Bioinformatics: Microarrays Analyses and Beyond
نویسنده
چکیده
We have witnessed in the past years the rapid progresses in the human genome project and biotechnologies. These advances result in many complex datasets associated with indepth scientific knowledge, e.g., genome sequences of many species, microarray expression profiles of different cell lines, single nucleotide polymorphisms (SNPs) in the human genome, etc. These data together with their underlying scientific challenges spawn the new field of Bioinformatics, which sprawls many academic disciplines as well as the pharmaceutical industry, and create one of the most exciting times for all quantitative researchers. There is no doubt that statistics will be pivotal in this new field, but it remains a challenge to us statisticians whether we can play a leading role in this biology and informatics revolution. This is not just a challenge, in fact, but also a golden opportunity for our discipline.
منابع مشابه
The SNPMaP package for R: a framework for genome-wide association using DNA pooling on microarrays
SUMMARY Large-scale genome-wide association (GWA) studies using thousands of high-density SNP microarrays are becoming an essential tool in the search for loci related to heritable variation in many phenotypes. However, the cost of GWA remains beyond the reach of many researchers. Fortunately, the majority of statistical power can still be obtained by estimating allele frequencies from DNA pool...
متن کاملDifferential coexpression analysis using microarray data and its application to human cancer
MOTIVATION Microarrays have been used to identify differential expression of individual genes or cluster genes that are coexpressed over various conditions. However, alteration in coexpression relationships has not been studied. Here we introduce a model for finding differential coexpression from microarrays and test its biological validity with respect to cancer. RESULTS We collected 10 publ...
متن کاملMolecular decomposition of complex clinical phenotypes using biologically structured analysis of microarray data
MOTIVATION Today, the characterization of clinical phenotypes by gene-expression patterns is widely used in clinical research. If the investigated phenotype is complex from the molecular point of view, new challenges arise and these have not been addressed systematically. For instance, the same clinical phenotype can be caused by various molecular disorders, such that one observes different cha...
متن کاملParaSAM: a parallelized version of the significance analysis of microarrays algorithm
MOTIVATION Significance analysis of microarrays (SAM) is a widely used permutation-based approach to identifying differentially expressed genes in microarray datasets. While SAM is freely available as an Excel plug-in and as an R-package, analyses are often limited for large datasets due to very high memory requirements. SUMMARY We have developed a parallelized version of the SAM algorithm ca...
متن کاملMGDB: crossing the marker genes of a user microarray with a database of public-microarrays marker genes
SUMMARY The microarrays performed by scientific teams grow exponentially. These microarray data could be useful for researchers around the world, but unfortunately they are underused. To fully exploit these data, it is necessary (i) to extract these data from a repository of the high-throughput gene expression data like Gene Expression Omnibus (GEO) and (ii) to make the data from different micr...
متن کامل