Thousands of non-coding SNPs have been linked to human diseases in the past. The identification of causal alleles within this pool of disease-associated non-coding SNPs is largely impossible due to the inability to accurately quantify the impact of non-coding variation. To overcome this challenge, we developed a computational model that uses ChIP-seq intensity variation in response to non-coding allelic change as a proxy to the quantification of the biological role of non-coding SNPs. We applied this model to HepG2 enhancers and detected 4796 enhancer SNPs capable of disrupting enhancer activity upon allelic change. These SNPs are significantly over-represented in the binding sites of HNF4 and FOXA families of liver transcription factors and liver eQTLs. In addition, these SNPs are strongly associated with liver GWAS traits, including type I diabetes, and are linked to the abnormal levels of HDL and LDL cholesterol. Our model is directly applicable to any enhancer set for mapping causal regulatory SNPs.
The Drosophila heart is composed of two distinct cell types, the contractile cardial cells (CCs) and the surrounding non-muscle pericardial cells (PCs), development of which is regulated by a network of conserved signaling molecules and transcription factors (TFs). Here, we used machine learning with array-based chromatin immunoprecipitation (ChIP) data and TF sequence motifs to computationally classify cell type-specific cardiac enhancers. Extensive testing of predicted enhancers at single-cell resolution revealed the added value of ChIP data for modeling cell type-specific activities. Furthermore, clustering the top-scoring classifier sequence features identified novel cardiac and cell type-specific regulatory motifs. For example, we found that the Myb motif learned by the classifier is crucial for CC activity, and the Myb TF acts in concert with two forkhead domain TFs and Polo kinase to regulate cardiac progenitor cell divisions. In addition, differential motif enrichment and cis-trans genetic studies revealed that the Notch signaling pathway TF Suppressor of Hairless [Su(H)] discriminates PC from CC enhancer activities. Collectively, these studies elucidate molecular pathways used in the regulatory decisions for proliferation and differentiation of cardiac progenitor cells, implicate Su(H) in regulating cell fate decisions of these progenitors, and document the utility of enhancer modeling in uncovering developmental regulatory subnetworks.
Machine learning; Gene regulation; Transcription factors; Progenitor specification; Cell division; Organogenesis; Drosophila
We investigated sequence features of enhancers separated from their target gene by at least one intermediate gene/exon (named tele-enhancers in this study) and enhancers residing inside their target gene locus. In this study, we used whole genome enhancer maps and gene expression profiles to establish a large panel of tele-enhancers. By contrasting tele-enhancers to proximal enhancers targeting heart genes, we observed that heart tele-enhancers use unique regulatory mechanisms based on the cardiac transcription factors SRF, TEAD, and NKX-2.5, whereas proximal heart enhancers rely on GATA4 instead. A functional analysis showed that tele-enhancers preferentially regulate house-keeping genes and genes with a metabolic role during heart development. In addition, tele-enhancers are significantly more conserved than their proximal counterparts. Similar trends have been observed for non-heart tissues and cell types, suggesting that our findings represent general characteristics of tele-enhancers.
enhancer; nucleotide divergence; single-nucleotide polymorphism; tissue specificity; transcription factor binding motif
Gene expression is controlled by proximal promoters and distal regulatory elements such as enhancers. While the activity of some promoters can be invariant across tissues, enhancers tend to be highly tissue-specific.
We compiled sets of tissue-specific promoters based on gene expression profiles of 79 human tissues and cell types. Putative transcription factor binding sites within each set of sequences were used to train a support vector machine classifier capable of distinguishing tissue-specific promoters from control sequences. We obtained reliable classifiers for 92% of the tissues, with an area under the receiver operating characteristic curve between 60% (for subthalamic nucleus promoters) and 98% (for heart promoters). We next used these classifiers to identify tissue-specific enhancers, scanning distal non-coding sequences in the loci of the 200 most highly and lowly expressed genes. Thirty percent of reliable classifiers produced consistent enhancer predictions, with significantly higher densities in the loci of the most highly expressed compared to lowly expressed genes. Liver enhancer predictions were assessed in vivo using the hydrodynamic tail vein injection assay. Fifty-eight percent of the predictions yielded significant enhancer activity in the mouse liver, whereas a control set of five sequences was completely negative.
We conclude that promoters of tissue-specific genes often contain unambiguous tissue-specific signatures that can be learned and used for the de novo prediction of enhancers.
