An overview of the wcd EST clustering tool.
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ABSTRACT: Unlabelled
The wcd system is an open source tool for clustering expressed sequence tags (EST) and other DNA and RNA sequences. wcd allows efficient all-versus-all comparison of ESTs using either the d(2) distance function or edit distance, improving existing implementations of d(2). It supports merging, refinement and reclustering of clusters. It is 'drop in' compatible with the StackPack clustering package. wcd supports parallelization under both shared memory and cluster architectures. It is distributed with an EMBOSS wrapper allowing wcd to be installed as part of an EMBOSS installation (and so provided by a web server).Availability
wcd is distributed under a GPL licence and is available from http://code.google.com/p/wcdest.Supplementary information
Additional experimental results. The wcd manual, a companion paper describing underlying algorithms, and all datasets used for experimentation can also be found at www.bioinf.wits.ac.za/~scott/wcdsupp.html.
SUBMITTER: Hazelhurst S
PROVIDER: S-EPMC2718666 | biostudies-literature | 2008 Jul
REPOSITORIES: biostudies-literature
Publications
An overview of the wcd EST clustering tool.
Hazelhurst Scott S Hide Winston W Lipták Zsuzsanna Z Nogueira Ramon R Starfield Richard R
Bioinformatics (Oxford, England) 20080514 13
<h4>Unlabelled</h4>The wcd system is an open source tool for clustering expressed sequence tags (EST) and other DNA and RNA sequences. wcd allows efficient all-versus-all comparison of ESTs using either the d(2) distance function or edit distance, improving existing implementations of d(2). It supports merging, refinement and reclustering of clusters. It is 'drop in' compatible with the StackPack clustering package. wcd supports parallelization under both shared memory and cluster architectures. ...[more]
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Project description:BACKGROUND: Single-pass, partial sequencing of complementary DNA (cDNA) libraries generates thousands of chromatograms that are processed into high quality expressed sequence tags (ESTs), and then assembled into contigs representative of putative genes. Usually, to be of value, ESTs and contigs must be associated with meaningful annotations, and made available to end-users. RESULTS: A web application, Expressed Sequence Tag Information Management and Annotation (ESTIMA), has been created to meet the EST annotation and data management requirements of multiple high-throughput EST sequencing projects. It is anchored on individual ESTs and organized around different properties of ESTs including chromatograms, base-calling quality scores, structure of assembled transcripts, and multiple sources of comparison to infer functional annotation, Gene Ontology associations, and cDNA library information. ESTIMA consists of a relational database schema and a set of interactive query interfaces. These are integrated with a suite of web-based tools that allow a user to query and retrieve information. Further, query results are interconnected among the various EST properties. ESTIMA has several unique features. Users may run their own EST processing pipeline, search against preferred reference genomes, and use any clustering and assembly algorithm. The ESTIMA database schema is very flexible and accepts output from any EST processing and assembly pipeline. ESTIMA has been used for the management of EST projects of many species, including honeybee (Apis mellifera), cattle (Bos taurus), songbird (Taeniopygia guttata), corn rootworm (Diabrotica vergifera), catfish (Ictalurus punctatus, Ictalurus furcatus), and apple (Malus x domestica). The entire resource may be downloaded and used as is, or readily adapted to fit the unique needs of other cDNA sequencing projects. CONCLUSIONS: The scripts used to create the ESTIMA interface are freely available to academic users in an archived format from http://titan.biotec.uiuc.edu/ESTIMA/. The entity-relationship (E-R) diagrams and the programs used to generate the Oracle database tables are also available. We have also provided detailed installation instructions and a tutorial at the same website. Presently the chromatograms, EST databases and their annotations have been made available for cattle and honeybee brain EST projects. Non-academic users need to contact the W.M. Keck Center for Functional and Comparative Genomics, University of Illinois at Urbana-Champaign, Urbana, IL, for licensing information.
