The value of position-specific priors in motif discovery using MEME

Machine learning-based approach KEVOLVE efficiently identifies SARS-CoV-2 variant-specific genomic signatures

Machine learning was shown to be effective at identifying distinctive genomic signatures among viral sequences. These signatures are defined as pervasive motifs in the viral genome that allow discrimination between species or variants. In the context of SARS-CoV-2, the identification of these signatures can assist in taxonomic and phylogenetic studies, improve in the recognition and definition of emerging variants, and aid in the characterization of functional properties of polymorphic gene products. In this paper, we assess KEVOLVE, an approach based on a genetic algorithm with a machine-learning kernel, to identify multiple genomic signatures based on minimal sets of k-mers. In a comparative study, in which we analyzed large SARS-CoV-2 genome dataset, KEVOLVE was more effective at identifying variant-discriminative signatures than several gold-standard statistical tools. Subsequently, these signatures were characterized using a new extension of KEVOLVE (KANALYZER) to highlight variations of the discriminative signatures among different classes of variants, their genomic location, and the mutations involved. The majority of identified signatures were associated with known mutations among the different variants, in terms of functional and pathological impact based on available literature. Here we showed that KEVOLVE is a robust machine learning approach to identify discriminative signatures among SARS-CoV-2 variants, which are frequently also biologically relevant, while bypassing multiple sequence alignments. The source code of the method and additional resources are available at: https://github.com/bioinfoUQAM/KEVOLVE.

BML: a versatile web server for bipartite motif discovery

Motif discovery and characterization are important for gene regulation analysis. The lack of intuitive and integrative web servers impedes the effective use of motifs. Most motif discovery web tools are either not designed for non-expert users or lacking optimization steps when using default settings. Here we describe bipartite motifs learning (BML), a parameter-free web server that provides a user-friendly portal for online discovery and analysis of sequence motifs, using high-throughput sequencing data as the input. BML utilizes both position weight matrix and dinucleotide weight matrix, the latter of which enables the expression of the interdependencies of neighboring bases. With input parameters concerning the motifs are given, the BML achieves significantly higher accuracy than other available tools for motif finding. When no parameters are given by non-expert users, unlike other tools, BML employs a learning method to identify motifs automatically and achieve accuracy comparable to the scenario where the parameters are set. The BML web server is freely available at http://motif.t-ridership.com/ (https://github.com/Mohammad-Vahed/BML).

Quinolone Resistance of Actinobacillus pleuropneumoniae Revealed through Genome and Transcriptome Analyses

Actinobacillus pleuropneumoniae is a pathogen that infects pigs and poses a serious threat to the pig industry. The emergence of quinolone-resistant strains of A.pleuropneumoniae further limits the choice of treatment. However, the mechanisms behind quinolone resistance in A.pleuropneumoniae remain unclear. The genomes of a ciprofloxacin-resistant strain, A. pleuropneumoniae SC1810 and its isogenic drug-sensitive counterpart were sequenced and analyzed using various bioinformatics tools, revealing 559 differentially expressed genes. The biological membrane, plasmid-mediated quinolone resistance genes and quinolone resistance-determining region were detected. Upregulated expression of efflux pump genes led to ciprofloxacin resistance. The expression of two porins, OmpP2B and LamB, was significantly downregulated in the mutant. Three nonsynonymous mutations in the mutant strain disrupted the water–metal ion bridge, subsequently reducing the affinity of the quinolone–enzyme complex for metal ions and leading to cross-resistance to multiple quinolones. The mechanism of quinolone resistance in A. pleuropneumoniae may involve inhibition of expression of the outer membrane protein genes ompP2B and lamB to decrease drug influx, overexpression of AcrB in the efflux pump to enhance its drug-pumping ability, and mutation in the quinolone resistance-determining region to weaken the binding of the remaining drugs. These findings will provide new potential targets for treatment.

PbCSE1 promotes lignification during stone cell development in pear (Pyrus bretschneideri) fruit

Pear [Pyrus bretschneideri cv. Dangshan Su] fruit quality is not always satisfactory owing to the presence of stone cells, and lignin is the main component of stone cells in pear fruits. Caffeoyl shikimate esterase (CSE) is a key enzyme in the lignin biosynthesis. Although CSE-like genes have been isolated from a variety of plant species, their orthologs are not characterized in pear. In this study, the CSE gene family (PbCSE) from P. bretschneideri was identified. According to the physiological data and quantitative RT-PCR (qRT-PCR), PbCSE1 was associated with lignin deposition and stone cell formation. The overexpression of PbCSE1 increased the lignin content in pear fruits. Relative to wild-type (WT) Arabidopsis, the overexpression of PbCSE1 delayed growth, increased the lignin deposition and lignin content in stems. Simultaneously, the expression of lignin biosynthetic genes were also increased in pear fruits and Arabidopsis. These results demonstrated that PbCSE1 plays an important role in cell lignification and will provide a potential molecular strategy to improve the quality of pear fruits.

An Unbiased Predictive Model to Detect DNA Methylation Propensity of CpG Islands in the Human Genome

Background:

Epigenetic repression mechanisms play an important role in gene regulation, specifically in cancer development. In many cases, a CpG island’s (CGI) susceptibility or resistance to methylation is shown to be contributed by local DNA sequence features.

Objective:

To develop unbiased machine learning models–individually and combined for different biological features–that predict the methylation propensity of a CGI.

Methods:

We developed our model consisting of CGI sequence features on a dataset of 75 sequences (28 prone, 47 resistant) representing a genome-wide methylation structure. We tested our model on two independent datasets that are chromosome (132 sequences) and disease (70 sequences) specific.

Results:

We provided improvements in prediction accuracy over previous models. Our results indicate that combined features better predict the methylation propensity of a CGI (area under the curve (AUC) ~0.81). Our global methylation classifier performs well on independent datasets reaching an AUC of ~0.82 for the complete model and an AUC of ~0.88 for the model using select sequences that better represent their classes in the training set. We report certain de novo motifs and transcription factor binding site (TFBS) motifs that are consistently better in separating prone and resistant CGIs.

Conclusion:

Predictive models for the methylation propensity of CGIs lead to a better understanding of disease mechanisms and can be used to classify genes based on their tendency to contain methylation prone CGIs, which may lead to preventative treatment strategies. MATLAB® and Python™ scripts used for model building, prediction, and downstream analyses are available at https://github.com/dicleyalcin/methylProp_predictor.

