Persagen Consulting: Bioinformatics
Bioinformatics is the application of statistics and computer science to the field of molecular biology. Bioinformatics entails the creation and advancement of databases, algorithms, computational and statistical techniques and theory to solve formal and practical problems arising from the management and analysis of the tremendous amount of molecular biology data being generated in various genomic studies. Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning different DNA and protein sequences to compare them, creating and viewing 3-D models of protein structures, and visualizing and analyzing the complex interactions of genes, and their products (proteins; RNA regulatory molecules, etc.) with one another. The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches is its focus on developing and applying computationally intensive techniques (e.g., pattern recognition, data mining, machine learning algorithms, and visualization) to achieve this goal. Major research efforts in the field include sequence alignment, gene finding, genome assembly, drug design, drug discovery, protein structure alignment, protein structure prediction, prediction of gene expression and protein-protein interactions, genome-wide association studies and the modeling of evolution. [Source: Wikipedia]
In my own research involving gene expression studies of the yeast Saccharomyces cerevisiae,
Stuart, G.R. et al. (2009) "Transcriptional response to mitochondrial NADH kinase deficiency in Saccharomyces cerevisiae." Mitochondrion 9:211-221. [PMID: 19254780], and
Stuart, G.R. et al. (2009) "Construction and application of a protein and genetic interaction network (yeast interactome)." Nucleic Acids Research 37:e54. [PMID: 19273534]
I developed and employed a high-quality, comprehensive yeast protein-protein and DNA-protein interaction network (an "interactome") for use with the bioinformatic platform Cytoscape, upon which I was able to overlay my yeast gene expression data, applying algorithms to find regions of the interactome that were significantly perturbed by the experimental treatment (e.g., deletion of a yeast gene, or treatment of the yeast strain with hydrogen peroxide, an oxidant), and visualize the results.
This work was aided by additional bioinformatic analyses involving Agilent GeneSpring GX (a gene expression analysis platform), GenMAPP (for metabolic pathway and gene ontology mapping), OmniViz (for data mining and visualization), GoMiner (for gene ontology analyses), and custom scripts that I developed to sort and analyze these data using Microsoft Excel and OpenOffice Calc spreadsheets.
In my more recent work (for a client through my consultancy; proprietary & unpublished) I have been analyzing genomic expression data for a U.S. federal client, involving the Rhesus monkey Macaca mulatta (Rhesus macaque). As in this study we are we are ultimately interested in corresponding health effects in humans, as for my yeast work, I created a human interactome for use with this dataset and Cytoscape; additional analyses again also involved Agilent GeneSpring GX, and custom scripts that I wrote in OpenOffice Calc (e.g. to calculate fold-changes, and to sort and parse the data based on statistical significance, based on standard deviations).
In addition to my genomics bioinformatics research, I am also employing a very sophisticated data mining and text / literature mining suite, RapidMiner.
This brief overview gives some indication of the types of bioinformatic analyses that I can provide; please contact me (mail @ persagen.com) to discuss how I may assist you with your own data.
This need not be limited to genetic / gene expression data; the same approaches can be applied to text mining and literature mining, of any body (database) of information: e.g. marketing / customer data; medical data; business records; etc.
As always, complete confidentiality is assured!
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