Despite continual progress in the cataloging of vertebrate regulatory elements, little is known about their organization and regulatory architecture. Here we describe a massively parallel experiment to systematically test the impact of copy number, spacing, combination and order of transcription factor binding sites on gene expression. A complex library of ~5,000 synthetic regulatory elements containing patterns from 1 2 liver-specific transcription factor binding sites was assayed in mice and in HepG2 cells. We find that certain transcription factors act as direct drivers of gene expression in homotypic clusters of binding sites, independent of spacing between sites, whereas others function only synergistically. Heterotypic enhancers are stronger than their homotypic analogs and favor specific transcription factor binding site combinations, mimicking putative native enhancers. Exhaustive testing of binding site permutations suggests that there is flexibility in binding site order. Our findings provide quantitative support for a flexible model of regulatory element activity and suggest a framework for the design of synthetic tissue-specific enhancers.
Identifying gene regulatory elements and their target genes in vertebrates remains a significant challenge. It is now recognized that transcriptional regulatory sequences are critical in orchestrating dynamic controls of tissue-specific gene expression during vertebrate development and in adult tissues, and that these elements can be positioned at great distances in relation to the promoters of the genes they control. While significant progress has been made in mapping DNA binding regions by combining chromatin immunoprecipitation and next generation sequencing, functional validation remains a limiting step in improving our ability to correlate in silico predictions with biological function. We recently developed a computational method that synergistically combines genome-wide gene-expression profiling, vertebrate genome comparisons, and transcription factor binding-site analysis to predict tissue-specific enhancers in the human genome. We applied this method to 270 genes highly expressed in skeletal muscle and predicted 190 putative cis-regulatory modules. Furthermore, we optimized Tol2 transgenic constructs in Xenopus laevis to interrogate 20 of these elements for their ability to function as skeletal muscle-specific transcriptional enhancers during embryonic development. We found 45% of these elements expressed only in the fast muscle fibers that are oriented in highly organized chevrons in the Xenopus laevis tadpole. Transcription factor binding site analysis identified >2 Mef2/MyoD sites within ∼200 bp regions in 6 of the validated enhancers, and systematic mutagenesis of these sites revealed that they are critical for the enhancer function. The data described herein introduces a new reporter system suitable for interrogating tissue-specific cis-regulatory elements which allows monitoring of enhancer activity in real time, throughout early stages of embryonic development, in Xenopus.
Multiple sequence alignment analysis is a powerful approach for translating the evolutionary selective power into phylogenetic relationships to localize functional coding and noncoding genomic elements. The tool Mulan (http://mulan.dcode.org/) has been designed to effectively perform multiple comparisons of genomic sequences necessary to facilitate bioinformatic-driven biological discoveries. The Mulan network server is capable of comparing both closely and distantly related genomes to identify conserved elements over a broad range of evolutionary time. Several novel algorithms are brought together in this tool: the tba multisequence aligner program used to rapidly identify local sequence conservation and the multiTF program to detect evolutionarily conserved transcription factor binding sites in alignments. Mulan is integrated with the ERC Browser, the UCSC Genome Browser for quick uploads of available sequences and supports two-way communication with the GALA database to overlay GALA functional genome annotation with sequence conservation profiles. Local multiple alignments computed by Mulan ensure reliable representation of short- and large-scale genomic rearrangements in distant organisms. Recently, we have also introduced the ability to handle duplications to permit the reliable reconstruction of evolutionary events that underlie the genome sequence data. Here, we describe the main features of the Mulan tool that include the interactive modification of critical conservation parameters, visualization options, and dynamic access to sequence data from visual graphs for flexible and easy-to-perform analysis of differentially evolving genomic regions.
Multiple alignment; alignment tool; evolutionary conservation; conserved elements; conserved transcription factor binding sites
Lineage-specific regulatory elements underlie adaptation of species and play a role in disease susceptibility. We compared functionally conserved and lineage-specific enhancers by cross-mapping 5042 human and 6564 mouse heart enhancers. Of these, 79 per cent are lineage-specific, lacking a functional orthologue. Heart enhancers tend to cluster and, commonly, there are multiple heart enhancers in a heart locus providing a regulatory stability to the locus. We observed little cross-clustering, however, between lineage-specific and functionally conserved heart enhancers suggesting regulatory function acquisition and development in loci previously lacking heart activity. We also identified 862 human-specific heart enhancers: 417 featuring sequence conservation with mouse (class II) and 445 with neither sequence nor function conservation (class III). Ninety-eight per cent of class III enhancers were deleted from the mouse genome, and we estimated a similar-sized enhancer gain in the human lineage. Human-specific enhancers display no detectable decrease in the negative selection pressure and are strongly associated with genes partaking in the heart regulatory programmes. The loss of a heart enhancer could be compensated by activity of a redundant heart enhancer; however, we observed redundancy in only 15 per cent of class II and III enhancer loci indicating a large-scale reprogramming of the heart regulatory programme in mammals.