Genome-wide discovery of G-quadruplexes in barley

G-quadruplexes (G4s) are four-stranded nucleic acid structures with closely spaced guanine bases forming square planar G-quartets. Aberrant formation of G4 structures has been associated with genomic instability. However, most plant species are lacking comprehensive studies of G4 motifs. In this study, genome-wide identification of G4 motifs in barley was performed, followed by a comparison of genomic distribution and molecular functions to other monocot species, such as wheat, maize, and rice. Similar to the reports on human and some plants like wheat, G4 motifs peaked around the 5′ untranslated region (5′ UTR), the first coding domain sequence, and the first intron start sites on antisense strands. Our comparative analyses in human, Arabidopsis, maize, rice, and sorghum demonstrated that the peak points could be erroneously merged into a single peak when large window sizes are used. We also showed that the G4 distributions around genic regions are relatively similar in the species studied, except in the case of Arabidopsis. G4 containing genes in monocots showed conserved molecular functions for transcription initiation and hydrolase activity. Additionally, we provided examples of imperfect G4 motifs.

PPARγ-p53-Mediated Vasculoregenerative Program to Reverse Pulmonary Hypertension

RATIONALE

In pulmonary arterial hypertension (PAH), endothelial dysfunction and obliterative vascular disease are associated with DNA damage and impaired signaling of BMPR2 (bone morphogenetic protein type 2 receptor) via two downstream transcription factors, PPARγ (peroxisome proliferator-activated receptor gamma), and p53.

OBJECTIVE

We investigated the vasculoprotective and regenerative potential of a newly identified PPARγ-p53 transcription factor complex in the pulmonary endothelium.

METHODS AND RESULTS

In this study, we identified a pharmacologically inducible vasculoprotective mechanism in pulmonary arterial and lung MV (microvascular) endothelial cells in response to DNA damage and oxidant stress regulated in part by a BMPR2 dependent transcription factor complex between PPARγ and p53. Chromatin immunoprecipitation sequencing and RNA-sequencing established an inducible PPARγ-p53 mediated regenerative program regulating 19 genes involved in lung endothelial cell survival, angiogenesis and DNA repair including, EPHA2 (ephrin type-A receptor 2), FHL2 (four and a half LIM domains protein 2), JAG1 (jagged 1), SULF2 (extracellular sulfatase Sulf-2), and TIGAR (TP53-inducible glycolysis and apoptosis regulator). Expression of these genes was partially impaired when the PPARγ-p53 complex was pharmacologically disrupted or when BMPR2 was reduced in pulmonary artery endothelial cells (PAECs) subjected to oxidative stress. In endothelial cell-specific Bmpr2-knockout mice unable to stabilize p53 in endothelial cells under oxidative stress, Nutlin-3 rescued endothelial p53 and PPARγ-p53 complex formation and induced target genes, such as APLN (apelin) and JAG1, to regenerate pulmonary microvessels and reverse pulmonary hypertension. In PAECs from BMPR2 mutant PAH patients, pharmacological induction of p53 and PPARγ-p53 genes repaired damaged DNA utilizing genes from the nucleotide excision repair pathway without provoking PAEC apoptosis.

CONCLUSIONS

We identified a novel therapeutic strategy that activates a vasculoprotective gene regulation program in PAECs downstream of dysfunctional BMPR2 to rehabilitate PAH PAECs, regenerate pulmonary microvessels, and reverse disease. Our studies pave the way for p53-based vasculoregenerative therapies for PAH by extending the therapeutic focus to PAEC dysfunction and to DNA damage associated with PAH progression.

Statistical modelling of bacterial promoter sequences for regulatory motif discovery with the help of transcriptome data: application to Listeria monocytogenes

Automatic de novo identification of the main regulons of a bacterium from genome and transcriptome data remains a challenge. To address this task, we propose a statistical model that can use information on exact positions of the transcription start sites and condition-dependent expression profiles. The central idea of this model is to improve the probabilistic representation of the promoter DNA sequences by incorporating covariates summarizing expression profiles (e.g. coordinates in projection spaces or hierarchical clustering trees). A dedicated trans-dimensional Markov chain Monte Carlo algorithm adjusts the width and palindromic properties of the corresponding position-weight matrices, the number of parameters to describe exact position relative to the transcription start site, and chooses the expression covariates relevant for each motif. All parameters are estimated simultaneously, for many motifs and many expression covariates. The method is applied to a dataset of transcription start sites and expression profiles available for Listeria monocytogenes. The results validate the approach and provide a new global view of the transcription regulatory network of this important pathogen. Remarkably, a previously unreported motif is found in promoter regions of ribosomal protein genes, suggesting a role in the regulation of growth.

Identifying TF Binding Motifs from a Partial Set of Target Genes and its Application to Regulatory Network Inference

Motif identification has been one of the most widely studied problems in bioinformatics. Many methods have been developed to discover binding motifs from a large set of genes. But when the given genes are only a partial set of target genes, the statistical significance usually contains a bias towards the input. If we can identify the TF binding motif from a partial set of target genes, we can save the labor costs and resources for doing many experiments. In this paper, we propose a method MISA (Motif Identification through Segments Assembly) to identify binding motifs from a subset of target genes. By ranking and assembling the segments, MISA discovers a set of binding motifs with the best length to fit our proposed objective function. We also predict the additional target genes as an application of regulatory network inference. We compare our approach with two widely used methods MEME and AlignACE by analyzing both the quality of the binding motif and network inference. Using two model organisms S. cerevisiae and E. coli, we show that with 20 percent of the target genes (minimum sample size of 20), we can achieve a motif similarity of 82 percent with the known motifs. Our results also show that 73 percent of target genes on average can be correctly predicted without introducing many false target genes.

Endothelial cell-glucocorticoid receptor interactions and regulation of Wnt signaling

Vascular inflammation is present in many cardiovascular diseases, and exogenous glucocorticoids have traditionally been used as a therapy to suppress inflammation. However, recent data have shown that endogenous glucocorticoids, acting through the endothelial glucocorticoid receptor, act as negative regulators of inflammation. Here, we performed ChIP for the glucocorticoid receptor, followed by next-generation sequencing in mouse endothelial cells to investigate how the endothelial glucocorticoid receptor regulates vascular inflammation. We identified a role of the Wnt signaling pathway in this setting and show that loss of the endothelial glucocorticoid receptor results in upregulation of Wnt signaling both in vitro and in vivo using our validated mouse model. Furthermore, we demonstrate glucocorticoid receptor regulation of a key gene in the Wnt pathway, Frzb, via a glucocorticoid response element gleaned from our genomic data. These results suggest a role for endothelial Wnt signaling modulation in states of vascular inflammation.

