MONKEY: identifying conserved transcription-factor binding sites in multiple alignments using a binding site-specific evolutionary model

Characterization of the promoter region of the murine Catsper2 gene

CATSPER2 (Cation channel sperm-associated protein 2) protein, which is part of the calcium CATSPER channel located in the membrane of the flagellar principal piece of the sperm cell, is only expressed in the testis during spermatogenesis. Deletions or mutations in the Catsper2 gene are associated with the deafness-infertility syndrome (DIS) and non-syndromic male infertility. However, the mechanisms by which Catsper2 is regulated are unknown. Here, we report the characterization of the promoter region of murine Catsper2 and the role of CTCF and CREMτ in its transcription. We report that the promoter region has transcriptional activity in both directions, as determined by observing luciferase activity in mouse Sertoli and GC-1 spg transfected cells. WGBS data analysis indicated that a CpG island identified in silico is non-methylated; Chromatin immunoprecipitation (ChIP)-seq data analysis revealed that histone marks H3K4me3 and H3K36me3 are present in the promoter and body of the Catsper2 gene respectively, indicating that Catsper2 is subject to epigenetic regulation. In addition, the murine Catsper2 core promoter was delimited to a region between -54/+189 relative to the transcription start site (TSS), where three CTCF and one CRE binding site were predicted. The functionality of these sites was determined by mutation of the CTCF sites and deletion of the CRE site. Finally, ChIP assays confirmed that CREMτ and CTCF bind to the Catsper2 minimal promoter region. This study represents the first functional analysis of the murine Catsper2 promoter region and the mechanisms that regulate its expression.

Transcription Factor Action Orchestrates the Complex Expression Pattern of CRABS CLAW in Arabidopsis

Angiosperm flowers are the most complex organs that plants generate, and in their center, the gynoecium forms, assuring sexual reproduction. Gynoecium development requires tight regulation of developmental regulators across time and tissues. How simple on and off regulation of gene expression is achieved in plants was described previously, but molecular mechanisms generating complex expression patterns remain unclear. We use the gynoecium developmental regulator CRABS CLAW (CRC) to study factors contributing to its sophisticated expression pattern. We combine in silico promoter analyses, global TF-DNA interaction screens, and mutant analyses. We find that miRNA action, DNA methylation, and chromatin remodeling do not contribute substantially to CRC regulation. However, 119 TFs, including SEP3, ETT, CAL, FUL, NGA2, and JAG bind to the CRC promoter in yeast. These TFs finetune transcript abundance as homodimers by transcriptional activation. Interestingly, temporal–spatial aspects of expression regulation may be under the control of redundantly acting genes and require higher order complex formation at TF binding sites. Our work shows that endogenous regulation of complex expression pattern requires orchestrated transcription factor action on several conserved promotor sites covering almost 4 kb in length. Our results highlight the utility of comprehensive regulators screens directly linking transcriptional regulators with their targets.

Prediction of disease-associated functional variants in noncoding regions through a comprehensive analysis by integrating datasets and features

One of the greatest challenges in human genetics is deciphering the link between functional variants in noncoding sequences and the pathophysiology of complex diseases. To address this issue, many methods have been developed to sort functional single-nucleotide variants (SNVs) for neutral SNVs in noncoding regions. In this study, we integrated well-established features and commonly used datasets and merged them into large-scale datasets based on a random forest model, which yielded promising performance and outperformed some cutting-edge approaches. Our analyses of feature importance and data coverage also provide certain clues for future research in enhancing the prediction of functional noncoding SNVs.

Evolutionary potential of transcription factors for gene regulatory rewiring

Gene regulatory networks evolve through rewiring of individual components-that is, through changes in regulatory connections. However, the mechanistic basis of regulatory rewiring is poorly understood. Using a canonical gene regulatory system, we quantify the properties of transcription factors that determine the evolutionary potential for rewiring of regulatory connections: robustness, tunability and evolvability. In vivo repression measurements of two repressors at mutated operator sites reveal their contrasting evolutionary potential: while robustness and evolvability were positively correlated, both were in trade-off with tunability. Epistatic interactions between adjacent operators alleviated this trade-off. A thermodynamic model explains how the differences in robustness, tunability and evolvability arise from biophysical characteristics of repressor-DNA binding. The model also uncovers that the energy matrix, which describes how mutations affect repressor-DNA binding, encodes crucial information about the evolutionary potential of a repressor. The biophysical determinants of evolutionary potential for regulatory rewiring constitute a mechanistic framework for understanding network evolution.

Unrealistic phylogenetic trees may improve phylogenetic footprinting

Motivation

The computational investigation of DNA binding motifs from binding sites is one of the classic tasks in bioinformatics and a prerequisite for understanding gene regulation as a whole. Due to the development of sequencing technologies and the increasing number of available genomes, approaches based on phylogenetic footprinting become increasingly attractive. Phylogenetic footprinting requires phylogenetic trees with attached substitution probabilities for quantifying the evolution of binding sites, but these trees and substitution probabilities are typically not known and cannot be estimated easily.

Results

Here, we investigate the influence of phylogenetic trees with different substitution probabilities on the classification performance of phylogenetic footprinting using synthetic and real data. For synthetic data we find that the classification performance is highest when the substitution probability used for phylogenetic footprinting is similar to that used for data generation. For real data, however, we typically find that the classification performance of phylogenetic footprinting surprisingly increases with increasing substitution probabilities and is often highest for unrealistically high substitution probabilities close to one. This finding suggests that choosing realistic model assumptions might not always yield optimal predictions in general and that choosing unrealistically high substitution probabilities close to one might actually improve the classification performance of phylogenetic footprinting.

Availability and Implementation

The proposed PF is implemented in JAVA and can be downloaded from https://github.com/mgledi/PhyFoo

Supplementary information

Supplementary data are available at Bioinformatics online.

Noncoding Variants Functional Prioritization Methods Based on Predicted Regulatory Factor Binding Sites

BACKGROUNDS

With the advent of the post genomic era, the research for the genetic mechanism of the diseases has found to be increasingly depended on the studies of the genes, the gene-networks and gene-protein interaction networks. To explore gene expression and regulation, the researchers have carried out many studies on transcription factors and their binding sites (TFBSs). Based on the large amount of transcription factor binding sites predicting values in the deep learning models, further computation and analysis have been done to reveal the relationship between the gene mutation and the occurrence of the disease. It has been demonstrated that based on the deep learning methods, the performances of the prediction for the functions of the noncoding variants are outperforming than those of the conventional methods. The research on the prediction for functions of Single Nucleotide Polymorphisms (SNPs) is expected to uncover the mechanism of the gene mutation affection on traits and diseases of human beings.

RESULTS

We reviewed the conventional TFBSs identification methods from different perspectives. As for the deep learning methods to predict the TFBSs, we discussed the related problems, such as the raw data preprocessing, the structure design of the deep convolution neural network (CNN) and the model performance measure et al. And then we summarized the techniques that usually used in finding out the functional noncoding variants from de novo sequence.

CONCLUSION

Along with the rapid development of the high-throughout assays, more and more sample data and chromatin features would be conducive to improve the prediction accuracy of the deep convolution neural network for TFBSs identification. Meanwhile, getting more insights into the deep CNN framework itself has been proved useful for both the promotion on model performance and the development for more suitable design to sample data. Based on the feature values predicted by the deep CNN model, the prioritization model for functional noncoding variants would contribute to reveal the affection of gene mutation on the diseases.

