Christopher T. Workman

Integration of biological networks and gene expression data using Cytoscape

Cytoscape is a free software package for visualizing, modeling and analyzing molecular and genetic interaction networks. This protocol explains how to use Cytoscape to analyze the results of mRNA expression profiling, and other functional genomics and proteomics experiments, in the context of an interaction network obtained for genes of interest. Five major steps are described: (i) obtaining a gene or protein network, (ii) displaying the network using layout algorithms, (iii) integrating with gene expression and other functional attributes, (iv) identifying putative complexes and functional modules and (v) identifying enriched Gene Ontology annotations in the network. These steps provide a broad sample of the types of analyses performed by Cytoscape.

Assessing computational tools for the discovery of transcription factor binding sites

The prediction of regulatory elements is a problem where computational methods offer great hope. Over the past few years, numerous tools have become available for this task. The purpose of the current assessment is twofold: to provide some guidance to users regarding the accuracy of currently available tools in various settings, and to provide a benchmark of data sets for assessing future tools.

A new non-linear normalization method for reducing variability in DNA microarray experiments

BackgroundMicroarray data are subject to multiple sources of variation, of which biological sources are of interest whereas most others are only confounding. Recent work has identified systematic sources of variation that are intensity-dependent and non-linear in nature. Systematic sources of variation are not limited to the differing properties of the cyanine dyes Cy5 and Cy3 as observed in cDNA arrays, but are the general case for both oligonucleotide microarray (Affymetrix GeneChips) and cDNA microarray data. Current normalization techniques are most often linear and therefore not capable of fully correcting for these effects.ResultsWe present here a simple and robust non-linear method for normalization using array signal distribution analysis and cubic splines. These methods compared favorably to normalization using robust local-linear regression (lowess). The application of these methods to oligonucleotide arrays reduced the relative error between replicates by 5-10% compared with a standard global normalization method. Application to cDNA arrays showed improvements over the standard method and over Cy3-Cy5 normalization based on dye-swap replication. In addition, a set of known differentially regulated genes was ranked higher by the t-test. In either cDNA or Affymetrix technology, signal-dependent bias was more than ten times greater than the observed print-tip or spatial effects.ConclusionsIntensity-dependent normalization is important for both high-density oligonucleotide array and cDNA array data. Both the regression and spline-based methods described here performed better than existing linear methods when assessed on the variability of replicate arrays. Dye-swap normalization was less effective at Cy3-Cy5 normalization than either regression or spline-based methods alone.

Prediction of human protein function from post-translational modifications and localization features

We have developed an entirely sequence-based method that identifies and integrates relevant features that can be used to assign proteins of unknown function to functional classes, and enzyme categories for enzymes. We show that strategies for the elucidation of protein function may benefit from a number of functional attributes that are more directly related to the linear sequence of amino acids, and hence easier to predict, than protein structure. These attributes include features associated with post-translational modifications and protein sorting, but also much simpler aspects such as the length, isoelectric point and composition of the polypeptide chain.

A Systems Approach to Mapping DNA Damage Response Pathways

Failure of cells to respond to DNA damage is a primary event associated with mutagenesis and environmental toxicity. To map the transcriptional network controlling the damage response, we measured genomewide binding locations for 30 damage-related transcription factors (TFs) after exposure of yeast to methyl-methanesulfonate (MMS). The resulting 5272 TF-target interactions revealed extensive changes in the pattern of promoter binding and identified damage-specific binding motifs. As systematic functional validation, we identified interactions for which the target changed expression in wild-type cells in response to MMS but was nonresponsive in cells lacking the TF. Validated interactions were assembled into causal pathway models that provide global hypotheses of how signaling, transcription, and phenotype are integrated after damage.

ANN-Spec: a method for discovering transcription factor binding sites with improved specificity.

This work describes ANN-Spec, a machine learning algorithm and its application to discovering un-gapped patterns in DNA sequence. The approach makes use of an Artificial Neural Network and a Gibbs sampling method to define the Specificity of a DNA-binding protein. ANN-Spec searches for the parameters of a simple network (or weight matrix) that will maximize the specificity for binding sequences of a positive set compared to a background sequence set. Binding sites in the positive data set are found with the resulting weight matrix and these sites are then used to define a local multiple sequence alignment. Training complexity is O(lN) where l is the width of the pattern and N is the size of the positive training data. A quantitative comparison of ANN-Spec and a few related programs is presented. The comparison shows that ANN-Spec finds patterns of higher specificity when training with a background data set. The program and documentation are available from the authors for UNIX systems.