Recent computational developments on CLIP-seq data analysis and microRNA targeting implications

Cross-Linking Immunoprecipitation associated to high-throughput sequencing (CLIP-seq) is a technique used to identify RNA directly bound to RNA-binding proteins across the entire transcriptome in cell or tissue samples. Recent technological and computational advances permit the analysis of many CLIP-seq samples simultaneously, allowing us to reveal the comprehensive network of RNA–protein interaction and to integrate it to other genome-wide analyses. Therefore, the design and quality management of the CLIP-seq analyses are of critical importance to extract clean and biological meaningful information from CLIP-seq experiments. The application of CLIP-seq technique to Argonaute 2 (Ago2) protein, the main component of the microRNA (miRNA)-induced silencing complex, reveals the direct binding sites of miRNAs, thus providing insightful information about the role played by miRNA(s). In this review, we summarize and discuss the most recent computational methods for CLIP-seq analysis, and discuss their impact on Ago2/miRNA-binding site identification and prediction with a regard toward human pathologies.

Toward an Alignment-Free Method for Feature Extraction and Accurate Classification of Viral Sequences

The classification of pathogens in emerging and re-emerging viruses represents major interests in taxonomic studies, functional genomics, host-pathogen interplay, prevention, and disease treatments. It consists of assigning a given sequence to its related group of known sequences sharing similar characteristics and traits. The challenges to such classification could be associated with several virus properties including recombination, mutation rate, multiplicity of motifs, and diversity. In domains such as pathogen monitoring and surveillance, it is important to detect and quantify known and novel taxa without exploiting the full and accurate alignments or virus family profiles. In this study, we propose an alignment-free method, CASTOR-KRFE, to detect discriminating subsequences within known pathogen sequences to classify accurately unknown pathogen sequences. This method includes three major steps: (1) vectorization of known viral genomic sequences based on k-mers to constitute the potential features, (2) efficient way of pattern extraction and evaluation maximizing classification performance, and (3) prediction of the minimal set of features fitting a given criterion (threshold of performance metric and maximum number of features). We assessed this method through a jackknife data partitioning on a dozen of various virus data sets, covering the seven major virus groups and including influenza virus, Ebola virus, human immunodeficiency virus 1, hepatitis C virus, hepatitis B virus, and human papillomavirus. CASTOR-KRFE provides a weighted average F-measure >0.96 over a wide range of viruses. Our method also shows better performance on complex virus data sets than multiple subsequences extractor for classification (MISSEL), a subsequence extraction method, and the Discriminative mode of MEME patterns extraction tool.

DEAD-box RNA helicases Dbp2, Ded1 and Mss116 bind to G-quadruplex nucleic acids and destabilize G-quadruplex RNA

We identified 29 G-quadruplex binding proteins by affinity purification and quantitative LC-MS/MS. We demonstrated that the DEAD-box RNA helicases Dbp2, Ded1 and Mss116 preferentially bind to G-quadruplex nucleic acids in vitro and destabilize RNA quadruplexes, suggesting new potential roles for these helicases in disruption of quadruplex structures in RNA.

Review of Different Sequence Motif Finding Algorithms

The DNA motif discovery is a primary step in many systems for studying gene function. Motif discovery plays a vital role in identification of Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Over the past decades, different algorithms were used to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approaches that many of them are time-consuming and easily trapped in a local optimum. Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome these problems. This paper presents a general classification of motif discovery algorithms with new sub-categories that facilitate building a successful motif discovery algorithm. It also presents a summary of comparison between them.

Comparative Analysis of DNA Motif Discovery Algorithms: A Systemic Review

Background:

Bioinformatics is an interdisciplinary field that combines biology and information technology to study how to deal with the biological data. The DNA motif discovery problem is the main challenge of genome biology and its importance is directly proportional to increasing sequencing technologies which produce large amounts of data. DNA motif is a repeated portion of DNA sequences of major biological interest with important structural and functional features. Motif discovery plays a vital role in the antibody-biomarker identification which is useful for diagnosis of disease and to identify Transcription Factor Binding Sites (TFBSs) that help in learning the mechanisms for regulation of gene expression. Recently, scientists discovered that the TFs have a mutation rate five times higher than the flanking sequences, so motif discovery also has a crucial role in cancer discovery.

Methods:

Over the past decades, many attempts use different algorithms to design fast and accurate motif discovery tools. These algorithms are generally classified into consensus or probabilistic approach.

Results:

Many of DNA motif discovery algorithms are time-consuming and easily trapped in a local optimum.

Conclusion:

Nature-inspired algorithms and many of combinatorial algorithms are recently proposed to overcome the problems of consensus and probabilistic approaches. This paper presents a general classification of motif discovery algorithms with new sub-categories. It also presents a summary comparison between them.

Synthetic Promoters: Designing the cis Regulatory Modules for Controlled Gene Expression

Designing the expression cassettes with desired properties remains the most important consideration of gene engineering technology. One of the challenges for predictive gene expression is the modeling of synthetic gene switches to regulate one or more target genes which would directly respond to specific chemical, environmental, and physiological stimuli. Assessment of natural promoter, high-throughput sequencing, and modern biotech inventory aided in deciphering the structure of cis elements and molding the native cis elements into desired synthetic promoter. Synthetic promoters which are molded by rearrangement of cis motifs can greatly benefit plant biotechnology applications. This review gives a glimpse of the manual in vivo gene regulation through synthetic promoters. It summarizes the integrative design strategy of synthetic promoters and enumerates five approaches for constructing synthetic promoters. Insights into the pattern of cis regulatory elements in the pursuit of desirable "gene switches" to date has also been reevaluated. Joint strategies of bioinformatics modeling and randomized biochemical synthesis are addressed in an effort to construct synthetic promoters for intricate gene regulation.

Ancient horizontally transferred genes in the genome of California two-spot octopus, Octopus bimaculoides

Horizontal gene transfer (HGT), a mechanism that shares genetic material between the host and donor from separated offspring branches, has been described as a means of producing novel and beneficial phenotypes for the host organisms. However, in molluscs, the second most diverse group, the existence of HGT is still controversial. In the present study, 12 HGT genes were identified from California two-spot octopus Octopus bimaculoides based on a similarity search, phylogenetic construction, gene composition analysis and PCR (Polymerase Chain Reaction) validation. Based on the phylogenetic topologies, ten HGT genes were identified to have been transferred into the possible molluscan ancestor, possibly before its radiation. Furthermore, most of the donor organisms were predicted to be familiar bacteria in marine environments. These horizontally transferred genes were under a strong negative selection and could be transcribed in octopus functionally. The predicted biochemical functions of these genes include metabolism, neurotransmission, immune defense and tissue integrity. Seven Zn-metalloproteinases were validated as the main type of HGT genes in octopus with divergent motif composition, intron presence and phylogenetic relationship to the endogenous ones. Furthermore, the functions of Zn-metalloproteinase were predicted to be responsible for immune defense and tissue remolding. Three HGT genes were distributed mainly in the nervous system and were predicted to regulate the neurotransmission through glia-neuronal interactions. The results collectively indicated the existence of HGT in molluscs and its potential contribution to the evolution of octopus with regards to functional innovation and adaptability.

