A generalized linear model for peak calling in ChIP-Seq data

Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) has become a routine for detecting genome-wide protein-DNA interaction. The success of ChIP-Seq data analysis highly depends on the quality of peak calling (i.e., to detect peaks of tag counts at a genomic location and evaluate if the peak corresponds to a real protein-DNA interaction event). The challenges in peak calling include (1) how to combine the forward and the reverse strand tag data to improve the power of peak calling and (2) how to account for the variation of tag data observed across different genomic locations. We introduce a new peak calling method based on the generalized linear model (GLMNB) that utilizes negative binomial distribution to model the tag count data and account for the variation of background tags that may randomly bind to the DNA sequence at varying levels due to local genomic structures and sequence contents. We allow local shifting of peaks observed on the forward and the reverse stands, such that at each potential binding site, a binding profile representing the pattern of a real peak signal is fitted to best explain the observed tag data with maximum likelihood. Our method can also detect multiple peaks within a local region if there are multiple binding sites in the region.     

A multiple regression method for analysing stage-frequency data

A linear regression method that allows survival rates to vary from stage to stage is described for the analysis of stage-frequency data. It has advantages over previously suggested methods since the calculations are not iterative, and it is not necessary to have independent estimates of stage durations, numbers entering stages, or the rate of entry to stage 1. Simulation is proposed to determine standard errors for estimates of population parameters, and to assess the goodness of fit of models.     

Use of the multiple copy simultaneous search (MCSS) method to design a new class of picornavirus capsid binding drugs

A combinatorial ligand design approach based on the multiple copy simultaneous search (MCSS) method and a simple scheme for joining MCSS functional group sites was applied to the binding pocket of P3/Sabin poliovirus and rhinovirus 14. The MCSS method determines where specific functional (chemical) groups have local potential energy minima in the binding site. Before the virus application, test calculations were run to determine the optimal set of input parameters to be used in evaluating the MCSS results. The MCSS minima are analyzed and selected minima are connected with (CH₂) _n linkers to form candidate ligands, whose structures are optimized in the binding site. Estimates of the binding strength were made for the ligands and compared with those for known drugs. The results indicate that the proposed ligands should bind to P3/Sabin poliovirus at least as well as the best of the existing drugs, and that they should also bind to P1/Mahoney poliovirus and rhinovirus 14. A detailed comparison of the poliovirus and rhinovirus binding pockets and an analysis of drug binding specificity is presented. Proteins 29:32–58, 1997. © 1997 Wiley-Liss, Inc.     

Empirical bayes method for incorporating data from multiple genome scans

Individual genome scans tend to have low power and can produce markedly biased estimates of QTL effects. Further, the confidence interval for their location is often prohibitively large for subsequent fine mapping and positional cloning. Given that a large number of genome scans have been conducted, not to mention the large number of variables and subsets tested, it is difficult to confidently rule out type 1 error as an explanation for significant effects even when there is apparent replication in a separate data set. We adapted Empirical Bayes (EB) methods [1] to analyze data from multiple genome scans simultaneously and alleviate each of these problems while still allowing for different QTL population effects across studies. We investigated the effects of using the EB method to include data from background studies to update the results of a single study of interest via simulation and demonstrated that it has a stable confidence level over a wide range of parameters defining the background studies and increased the power to detect linkage, even when some of the background studies were null or had QTL effect at other markers. This EB method for incorporating data from multiple studies into genome scan analyses seems promising.     

Normalization method for metabolomics data using optimal selection of multiple internal standards

Background  

Success of metabolomics as the phenotyping platform largely depends on its ability to detect various sources of biological variability. Removal of platform-specific sources of variability such as systematic error is therefore one of the foremost priorities in data preprocessing. However, chemical diversity of molecular species included in typical metabolic profiling experiments leads to different responses to variations in experimental conditions, making normalization a very demanding task.     

MISHIMA - a new method for high speed multiple alignment of nucleotide sequences of bacterial genome scale data

Background  

Large nucleotide sequence datasets are becoming increasingly common objects of comparison. Complete bacterial genomes are reported almost everyday. This creates challenges for developing new multiple sequence alignment methods. Conventional multiple alignment methods are based on pairwise alignment and/or progressive alignment techniques. These approaches have performance problems when the number of sequences is large and when dealing with genome scale sequences.     

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation

There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.