Evolutionary dynamics of bacteria in a human host environment

Laboratory evolution experiments have led to important findings relating organism adaptation and genomic evolution. However, continuous monitoring of long-term evolution has been lacking for natural systems, limiting our understanding of these processes in situ. Here we characterize the evolutionary dynamics of a lineage of a clinically important opportunistic bacterial pathogen, Pseudomonas aeruginosa, as it adapts to the airways of several individual cystic fibrosis patients over 200,000 bacterial generations, and provide estimates of mutation rates of bacteria in a natural environment. In contrast to predictions based on in vitro evolution experiments, we document limited diversification of the evolving lineage despite a highly structured and complex host environment. Notably, the lineage went through an initial period of rapid adaptation caused by a small number of mutations with pleiotropic effects, followed by a period of genetic drift with limited phenotypic change and a genomic signature of negative selection, suggesting that the evolving lineage has reached a major adaptive peak in the fitness landscape. This contrasts with previous findings of continued positive selection from long-term in vitro evolution experiments. The evolved phenotype of the infecting bacteria further suggests that the opportunistic pathogen has transitioned to become a primary pathogen for cystic fibrosis patients.

enoLOGOS: a versatile web tool for energy normalized sequence logos

enoLOGOS is a web-based tool that generates sequence logos from various input sources. Sequence logos have become a popular way to graphically represent DNA and amino acid sequence patterns from a set of aligned sequences. Each position of the alignment is represented by a column of stacked symbols with its total height reflecting the information content in this position. Currently, the available web servers are able to create logo images from a set of aligned sequences, but none of them generates weighted sequence logos directly from energy measurements or other sources. With the advent of high-throughput technologies for estimating the contact energy of different DNA sequences, tools that can create logos directly from binding affinity data are useful to researchers. enoLOGOS generates sequence logos from a variety of input data, including energy measurements, probability matrices, alignment matrices, count matrices and aligned sequences. Furthermore, enoLOGOS can represent the mutual information of different positions of the consensus sequence, a unique feature of this tool. Another web interface for our software, C2H2-enoLOGOS, generates logos for the DNA-binding preferences of the C2H2 zinc-finger transcription factor family members. enoLOGOS and C2H2-enoLOGOS are accessible over the web at .

Obesity and Bariatric Surgery Drive Epigenetic Variation of Spermatozoa in Humans.

Obesity is a heritable disorder, with children of obese fathers at higher risk of developing obesity. Environmental factors epigenetically influence somatic tissues, but the contribution of these factors to the establishment of epigenetic patterns in human gametes is unknown. Here, we hypothesized that weight loss remodels the epigenetic signature of spermatozoa in human obesity. Comprehensive profiling of the epigenome of sperm from lean and obese men showed similar histone positioning, but small non-coding RNA expression and DNA methylation patterns were markedly different. In a separate cohort of morbidly obese men, surgery-induced weight loss was associated with a dramatic remodeling of sperm DNA methylation, notably at genetic locations implicated in the central control of appetite. Our data provide evidence that the epigenome of human spermatozoa dynamically changes under environmental pressure and offers insight into how obesity may propagate metabolic dysfunction to the next generation.

Enrichment of regulatory signals in conserved non-coding genomic sequence

MOTIVATION Whole genome shotgun sequencing strategies generate sequence data prior to the application of assembly methodologies that result in contiguous sequence. Sequence reads can be employed to indicate regions of conservation between closely related species for which only one genome has been assembled. Consequently, by using pairwise sequence alignments methods it is possible to identify novel, non-repetitive, conserved segments in non-coding sequence that exist between the assembled human genome and mouse whole genome shotgun sequencing fragments. Conserved non-coding regions identify potentially functional DNA that could be involved in transcriptional regulation. RESULTS Local sequence alignment methods were applied employing mouse fragments and the assembled human genome. In addition, transcription factor binding sites were detected by aligning their corresponding positional weight matrices to the sequence regions. These methods were applied to a set of transcripts corresponding to 502 genes associated with a variety of different human diseases taken from the Online Mendelian Inheritance in Man database. Using statistical arguments we have shown that conserved non-coding segments contain an enrichment of transcription factor binding sites when compared to the sequence background in which the conserved segments are located. This enrichment of binding sites was not observed in coding sequence. Conserved non-coding segments are not extensively repeated in the genome and therefore their identification provides a rapid means of finding genes with related conserved regions, and consequently potentially related regulatory mechanism. Conserved segments in upstream regions are found to contain binding sites that are co-localized in a manner consistent with experimentally known transcription factor pairwise co-occurrences and afford the identification of novel co-occurring Transcription Factor (TF) pairs. This study provides a methodology and more evidence to suggest that conserved non-coding regions are biologically significant since they contain a statistical enrichment of regulatory signals and pairs of signals that enable the construction of regulatory models for human genes. CONTACT samuel.levy@celera.com.