Data on horizontally transferred genes in California two-spot octopus, Octopus bimaculoides

Horizontal gene transfer (HGT), a mechanism that shares genetic material between the host and donor from separated offspring branches, has been described as a means of producing novel and beneficial phenotypes for the host organisms. In the present study, 12 HGT genes were identified from California two-spot octopus Octopus bimaculoides based on a similarity search, phylogenetic construction, gene composition analysis and PCR (Polymerase Chain Reaction) validation. The data collected from the HGT genes from octopus, indicating the phylogenetic incongruences, CodonW analysis, PCR products, detailed motifs and organisms used in screening. In phylogenetic screening, those genes were nested within bacteria homologs and identified as HGT genes transferred from the bacteria to the octopus. The motifs were similar in proteins of the horizontally acquired Zn-metalloproteinases, but differed to endogenous proteins. CodonW was employed to investigate the codon usage bias between HGT genes and other genes in the octopus genome. In PCR validation, all the HGT genes could be produced as amplified fragments. The results collectively indicated the existence of HGT in molluscs and its potential l contribution to the evolution of octopus with regards to functional innovation and adaptability.

Genome scanning for identification and mapping of receptor-like kinase (RLK) gene superfamily in Solanum tuberosum

Receptor-like kinases (RLKs) are a key class of genes that contribute to diverse phenomena from plant development to defense responses. The availability of completed potato genome sequences provide an excellent opportunity to identify and characterize RLK gene superfamily in this lineage. We identified 747 non-redundant RLK genes in the potato genome that were classified into 52 subfamilies, of which 58% members organized into tandem repeats. Nine of potato RLK subfamilies organized into tandem repeats. Also, six subfamilies exhibited lineage-specific expansion compared to Arabidopsis. The majority of RLK genes were physically organized within heterogeneous and homogeneous clusters on chromosomes and were unevenly distributed on the genome. Chromosome 2, 3 and 7 contained the highest number of RLK genes and the most underrepresented chromosomes were chromosome 8, 10 and 11. Taken together, our results provide a framework for future efforts on comparative, evolutionary and functional studies of the members of RLK superfamily.

PISMA: A Visual Representation of Motif Distribution in DNA Sequences

Background:

Because the graphical presentation and analysis of motif distribution can provide insights for experimental hypothesis, PISMA aims at identifying motifs on DNA sequences, counting and showing them graphically. The motif length ranges from 2 to 10 bases, and the DNA sequences range up to 10 kb. The motif distribution is shown as a bar-code–like, as a gene-map–like, and as a transcript scheme.

Results:

We obtained graphical schemes of the CpG site distribution from 91 human papillomavirus genomes. Also, we present 2 analyses: one of DNA motifs associated with either methylation-resistant or methylation-sensitive CpG islands and another analysis of motifs associated with exosome RNA secretion.

Availability and Implementation:

PISMA is developed in Java; it is executable in any type of hardware and in diverse operating systems. PISMA is freely available to noncommercial users. The English version and the User Manual are provided in Supplementary Files 1 and 2, and a Spanish version is available at www.biomedicas.unam.mx/wp-content/software/pisma.zip and www.biomedicas.unam.mx/wp-content/pdf/manual/pisma.pdf .

β-Propeller phytases: Diversity, catalytic attributes, current developments and potential biotechnological applications

Phytases are phosphatases which stepwise remove phosphates from phytic acid or its salts. β-Propeller phytase (BPPhy) belongs to a special class of microbial phytases that is regarded as most diverse, isolated and characterized from different microbes, mainly from Bacillus spp. BPPhy class is unique for its Ca²⁺-dependent catalytic activity, strict substrate specificity, active at neutral to alkaline pH and high thermostability. Numerous sequence and structure based studies have revealed unique attributes and catalytic properties of this class, as compared to other classes of phytases. Recent studies including cloning and expression and genetic engineering approaches have led to improvements in BPPhy which provide an opportunity for extended utilization of this class of phytases in improving animal nutrition, human health, plant growth promotion, and environmental protection, etc. This review describes the sources and diversity of BPPhy genes, biochemical properties, Ca²⁺ dependence, current developments in structural elucidation, heterogeneous expression and catalytic improvements, and multifarious applications of BPPhy.

Divergent DNA Methylation Provides Insights into the Evolution of Duplicate Genes in Zebrafish

The evolutionary mechanism, fate and function of duplicate genes in various taxa have been widely studied; however, the mechanism underlying the maintenance and divergence of duplicate genes in Danio rerio remains largely unexplored. Whether and how the divergence of DNA methylation between duplicate pairs is associated with gene expression and evolutionary time are poorly understood. In this study, by analyzing bisulfite sequencing (BS-seq) and RNA-seq datasets from public data, we demonstrated that DNA methylation played a critical role in duplicate gene evolution in zebrafish. Initially, we found promoter methylation of duplicate genes generally decreased with evolutionary time as measured by synonymous substitution rate between paralogous duplicates (Ks). Importantly, promoter methylation of duplicate genes was negatively correlated with gene expression. Interestingly, for 665 duplicate gene pairs, one gene was consistently promoter methylated, while the other was unmethylated across nine different datasets we studied. Moreover, one motif enriched in promoter methylated duplicate genes tended to be bound by the transcription repression factor FOXD3, whereas a motif enriched in the promoter unmethylated sequences interacted with the transcription activator Sp1, indicating a complex interaction between the genomic environment and epigenome. Besides, body-methylated genes showed longer length than body-unmethylated genes. Overall, our results suggest that DNA methylation is highly important in the differential expression and evolution of duplicate genes in zebrafish.

An Entropy-Based Position Projection Algorithm for Motif Discovery

Motif discovery problem is crucial for understanding the structure and function of gene expression. Over the past decades, many attempts using consensus and probability training model for motif finding are successful. However, the most existing motif discovery algorithms are still time-consuming or easily trapped in a local optimum. To overcome these shortcomings, in this paper, we propose an entropy-based position projection algorithm, called EPP, which designs a projection process to divide the dataset and explores the best local optimal solution. The experimental results on real DNA sequences, Tompa data, and ChIP-seq data show that EPP is advantageous in dealing with the motif discovery problem and outperforms current widely used algorithms.

