host–pathogen or other evolutionary processes. ButterflyBase is expanding to include additional genomic sequence, ecological and mapping data for key species

Collembase: a repository for springtail genomics and soil quality assessment

<p>Abstract</p> <p>Background</p> <p>Environmental quality assessment is traditionally based on responses of reproduction and survival of indicator organisms. For soil assessment the springtail <it>Folsomia candida </it>(Collembola) is an accepted standard test organism. We argue that environmental quality assessment using gene expression profiles of indicator organisms exposed to test substrates is more sensitive, more toxicant specific and significantly faster than current risk assessment methods. To apply this species as a genomic model for soil quality testing we conducted an EST sequencing project and developed an online database.</p> <p>Description</p> <p>Collembase is a web-accessible database comprising springtail (<it>F. candida</it>) genomic data. Presently, the database contains information on 8686 ESTs that are assembled into 5952 unique gene objects. Of those gene objects ~40% showed homology to other protein sequences available in GenBank (blastx analysis; non-redundant (nr) database; expect-value < 10^-5). Software was applied to infer protein sequences. The putative peptides, which had an average length of 115 amino-acids (ranging between 23 and 440) were annotated with Gene Ontology (GO) terms. In total 1025 peptides (~17% of the gene objects) were assigned at least one GO term (expect-value < 10^-25). Within Collembase searches can be conducted based on BLAST and GO annotation, cluster name or using a BLAST server. The system furthermore enables easy sequence retrieval for functional genomic and Quantitative-PCR experiments. Sequences are submitted to GenBank (Accession numbers: <ext-link ext-link-type="gen" ext-link-id="EV473060">EV473060</ext-link> – <ext-link ext-link-type="gen" ext-link-id="EV481745">EV481745</ext-link>).</p> <p>Conclusion</p> <p>Collembase <url>http://www.collembase.org</url> is a resource of sequence data on the springtail <it>F. candida</it>. The information within the database will be linked to a custom made microarray, based on the Agilent platform, which can be applied for soil quality testing. In addition, Collembase supplies information that is valuable for related scientific disciplines such as molecular ecology, ecogenomics, molecular evolution and phylogenetics.</p