Discriminating between HuR and TTP binding sites using the k-spectrum kernel method

Background

The RNA binding proteins (RBPs) human antigen R (HuR) and Tristetraprolin (TTP) are known to exhibit competitive binding but have opposing effects on the bound messenger RNA (mRNA). How cells discriminate between the two proteins is an interesting problem. Machine learning approaches, such as support vector machines (SVMs), may be useful in the identification of discriminative features. However, this method has yet to be applied to studies of RNA binding protein motifs.

Results

Applying the k-spectrum kernel to a support vector machine (SVM), we first verified the published binding sites of both HuR and TTP. Additional feature engineering highlighted the U-rich binding preference of HuR and AU-rich binding preference for TTP. Domain adaptation along with multi-task learning was used to predict the common binding sites.

Conclusion

The distinction between HuR and TTP binding appears to be subtle content features. HuR prefers strongly U-rich sequences whereas TTP prefers AU-rich as with increasing A content, the sequences are more likely to be bound only by TTP. Our model is consistent with competitive binding of the two proteins, particularly at intermediate AU-balanced sequences. This suggests that fine changes in the A/U balance within a untranslated region (UTR) can alter the binding and subsequent stability of the message. Both feature engineering and domain adaptation emphasized the extent to which these proteins recognize similar general sequence features. This work suggests that the k-spectrum kernel method could be useful when studying RNA binding proteins and domain adaptation techniques such as feature augmentation could be employed particularly when examining RBPs with similar binding preferences.

Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies

Background

Transcriptional gene regulation is a fundamental process in nature, and the experimental and computational investigation of DNA binding motifs and their binding sites is a prerequisite for elucidating this process. Approaches for de-novo motif discovery can be subdivided in phylogenetic footprinting that takes into account phylogenetic dependencies in aligned sequences of more than one species and non-phylogenetic approaches based on sequences from only one species that typically take into account intra-motif dependencies. It has been shown that modeling (i) phylogenetic dependencies as well as (ii) intra-motif dependencies separately improves de-novo motif discovery, but there is no approach capable of modeling both (i) and (ii) simultaneously.

Results

Here, we present an approach for de-novo motif discovery that combines phylogenetic footprinting with motif models capable of taking into account intra-motif dependencies. We study the degree of intra-motif dependencies inferred by this approach from ChIP-seq data of 35 transcription factors. We find that significant intra-motif dependencies of orders 1 and 2 are present in all 35 datasets and that intra-motif dependencies of order 2 are typically stronger than those of order 1. We also find that the presented approach improves the classification performance of phylogenetic footprinting in all 35 datasets and that incorporating intra-motif dependencies of order 2 yields a higher classification performance than incorporating such dependencies of only order 1.

Conclusion

Combining phylogenetic footprinting with motif models incorporating intra-motif dependencies leads to an improved performance in the classification of transcription factor binding sites. This may advance our understanding of transcriptional gene regulation and its evolution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1495-1) contains supplementary material, which is available to authorized users.

What does it take to evolve an enhancer? A simulation-based study of factors influencing the emergence of combinatorial regulation

There is widespread interest today in understanding enhancers, which are regulatory elements typically harboring several transcription factor binding sites and mediating the combinatorial effect of transcription factors on gene expression. The evolution of enhancers poses interesting unanswered questions, for example, the evolutionary time taken for a typical enhancer to emerge or the factors shaping its evolution. Existing approaches to cis-regulatory evolution have often ignored the combinatorial nature and varied biochemical mechanisms of gene regulation encoded in enhancers. We report on our investigation of enhancer evolution through the use of PEBCRES, a framework for evolutionary simulation of enhancers that employs a mechanistic and well-supported sequence-to-expression model to assign fitness to the evolving enhancer genotype. We estimated the time necessary to evolve, from genomic background, enhancers capable of driving complex gene expression patterns similar to those involved in early development in Drosophila. We found the time-to-evolve to range between 0.5 and 10 Myr, and to vary greatly with the target expression pattern, complexity of the real enhancer known to encode that pattern, and the strength of input from specific transcription factors. To our knowledge, this is the first estimate of waiting times for realistic enhancers to evolve. The in silico evolved enhancers had, with a few interesting exceptions, site compositions similar to those seen in real enhancers for the same patterns. Our simulations also revealed that certain features of an enhancer might evolve not due to their biological function but as aids to the evolutionary process itself.

Signatures of natural selection at the FTO (fat mass and obesity associated) locus in human populations

Background and aims

Polymorphisms in the first intron of FTO have been robustly replicated for associations with obesity. In the Sorbs, a Slavic population resident in Germany, the strongest effect on body mass index (BMI) was found for a variant in the third intron of FTO (rs17818902). Since this may indicate population specific effects of FTO variants, we initiated studies testing FTO for signatures of selection in vertebrate species and human populations.

Methods

First, we analyzed the coding region of 35 vertebrate FTO orthologs with Phylogenetic Analysis by Maximum Likelihood (PAML, ω = dN/dS) to screen for signatures of selection among species. Second, we investigated human population (Europeans/CEU, Yoruba/YRI, Chinese/CHB, Japanese/JPT, Sorbs) SNP data for footprints of selection using DnaSP version 4.5 and the Haplotter/PhaseII. Finally, using ConSite we compared transcription factor (TF) binding sites at sequences harbouring FTO SNPs in intron three.

Results

PAML analyses revealed strong conservation in coding region of FTO (ω<1). Sliding-window results from population genetic analyses provided highly significant (p<0.001) signatures for balancing selection specifically in the third intron (e.g. Tajima’s D in Sorbs = 2.77). We observed several alterations in TF binding sites, e.g. TCF3 binding site introduced by the rs17818902 minor allele.

Conclusion

Population genetic analysis revealed signatures of balancing selection at the FTO locus with a prominent signal in intron three, a genomic region with strong association with BMI in the Sorbs. Our data support the hypothesis that genes associated with obesity may have been under evolutionary selective pressure.

Modeling evolution using the probability of fixation: history and implications

Many models of evolution calculate the rate of evolution by multiplying the rate at which new mutations originate within a population by a probability of fixation. Here we review the historical origins, contemporary applications, and evolutionary implications of these "origin-fixation" models, which are widely used in evolutionary genetics, molecular evolution, and phylogenetics. Origin-fixation models were first introduced in 1969, in association with an emerging view of "molecular" evolution. Early origin-fixation models were used to calculate an instantaneous rate of evolution across a large number of independently evolving loci; in the 1980s and 1990s, a second wave of origin-fixation models emerged to address a sequence of fixation events at a single locus. Although origin fixation models have been applied to a broad array of problems in contemporary evolutionary research, their rise in popularity has not been accompanied by an increased appreciation of their restrictive assumptions or their distinctive implications. We argue that origin-fixation models constitute a coherent theory of mutation-limited evolution that contrasts sharply with theories of evolution that rely on the presence of standing genetic variation. A major unsolved question in evolutionary biology is the degree to which these models provide an accurate approximation of evolution in natural populations.

A general pairwise interaction model provides an accurate description of in vivo transcription factor binding sites

The identification of transcription factor binding sites (TFBSs) on genomic DNA is of crucial importance for understanding and predicting regulatory elements in gene networks. TFBS motifs are commonly described by Position Weight Matrices (PWMs), in which each DNA base pair contributes independently to the transcription factor (TF) binding. However, this description ignores correlations between nucleotides at different positions, and is generally inaccurate: analysing fly and mouse in vivo ChIPseq data, we show that in most cases the PWM model fails to reproduce the observed statistics of TFBSs. To overcome this issue, we introduce the pairwise interaction model (PIM), a generalization of the PWM model. The model is based on the principle of maximum entropy and explicitly describes pairwise correlations between nucleotides at different positions, while being otherwise as unconstrained as possible. It is mathematically equivalent to considering a TF-DNA binding energy that depends additively on each nucleotide identity at all positions in the TFBS, like the PWM model, but also additively on pairs of nucleotides. We find that the PIM significantly improves over the PWM model, and even provides an optimal description of TFBS statistics within statistical noise. The PIM generalizes previous approaches to interdependent positions: it accounts for co-variation of two or more base pairs, and predicts secondary motifs, while outperforming multiple-motif models consisting of mixtures of PWMs. We analyse the structure of pairwise interactions between nucleotides, and find that they are sparse and dominantly located between consecutive base pairs in the flanking region of TFBS. Nonetheless, interactions between pairs of non-consecutive nucleotides are found to play a significant role in the obtained accurate description of TFBS statistics. The PIM is computationally tractable, and provides a general framework that should be useful for describing and predicting TFBSs beyond PWMs.