Transcriptome-Wide Analysis Reveals Modulation of Human Macrophage Inflammatory Phenotype Through Alternative Splicing

OBJECTIVE

Human macrophages can shift phenotype across the inflammatory M1 and reparative M2 spectrum in response to environmental challenges, but the mechanisms promoting inflammatory and cardiometabolic disease-associated M1 phenotypes remain incompletely understood. Alternative splicing (AS) is emerging as an important regulator of cellular function, yet its role in macrophage activation is largely unknown. We investigated the extent to which AS occurs in M1 activation within the cardiometabolic disease context and validated a functional genomic cell model for studying human macrophage-related AS events.

APPROACH AND RESULTS

From deep RNA-sequencing of resting, M1, and M2 primary human monocyte-derived macrophages, we found 3860 differentially expressed genes in M1 activation and detected 233 M1-induced AS events; the majority of AS events were cell- and M1-specific with enrichment for pathways relevant to macrophage inflammation. Using genetic variant data for 10 cardiometabolic traits, we identified 28 trait-associated variants within the genomic loci of 21 alternatively spliced genes and 15 variants within 7 differentially expressed regulatory splicing factors in M1 activation. Knockdown of 1 such splicing factor, CELF1, in primary human macrophages led to increased inflammatory response to M1 stimulation, demonstrating CELF1's potential modulation of the M1 phenotype. Finally, we demonstrated that an induced pluripotent stem cell-derived macrophage system recapitulates M1-associated AS events and provides a high-fidelity macrophage AS model.

CONCLUSIONS

AS plays a role in defining macrophage phenotype in a cell- and stimulus-specific fashion. Alternatively spliced genes and splicing factors with trait-associated variants may reveal novel pathways and targets in cardiometabolic diseases.

Regulation of normal B-cell differentiation and malignant B-cell survival by OCT2

The requirement for the B-cell transcription factor OCT2 (octamer-binding protein 2, encoded by Pou2f2) in germinal center B cells has proved controversial. Here, we report that germinal center B cells are formed normally after depletion of OCT2 in a conditional knockout mouse, but their proliferation is reduced and in vivo differentiation to antibody-secreting plasma cells is blocked. This finding led us to examine the role of OCT2 in germinal center-derived lymphomas. shRNA knockdown showed that almost all diffuse large B-cell lymphoma (DLBCL) cell lines are addicted to the expression of OCT2 and its coactivator OCA-B. Genome-wide chromatin immunoprecipitation (ChIP) analysis and gene-expression profiling revealed the broad transcriptional program regulated by OCT2 that includes the expression of STAT3, IL-10, ELL2, XBP1, MYC, TERT, and ADA. Importantly, genetic alteration of OCT2 is not a requirement for cellular addiction in DLBCL. However, we detected amplifications of the POU2F2 locus in DLBCL tumor biopsies and a recurrent mutation of threonine 223 in the DNA-binding domain of OCT2. This neomorphic mutation subtly alters the DNA-binding preference of OCT2, leading to the transactivation of noncanonical target genes including HIF1a and FCRL3 Finally, by introducing mutations designed to disrupt the OCT2-OCA-B interface, we reveal a requirement for this protein-protein interface that ultimately might be exploited therapeutically. Our findings, combined with the predominantly B-cell-restricted expression of OCT2 and the absence of a systemic phenotype in our knockout mice, suggest that an OCT2-targeted therapeutic strategy would be efficacious in both major subtypes of DLBCL while avoiding systemic toxicity.

Intracellular Dynamics of Synucleins: "Here, There and Everywhere"

Synucleins are small, soluble proteins expressed primarily in neural tissue and in certain tumors. The synuclein family consists of three members: α-, β-, and γ-synucleins present only in vertebrates. Members of the synuclein family have high sequence identity, especially in the N-terminal regions. The synuclein gene family came into the spotlight, when one of its members, α-synuclein, was found to be associated with Parkinson's disease and other neurodegenerative disorders, whereas γ-synuclein was linked to several forms of cancer. There are a lot of controversy and exciting debates concerning members of the synuclein family, including their normal functions, toxicity, role in pathology, transmission between cells and intracellular localization. Important findings which remain undisputable for many years are synuclein localization in synapses and their role in the regulation of synaptic vesicle trafficking, whereas their presence and function in mitochondria and nucleus is a debated topic. In this review, we present the data on the localization of synucleins in two intracellular organelles: the nucleus and mitochondria.

A Fast Cluster Motif Finding Algorithm for ChIP-Seq Data Sets

New high-throughput technique ChIP-seq, coupling chromatin immunoprecipitation experiment with high-throughput sequencing technologies, has extended the identification of binding locations of a transcription factor to the genome-wide regions. However, the most existing motif discovery algorithms are time-consuming and limited to identify binding motifs in ChIP-seq data which normally has the significant characteristics of large scale data. In order to improve the efficiency, we propose a fast cluster motif finding algorithm, named as FCmotif, to identify the (l,  d) motifs in large scale ChIP-seq data set. It is inspired by the emerging substrings mining strategy to find the enriched substrings and then searching the neighborhood instances to construct PWM and cluster motifs in different length. FCmotif is not following the OOPS model constraint and can find long motifs. The effectiveness of proposed algorithm has been proved by experiments on the ChIP-seq data sets from mouse ES cells. The whole detection of the real binding motifs and processing of the full size data of several megabytes finished in a few minutes. The experimental results show that FCmotif has advantageous to deal with the (l,  d) motif finding in the ChIP-seq data; meanwhile it also demonstrates better performance than other current widely-used algorithms such as MEME, Weeder, ChIPMunk, and DREME.

TPpred3 detects and discriminates mitochondrial and chloroplastic targeting peptides in eukaryotic proteins

MOTIVATION

Molecular recognition of N-terminal targeting peptides is the most common mechanism controlling the import of nuclear-encoded proteins into mitochondria and chloroplasts. When experimental information is lacking, computational methods can annotate targeting peptides, and determine their cleavage sites for characterizing protein localization, function, and mature protein sequences. The problem of discriminating mitochondrial from chloroplastic propeptides is particularly relevant when annotating proteomes of photosynthetic Eukaryotes, endowed with both types of sequences.