gene regulation; lineage-specific heart enhancers; cis-regulatory evolution
The mammalian telencephalon plays critical roles in cognition, motor function, and emotion. While many of the genes required for its development have been identified, the distant-acting regulatory sequences orchestrating their in vivo expression are mostly unknown. Here we describe a digital atlas of in vivo enhancers active in subregions of the developing telencephalon. We identified over 4,600 candidate embryonic forebrain enhancers and studied the in vivo activity of 329 of these sequences in transgenic mouse embryos. We generated serial sets of histological brain sections for 145 reproducible forebrain enhancers, resulting in a publicly accessible web-based data collection comprising over 32,000 sections. We also used epigenomic analysis of human and mouse cortex tissue to directly compare the genome-wide enhancer architecture in these species. These data provide a primary resource for investigating gene regulatory mechanisms of telencephalon development and enable studies of the role of distant-acting enhancers in neurodevelopmental disorders.
From initial seed germination through reproduction, plants continuously reprogram their transcriptional repertoire to facilitate growth and development. This dynamic is mediated by a diverse but inextricably-linked catalog of regulatory proteins called transcription factors (TFs). Statistically quantifying TF binding site (TFBS) abundance in promoters of differentially expressed genes can be used to identify binding site patterns in promoters that are closely related to stress-response. Output from today’s transcriptomic assays necessitates statistically-oriented software to handle large promoter-sequence sets in a computationally tractable fashion.
We present Marina, an open-source software for identifying over-represented TFBSs from amongst large sets of promoter sequences, using an ensemble of 7 statistical metrics and binding-site profiles. Through software comparison, we show that Marina can identify considerably more over-represented plant TFBSs compared to a popular software alternative.
Marina was used to identify over-represented TFBSs in a two time-point RNA-Seq study exploring the transcriptomic interplay between soybean (Glycine max) and soybean rust (Phakopsora pachyrhizi). Marina identified numerous abundant TFBSs recognized by transcription factors that are associated with defense-response such as WRKY, HY5 and MYB2. Comparing results from Marina to that of a popular software alternative suggests that regardless of the number of promoter-sequences, Marina is able to identify significantly more over-represented TFBSs.
CLARE is a computational method designed to reveal sequence encryption of tissue-specific regulatory elements. Starting with a set of regulatory elements known to be active in a particular tissue/process, it learns the sequence code of the input set and builds a predictive model from features specific to those elements. The resulting model can then be applied to user-supplied genomic regions to identify novel candidate regulatory elements. CLARE's model also provides a detailed analysis of transcription factors that most likely bind to the elements, making it an invaluable tool for understanding mechanisms of tissue-specific gene regulation.
Availability: CLARE is freely accessible at http://clare.dcode.org/.
Supplementary data are available at Bioinformatics online.
Transcriptional enhancers integrate the contributions of multiple classes of transcription factors (TFs) to orchestrate the myriad spatio-temporal gene expression programs that occur during development. A molecular understanding of enhancers with similar activities requires the identification of both their unique and their shared sequence features. To address this problem, we combined phylogenetic profiling with a DNA–based enhancer sequence classifier that analyzes the TF binding sites (TFBSs) governing the transcription of a co-expressed gene set. We first assembled a small number of enhancers that are active in Drosophila melanogaster muscle founder cells (FCs) and other mesodermal cell types. Using phylogenetic profiling, we increased the number of enhancers by incorporating orthologous but divergent sequences from other Drosophila species. Functional assays revealed that the diverged enhancer orthologs were active in largely similar patterns as their D. melanogaster counterparts, although there was extensive evolutionary shuffling of known TFBSs. We then built and trained a classifier using this enhancer set and identified additional related enhancers based on the presence or absence of known and putative TFBSs. Predicted FC enhancers were over-represented in proximity to known FC genes; and many of the TFBSs learned by the classifier were found to be critical for enhancer activity, including POU homeodomain, Myb, Ets, Forkhead, and T-box motifs. Empirical testing also revealed that the T-box TF encoded by org-1 is a previously uncharacterized regulator of muscle cell identity. Finally, we found extensive diversity in the composition of TFBSs within known FC enhancers, suggesting that motif combinatorics plays an essential role in the cellular specificity exhibited by such enhancers. In summary, machine learning combined with evolutionary sequence analysis is useful for recognizing novel TFBSs and for facilitating the identification of cognate TFs that coordinate cell type–specific developmental gene expression patterns.
