BLAST-EXPLORER helps you building datasets for phylogenetic analysis

<p>Abstract</p> <p>Background</p> <p>The right sampling of homologous sequences for phylogenetic or molecular evolution analyses is a crucial step, the quality of which can have a significant impact on the final interpretation of the study. There is no single way for constructing datasets suitable for phylogenetic analysis, because this task intimately depends on the scientific question we want to address, Moreover, database mining softwares such as BLAST which are routinely used for searching homologous sequences are not specifically optimized for this task.</p> <p>Results</p> <p>To fill this gap, we designed BLAST-Explorer, an original and friendly web-based application that combines a BLAST search with a suite of tools that allows interactive, phylogenetic-oriented exploration of the BLAST results and flexible selection of homologous sequences among the BLAST hits. Once the selection of the BLAST hits is done using BLAST-Explorer, the corresponding sequence can be imported locally for external analysis or passed to the phylogenetic tree reconstruction pipelines available on the Phylogeny.fr platform.</p> <p>Conclusions</p> <p>BLAST-Explorer provides a simple, intuitive and interactive graphical representation of the BLAST results and allows selection and retrieving of the BLAST hit sequences based a wide range of criterions. Although BLAST-Explorer primarily aims at helping the construction of sequence datasets for further phylogenetic study, it can also be used as a standard BLAST server with enriched output. BLAST-Explorer is available at <url>http://www.phylogeny.fr</url></p

SAT, a flexible and optimized Web application for SSR marker development

<p>Abstract</p> <p>Background</p> <p>Simple Sequence Repeats (SSRs), or microsatellites, are among the most powerful genetic markers known. A common method for the development of SSR markers is the construction of genomic DNA libraries enriched for SSR sequences, followed by DNA sequencing. However, designing optimal SSR markers from bulk sequence data is a laborious and time-consuming process.</p> <p>Results</p> <p>SAT (SSR Analysis Tool) is a user-friendly Web application developed to minimize tedious manual operations and reduce errors. This tool facilitates the integration, analysis and display of sequence data from SSR-enriched libraries.</p> <p>SAT is designed to successively perform base calling and quality evaluation of chromatograms, eliminate cloning vector, adaptors and low quality sequences, detect chimera or partially digested sequences, search for SSR motifs, cluster and assemble the redundant sequences, and design SSR primer pairs. An additional virtual PCR step establishes primer specificity. Users may modify the different parameters of each step of the SAT analysis.</p> <p>Although certain steps are compulsory, such as SSR motifs search and sequence assembly, users do not have to run the entire pipeline, and they can choose selectively which steps to perform. A database allows users to store and query results, and to redo individual steps of the workflow.</p> <p>Conclusion</p> <p>The SAT Web application is available at <url>http://sat.cirad.fr/sat</url>, and a standalone command-line version is also freely downloadable. Users must send an email to the SAT administrator <email>[email protected]</email> to request a login and password.</p

Gigwa—Genotype investigator for genome-wide analyses

Background Exploring the structure of genomes and analyzing their evolution is essential to understanding the ecological adaptation of organisms. However, with the large amounts of data being produced by next-generation sequencing, computational challenges arise in terms of storage, search, sharing, analysis and visualization. This is particularly true with regards to studies of genomic variation, which are currently lacking scalable and user-friendly data exploration solutions. Description Here we present Gigwa, a web-based tool that provides an easy and intuitive way to explore large amounts of genotyping data by filtering it not only on the basis of variant features, including functional annotations, but also on genotype patterns. The data storage relies on MongoDB, which offers good scalability properties. Gigwa can handle multiple databases and may be deployed in either single- or multi-user mode. In addition, it provides a wide range of popular export formats. Conclusions The Gigwa application is suitable for managing large amounts of genomic variation data. Its user-friendly web interface makes such processing widely accessible. It can either be simply deployed on a workstation or be used to provide a shared data portal for a given community of researchers. (Résumé d'auteur