Imogene: identification of motifs and cis-regulatory modules underlying gene co-regulation

Cis-regulatory modules (CRMs) and motifs play a central role in tissue and condition-specific gene expression. Here we present Imogene, an ensemble of statistical tools that we have developed to facilitate their identification and implemented in a publicly available software. Starting from a small training set of mammalian or fly CRMs that drive similar gene expression profiles, Imogene determines de novocis-regulatory motifs that underlie this co-expression. It can then predict on a genome-wide scale other CRMs with a regulatory potential similar to the training set. Imogene bypasses the need of large datasets for statistical analyses by making central use of the information provided by the sequenced genomes of multiple species, based on the developed statistical tools and explicit models for transcription factor binding site evolution. We test Imogene on characterized tissue-specific mouse developmental CRMs. Its ability to identify CRMs with the same specificity based on its de novo created motifs is comparable to that of previously evaluated ‘motif-blind’ methods. We further show, both in flies and in mammals, that Imogene de novo generated motifs are sufficient to discriminate CRMs related to different developmental programs. Notably, purely relying on sequence data, Imogene performs as well in this discrimination task as a previously reported learning algorithm based on Chromatin Immunoprecipitation (ChIP) data for multiple transcription factors at multiple developmental stages.

Systematic discovery and characterization of regulatory motifs in ENCODE TF binding experiments

Recent advances in technology have led to a dramatic increase in the number of available transcription factor ChIP-seq and ChIP-chip data sets. Understanding the motif content of these data sets is an important step in understanding the underlying mechanisms of regulation. Here we provide a systematic motif analysis for 427 human ChIP-seq data sets using motifs curated from the literature and also discovered de novo using five established motif discovery tools. We use a systematic pipeline for calculating motif enrichment in each data set, providing a principled way for choosing between motif variants found in the literature and for flagging potentially problematic data sets. Our analysis confirms the known specificity of 41 of the 56 analyzed factor groups and reveals motifs of potential cofactors. We also use cell type-specific binding to find factors active in specific conditions. The resource we provide is accessible both for browsing a small number of factors and for performing large-scale systematic analyses. We provide motif matrices, instances and enrichments in each of the ENCODE data sets. The motifs discovered here have been used in parallel studies to validate the specificity of antibodies, understand cooperativity between data sets and measure the variation of motif binding across individuals and species.

Simulations of enhancer evolution provide mechanistic insights into gene regulation

There is growing interest in models of regulatory sequence evolution. However, existing models specifically designed for regulatory sequences consider the independent evolution of individual transcription factor (TF)-binding sites, ignoring that the function and evolution of a binding site depends on its context, typically the cis-regulatory module (CRM) in which the site is located. Moreover, existing models do not account for the gene-specific roles of TF-binding sites, primarily because their roles often are not well understood. We introduce two models of regulatory sequence evolution that address some of the shortcomings of existing models and implement simulation frameworks based on them. One model simulates the evolution of an individual binding site in the context of a CRM, while the other evolves an entire CRM. Both models use a state-of-the art sequence-to-expression model to predict the effects of mutations on the regulatory output of the CRM and determine the strength of selection. We use the new framework to simulate the evolution of TF-binding sites in 37 well-studied CRMs belonging to the anterior-posterior patterning system in Drosophila embryos. We show that these simulations provide accurate fits to evolutionary data from 12 Drosophila genomes, which includes statistics of binding site conservation on relatively short evolutionary scales and site loss across larger divergence times. The new framework allows us, for the first time, to test hypotheses regarding the underlying cis-regulatory code by directly comparing the evolutionary implications of the hypothesis with the observed evolutionary dynamics of binding sites. Using this capability, we find that explicitly modeling self-cooperative DNA binding by the TF Caudal (CAD) provides significantly better fits than an otherwise identical evolutionary simulation that lacks this mechanistic aspect. This hypothesis is further supported by a statistical analysis of the distribution of intersite spacing between adjacent CAD sites. Experimental tests confirm direct homodimeric interaction between CAD molecules as well as self-cooperative DNA binding by CAD. We note that computational modeling of the D. melanogaster CRMs alone did not yield significant evidence to support CAD self-cooperativity. We thus demonstrate how specific mechanistic details encoded in CRMs can be revealed by modeling their evolution and fitting such models to multispecies data.

A compendium of RNA-binding motifs for decoding gene regulation

RNA-binding proteins are key regulators of gene expression, yet only a small fraction have been functionally characterized. Here we report a systematic analysis of the RNA motifs recognized by RNA-binding proteins, encompassing 205 distinct genes from 24 diverse eukaryotes. The sequence specificities of RNA-binding proteins display deep evolutionary conservation, and the recognition preferences for a large fraction of metazoan RNA-binding proteins can thus be inferred from their RNA-binding domain sequence. The motifs that we identify in vitro correlate well with in vivo RNA-binding data. Moreover, we can associate them with distinct functional roles in diverse types of post-transcriptional regulation, enabling new insights into the functions of RNA-binding proteins both in normal physiology and in human disease. These data provide an unprecedented overview of RNA-binding proteins and their targets, and constitute an invaluable resource for determining post-transcriptional regulatory mechanisms in eukaryotes.

Systematic dissection of regulatory motifs in 2000 predicted human enhancers using a massively parallel reporter assay

Genome-wide chromatin annotations have permitted the mapping of putative regulatory elements across multiple human cell types. However, their experimental dissection by directed regulatory motif disruption has remained unfeasible at the genome scale. Here, we use a massively parallel reporter assay (MPRA) to measure the transcriptional levels induced by 145-bp DNA segments centered on evolutionarily conserved regulatory motif instances within enhancer chromatin states. We select five predicted activators (HNF1, HNF4, FOXA, GATA, NFE2L2) and two predicted repressors (GFI1, ZFP161) and measure reporter expression in erythroleukemia (K562) and liver carcinoma (HepG2) cell lines. We test 2104 wild-type sequences and 3314 engineered enhancer variants containing targeted motif disruptions, each using 10 barcode tags and two replicates. The resulting data strongly confirm the enhancer activity and cell-type specificity of enhancer chromatin states, the ability of 145-bp segments to recapitulate both, the necessary role of regulatory motifs in enhancer function, and the complementary roles of activator and repressor motifs. We find statistically robust evidence that (1) disrupting the predicted activator motifs abolishes enhancer function, while silent or motif-improving changes maintain enhancer activity; (2) evolutionary conservation, nucleosome exclusion, binding of other factors, and strength of the motif match are predictive of enhancer activity; (3) scrambling repressor motifs leads to aberrant reporter expression in cell lines where the enhancers are usually inactive. Our results suggest a general strategy for deciphering cis-regulatory elements by systematic large-scale manipulation and provide quantitative enhancer activity measurements across thousands of constructs that can be mined to develop predictive models of gene expression.