The development of multicellular organisms requires the formation of a diversity of cell types. Each cell has a unique genetic program that is orchestrated by regulatory sequences called enhancers, comprising multiple short DNA sequences that bind distinct transcription factors. Understanding developmental regulatory networks requires knowledge of the sequence features of functionally related enhancers. We developed an integrated evolutionary and computational approach for deciphering enhancer regulatory codes and applied this method to discover new components of the transcriptional network controlling muscle development in the fruit fly, Drosophila melanogaster. Our method involves assembling known muscle enhancers, expanding this set with evolutionarily conserved sequences, computationally classifying these enhancers based on their shared sequence features, and scanning the entire Drosophila genome to predict additional related enhancers. Using this approach, we created a map of 5,500 putative muscle enhancers, identified candidate transcription factors to which they bind, observed a strong correlation between mapped enhancers and muscle gene expression, and uncovered extensive heterogeneity among combinations of transcription factor binding sites in validated muscle enhancers, a feature that may contribute to the individual cellular specificities of these regulatory elements. Our strategy can readily be generalized to study transcriptional networks in other organisms and developmental contexts.
Researchers seeking to unlock the genetic basis of human physiology and diseases have been studying gene transcription regulation. The temporal and spatial patterns of gene expression are controlled by mainly non-coding elements known as cis-regulatory modules (CRMs) and epigenetic factors. CRMs modulating related genes share the regulatory signature which consists of transcription factor (TF) binding sites (TFBSs). Identifying such CRMs is a challenging problem due to the prohibitive number of sequence sets that need to be analyzed.
We formulated the challenge as a supervised classification problem even though experimentally validated CRMs were not required. Our efforts resulted in a software system named CrmMiner. The system mines for CRMs in the vicinity of related genes. CrmMiner requires two sets of sequences: a mixed set and a control set. Sequences in the vicinity of the related genes comprise the mixed set, whereas the control set includes random genomic sequences. CrmMiner assumes that a large percentage of the mixed set is made of background sequences that do not include CRMs. The system identifies pairs of closely located motifs representing vertebrate TFBSs that are enriched in the training mixed set consisting of 50% of the gene loci. In addition, CrmMiner selects a group of the enriched pairs to represent the tissue-specific regulatory signature. The mixed and the control sets are searched for candidate sequences that include any of the selected pairs. Next, an optimal Bayesian classifier is used to distinguish candidates found in the mixed set from their control counterparts. Our study proposes 62 tissue-specific regulatory signatures and putative CRMs for different human tissues and cell types. These signatures consist of assortments of ubiquitously expressed TFs and tissue-specific TFs. Under controlled settings, CrmMiner identified known CRMs in noisy sets up to 1:25 signal-to-noise ratio. CrmMiner was 21-75% more precise than a related CRM predictor. The sensitivity of the system to locate known human heart enhancers reached up to 83%. CrmMiner precision reached 82% while mining for CRMs specific to the human CD4+ T cells. On several data sets, the system achieved 99% specificity.
These results suggest that CrmMiner predictions are accurate and likely to be tissue-specific CRMs. We expect that the predicted tissue-specific CRMs and the regulatory signatures broaden our knowledge of gene transcription regulation.
