Effect of false positive and false negative rates on inference of binding target conservation across different conditions and species from ChIP-chip data

<p>Abstract</p> <p>Background</p> <p>ChIP-chip data are routinely used to identify transcription factor binding targets. However, the presence of false positives and false negatives in ChIP-chip data complicates and hinders analyses, especially when the binding targets for a specific transcription factor are compared across conditions or species.</p> <p>Results</p> <p>We propose an Expectation Maximization based approach to infer the underlying true counts of "positives" and "negatives" from the observed counts. Based on this approach, we study the effect of false positives and false negatives on inferences related to transcription regulation.</p> <p>Conclusion</p> <p>Our results indicate that if there is a significant degree of association among the binding targets across conditions/species (log odds ratio > 4), moderate values of false positive and false negative rates (0.005 and 0.4 respectively) would not change our inference qualitatively (i.e. the presence or absence of conservation) based on the observed experimental data despite a significant change in the observed counts. However, if the underlying association is marginal, with odds ratios close to 1, moderate to large values of false positive and false negative rates (0.01 and 0.2 respectively) could mask the underlying association.</p

Strategies for increasing the applicability of biological network inference

The manipulation of cellular state has many promising applications, including stem cell biology and regenerative medicine, biofuel production, and stress resistant crop development. The construction of interaction maps promises to enhance our ability to engineer cellular behavior. Within the last 15 years, many methods have been developed to infer the structure of the gene regulatory interaction map from gene abundance snapshots provided by high-throughput experimental data. However, relatively little research has focused on using gene regulatory network models for the prediction and manipulation of cellular behavior. This dissertation examines and applies strategies to utilize the predictive power of gene network models to guide experimentation and engineering efforts. First, we developed methods to improve gene network models by integrating interaction evidence sources, in order to utilize the full predictive power of the models. Next, we explored the power of networks models to guide experimental efforts through inference and analysis of a regulatory network in the pathogenic fungus Cryptococcus neoformans. Finally, we develop a novel, network-guided algorithm to select genetic interventions for engineering transcriptional state. We apply this method to select intervention strains for improving biofuel production in a mixed glucose-xylose environment. The contributions in this dissertation provide the first thorough examination, systematic application, and quantitative evaluation of the utilization of network models for guiding cellular engineering

Evidence-ranked motif identification

A new computational method for the identification of regulatory motifs from large genomic datasets is presented her

Contribution of Transcription Factor Binding Site Motif Variants to Condition-Specific Gene Expression Patterns in Budding Yeast

It is now experimentally well known that variant sequences of a cis transcription factor binding site motif can contribute to differential regulation of genes. We characterize the relationship between motif variants and gene expression by analyzing expression microarray data and binding site predictions. To accomplish this, we statistically detect motif variants with effects that differ among environments. Such environmental specificity may be due to either affinity differences between variants or, more likely, differential interactions of TFs bound to these variants with cofactors, and with differential presence of cofactors across environments. We examine conservation of functional variants across four Saccharomyces species, and find that about a third of transcription factors have target genes that are differentially expressed in a condition-specific manner that is correlated with the nucleotide at variant motif positions. We find good correspondence between our results and some cases in the experimental literature (Reb1, Sum1, Mcm1, and Rap1). These results and growing consensus in the literature indicates that motif variants may often be functionally distinct, that this may be observed in genomic data, and that variants play an important role in condition-specific gene regulation

Modeling the Evolution of Regulatory Elements by Simultaneous Detection and Alignment with Phylogenetic Pair HMMs

The computational detection of regulatory elements in DNA is a difficult but important problem impacting our progress in understanding the complex nature of eukaryotic gene regulation. Attempts to utilize cross-species conservation for this task have been hampered both by evolutionary changes of functional sites and poor performance of general-purpose alignment programs when applied to non-coding sequence. We describe a new and flexible framework for modeling binding site evolution in multiple related genomes, based on phylogenetic pair hidden Markov models which explicitly model the gain and loss of binding sites along a phylogeny. We demonstrate the value of this framework for both the alignment of regulatory regions and the inference of precise binding-site locations within those regions. As the underlying formalism is a stochastic, generative model, it can also be used to simulate the evolution of regulatory elements. Our implementation is scalable in terms of numbers of species and sequence lengths and can produce alignments and binding-site predictions with accuracy rivaling or exceeding current systems that specialize in only alignment or only binding-site prediction. We demonstrate the validity and power of various model components on extensive simulations of realistic sequence data and apply a specific model to study Drosophila enhancers in as many as ten related genomes and in the presence of gain and loss of binding sites. Different models and modeling assumptions can be easily specified, thus providing an invaluable tool for the exploration of biological hypotheses that can drive improvements in our understanding of the mechanisms and evolution of gene regulation

Normalization and centering of array-based heterologous genome hybridization based on divergent control probes

<p>Abstract</p> <p>Background</p> <p>Hybridization of heterologous (non-specific) nucleic acids onto arrays designed for model-organisms has been proposed as a viable genomic resource for estimating sequence variation and gene expression in non-model organisms. However, conventional methods of normalization that assume equivalent distributions (such as quantile normalization) are inappropriate when applied to non-specific (heterologous) hybridization. We propose an algorithm for normalizing and centering intensity data from heterologous hybridization that makes no prior assumptions of distribution, reduces the false appearance of homology, and provides a way for researchers to confirm whether heterologous hybridization is suitable.</p> <p>Results</p> <p>Data are normalized by adjusting for Gibbs free energy binding, and centered by adjusting for the median of a common set of control probes assumed to be equivalently dissimilar for all species. This procedure was compared to existing approaches and found to be as successful as Loess normalization at detecting sequence variations (deletions) and even more successful than quantile normalization at reducing the accumulation of false positive probe matches between two related nematode species, <it>Caenorhabditis elegans </it>and <it>C. briggsae</it>. Despite the improvements, we still found that probe fluorescence intensity was too poorly correlated with sequence similarity to result in reliable detection of matching probe sequence.</p> <p>Conclusions</p> <p>Cross-species hybridizations can be a way to adapt genome-enabled tools for closely related non-model organisms, but data must be appropriately normalized and centered in a way that accommodates hybridization of nucleic acids with diverged sequence. For short, 25-mer probes, hybridization intensity alone may be insufficiently correlated with sequence similarity to allow reliable inference of homology at the probe level.</p

Contribution of Transcription Factor Binding Site Motif Variants to Condition-Specific Gene Expression Patterns in Budding Yeast

It is now experimentally well known that variant sequences of a cis transcription factor binding site motif can contribute to differential regulation of genes. We characterize the relationship between motif variants and gene expression by analyzing expression microarray data and binding site predictions. To accomplish this, we statistically detect motif variants with effects that differ among environments. Such environmental specificity may be due to either affinity differences between variants or, more likely, differential interactions of TFs bound to these variants with cofactors, and with differential presence of cofactors across environments. We examine conservation of functional variants across four Saccharomyces species, and find that about a third of transcription factors have target genes that are differentially expressed in a condition-specific manner that is correlated with the nucleotide at variant motif positions. We find good correspondence between our results and some cases in the experimental literature (Reb1, Sum1, Mcm1, and Rap1). These results and growing consensus in the literature indicates that motif variants may often be functionally distinct, that this may be observed in genomic data, and that variants play an important role in condition-specific gene regulation.</p