A primer on thermodynamic-based models for deciphering transcriptional regulatory logic

A rigorous analysis of transcriptional regulation at the DNA level is crucial to the understanding of many biological systems. Mathematical modeling has offered researchers a new approach to understanding this central process. In particular, thermodynamic-based modeling represents the most biophysically informed approach aimed at connecting DNA level regulatory sequences to the expression of specific genes. The goal of this review is to give biologists a thorough description of the steps involved in building, analyzing, and implementing a thermodynamic-based model of transcriptional regulation. The data requirements for this modeling approach are described, the derivation for a specific regulatory region is shown, and the challenges and future directions for the quantitative modeling of gene regulation are discussed.

Exploiting ancestral mammalian genomes for the prediction of human transcription factor binding sites

Background

The computational prediction of Transcription Factor Binding Sites (TFBS) remains a challenge due to their short length and low information content. Comparative genomics approaches that simultaneously consider several related species and favor sites that have been conserved throughout evolution improve the accuracy (specificity) of the predictions but are limited due to a phenomenon called binding site turnover, where sequence evolution causes one TFBS to replace another in the same region. In parallel to this development, an increasing number of mammalian genomes are now sequenced and it is becoming possible to infer, to a surprisingly high degree of accuracy, ancestral mammalian sequences.

Results

We propose a TFBS prediction approach that makes use of the availability of inferred ancestral mammalian genomes to improve its accuracy. This method aims to identify binding loci, which are regions of a few hundred base pairs that have preserved their potential to bind a given transcription factor over evolutionary time. After proposing a neutral evolutionary model of predicted TFBS counts in a DNA region of a given length, we use it to identify regions that have preserved the number of predicted TFBS they contain to an unexpected degree given their divergence. The approach is applied to human chromosome 1 and shows significant gains in accuracy as compared to both existing single-species and multi-species TFBS prediction approaches, in particular for transcription factors that are subject to high turnover rates.

Availability

The source code and predictions made by the program are available at http://www.cs.mcgill.ca/~blanchem/bindingLoci.

SwissRegulon, a database of genome-wide annotations of regulatory sites: recent updates

Identification of genomic regulatory elements is essential for understanding the dynamics of cellular processes. This task has been substantially facilitated by the availability of genome sequences for many species and high-throughput data of transcripts and transcription factor (TF) binding. However, rigorous computational methods are necessary to derive accurate genome-wide annotations of regulatory sites from such data. SwissRegulon (http://swissregulon.unibas.ch) is a database containing genome-wide annotations of regulatory motifs, promoters and TF binding sites (TFBSs) in promoter regions across model organisms. Its binding site predictions were obtained with rigorous Bayesian probabilistic methods that operate on orthologous regions from related genomes, and use explicit evolutionary models to assess the evidence of purifying selection on each site. New in the current version of SwissRegulon is a curated collection of 190 mammalian regulatory motifs associated with ∼340 TFs, and TFBS annotations across a curated set of ∼35 000 promoters in both human and mouse. Predictions of TFBSs for Saccharomyces cerevisiae have also been significantly extended and now cover 158 of yeast’s ∼180 TFs. All data are accessible through both an easily navigable genome browser with search functions, and as flat files that can be downloaded for further analysis.

Interpreting noncoding genetic variation in complex traits and human disease

Association studies provide genome-wide information about the genetic basis of complex disease, but medical research has primarily focused on protein-coding variants, due to the difficulty of interpreting non-coding mutations. This picture has changed with advances in the systematic annotation of functional non-coding elements. Evolutionary conservation, functional genomics, chromatin state, sequence motifs, and molecular quantitative trait loci all provide complementary information about non-coding function. These functional maps can help prioritize variants on risk haplotypes, filter mutations encountered in the clinic, and perform systems-level analyses to reveal processes underlying disease associations. Advances in predictive modeling can enable dataset integration to reveal pathways shared across loci and alleles, and richer regulatory models can guide the search for epistatic interactions. Lastly, new massively parallel reporter experiments can systematically validate regulatory predictions. Ultimately, advances in regulatory and systems genomics can help unleash the value of whole-genome sequencing for personalized genomic risk assessment, diagnosis, and treatment.

Cell-type specificity of ChIP-predicted transcription factor binding sites

BACKGROUND

Context-dependent transcription factor (TF) binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identifies genome-wide TF binding sites for one particular context-the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding?

RESULTS

We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity.

CONCLUSIONS

Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data-ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure-we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.

Identification of molecular compartments and genetic circuitry in the developing mammalian kidney

Lengthy developmental programs generate cell diversity within an organotypic framework, enabling the later physiological actions of each organ system. Cell identity, cell diversity and cell function are determined by cell type-specific transcriptional programs; consequently, transcriptional regulatory factors are useful markers of emerging cellular complexity, and their expression patterns provide insights into the regulatory mechanisms at play. We performed a comprehensive genome-scale in situ expression screen of 921 transcriptional regulators in the developing mammalian urogenital system. Focusing on the kidney, analysis of regional-specific expression patterns identified novel markers and cell types associated with development and patterning of the urinary system. Furthermore, promoter analysis of synexpressed genes predicts transcriptional control mechanisms that regulate cell differentiation. The annotated informational resource (www.gudmap.org) will facilitate functional analysis of the mammalian kidney and provides useful information for the generation of novel genetic tools to manipulate emerging cell populations.

MotEvo: integrated Bayesian probabilistic methods for inferring regulatory sites and motifs on multiple alignments of DNA sequences

MOTIVATION

Probabilistic approaches for inferring transcription factor binding sites (TFBSs) and regulatory motifs from DNA sequences have been developed for over two decades. Previous work has shown that prediction accuracy can be significantly improved by incorporating features such as the competition of multiple transcription factors (TFs) for binding to nearby sites, the tendency of TFBSs for co-regulated TFs to cluster and form cis-regulatory modules and explicit evolutionary modeling of conservation of TFBSs across orthologous sequences. However, currently available tools only incorporate some of these features, and significant methodological hurdles hampered their synthesis into a single consistent probabilistic framework.

RESULTS

We present MotEvo, a integrated suite of Bayesian probabilistic methods for the prediction of TFBSs and inference of regulatory motifs from multiple alignments of phylogenetically related DNA sequences, which incorporates all features just mentioned. In addition, MotEvo incorporates a novel model for detecting unknown functional elements that are under evolutionary constraint, and a new robust model for treating gain and loss of TFBSs along a phylogeny. Rigorous benchmarking tests on ChIP-seq datasets show that MotEvo's novel features significantly improve the accuracy of TFBS prediction, motif inference and enhancer prediction.

AVAILABILITY

Source code, a user manual and files with several example applications are available at www.swissregulon.unibas.ch.

Accurate reconstruction of insertion-deletion histories by statistical phylogenetics

The Multiple Sequence Alignment (MSA) is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history), it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm) to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes.

LOESS correction for length variation in gene set-based genomic sequence analysis

MOTIVATION

Sequence analysis algorithms are often applied to sets of DNA, RNA or protein sequences to identify common or distinguishing features. Controlling for sequence length variation is critical to properly score sequence features and identify true biological signals rather than length-dependent artifacts.

RESULTS

Several cis-regulatory module discovery algorithms exhibit a substantial dependence between DNA sequence score and sequence length. Our newly developed LOESS method is flexible in capturing diverse score-length relationships and is more effective in correcting DNA sequence scores for length-dependent artifacts, compared with four other approaches. Application of this method to genes co-expressed during Drosophila melanogaster embryonic mesoderm development or neural development scored by the Lever motif analysis algorithm resulted in successful recovery of their biologically validated cis-regulatory codes. The LOESS length-correction method is broadly applicable, and may be useful not only for more accurate inference of cis-regulatory codes, but also for detection of other types of patterns in biological sequences.