RESULTS

Here, we introduce TPpred3, a computational method that given any Eukaryotic protein sequence performs three different tasks: (i) the detection of targeting peptides; (ii) their classification as mitochondrial or chloroplastic and (iii) the precise localization of the cleavage sites in an organelle-specific framework. Our implementation is based on our TPpred previously introduced. Here, we integrate a new N-to-1 Extreme Learning Machine specifically designed for the classification task (ii). For the last task, we introduce an organelle-specific Support Vector Machine that exploits sequence motifs retrieved with an extensive motif-discovery analysis of a large set of mitochondrial and chloroplastic proteins. We show that TPpred3 outperforms the state-of-the-art methods in all the three tasks.

AVAILABILITY AND IMPLEMENTATION

The method server and datasets are available at http://tppred3.biocomp.unibo.it.

CONTACT

gigi@biocomp.unibo.it

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Repulsive parallel MCMC algorithm for discovering diverse motifs from large sequence sets

Motivation: The motif discovery problem consists of finding recurring patterns of short strings in a set of nucleotide sequences. This classical problem is receiving renewed attention as most early motif discovery methods lack the ability to handle large data of recent genome-wide ChIP studies. New ChIP-tailored methods focus on reducing computation time and pay little regard to the accuracy of motif detection. Unlike such methods, our method focuses on increasing the detection accuracy while maintaining the computation efficiency at an acceptable level. The major advantage of our method is that it can mine diverse multiple motifs undetectable by current methods.

Results: The repulsive parallel Markov chain Monte Carlo (RPMCMC) algorithm that we propose is a parallel version of the widely used Gibbs motif sampler. RPMCMC is run on parallel interacting motif samplers. A repulsive force is generated when different motifs produced by different samplers near each other. Thus, different samplers explore different motifs. In this way, we can detect much more diverse motifs than conventional methods can. Through application to 228 transcription factor ChIP-seq datasets of the ENCODE project, we show that the RPMCMC algorithm can find many reliable cofactor interacting motifs that existing methods are unable to discover.

Availability and implementation: A C++ implementation of RPMCMC and discovered cofactor motifs for the 228 ENCODE ChIP-seq datasets are available from http://daweb.ism.ac.jp/yoshidalab/motif.

Contact:ikebata.hisaki@ism.ac.jp, yoshidar@ism.ac.jp

Supplementary information:Supplementary data are available from Bioinformatics online.

Stochastic EM-based TFBS motif discovery with MITSU

Motivation: The Expectation–Maximization (EM) algorithm has been successfully applied to the problem of transcription factor binding site (TFBS) motif discovery and underlies the most widely used motif discovery algorithms. In the wider field of probabilistic modelling, the stochastic EM (sEM) algorithm has been used to overcome some of the limitations of the EM algorithm; however, the application of sEM to motif discovery has not been fully explored.

Results: We present MITSU (Motif discovery by ITerative Sampling and Updating), a novel algorithm for motif discovery, which combines sEM with an improved approximation to the likelihood function, which is unconstrained with regard to the distribution of motif occurrences within the input dataset. The algorithm is evaluated quantitatively on realistic synthetic data and several collections of characterized prokaryotic TFBS motifs and shown to outperform EM and an alternative sEM-based algorithm, particularly in terms of site-level positive predictive value.

Availability and implementation: Java executable available for download at http://www.sourceforge.net/p/mitsu-motif/, supported on Linux/OS X.

Contact:a.m.kilpatrick@sms.ed.ac.uk

Computational Methods for CLIP-seq Data Processing

RNA-binding proteins (RBPs) are at the core of post-transcriptional regulation and thus of gene expression control at the RNA level. One of the principal challenges in the field of gene expression regulation is to understand RBPs mechanism of action. As a result of recent evolution of experimental techniques, it is now possible to obtain the RNA regions recognized by RBPs on a transcriptome-wide scale. In fact, CLIP-seq protocols use the joint action of CLIP, crosslinking immunoprecipitation, and high-throughput sequencing to recover the transcriptome-wide set of interaction regions for a particular protein. Nevertheless, computational methods are necessary to process CLIP-seq experimental data and are a key to advancement in the understanding of gene regulatory mechanisms. Considering the importance of computational methods in this area, we present a review of the current status of computational approaches used and proposed for CLIP-seq data.

Genome-wide profiling of untranslated regions by paired-end ditag sequencing reveals unexpected transcriptome complexity in yeast

The identification of structural and functional elements encoded in a genome is a challenging task. Although the transcriptome of budding yeast has been extensively analyzed, the boundaries and untranslated regions of yeast genes remain elusive. To address this least-explored field of yeast genomics, we performed a transcript profiling analysis through paired-end ditag (PET) approach coupled with deep sequencing. With 562,133 PET sequences we accurately defined the boundaries and untranslated regions of 3,409 ORFs, suggesting many yeast genes have multiple transcription start sites (TSSs). We also identified 85 previously uncharacterized transcripts either in intergenic regions or from the opposite strand of reported genomic features. Furthermore, our data revealed the extensive 3' end heterogeneity of yeast genes and identified a novel putative motif for polyadenylation. Our results indicate the yeast transcriptome is more complex than expected. This study would serve as an invaluable resource for elucidating the regulation and evolution of yeast genes.

Global characterization of the oocyte-to-embryo transition in Caenorhabditis elegans uncovers a novel mRNA clearance mechanism

The oocyte-to-embryo transition (OET) is thought to be mainly driven by post-transcriptional gene regulation. However, expression of both RNAs and proteins during the OET has not been comprehensively assayed. Furthermore, specific molecular mechanisms that regulate gene expression during OET are largely unknown. Here, we quantify and analyze transcriptome-wide, expression of mRNAs and thousands of proteins in Caenorhabditis elegans oocytes, 1-cell, and 2-cell embryos. This represents a first comprehensive gene expression atlas during the OET in animals. We discovered a first wave of degradation in which thousands of mRNAs are cleared shortly after fertilization. Sequence analysis revealed a statistically highly significant presence of a polyC motif in the 3' untranslated regions of most of these degraded mRNAs. Transgenic reporter assays demonstrated that this polyC motif is required and sufficient for mRNA degradation after fertilization. We show that orthologs of human polyC-binding protein specifically bind this motif. Our data suggest a mechanism in which the polyC motif and binding partners direct degradation of maternal mRNAs. Our data also indicate that endogenous siRNAs but not miRNAs promote mRNA clearance during the OET.