| S-EPMC533868 | biostudies-literature
Project description:BackgroundAllium sativum., commonly known as garlic, is a species in the onion genus (Allium), which is a large and diverse one containing over 1,250 species. Its close relatives include chives, onion, leek and shallot. Garlic has been used throughout recorded history for culinary, medicinal use and health benefits. Currently, the interest in garlic is highly increasing due to nutritional and pharmaceutical value including high blood pressure and cholesterol, atherosclerosis and cancer. For all that, there are no comprehensive databases available for Expressed Sequence Tags(EST) of garlic for gene discovery and future efforts of genome annotation. That is why we developed a new garlic database and applications to enable comprehensive analysis of garlic gene expression.DescriptionGarlicESTdb is an integrated database and mining tool for large-scale garlic (Allium sativum) EST sequencing. A total of 21,595 ESTs collected from an in-house cDNA library were used to construct the database. The analysis pipeline is an automated system written in JAVA and consists of the following components: automatic preprocessing of EST reads, assembly of raw sequences, annotation of the assembled sequences, storage of the analyzed information into MySQL databases, and graphic display of all processed data. A web application was implemented with the latest J2EE (Java 2 Platform Enterprise Edition) software technology (JSP/EJB/JavaServlet) for browsing and querying the database, for creation of dynamic web pages on the client side, and for mapping annotated enzymes to KEGG pathways, the AJAX framework was also used partially. The online resources, such as putative annotation, single nucleotide polymorphisms (SNP) and tandem repeat data sets, can be searched by text, explored on the website, searched using BLAST, and downloaded. To archive more significant BLAST results, a curation system was introduced with which biologists can easily edit best-hit annotation information for others to view. The GarlicESTdb web application is freely available at http://garlicdb.kribb.re.kr.ConclusionGarlicESTdb is the first incorporated online information database of EST sequences isolated from garlic that can be freely accessed and downloaded. It has many useful features for interactive mining of EST contigs and datasets from each library, including curation of annotated information, expression profiling, information retrieval, and summary of statistics of functional annotation. Consequently, the development of GarlicESTdb will provide a crucial contribution to biologists for data-mining and more efficient experimental studies.
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Project description:The Enzyme Function Initiative, an NIH/NIGMS-supported Large-Scale Collaborative Project (EFI; U54GM093342; http://enzymefunction.org/), is focused on devising and disseminating bioinformatics and computational tools as well as experimental strategies for the prediction and assignment of functions (in vitro activities and in vivo physiological/metabolic roles) to uncharacterized enzymes discovered in genome projects. Protein sequence similarity networks (SSNs) are visually powerful tools for analyzing sequence relationships in protein families (H.J. Atkinson, J.H. Morris, T.E. Ferrin, and P.C. Babbitt, PLoS One 2009, 4, e4345). However, the members of the biological/biomedical community have not had access to the capability to generate SSNs for their "favorite" protein families. In this article we announce the EFI-EST (Enzyme Function Initiative-Enzyme Similarity Tool) web tool (http://efi.igb.illinois.edu/efi-est/) that is available without cost for the automated generation of SSNs by the community. The tool can create SSNs for the "closest neighbors" of a user-supplied protein sequence from the UniProt database (Option A) or of members of any user-supplied Pfam and/or InterPro family (Option B). We provide an introduction to SSNs, a description of EFI-EST, and a demonstration of the use of EFI-EST to explore sequence-function space in the OMP decarboxylase superfamily (PF00215). This article is designed as a tutorial that will allow members of the community to use the EFI-EST web tool for exploring sequence/function space in protein families.
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Project description:The Diatom Expressed Sequence Tag (EST) Database was constructed to provide integral access to ESTs from these ecologically and evolutionarily interesting microalgae. It has now been updated with 130,000 Phaeodactylum tricornutum ESTs from 16 cDNA libraries and 77,000 Thalassiosira pseudonana ESTs from seven libraries, derived from cells grown in different nutrient and stress regimes. The updated relational database incorporates results from statistical analyses such as log-likelihood ratios and hierarchical clustering, which help to identify differentially expressed genes under different conditions, and allow similarities in gene expression in different libraries to be investigated in a functional context. The database also incorporates links to the recently sequenced genomes of P. tricornutum and T. pseudonana, enabling an easy cross-talk between the expression pattern of diatom orthologs and the genome browsers. These improvements will facilitate exploration of diatom responses to conditions of ecological relevance and will aid gene function identification of diatom-specific genes and in silico gene prediction in this largely unexplored class of eukaryotes. The updated Diatom EST Database is available at http://www.biologie.ens.fr/diatomics/EST3.