AVAILABILITY

Source code and compiled code are available from http://thebrain.bwh.harvard.edu/LM_LOESS/

Use of ChIP-Seq data for the design of a multiple promoter-alignment method

We address the challenge of regulatory sequence alignment with a new method, Pro-Coffee, a multiple aligner specifically designed for homologous promoter regions. Pro-Coffee uses a dinucleotide substitution matrix estimated on alignments of functional binding sites from TRANSFAC. We designed a validation framework using several thousand families of orthologous promoters. This dataset was used to evaluate the accuracy for predicting true human orthologs among their paralogs. We found that whereas other methods achieve on average 73.5% accuracy, and 77.6% when trained on that same dataset, the figure goes up to 80.4% for Pro-Coffee. We then applied a novel validation procedure based on multi-species ChIP-seq data. Trained and untrained methods were tested for their capacity to correctly align experimentally detected binding sites. Whereas the average number of correctly aligned sites for two transcription factors is 284 for default methods and 316 for trained methods, Pro-Coffee achieves 331, 16.5% above the default average. We find a high correlation between a method's performance when classifying orthologs and its ability to correctly align proven binding sites. Not only has this interesting biological consequences, it also allows us to conclude that any method that is trained on the ortholog data set will result in functionally more informative alignments.

Predicting site-specific human selective pressure using evolutionary signatures

Motivation: The identification of non-coding functional regions of the human genome remains one of the main challenges of genomics. By observing how a given region evolved over time, one can detect signs of negative or positive selection hinting that the region may be functional. With the quickly increasing number of vertebrate genomes to compare with our own, this type of approach is set to become extremely powerful, provided the right analytical tools are available.

Results: A large number of approaches have been proposed to measure signs of past selective pressure, usually in the form of reduced mutation rate. Here, we propose a radically different approach to the detection of non-coding functional region: instead of measuring past evolutionary rates, we build a machine learning classifier to predict current substitution rates in human based on the inferred evolutionary events that affected the region during vertebrate evolution. We show that different types of evolutionary events, occurring along different branches of the phylogenetic tree, bring very different amounts of information. We propose a number of simple machine learning classifiers and show that a Support-Vector Machine (SVM) predictor clearly outperforms existing tools at predicting human non-coding functional sites. Comparison to external evidences of selection and regulatory function confirms that these SVM predictions are more accurate than those of other approaches.

Availability: The predictor and predictions made are available at http://www.mcb.mcgill.ca/~blanchem/sadri.

Contact:blanchem@mcb.mcgill.ca

Formation of regulatory modules by local sequence duplication

Turnover of regulatory sequence and function is an important part of molecular evolution. But what are the modes of sequence evolution leading to rapid formation and loss of regulatory sites? Here we show that a large fraction of neighboring transcription factor binding sites in the fly genome have formed from a common sequence origin by local duplications. This mode of evolution is found to produce regulatory information: duplications can seed new sites in the neighborhood of existing sites. Duplicate seeds evolve subsequently by point mutations, often towards binding a different factor than their ancestral neighbor sites. These results are based on a statistical analysis of 346 cis-regulatory modules in the Drosophila melanogaster genome, and a comparison set of intergenic regulatory sequence in Saccharomyces cerevisiae. In fly regulatory modules, pairs of binding sites show significantly enhanced sequence similarity up to distances of about 50 bp. We analyze these data in terms of an evolutionary model with two distinct modes of site formation: (i) evolution from independent sequence origin and (ii) divergent evolution following duplication of a common ancestor sequence. Our results suggest that pervasive formation of binding sites by local sequence duplications distinguishes the complex regulatory architecture of higher eukaryotes from the simpler architecture of unicellular organisms.

Since Jacob and Monod stressed the importance of gene regulation in evolution, our understanding of the mechanisms of regulation has substantially advanced. In higher eukaryotes, genes often have complex regulatory input, which is encoded in cis-regulatory sequence with multiple transcription factor binding sites. However, the modes of genome evolution generating regulatory complexity are much less understood. This study reports a surprising finding: in fly regulatory modules, the majority of transcription factor binding sites show evidence of a local sequence duplication in their evolutionary history, which relates their sequence information to that of neighboring binding sites. Our analysis suggests that local sequence duplications are a pervasive production mode of regulatory information. This mode appears to be specific to higher eukaryotes; we have not found evidence of frequent local duplications in the yeast genome. Our results affect genomic sequence analysis, in particular, computational identification of cis-regulatory elements and alignment of regulatory DNA. At the same time, they address fundamental questions on the evolution of regulation: How much of the regulatory “grammar” observed in higher eukaryotes is due to optimization of function, and how much reflects the underlying sequence evolution modes? What is the result and what is the substrate of natural selection?

Tissue-specific prediction of directly regulated genes

Direct binding by a transcription factor (TF) to the proximal promoter of a gene is a strong evidence that the TF regulates the gene. Assaying the genome-wide binding of every TF in every cell type and condition is currently impractical. Histone modifications correlate with tissue/cell/condition-specific ('tissue specific') TF binding, so histone ChIP-seq data can be combined with traditional position weight matrix (PWM) methods to make tissue-specific predictions of TF-promoter interactions.

RESULTS

We use supervised learning to train a naïve Bayes predictor of TF-promoter binding. The predictor's features are the histone modification levels and a PWM-based score for the promoter. Training and testing uses sets of promoters labeled using TF ChIP-seq data, and we use cross-validation on 23 such datasets to measure the accuracy. A PWM+histone naïve Bayes predictor using a single histone modification (H3K4me3) is substantially more accurate than a PWM score or a conservation-based score (phylogenetic motif model). The naïve Bayes predictor is more accurate (on average) at all sensitivity levels, and makes only half as many false positive predictions at sensitivity levels from 10% to 80%. On average, it correctly predicts 80% of bound promoters at a false positive rate of 20%. Accuracy does not diminish when we test the predictor in a different cell type (and species) from training. Accuracy is barely diminished even when we train the predictor without using TF ChIP-seq data.

AVAILABILITY

Our tissue-specific predictor of promoters bound by a TF is called Dr Gene and is available at http://bioinformatics.org.au/drgene.

CONTACT

t.bailey@imb.uq.edu.au

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Faster than neutral evolution of constrained sequences: the complex interplay of mutational biases and weak selection

Comparative genomics has become widely accepted as the major framework for the ascertainment of functionally important regions in genomes. The underlying paradigm of this approach is that most of the functional regions are assumed to be under selective constraint, which in turn reduces the rate of evolution relative to neutrality. This assumption allows detection of functional regions through sequence conservation. However, constraint does not always lead to sequence conservation. When purifying selection is weak and mutation is biased, constrained regions can even evolve faster than neutral sequences and thus can appear to be under positive selection. Moreover, conservation estimates depend also on the orientation of selection relative to mutational biases and can vary over time. In the light of recent data of the ubiquity of mutational biases and weak selective forces, these effects should reduce the power of conservation analyses to define functional regions using comparative genomics data. We argue that the estimation of true mutational biases and the use of explicit evolutionary models are essential to improve methods inferring the action of natural selection and functionality in genome sequences.