Encoded expansion: an efficient algorithm to discover identical string motifs

A major task in computational biology is the discovery of short recurring string patterns known as motifs. Most of the schemes to discover motifs are either stochastic or combinatorial in nature. Stochastic approaches do not guarantee finding the correct motifs, while the combinatorial schemes tend to have an exponential time complexity with respect to motif length. To alleviate the cost, the combinatorial approach exploits dynamic data structures such as trees or graphs. Recently (Karci (2009) Efficient automatic exact motif discovery algorithms for biological sequences, Expert Systems with Applications 36:7952-7963) devised a deterministic algorithm that finds all the identical copies of string motifs of all sizes [Formula: see text] in theoretical time complexity of [Formula: see text] and a space complexity of [Formula: see text] where [Formula: see text] is the length of the input sequence and [Formula: see text] is the length of the longest possible string motif. In this paper, we present a significant improvement on Karci's original algorithm. The algorithm that we propose reports all identical string motifs of sizes [Formula: see text] that occur at least [Formula: see text] times. Our algorithm starts with string motifs of size 2, and at each iteration it expands the candidate string motifs by one symbol throwing out those that occur less than [Formula: see text] times in the entire input sequence. We use a simple array and data encoding to achieve theoretical worst-case time complexity of [Formula: see text] and a space complexity of [Formula: see text] Encoding of the substrings can speed up the process of comparison between string motifs. Experimental results on random and real biological sequences confirm that our algorithm has indeed a linear time complexity and it is more scalable in terms of sequence length than the existing algorithms.

DNA methylation and evolution of duplicate genes

The evolutionary mechanisms underlying duplicate gene maintenance and divergence remain highly debated. Epigenetic modifications, such as DNA methylation, may contribute to duplicate gene evolution by facilitating tissue-specific regulation. However, the role of epigenetic divergence on duplicate gene evolution remains little understood. Here we show, using comprehensive data across 10 diverse human tissues, that DNA methylation plays critical roles in several aspects of duplicate gene evolution. We first demonstrate that duplicate genes are initially heavily methylated, before gradually losing DNA methylation as they age. Within each pair, DNA methylation divergence between duplicate partners increases with evolutionary age. Importantly, tissue-specific DNA methylation of duplicates correlates with tissue-specific expression, implicating DNA methylation as a causative factor for functional divergence of duplicate genes. These patterns are apparent in promoters but not in gene bodies, in accord with the complex relationship between gene-body DNA methylation and transcription. Remarkably, many duplicate gene pairs exhibit consistent division of DNA methylation across multiple, divergent tissues: For the majority (73%) of duplicate gene pairs, one partner is always hypermethylated compared with the other. This is indicative of a common underlying determinant of DNA methylation. The division of DNA methylation is also consistent with their chromatin accessibility profiles. Moreover, at least two sequence motifs known to interact with the Sp1 transcription factor mark promoters of more hypomethylated duplicate partners. These results demonstrate critical roles of DNA methylation, as well as complex interaction between genome and epigenome, on duplicate gene evolution.

STEME: a robust, accurate motif finder for large data sets

Motif finding is a difficult problem that has been studied for over 20 years. Some older popular motif finders are not suitable for analysis of the large data sets generated by next-generation sequencing. We recently published an efficient approximation (STEME) to the EM algorithm that is at the core of many motif finders such as MEME. This approximation allows the EM algorithm to be applied to large data sets. In this work we describe several efficient extensions to STEME that are based on the MEME algorithm. Together with the original STEME EM approximation, these extensions make STEME a fully-fledged motif finder with similar properties to MEME. We discuss the difficulty of objectively comparing motif finders. We show that STEME performs comparably to existing prominent discriminative motif finders, DREME and Trawler, on 13 sets of transcription factor binding data in mouse ES cells. We demonstrate the ability of STEME to find long degenerate motifs which these discriminative motif finders do not find. As part of our method, we extend an earlier method due to Nagarajan et al. for the efficient calculation of motif E-values. STEME's source code is available under an open source license and STEME is available via a web interface.

EXTREME: an online EM algorithm for motif discovery

MOTIVATION

Identifying regulatory elements is a fundamental problem in the field of gene transcription. Motif discovery-the task of identifying the sequence preference of transcription factor proteins, which bind to these elements-is an important step in this challenge. MEME is a popular motif discovery algorithm. Unfortunately, MEME's running time scales poorly with the size of the dataset. Experiments such as ChIP-Seq and DNase-Seq are providing a rich amount of information on the binding preference of transcription factors. MEME cannot discover motifs in data from these experiments in a practical amount of time without a compromising strategy such as discarding a majority of the sequences.

RESULTS

We present EXTREME, a motif discovery algorithm designed to find DNA-binding motifs in ChIP-Seq and DNase-Seq data. Unlike MEME, which uses the expectation-maximization algorithm for motif discovery, EXTREME uses the online expectation-maximization algorithm to discover motifs. EXTREME can discover motifs in large datasets in a practical amount of time without discarding any sequences. Using EXTREME on ChIP-Seq and DNase-Seq data, we discover many motifs, including some novel and infrequent motifs that can only be discovered by using the entire dataset. Conservation analysis of one of these novel infrequent motifs confirms that it is evolutionarily conserved and possibly functional.

AVAILABILITY AND IMPLEMENTATION

All source code is available at the Github repository http://github.com/uci-cbcl/EXTREME.

A comprehensive promoter landscape identifies a novel promoter for CD133 in restricted tissues, cancers, and stem cells

PROM1 is the gene encoding prominin-1 or CD133, an important cell surface marker for the isolation of both normal and cancer stem cells. PROM1 transcripts initiate at a range of transcription start sites (TSS) associated with distinct tissue and cancer expression profiles. Using high resolution Cap Analysis of Gene Expression (CAGE) sequencing we characterize TSS utilization across a broad range of normal and developmental tissues. We identify a novel proximal promoter (P6) within CD133⁺ melanoma cell lines and stem cells. Additional exon array sampling finds P6 to be active in populations enriched for mesenchyme, neural stem cells and within CD133⁺ enriched Ewing sarcomas. The P6 promoter is enriched with respect to previously characterized PROM1 promoters for a HMGI/Y (HMGA1) family transcription factor binding site motif and exhibits different epigenetic modifications relative to the canonical promoter region of PROM1.