Detailed information about stage-specific changes in gene expression is crucial for understanding the gene regulatory networks underlying development and the various signal transduction pathways contributing to morphogenesis. Here we describe the global gene expression dynamics during early murine limb development, when cartilage, tendons, muscle, joints, vasculature and nerves are specified and the musculoskeletal system of limbs is established. We used whole-genome microarrays to identify genes with differential expression at 5 stages of limb development (E9.5 to 13.5), during fore- and hind-limb patterning. We found that the onset of limb formation is characterized by an up-regulation of transcription factors, which is followed by a massive activation of genes during E10.5 and E11.5 which levels off at later time points. Among the 3520 genes identified as significantly up-regulated in the limb, we find ∼30% to be novel, dramatically expanding the repertoire of candidate genes likely to function in the limb. Hierarchical and stage-specific clustering identified expression profiles that are likely to correlate with functional programs during limb development and further characterization of these transcripts will provide new insights into specific tissue patterning processes. Here, we provide for the first time a comprehensive analysis of developmentally regulated genes during murine limb development, and provide some novel insights into the expression dynamics governing limb morphogenesis.
Many genomic alterations associated to human diseases localize in non-coding regulatory elements located far from the promoters they regulate, making the association of non-coding mutations or risk associated variants to target genes challenging. The range of action of a given set of enhancers is thought to be defined by insulator elements bound by CTCF. Here, we analyzed the genomic distribution of CTCF in various human, mouse and chicken cell types, demonstrating the existence of evolutionarily conserved CTCF-bound sites beyond mammals. These sites preferentially flank transcription factor-encoding genes, often associated to human diseases, and function as enhancer blockers in vivo, suggesting that they act as evolutionary invariant gene boundaries. We then applied this concept to predict and functionally demonstrate that the polymorphic variants associated to multiple sclerosis located within the EVI5 gene are actually impinging on the adjacent gene GFI1.
High mobility group N (HMGN) is a family of intrinsically disordered nuclear proteins that bind to nucleosomes, alters the structure of chromatin and affects transcription. A major unresolved question is the extent of functional specificity, or redundancy, between the various members of the HMGN protein family. Here, we analyze the transcriptional profile of cells in which the expression of various HMGN proteins has been either deleted or doubled. We find that both up- and downregulation of HMGN expression altered the cellular transcription profile. Most, but not all of the changes were variant specific, suggesting limited redundancy in transcriptional regulation. Analysis of point and swap HMGN mutants revealed that the transcriptional specificity is determined by a unique combination of a functional nucleosome-binding domain and C-terminal domain. Doubling the amount of HMGN had a significantly larger effect on the transcription profile than total deletion, suggesting that the intrinsically disordered structure of HMGN proteins plays an important role in their function. The results reveal an HMGN-variant-specific effect on the fidelity of the cellular transcription profile, indicating that functionally the various HMGN subtypes are not fully redundant.
The western clawed frog Xenopus tropicalis is an important model for vertebrate development that combines experimental advantages of the African clawed frog Xenopus laevis with more tractable genetics. Here we present a draft genome sequence assembly of X. tropicalis. This genome encodes over 20,000 protein-coding genes, including orthologs of at least 1,700 human disease genes. Over a million expressed sequence tags validated the annotation. More than one-third of the genome consists of transposable elements, with unusually prevalent DNA transposons. Like other tetrapods, the genome contains gene deserts enriched for conserved non-coding elements. The genome exhibits remarkable shared synteny with human and chicken over major parts of large chromosomes, broken by lineage-specific chromosome fusions and fissions, mainly in the mammalian lineage.
Transcriptome diversity provides the key to cellular identity. One important contribution to expression diversity is the use of alternative promoters, which creates mRNA isoforms by expanding the choice of transcription initiation sites of a gene. The proximity of the basal promoter to the transcription initiation site enables prediction of a promoter's location based on the gene annotations. We show that annotation of alternative promoters regulating expression of transcripts with distinct first exons enables a novel methodology to quantify expression levels and tissue specificity of mRNA isoforms.
The use of distinct alternative first exons in 3,296 genes was examined using exon-microarray data from 11 human tissues. Comparing two transcripts from each gene we found that the activity of alternative promoters (i.e., P1 and P2) was not correlated through tissue specificity or level of expression. Furthermore neither P1 nor P2 conferred any bias for tissue-specific or ubiquitous expression. Genes associated with specific diseases produced transcripts whose limited expression patterns were consistent with the tissue affected in disease. Notably, genes that were historically designated as tissue-specific or housekeeping had alternative isoforms that showed differential expression. Furthermore, only a small number of alternative promoters showed expression exclusive to a single tissue indicating that “tissue preference” provides a better description of promoter activity than tissue specificity. When compared to gene expression data in public databases, as few as 22% of the genes had detailed information for more than one isoform, whereas the remainder collapsed the expression patterns from individual transcripts into one profile.