Germline fitness-based scoring of cancer mutations

A key goal in cancer research is to find the genomic alterations that underlie malignant cells. Genomics has proved successful in identifying somatic variants at a large scale. However, it has become evident that a typical cancer exhibits a heterogenous mutation pattern across samples. Cases where the same alteration is observed repeatedly seem to be the exception rather than the norm. Thus, pinpointing the key alterations (driver mutations) from a background of variations with no direct causal link to cancer (passenger mutations) is difficult. Here we analyze somatic missense mutations from cancer samples and their healthy tissue counterparts (germline mutations) from the viewpoint of germline fitness. We calibrate a scoring system from protein domain alignments to score mutations and their target loci. We show first that this score predicts to a good degree the rate of polymorphism of the observed germline variation. The scoring is then applied to somatic mutations. We show that candidate cancer genes prone to copy number loss harbor mutations with germline fitness effects that are significantly more deleterious than expected by chance. This suggests that missense mutations play a driving role in tumor suppressor genes. Furthermore, these mutations fall preferably onto loci in sequence neighborhoods that are high scoring in terms of germline fitness. In contrast, for somatic mutations in candidate onco genes we do not observe a statistically significant effect. These results help to inform how to exploit germline fitness predictions in discovering new genes and mutations responsible for cancer.

Evolution of gene regulation of pluripotency--the case for wiki tracks at genome browsers

Background

Experimentally validated data on gene regulation are hard to obtain. In particular, information about transcription factor binding sites in regulatory regions are scattered around in the literature. This impedes their systematic in-context analysis, e.g. the inference of their conservation in evolutionary history.

Results

We demonstrate the power of integrative bioinformatics by including curated transcription factor binding site information into the UCSC genome browser, using wiki and custom tracks, which enable easy publication of annotation data. Data integration allows to investigate the evolution of gene regulation of the pluripotency-associated genes Oct4, Sox2 and Nanog. For the first time, experimentally validated transcription factor binding sites in the regulatory regions of all three genes were assembled together based on manual curation of data from 39 publications. Using the UCSC genome browser, these data were then visualized in the context of multi-species conservation based on genomic alignment. We confirm previous hypotheses regarding the evolutionary age of specific regulatory patterns, establishing their "deep homology". We also confirm some other principles of Carroll's "Genetic theory of Morphological Evolution", such as "mosaic pleiotropy", exemplified by the dual role of Sox2 reflected in its regulatory region.

Conclusions

We were able to elucidate some aspects of the evolution of gene regulation for three genes associated with pluripotency. Based on the expected return on investment for the community, we encourage other scientists to contribute experimental data on gene regulation (original work as well as data collected for reviews) to the UCSC system, to enable studies of the evolution of gene regulation on a large scale, and to report their findings.

Reviewers

This article was reviewed by Dr. Gustavo Glusman and Dr. Juan Caballero, Institute for Systems Biology, Seattle, USA (nominated by Dr. Doron Lancet, Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, Israel), Dr. Niels Grabe, TIGA Center (BIOQUANT) and Medical Systems Biology Group, Institute of Medical Biometry and Informatics, University Hospital Heidelberg, Germany (nominated by Dr. Mikhail Gelfand, Department of Bioinformatics, Institute of Information Transfer Problems, Russian Academy of Science, Moscow, Russian Federation) and Dr. Franz-Josef Müller, Center for Regenerative Medicine, The Scripps Research Institute, La Jolla, CA, USA and University Hospital for Psychiatry and Psychotherapy (part of ZIP gGmbH), University of Kiel, Germany (nominated by Dr. Trey Ideker, University of California, San Diego, La Jolla CA, United States).

PHAST and RPHAST: phylogenetic analysis with space/time models

The PHylogenetic Analysis with Space/Time models (PHAST) software package consists of a collection of command-line programs and supporting libraries for comparative genomics. PHAST is best known as the engine behind the Conservation tracks in the University of California, Santa Cruz (UCSC) Genome Browser. However, it also includes several other tools for phylogenetic modeling and functional element identification, as well as utilities for manipulating alignments, trees and genomic annotations. PHAST has been in development since 2002 and has now been downloaded more than 1000 times, but so far it has been released only as provisional ('beta') software. Here, we describe the first official release (v1.0) of PHAST, with improved stability, portability and documentation and several new features. We outline the components of the package and detail recent improvements. In addition, we introduce a new interface to the PHAST libraries from the R statistical computing environment, called RPHAST, and illustrate its use in a series of vignettes. We demonstrate that RPHAST can be particularly useful in applications involving both large-scale phylogenomics and complex statistical analyses. The R interface also makes the PHAST libraries acccessible to non-C programmers, and is useful for rapid prototyping. PHAST v1.0 and RPHAST v1.0 are available for download at http://compgen.bscb.cornell.edu/phast, under the terms of an unrestrictive BSD-style license. RPHAST can also be obtained from the Comprehensive R Archive Network (CRAN; http://cran.r-project.org).

Assessing computational methods of cis-regulatory module prediction

Computational methods attempting to identify instances of cis-regulatory modules (CRMs) in the genome face a challenging problem of searching for potentially interacting transcription factor binding sites while knowledge of the specific interactions involved remains limited. Without a comprehensive comparison of their performance, the reliability and accuracy of these tools remains unclear. Faced with a large number of different tools that address this problem, we summarized and categorized them based on search strategy and input data requirements. Twelve representative methods were chosen and applied to predict CRMs from the Drosophila CRM database REDfly, and across the human ENCODE regions. Our results show that the optimal choice of method varies depending on species and composition of the sequences in question. When discriminating CRMs from non-coding regions, those methods considering evolutionary conservation have a stronger predictive power than methods designed to be run on a single genome. Different CRM representations and search strategies rely on different CRM properties, and different methods can complement one another. For example, some favour homotypical clusters of binding sites, while others perform best on short CRMs. Furthermore, most methods appear to be sensitive to the composition and structure of the genome to which they are applied. We analyze the principal features that distinguish the methods that performed well, identify weaknesses leading to poor performance, and provide a guide for users. We also propose key considerations for the development and evaluation of future CRM-prediction methods.

Transcriptional regulation involves multiple transcription factors binding to DNA sequences. A limited repertoire of transcription factors performs this complex regulatory step through various spatial and temporal interactions between themselves and their binding sites. These transcription factor binding interactions are clustered as distinct modules: cis-regulatory modules (CRMs). Computational methods attempting to identify instances of CRMs in the genome face a challenging problem because a majority of these interactions between transcription factors remain unknown. To investigate the reliability and accuracy of these methods, we chose twelve representative methods and applied them to predict CRMs on both the fly and human genomes. Our results show that the optimal choice of method varies depending on species and composition of the sequences in question. Different CRM representations and search strategies rely on different CRM properties, and different methods can complement one another. We provide a guide for users and key considerations for developers. We also expect that, along with new technology generating new types of genomic data, future CRM prediction methods will be able to reveal transcription binding interactions in three-dimensional space.

NFIA controls telencephalic progenitor cell differentiation through repression of the Notch effector Hes1

The balance between self-renewal and differentiation of neural progenitor cells is an absolute requirement for the correct formation of the nervous system. Much is known about both the pathways involved in progenitor cell self-renewal, such as Notch signaling, and the expression of genes that initiate progenitor differentiation. However, whether these fundamental processes are mechanistically linked, and specifically how repression of progenitor self-renewal pathways occurs, is poorly understood. Nuclear factor I A (Nfia), a gene known to regulate spinal cord and neocortical development, has recently been implicated as acting downstream of Notch to initiate the expression of astrocyte-specific genes within the cortex. Here we demonstrate that, in addition to activating the expression of astrocyte-specific genes, Nfia also downregulates the activity of the Notch signaling pathway via repression of the key Notch effector Hes1. These data provide a significant conceptual advance in our understanding of neural progenitor differentiation, revealing that a single transcription factor can control both the activation of differentiation genes and the repression of the self-renewal genes, thereby acting as a pivotal regulator of the balance between progenitor and differentiated cell states.

