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A strong bioinformatics platform is essential to the storage, analysis and interpretation of Medicago functional genomics data generated from high throughput transcriptomics, proteomics and metabolomics experiments. We are developing computational biology / bioinformatics solutions and data mining tools to facilitate integrated legume biology research using systems biology approaches and "omics" technologies.
The central methods of Bioinformatics in Medicago the Medicago functional and translational genomics at the Noble Foundation are gene sequence and expression analysis using statistical pattern recognition, phylogenetic analysis, artificial intelligence and machine learning data-mining methods, biology network modeling, databases integration and "wet"-lab and workbench informatics. We are applying advanced mathematics, statistics and computer science algorithms for functional annotation of biological sequence, understanding regulation of gene expression, pathways construction and discovery, protein interaction, metabolic control theory and modeling and construction of integrated databases across genomics technology platforms.
Our research areas are focused on:| • Development of computational models for the study of gene expression regulatory mechanisms in reference legumes, Medicago truncatula, Glycine max and Lotus japonicus. The aim of the study is to determine genome-wide transcription networks of model legumes. This includes identifying key regulators (such as transcription factors and micro-RNA), modeling DNA-binding proteins and cognate promoter sequences interactions, and understating transcription networks. | |
| • Integrated informatics in functional genomics of Legume transporters. The aim of the study is to identify gene networks that control the uptake and translocation of mineral nutrients and toxic metals in nitrogen-fixing legume nodules. | |
| • Development of the comparative Legume Gene Expression Atlas (e.g. which genes are expressed in which tissues and under what conditions) for the major organ systems of the two reference legumes, Medicago truncatula and Lotus japonicus. | |
| • Enhancement of legume genomics through integration and mining of heterogeneous data from high-throughput “omics” platforms, such as Affymetrix microarrays and x-MS platforms (gene expression and metabolite levels). | |
| • Integration of metabolic profiles with gene expression patterns in intact M. truncatula tissues in order to assist in functional annotation of genes. | |
| • Various informatics support work for the forage grass and legume breeding programs. |
We are developing database systems and data analysis tools for the genomics studies in the center, with focus on the following areas:
Databases:A Database for Transcription Factor Prediction in Medicago truncatula The study of transcription factors plays an important role in the understanding of the molecular mechanism of gene regulation as a whole in plants. While several transcription factor databases of Arabidopsis have being actively developed, there is no reported work on the model legume species, Medicago truncatula, of which the gene-space genomic sequencing will be completed in the near future. We developed a pipeline and a relational database for the prediction of Medicago truncatula transcription factors. The prediction was based on transcription factor binding sites and Hidden Markov Models (HMM). The models were built mainly on documented transcription factors and their family information in Arabidopsis and a small number of known transcription factors from legume (soybean, Alfalfa and Medicago truncatula). The prediction was made on the putative genes released by the International Medicago Genome Annotation Group (IMGAG). The prediction results were further grouped into multiple families and sub-families according to the classification of their functions. We also annotated a number of transcription factors that are related to nodule mechanism. The pipeline is being periodically executed to synchronize with the monthly release of IMGAG gene calls and daily update of Medicago truncatula genomic assembly. Medicago truncatula Mutagenesis Database The Noble Foundation has initiated a Medicago truncatula insertional mutagenesis project for a near-saturation mutagenesis of Medicago truncatula using a tobacco retrotransposon Tnt1. Contingent upon funding, approximately 15,000 Tnt1 tagged M. truncatula lines, with an average of 20 insertions per line, will be produced during the next 5 years. The database aims to host the comprehensive data being generated from this project. |
Data analysis tools:
| • NEST-PIPE (Noble EST-Pipeline) is an automated pipeline for the analysis of EST sequences. Features of NEST-PIPE include sequencing error detection, sequence cleaning, vector removal, sequence assembling, and functional annotation. | |
| • PLAN (Post bLAst Navigator) is a comprehensive web application for organizing, managing and mining sequence alignment results based on BLAST search. More information is available at bioinfo.noble.org/plan/ | |
| • MET-IDEA: A Data Extraction Tool For Mass Spectrometry-based Metabolomics. MET-IDEA greatly accelerates the metabolomic data process by streamlining the critical step of transforming raw data files into an organized data matrix. It generates reliable and comprehensive datasets from major MS platforms with improved sensitivity and selectivity compared to traditional ion integration methods. MET-IDEA is compatible with a diversity of chromatographically coupled mass spectrometry systems, generates an output similar to traditional quantification methods, utilizes the sensitivity and selectivity associated with selected ion quantification, and greatly reduces the time and effort necessary to obtain large-scale organized datasets by several orders of magnitude. MET-IDEA software is available through the download area of bioinfo.noble.org | |
| • phpSSRMiner (Simple Sequence Repeat Marker Miner) effectively streamlines the process of SSR (Simple Sequence Repeat) identification, primer design, and in-silicon primer verification in a single web interface. The system is available at bioinfo.noble.org/phpssrminer |
We are also interested in application of machine learning techniques to the analysis of data from microarrays, proteomics and metabolomics in collaboration with other research groups at the Noble Foundation and the community.
See Also:
Patrick Xuechun Zhao Group: Bioinformatics / Computational Biology