We describe a computational pipeline that uses microarray data to assess the level of expression and breadth of tissue profiles for transcripts with distinct first exons regulated by alternative promoters. We conclude that alternative promoters provide individualized regulation that is confirmed through expression levels, tissue preference and chromatin modifications. Although the selective use of alternative promoters often goes uncharacterized in gene expression analyses, transcripts produced in this manner make unique contributions to the cell that requires further exploration.
Proper development and functioning of an organism depends on precise spatial and temporal expression of all its genes. These coordinated expression-patterns are maintained primarily through the process of transcriptional regulation. Transcriptional regulation is mediated by proteins binding to regulatory elements on the DNA in a combinatorial manner, where particular combinations of transcription factor binding sites establish specific regulatory codes. In this review, we survey experimental and computational approaches geared towards the identification of proximal and distal gene regulatory elements in the genomes of complex eukaryotes. Available approaches that decipher the genetic structure and function of regulatory elements by exploiting various sources of information like gene expression data, chromatin structure, DNA-binding specificities of transcription factors, cooperativity of transcription factors, etc. are highlighted. We also discuss the relevance of regulatory elements in the context of human health through examples of mutations in some of these regions having serious implications in misregulation of genes and being strongly associated with human disorders.
transcriptional regulation; enhancers; silencers; tissue-specific regulatory elements; population variation; non-coding diseases; computational analysis of regulatory element sequence composition
The landscape of the human genome consists of millions of short islands of conservation that are 100% conserved across multiple vertebrate genomes (termed “bricks”), the majority of which are located in noncoding regions. Several hundred thousand bricks are deeply conserved reaching the genomes of amphibians and fish. Deep phylogenetic conservation of noncoding DNA has been reported to be strongly associated with the presence of gene regulatory elements, introducing bricks as a proxy to the functional noncoding landscape of the human genome. Here, we report a significant overrepresentation of bricks in the promoters of transcription factors and developmental genes, where the high level of phylogenetic conservation correlates with an increase in brick overrepresentation. We also found that the presence of a brick dictates a predisposition to evolutionary constraint, with only 0.7% of the amniota brick central nucleotides being diverged within the primate lineage—an 11-fold reduction in the divergence rate compared with random expectation. Human single-nucleotide polymorphism (SNP) data explains only 3% of primate-specific variation in amniota bricks, thus arguing for a widespread fixation of brick mutations within the primate lineage and prior to human radiation. This variation, in turn, might have been utilized as a driving force for primate- and hominoid-specific adaptation. We also discovered a pronounced deviation from the evolutionary predisposition in the human lineage, with over 20-fold increase in the substitution rate at brick SNP sites over expected values. In addition, contrary to typical brick mutations, brick variation commonly encountered in the human population displays limited, if any, signatures of negative selection as measured by the minor allele frequency and population differentiation (F-statistical measure) measures. These observations argue for the plasticity of gene regulatory mechanisms in vertebrates—with evidence of strong purifying selection acting on the gene regulatory landscape of the human genome, where widespread advantageous mutations in putative regulatory elements are likely utilized in functional diversification and adaptation of species.
gene regulation; enhancer evolution; selection and adaptation; sequence conservation
The distribution and evolution of ultraconserved elements (UCEs, DNA stretches that are perfectly identical in primates and rodents) were examined in genomes of three primate species (human, chimpanzee, and rhesus macaque). It was found that the number of UCEs has decreased throughout primate evolution. At least 26% of ancestral UCEs have diverged in hominoids, while an additional 17% have accumulated one or more single nucleotide polymorphisms (SNPs) in the human genome. Sequence polymorphism analyses indicate that mutation fixation within an UCE can trigger a relaxation in the selective constraint on that element. Homogeneous mutation accumulations in UCEs served as a template by which purifying selection acted more effectively on protein-coding UCEs. Gene ontology annotation suggests that UCE sequence variation, primarily occurring in noncoding regions, might be linked to the reprogramming of the expression pattern of transcription factors and developmentally important genes. Many of these genes are expressed in the central nervous system. Finally, UCE sequence variability within human populations has been identified, including population-specific non-synonymous changes in protein-coding regions.