Evolution of gene regulation--on the road towards computational inferences

If fragments of DNA are transcribed (expressed), they deserve to be called (parts of) a gene. Whether transcription takes place depends on the 'gene regulatory network'. This network is defined as the complex interplay of the sequence, biochemical modifications and structure of the chromosomal DNA with the regulatory proteins/RNA (transcription factors, co-factors, regulating RNA and the transcriptional apparatus itself). Gene regulatory networks play a role in various stages of development as well as in the maintenance of the organism; in this review we will concentrate on the former. Their evolutionary reconstruction is daunting (to say the least), and bioinformatics tools are in their infancy. However, gain of understanding offers a reward beyond itself, since evolutionary considerations can enable discoveries in the first place, e.g. the computational identification of conserved transcription factor binding sites. We discuss the evolution of gene regulation in the context of the 'Genetic Theory of Morphological Evolution' as described by Carroll, identifying those parts of the theory that are relevant for bioinformatics, and their implications. We discuss the important question of how bioinformatics analysis results on the evolution of gene regulation may be validated. Finally, we briefly exemplify use of the UCSC genome browser, exploiting its pre-computed alignments to describe the evolution of gene regulation.

Genome-wide identification of cis-regulatory motifs and modules underlying gene coregulation using statistics and phylogeny

Cell fate determination depends in part on the establishment of specific transcriptional programs of gene expression. These programs result from the interpretation of the genomic cis-regulatory information by sequence-specific factors. Decoding this information in sequenced genomes is an important issue. Here, we developed statistical analysis tools to computationally identify the cis-regulatory elements that control gene expression in a set of coregulated genes. Starting with a small number of validated and/or predicted cis-regulatory modules (CRMs) in a reference species as a training set, but with no a priori knowledge of the factors acting in trans, we computationally predicted transcription factor binding sites (TFBSs) and genomic CRMs underlying coregulation. This method was applied to the gene expression program active in Drosophila melanogaster sensory organ precursor cells (SOPs), a specific type of neural progenitor cells. Mutational analysis showed that four, including one newly characterized, out of the five top-ranked families of predicted TFBSs were required for SOP-specific gene expression. Additionaly, 19 out of the 29 top-ranked predicted CRMs directed gene expression in neural progenitor cells, i.e., SOPs or larval brain neuroblasts, with a notable fraction active in SOPs (11/29). We further identified the lola gene as the target of two SOP-specific CRMs and found that the lola gene contributed to SOP specification. The statistics and phylogeny-based tools described here can be more generally applied to identify the cis-regulatory elements of specific gene regulatory networks in any family of related species with sequenced genomes.

Phyloscan: locating transcription-regulating binding sites in mixed aligned and unaligned sequence data

The transcription of a gene from its DNA template into an mRNA molecule is the first, and most heavily regulated, step in gene expression. Especially in bacteria, regulation is typically achieved via the binding of a transcription factor (protein) or small RNA molecule to the chromosomal region upstream of a regulated gene. The protein or RNA molecule recognizes a short, approximately conserved sequence within a gene's promoter region and, by binding to it, either enhances or represses expression of the nearby gene. Since the sought-for motif (pattern) is short and accommodating to variation, computational approaches that scan for binding sites have trouble distinguishing functional sites from look-alikes. Many computational approaches are unable to find the majority of experimentally verified binding sites without also finding many false positives. Phyloscan overcomes this difficulty by exploiting two key features of functional binding sites: (i) these sites are typically more conserved evolutionarily than are non-functional DNA sequences; and (ii) these sites often occur two or more times in the promoter region of a regulated gene. The website is free and open to all users, and there is no login requirement. Address: (http://bayesweb.wadsworth.org/phyloscan/).

A systems biology approach to transcription factor binding site prediction

BACKGROUND

The elucidation of mammalian transcriptional regulatory networks holds great promise for both basic and translational research and remains one the greatest challenges to systems biology. Recent reverse engineering methods deduce regulatory interactions from large-scale mRNA expression profiles and cross-species conserved regulatory regions in DNA. Technical challenges faced by these methods include distinguishing between direct and indirect interactions, associating transcription regulators with predicted transcription factor binding sites (TFBSs), identifying non-linearly conserved binding sites across species, and providing realistic accuracy estimates.

METHODOLOGY/PRINCIPAL FINDINGS

We address these challenges by closely integrating proven methods for regulatory network reverse engineering from mRNA expression data, linearly and non-linearly conserved regulatory region discovery, and TFBS evaluation and discovery. Using an extensive test set of high-likelihood interactions, which we collected in order to provide realistic prediction-accuracy estimates, we show that a careful integration of these methods leads to significant improvements in prediction accuracy. To verify our methods, we biochemically validated TFBS predictions made for both transcription factors (TFs) and co-factors; we validated binding site predictions made using a known E2F1 DNA-binding motif on E2F1 predicted promoter targets, known E2F1 and JUND motifs on JUND predicted promoter targets, and a de novo discovered motif for BCL6 on BCL6 predicted promoter targets. Finally, to demonstrate accuracy of prediction using an external dataset, we showed that sites matching predicted motifs for ZNF263 are significantly enriched in recent ZNF263 ChIP-seq data.

CONCLUSIONS/SIGNIFICANCE

Using an integrative framework, we were able to address technical challenges faced by state of the art network reverse engineering methods, leading to significant improvement in direct-interaction detection and TFBS-discovery accuracy. We estimated the accuracy of our framework on a human B-cell specific test set, which may help guide future methodological development.

An effective model for natural selection in promoters

We have produced an evolutionary model for promoters, analogous to the commonly used synonymous/nonsynonymous mutation models for protein-coding sequences. Although our model, called Sunflower, relies on some simple assumptions, it captures enough of the biology of transcription factor action to show clear correlation with other biological features. Sunflower predicts a binding profile of transcription factors to DNA sequences, in which different factors compete for the same potential binding sites. The parametrized model simultaneously estimates a continuous measurement of binding occupancy across the genomic sequence for each factor. We can then introduce a localized mutation, rerun the binding model, and record the difference in binding profiles. A single mutation can alter interactions both upstream and downstream of its position due to potential overlapping binding sites, and our statistic captures this domino effect. Over evolutionary time, we observe a clear excess of low-scoring mutations fixed in promoters, consistent with most changes being neutral. However, this is not consistent across all promoters, and some promoters show more rapid divergence. This divergence often occurs in the presence of relatively constant protein-coding divergence. Interestingly, different classes of promoters show different sensitivity to mutations, with phosphorylation-related genes having promoters inherently more sensitive to mutations than immune genes. Although there have previously been a number of models attempting to handle transcription factor binding, Sunflower provides a richer biological model, incorporating weak binding sites and the possibility of competition. The results show the first clear correlations between such a model and evolutionary processes.

Assigning roles to DNA regulatory motifs using comparative genomics

Motivation: Transcription factors (TFs) are crucial during the lifetime of the cell. Their functional roles are defined by the genes they regulate. Uncovering these roles not only sheds light on the TF at hand but puts it into the context of the complete regulatory network.

Results: Here, we present an alignment- and threshold-free comparative genomics approach for assigning functional roles to DNA regulatory motifs. We incorporate our approach into the Gomo algorithm, a computational tool for detecting associations between a user-specified DNA regulatory motif [expressed as a position weight matrix (PWM)] and Gene Ontology (GO) terms. Incorporating multiple species into the analysis significantly improves Gomo's ability to identify GO terms associated with the regulatory targets of TFs. Including three comparative species in the process of predicting TF roles in Saccharomyces cerevisiae and Homo sapiens increases the number of significant predictions by 75 and 200%, respectively. The predicted GO terms are also more specific, yielding deeper biological insight into the role of the TF. Adjusting motif (binding) affinity scores for individual sequence composition proves to be essential for avoiding false positive associations. We describe a novel DNA sequence-scoring algorithm that compensates a thermodynamic measure of DNA-binding affinity for individual sequence base composition. Gomo's prediction accuracy proves to be relatively insensitive to how promoters are defined. Because Gomo uses a threshold-free form of gene set analysis, there are no free parameters to tune. Biologists can investigate the potential roles of DNA regulatory motifs of interest using Gomo via the web (http://meme.nbcr.net).