| S-EPMC2686495 | biostudies-literature
Project description:Chromatin Immuno Precipitation (ChIP) profiling detects in vivo protein-DNA binding, and has revealed a large combinatorial complexity in the binding of chromatin associated proteins and their post-translational modifications. To fully explore the spatial and combinatorial patterns in ChIP-profiling data and detect potentially meaningful patterns, the areas of enrichment must be aligned and clustered, which is an algorithmically and computationally challenging task. We have developed CATCHprofiles, a novel tool for exhaustive pattern detection in ChIP profiling data. CATCHprofiles is built upon a computationally efficient implementation for the exhaustive alignment and hierarchical clustering of ChIP profiling data. The tool features a graphical interface for examination and browsing of the clustering results. CATCHprofiles requires no prior knowledge about functional sites, detects known binding patterns "ab initio", and enables the detection of new patterns from ChIP data at a high resolution, exemplified by the detection of asymmetric histone and histone modification patterns around H2A.Z-enriched sites. CATCHprofiles' capability for exhaustive analysis combined with its ease-of-use makes it an invaluable tool for explorative research based on ChIP profiling data. CATCHprofiles and the CATCH algorithm run on all platforms and is available for free through the CATCH website: http://catch.cmbi.ru.nl/. User support is available by subscribing to the mailing list catch-users@bioinformatics.org.
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Project description:Sequence clustering is a fundamental step in analyzing DNA sequences. Widely-used software tools for sequence clustering utilize greedy approaches that are not guaranteed to produce the best results. These tools are sensitive to one parameter that determines the similarity among sequences in a cluster. Often times, a biologist may not know the exact sequence similarity. Therefore, clusters produced by these tools do not likely match the real clusters comprising the data if the provided parameter is inaccurate. To overcome this limitation, we adapted the mean shift algorithm, an unsupervised machine-learning algorithm, which has been used successfully thousands of times in fields such as image processing and computer vision. The theory behind the mean shift algorithm, unlike the greedy approaches, guarantees convergence to the modes, e.g. cluster centers. Here we describe the first application of the mean shift algorithm to clustering DNA sequences. MeShClust is one of few applications of the mean shift algorithm in bioinformatics. Further, we applied supervised machine learning to predict the identity score produced by global alignment using alignment-free methods. We demonstrate MeShClust's ability to cluster DNA sequences with high accuracy even when the sequence similarity parameter provided by the user is not very accurate.
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Project description:The accelerating growth of the public microbial genomic data imposes substantial burden on the research community that uses such resources. Building databases for non-redundant reference sequences from massive microbial genomic data based on clustering analysis is essential. However, existing clustering algorithms perform poorly on long genomic sequences. In this article, we present Gclust, a parallel program for clustering complete or draft genomic sequences, where clustering is accelerated with a novel parallelization strategy and a fast sequence comparison algorithm using sparse suffix arrays (SSAs). Moreover, genome identity measures between two sequences are calculated based on their maximal exact matches (MEMs). In this paper, we demonstrate the high speed and clustering quality of Gclust by examining four genome sequence datasets. Gclust is freely available for non-commercial use at https://github.com/niu-lab/gclust. We also introduce a web server for clustering user-uploaded genomes at http://niulab.scgrid.cn/gclust.
| S-EPMC7056916 | biostudies-literature
Project description:BackgroundUnsupervised clustering is a common and exceptionally useful tool for large biological datasets. However, clustering requires upfront algorithm and hyperparameter selection, which can introduce bias into the final clustering labels. It is therefore advisable to obtain a range of clustering results from multiple models and hyperparameters, which can be cumbersome and slow.ResultsWe present hypercluster, a python package and SnakeMake pipeline for flexible and parallelized clustering evaluation and selection. Users can efficiently evaluate a huge range of clustering results from multiple models and hyperparameters to identify an optimal model.ConclusionsHypercluster improves ease of use, robustness and reproducibility for unsupervised clustering application for high throughput biology. Hypercluster is available on pip and bioconda; installation, documentation and example workflows can be found at: https://github.com/ruggleslab/hypercluster .
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