Motivation: Several functional gene annotation databases have been developed in the recent years, and are widely used to infer the biological function of gene sets, by scrutinizing the attributes that appear over- and underrepresented. However, this strategy is not directly applicable to the study of non-coding DNA, as the non-coding sequence span varies greatly among different gene loci in the human genome and longer loci have a higher likelihood of being selected purely by chance. Therefore, conclusions involving the function of non-coding elements that are drawn based on the annotation of neighboring genes are often biased. We assessed the systematic bias in several particular Gene Ontology (GO) categories using the standard hypergeometric test, by randomly sampling non-coding elements from the human genome and inferring their function based on the functional annotation of the closest genes. While no category is expected to occur significantly over- or underrepresented for a random selection of elements, categories such as ‘cell adhesion’, ‘nervous system development’ and ‘transcription factor activities’ appeared to be systematically overrepresented, while others such as ‘olfactory receptor activity’—underrepresented.
Results: Our results suggest that functional inference for non-coding elements using gene annotation databases requires a special correction. We introduce a set of correction coefficients for the probabilities of the GO categories that accounts for the variability in the length of the non-coding DNA across different loci and effectively eliminates the ascertainment bias from the functional characterization of non-coding elements. Our approach can be easily generalized to any other gene annotation database.
Supplementary data are available at Bioinformatics Online.
The distribution and evolution of ultraconserved elements (UCEs, DNA stretches that are perfectly identical in primates and rodents) were examined in genomes of 3 primate species (human, chimpanzee, and rhesus macaque). It was found that the number of UCEs has decreased throughout primate evolution. At least 26% of ancestral UCEs have diverged in hominoids, whereas an additional 17% have accumulated one or more single nucleotide polymorphisms in the human genome. Sequence polymorphism analyses indicate that mutation fixation within an UCE can trigger a relaxation in the selective constraint on that element. Homogeneous mutation accumulations in UCEs served as a template by which purifying selection acted more effectively on protein-coding UCEs. Gene ontology annotation suggests that UCE sequence variation, primarily occurring in noncoding regions, might be linked to the reprogramming of the expression pattern of transcription factors and developmentally important genes. Many of these genes are expressed in the central nervous system. Finally, UCE sequence variability within human populations has been identified, including population-specific nonsynonymous changes in protein-coding regions.
evolution; ultraconservation; gene regulation; comparative genomics
Regulation of gene expression in eukaryotic genomes is established through a complex cooperative activity of proximal promoters and distant regulatory elements (REs) such as enhancers, repressors and silencers. We have developed a web server named DiRE, based on the Enhancer Identification (EI) method, for predicting distant regulatory elements in higher eukaryotic genomes, namely for determining their chromosomal location and functional characteristics. The server uses gene co-expression data, comparative genomics and profiles of transcription factor binding sites (TFBSs) to determine TFBS-association signatures that can be used for discriminating specific regulatory functions. DiRE's unique feature is its ability to detect REs outside of proximal promoter regions, as it takes advantage of the full gene locus to conduct the search. DiRE can predict common REs for any set of input genes for which the user has prior knowledge of co-expression, co-function or other biologically meaningful grouping. The server predicts function-specific REs consisting of clusters of specifically-associated TFBSs and it also scores the association of individual transcription factors (TFs) with the biological function shared by the group of input genes. Its integration with the Array2BIO server allows users to start their analysis with raw microarray expression data. The DiRE web server is freely available at http://dire.dcode.org.
There are several isolated tools for partial analysis of microarray expression data. To provide an integrative, easy-to-use and automated toolkit for the analysis of Affymetrix microarray expression data we have developed Array2BIO, an application that couples several analytical methods into a single web based utility.
Array2BIO converts raw intensities into probe expression values, automatically maps those to genes, and subsequently identifies groups of co-expressed genes using two complementary approaches: (1) comparative analysis of signal versus control and (2) clustering analysis of gene expression across different conditions. The identified genes are assigned to functional categories based on Gene Ontology classification and KEGG protein interaction pathways. Array2BIO reliably handles low-expressor genes and provides a set of statistical methods for quantifying expression levels, including Benjamini-Hochberg and Bonferroni multiple testing corrections. An automated interface with the ECR Browser provides evolutionary conservation analysis for the identified gene loci while the interconnection with Crème allows prediction of gene regulatory elements that underlie observed expression patterns.
We have developed Array2BIO – a web based tool for rapid comprehensive analysis of Affymetrix microarray expression data, which also allows users to link expression data to Dcode.org comparative genomics tools and integrates a system for translating co-expression data into mechanisms of gene co-regulation. Array2BIO is publicly available at