Searches protein database using a translated nucleotide query. BLASTX is a BLAST search application that compares the six-frame conceptual translation products of a nucleotide query sequence (both strands) against a protein sequence database. This application can also work in Blast2Sequences mode and can send BLAST searches over the network to public NCBI server if desired.
Provides a web-based analytical pipeline for high-throughput metabolomics studies. MetaboAnalyst aims to offer a variety of commonly used procedures for metabolomic data processing, normalization, multivariate statistical analysis, as well as data annotation. The current implementation focuses on exploratory statistical analysis, functional interpretation, and advanced statistics for translational metabolomics studies. This tool is also available as desktop version.
Accelerates discovery research with systems biology content, analytics, and expertise. MetaCore is an integrated software suite for functional analysis of Next Generation Sequencing (NGS), gene expression, copy number variation (CNV), metabolic, proteomics, microRNA, and screening data. This method is based on a high-quality, manually-curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism and toxicity information.
Assists users in performing microbial genome annotation, data management and comparative analysis. MicroScope furnishes an environment permitting researchers to perform specifically comparative analysis of prokaryotic genomes, and manual curation of gene function in a comparative genomics and metabolic context. Moreover, this tool was used for annotating microbial genomes, transcriptomic and re-sequencing data.
A web-based tool for the visualization and analysis of cellular pathways. Its primary map summarizes the metabolism in biological systems as annotated to date. Nodes in the map correspond to various chemical compounds and edges represent series of enzymatic reactions.
An algorithm for the inference of gene regulatory networks from expression data. It decomposes the prediction of a regulatory network between p genes into p different regression problems. In each of the regression problems, the expression pattern of one of the genes (target gene) is predicted from the expression patterns of all the other genes (input genes), using tree-based ensemble methods Random Forests or Extra-Trees. The importance of an input gene in the prediction of the target gene expression pattern is taken as an indication of a putative regulatory link.
Offers a model investigating longitudinal and progressive dynamics of metabolic alterations of metabolic syndrome (MetS). MINGLeD depicts several metabolic pathways of glucose and lipids intending to represent the different stage of development of MetS by the use of a combination of a nonlinear ordinary differential equations (ODEs). This application can be used in conjunction with other methods to predict phenotypes.