Owe Ohler's research focus on sequence analysis. His previous ans current research projects include: Regulation of gene expression in Arabidopsis root development; Prediction and validation of skipped mammalian exons; Analysis of transcription start sites in fungal genomes; Motif finding with Bayesian approaches; Identification of core promoter elements in Drosophila; Post-transcriptional regulation with RNA-binding proteins; Regulation of neuronal gene expression in C elegans Pavel Tomancak; Embryonic expression patterns in Drosophila. It is worth mentioning that his research also deal with gene expression analysis, but I am not familiar with his thoughts and methods in this field. So I will focus on part of his research: alternative splicing site identification and promoter prediction.
Ohler U, Shomron N, Burge CB (2005) Recognition of Unknown Conserved Alternatively Spliced Exons. PLoS Comput Biol 1(2): e15 doi:10.1371/journal.pcbi.0010015
Ohler has scientific collaboration with Christopher B. Burge, from MIT, probably a BIG guy in this area. Pay attention to him.
What use is the identification of alternative splicing sites of. The author says that "The identification of such variants has until recently relied solely on the sequencing and comparison of expressed sequence tags (ESTs), but the number of available ESTs is not large enough to cover all variants under all conditions" According a Nature Genetics Review, which I reviewed in last post, the development of microarray platform for finding unknown exons are on the way. Probably, even a microarray experiment can not still covers all variants under all conditions. Thus a preliminary computational prediction gives many possible alternative splicing sites, among which many may be false positive, which can be tested by a microarray experiment. Such prediction may also help the design of the array.
Method: pair hidden Markov model
Patterns of flanking sequence conservation and a characteristic upstream motif for microRNA gene identification RNA (2004), 10:1309-1322
Quantification of transcription factor expression from Arabidopsis images Bioinformatics 2006 22(14):e323-e331; doi:10.1093/bioinformatics/btl228In spite of the great success of microarray technique in gene expression profiling, it fails to detect spatial features of gene expression, thus the confocal microscopy can also provide quantitative information of gene expression with greater spatial and temporal resolution. This paper describes a software protocol of analyzing confocal microscopy images. (How the high-throughput is achieved?)
GFP transcriptional fusion GFP serves as marker of mRNA expression level
GFP translational fusion