Contact:t.bailey@uq.edu.au

Supplementary information:Supplementary data are available at Bioinformatics online.

Impact of DNA-binding position variants on yeast gene expression

Transcription factors (TFs) regulate gene expression by binding to specific binding sites (TFBSs) in gene promoters. TFBS motifs may contain one or more variable positions. Although the prevailing assumption is that nucleotide variants at such positions are functionally equivalent, there is increasing evidence that such variants play a role in regulation of gene expression. In this article, we propose a method for studying the relationship between the expression of target genes and nucleotide variants in TFBS motifs at a genome-wide scale in Saccharomyces cerevisiae, especially the combinatorial effects of variants at two positions. Our analysis shows that nucleotide variations in more than one-third of variable positions and in 20% of dependent position pairs are highly correlated to gene expression. We define such positions as 'functional'. However, some positions are only functional as dependent pairs, but not individually. In addition, a significant proportion of the functional positions have been well conserved across all yeast-related species studied. We also find that some positions require the presence of co-occurring TFs, while others maintain their functionality in the absence of a co-occurring TF. Our analysis supports the importance of nucleotide variants at variable positions of TFBSs in gene regulation.

Evolution of cis-regulatory sequences in Drosophila

Cross-species comparison is an emerging paradigm for identifying cis-regulatory sequences and understanding their function and evolution. In this chapter, we review probabilistic models of evolution of transcription factor binding sites, which provide the theoretical basis for a number of new bioinformatics tools for comparative sequence analysis. We illustrate how important functional and evolutionary insights on binding site gain and loss can be acquired through sequence comparison. This includes the observation that binding site turnover follows a molecular clock and that its rate correlates with the strength of binding sites and the presence of other sites in the neighborhood. We also comment on emerging trends that go beyond individual binding sites to a more holistic study of regulatory evolution. We point out common technical challenges, such as reliable sequence alignment and binding site prediction, when doing comparative regulatory sequence analysis and note some potential solutions thereof.

Functional evolution of cis-regulatory modules at a homeotic gene in Drosophila

It is a long-held belief in evolutionary biology that the rate of molecular evolution for a given DNA sequence is inversely related to the level of functional constraint. This belief holds true for the protein-coding homeotic (Hox) genes originally discovered in Drosophila melanogaster. Expression of the Hox genes in Drosophila embryos is essential for body patterning and is controlled by an extensive array of cis-regulatory modules (CRMs). How the regulatory modules functionally evolve in different species is not clear. A comparison of the CRMs for the Abdominal-B gene from different Drosophila species reveals relatively low levels of overall sequence conservation. However, embryonic enhancer CRMs from other Drosophila species direct transgenic reporter gene expression in the same spatial and temporal patterns during development as their D. melanogaster orthologs. Bioinformatic analysis reveals the presence of short conserved sequences within defined CRMs, representing gap and pair-rule transcription factor binding sites. One predicted binding site for the gap transcription factor KRUPPEL in the IAB5 CRM was found to be altered in Superabdominal (Sab) mutations. In Sab mutant flies, the third abdominal segment is transformed into a copy of the fifth abdominal segment. A model for KRUPPEL-mediated repression at this binding site is presented. These findings challenge our current understanding of the relationship between sequence evolution at the molecular level and functional activity of a CRM. While the overall sequence conservation at Drosophila CRMs is not distinctive from neighboring genomic regions, functionally critical transcription factor binding sites within embryonic enhancer CRMs are highly conserved. These results have implications for understanding mechanisms of gene expression during embryonic development, enhancer function, and the molecular evolution of eukaryotic regulatory modules.

Detection of nonneutral substitution rates on mammalian phylogenies

Methods for detecting nucleotide substitution rates that are faster or slower than expected under neutral drift are widely used to identify candidate functional elements in genomic sequences. However, most existing methods consider either reductions (conservation) or increases (acceleration) in rate but not both, or assume that selection acts uniformly across the branches of a phylogeny. Here we examine the more general problem of detecting departures from the neutral rate of substitution in either direction, possibly in a clade-specific manner. We consider four statistical, phylogenetic tests for addressing this problem: a likelihood ratio test, a score test, a test based on exact distributions of numbers of substitutions, and the genomic evolutionary rate profiling (GERP) test. All four tests have been implemented in a freely available program called phyloP. Based on extensive simulation experiments, these tests are remarkably similar in statistical power. With 36 mammalian species, they all appear to be capable of fairly good sensitivity with low false-positive rates in detecting strong selection at individual nucleotides, moderate selection in 3-bp elements, and weaker or clade-specific selection in longer elements. By applying phyloP to mammalian multiple alignments from the ENCODE project, we shed light on patterns of conservation/acceleration in known and predicted functional elements, approximate fractions of sites subject to constraint, and differences in clade-specific selection in the primate and glires clades. We also describe new "Conservation" tracks in the UCSC Genome Browser that display both phyloP and phastCons scores for genome-wide alignments of 44 vertebrate species.

Assessing phylogenetic motif models for predicting transcription factor binding sites

MOTIVATION

A variety of algorithms have been developed to predict transcription factor binding sites (TFBSs) within the genome by exploiting the evolutionary information implicit in multiple alignments of the genomes of related species. One such approach uses an extension of the standard position-specific motif model that incorporates phylogenetic information via a phylogenetic tree and a model of evolution. However, these phylogenetic motif models (PMMs) have never been rigorously benchmarked in order to determine whether they lead to better prediction of TFBSs than obtained using simple position weight matrix scanning.

RESULTS

We evaluate three PMM-based prediction algorithms, each of which uses a different treatment of gapped alignments, and we compare their prediction accuracy with that of a non-phylogenetic motif scanning approach. Surprisingly, all of these algorithms appear to be inferior to simple motif scanning, when accuracy is measured using a gold standard of validated yeast TFBSs. However, the PMM scanners perform much better than simple motif scanning when we abandon the gold standard and consider the number of statistically significant sites predicted, using column-shuffled 'random' motifs to measure significance. These results suggest that the common practice of measuring the accuracy of binding site predictors using collections of known sites may be dangerously misleading since such collections may be missing 'weak' sites, which are exactly the type of sites needed to discriminate among predictors. We then extend our previous theoretical model of the statistical power of PMM-based prediction algorithms to allow for loss of binding sites during evolution, and show that it gives a more accurate upper bound on scanner accuracy. Finally, utilizing our theoretical model, we introduce a new method for predicting the number of real binding sites in a genome. The results suggest that the number of true sites for a yeast TF is in general several times greater than the number of known sites listed in the Saccharomyces cerevisiae Database (SCPD). Among the three scanning algorithms that we test, the MONKEY algorithm has the highest accuracy for predicting yeast TFBSs.

The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line

Using deep sequencing (deepCAGE), the FANTOM4 study measured the genome-wide dynamics of transcription-start-site usage in the human monocytic cell line THP-1 throughout a time course of growth arrest and differentiation. Modeling the expression dynamics in terms of predicted cis-regulatory sites, we identified the key transcription regulators, their time-dependent activities and target genes. Systematic siRNA knockdown of 52 transcription factors confirmed the roles of individual factors in the regulatory network. Our results indicate that cellular states are constrained by complex networks involving both positive and negative regulatory interactions among substantial numbers of transcription factors and that no single transcription factor is both necessary and sufficient to drive the differentiation process.