Improved statistical inference from DNA microarray data using analysis of variance and a Bayesian statistical framework. Analysis of global gene expression in Escherichia coli K12

We describe statistical methods based on the t test that can be conveniently used on high density array data to test for statistically significant differences between treatments. These t tests employ either the observed variance among replicates within treatments or a Bayesian estimate of the variance among replicates within treatments based on a prior estimate obtained from a local estimate of the standard deviation. The Bayesian prior allows statistical inference to be made from microarray data even when experiments are only replicated at nominal levels. We apply these new statistical tests to a data set that examined differential gene expression patterns in IHF(+) and IHF(-) Escherichia coli cells (Arfin, S. M., Long, A. D., Ito, E. T., Tolleri, L., Riehle, M. M., Paegle, E. S., and Hatfield, G. W. (2000) J. Biol. Chem. 275, 29672-29684). These analyses identify a more biologically reasonable set of candidate genes than those identified using statistical tests not incorporating a Bayesian prior. We also show that statistical tests based on analysis of variance and a Bayesian prior identify genes that are up- or down-regulated following an experimental manipulation more reliably than approaches based only on a t test or fold change. All the described tests are implemented in a simple-to-use web interface called Cyber-T that is located on the University of California at Irvine genomics web site.     

Analysis of superfamily specific profile-profile recognition accuracy

Background  

Annotation of sequences that share little similarity to sequences of known function remains a major obstacle in genome annotation. Some of the best methods of detecting remote relationships between protein sequences are based on matching sequence profiles. We analyse the superfamily specific performance of sequence profile-profile matching. Our benchmark consists of a set of 16 protein superfamilies that are highly diverse at the sequence level. We relate the performance to the number of sequences in the profiles, the profile diversity and the extent of structural conservation in the superfamily.     

Characterization of the mouse hnRNP A2/B1/B0 gene and identification of processed pseudogenes

The mouse hnRNP A2/B1/B0 gene has been cloned using a PCR-based strategy and sequenced. Analysis of this sequence showed that the gene organization closely follows that of the human orthologue with 12 exons and 11 introns. The hnRNP A2/B1/B0 gene gives rise to four splice variants through alternative splicing of exons 2 and 9. RT-PCR assays indicated that all splice variants were expressed in mouse brain, skin, and stomach tissues of varying ages, although their ratios to one another varied with age and tissue type. We also identified a small subset of all polyadenylated splice variants that included intron 11, which shows 94% sequence identity between human and mouse. Several processed pseudogenes were identified in the mouse genome. A search of the mouse genome databases located five pseudogenes, four of which are presumed to be non-functional because of the presence of premature stop codons, large deletions or rearrangements within the coding region. The fifth, which possesses putative promoter elements and has a coding sequence identical to that of the hnRNP A2 mRNA variant, may be functional.     

Diagnostic criteria for diabetes revisited: making use of combined criteria

Background  

Existing cut-offs for fasting plasma glucose (FPG) and post-load glucose (2hPG) criteria are not equivalent in the diagnosis of diabetes and glucose intolerance. Adjusting cut-offs of single measurements have not helped so we undertook this project to see if they could be complementary.     

DNA sequence and structure: direct and indirect recognition in protein-DNA binding

MOTIVATION: Direct recognition, or direct readout, of DNA bases by a DNA-binding protein involves amino acids that interact directly with features specific to each base. Experimental evidence also shows that in many cases the protein achieves partial sequence specificity by indirect recognition, i.e., by recognizing structural properties of the DNA. (1) Could threading a DNA sequence onto a crystal structure of bound DNA help explain the indirect recognition component of sequence specificity? (2) Might the resulting pure-structure computational motif manifest itself in familiar sequence-based computational motifs? RESULTS: The starting structure motif was a crystal structure of DNA bound to the integration host factor protein (IHF) of E. coli. IHF is known to exhibit both direct and indirect recognition of its binding sites. (1) Threading DNA sequences onto the crystal structure showed statistically significant partial separation of 60 IHF binding sites from random and intragenic sequences and was positively correlated with binding affinity. (2) The crystal structure was shown to be equivalent to a linear Markov network, and so, to a joint probability distribution over sequences, computable in linear time. It was transformed algorithmically into several common pure-sequence representations, including (a) small sets of short exact strings, (b) weight matrices, (c) consensus regular patterns, (d) multiple sequence alignments, and (e) phylogenetic trees. In all cases the pure-sequence motifs retained statistically significant partial separation of the IHF binding sites from random and intragenic sequences. Most exhibited positive correlation with binding affinity. The multiple alignment showed some conserved columns, and the phylogenetic tree partially mixed low-energy sequences with IHF binding sites but separated high-energy sequences. The conclusion is that deformation energy explains part of indirect recognition, which explains part of IHF sequence-specific binding.