Genome-wide identification of chromatin transitional regions reveals diverse mechanisms defining the boundary of facultative heterochromatin

Due to the self-propagating nature of the heterochromatic modification H3K27me3, chromatin barrier activities are required to demarcate the boundary and prevent it from encroaching into euchromatic regions. Studies in Drosophila and vertebrate systems have revealed several important chromatin barrier elements and their respective binding factors. However, epigenomic data indicate that the binding of these factors are not exclusive to chromatin boundaries. To gain a comprehensive understanding of facultative heterochromatin boundaries, we developed a two-tiered method to identify the Chromatin Transitional Region (CTR), i.e. the nucleosomal region that shows the greatest transition rate of the H3K27me3 modification as revealed by ChIP-Seq. This approach was applied to identify CTRs in Drosophila S2 cells and human HeLa cells. Although many insulator proteins have been characterized in Drosophila, less than half of the CTRs in S2 cells are associated with known insulator proteins, indicating unknown mechanisms remain to be characterized. Our analysis also revealed that the peak binding of insulator proteins are usually 1–2 nucleosomes away from the CTR. Comparison of CTR-associated insulator protein binding sites vs. those in heterochromatic region revealed that boundary-associated binding sites are distinctively flanked by nucleosome destabilizing sequences, which correlates with significant decreased nucleosome density and increased binding intensities of co-factors. Interestingly, several subgroups of boundaries have enhanced H3.3 incorporation but reduced nucleosome turnover rate. Our genome-wide study reveals that diverse mechanisms are employed to define the boundaries of facultative heterochromatin. In both Drosophila and mammalian systems, only a small fraction of insulator protein binding sites co-localize with H3K27me3 boundaries. However, boundary-associated insulator binding sites are distinctively flanked by nucleosome destabilizing sequences, which correlates with significantly decreased nucleosome density and increased binding of co-factors.

MCOIN: a novel heuristic for determining transcription factor binding site motif width

BACKGROUND

In transcription factor binding site discovery, the true width of the motif to be discovered is generally not known a priori. The ability to compute the most likely width of a motif is therefore a highly desirable property for motif discovery algorithms. However, this is a challenging computational problem as a result of changing model dimensionality at changing motif widths. The complexity of the problem is increased as the discovered model at the true motif width need not be the most statistically significant in a set of candidate motif models. Further, the core motif discovery algorithm used cannot guarantee to return the best possible result at each candidate width.

RESULTS

We present MCOIN, a novel heuristic for automatically determining transcription factor binding site motif width, based on motif containment and information content. Using realistic synthetic data and previously characterised prokaryotic data, we show that MCOIN outperforms the current most popular method (E-value of the resulting multiple alignment) as a predictor of motif width, based on mean absolute error. MCOIN is also shown to choose models which better match known sites at higher levels of motif conservation, based on ROC analysis.

CONCLUSIONS

We demonstrate the performance of MCOIN as part of a deterministic motif discovery algorithm and conclude that MCOIN outperforms current methods for determining motif width.

ER-stress-induced transcriptional regulation increases protein synthesis leading to cell death

Protein misfolding in the endoplasmic reticulum (ER) leads to cell death through PERK-mediated phosphorylation of eIF2α, although the mechanism is not understood. ChIP-seq and mRNA-seq of activating transcription factor 4 (ATF4) and C/EBP homologous protein (CHOP), key transcription factors downstream of p-eIF2α, demonstrated that they interact to directly induce genes encoding protein synthesis and the unfolded protein response, but not apoptosis. Forced expression of ATF4 and CHOP increased protein synthesis and caused ATP depletion, oxidative stress and cell death. The increased protein synthesis and oxidative stress were necessary signals for cell death. We show that eIF2α-phosphorylation-attenuated protein synthesis, and not Atf4 mRNA translation, promotes cell survival. These results show that transcriptional induction through ATF4 and CHOP increases protein synthesis leading to oxidative stress and cell death. The findings suggest that limiting protein synthesis will be therapeutic for diseases caused by protein misfolding in the ER.

Predicting cell-type-specific gene expression from regions of open chromatin

Complex patterns of cell-type-specific gene expression are thought to be achieved by combinatorial binding of transcription factors (TFs) to sequence elements in regulatory regions. Predicting cell-type-specific expression in mammals has been hindered by the oftentimes unknown location of distal regulatory regions. To alleviate this bottleneck, we used DNase-seq data from 19 diverse human cell types to identify proximal and distal regulatory elements at genome-wide scale. Matched expression data allowed us to separate genes into classes of cell-type-specific up-regulated, down-regulated, and constitutively expressed genes. CG dinucleotide content and DNA accessibility in the promoters of these three classes of genes displayed substantial differences, highlighting the importance of including these aspects in modeling gene expression. We associated DNase I hypersensitive sites (DHSs) with genes, and trained classifiers for different expression patterns. TF sequence motif matches in DHSs provided a strong performance improvement in predicting gene expression over the typical baseline approach of using proximal promoter sequences. In particular, we achieved competitive performance when discriminating up-regulated genes from different cell types or genes up- and down-regulated under the same conditions. We identified previously known and new candidate cell-type-specific regulators. The models generated testable predictions of activating or repressive functions of regulators. DNase I footprints for these regulators were indicative of their direct binding to DNA. In summary, we successfully used information of open chromatin obtained by a single assay, DNase-seq, to address the problem of predicting cell-type-specific gene expression in mammalian organisms directly from regulatory sequence.

MotifLab: a tools and data integration workbench for motif discovery and regulatory sequence analysis

Background

Traditional methods for computational motif discovery often suffer from poor performance. In particular, methods that search for sequence matches to known binding motifs tend to predict many non-functional binding sites because they fail to take into consideration the biological state of the cell. In recent years, genome-wide studies have generated a lot of data that has the potential to improve our ability to identify functional motifs and binding sites, such as information about chromatin accessibility and epigenetic states in different cell types. However, it is not always trivial to make use of this data in combination with existing motif discovery tools, especially for researchers who are not skilled in bioinformatics programming.

Results

Here we present MotifLab, a general workbench for analysing regulatory sequence regions and discovering transcription factor binding sites and cis-regulatory modules. MotifLab supports comprehensive motif discovery and analysis by allowing users to integrate several popular motif discovery tools as well as different kinds of additional information, including phylogenetic conservation, epigenetic marks, DNase hypersensitive sites, ChIP-Seq data, positional binding preferences of transcription factors, transcription factor interactions and gene expression. MotifLab offers several data-processing operations that can be used to create, manipulate and analyse data objects, and complete analysis workflows can be constructed and automatically executed within MotifLab, including graphical presentation of the results.

Conclusions

We have developed MotifLab as a flexible workbench for motif analysis in a genomic context. The flexibility and effectiveness of this workbench has been demonstrated on selected test cases, in particular two previously published benchmark data sets for single motifs and modules, and a realistic example of genes responding to treatment with forskolin. MotifLab is freely available at http://www.motiflab.org.

Comprehensive human transcription factor binding site map for combinatory binding motifs discovery

To know the map between transcription factors (TFs) and their binding sites is essential to reverse engineer the regulation process. Only about 10%-20% of the transcription factor binding motifs (TFBMs) have been reported. This lack of data hinders understanding gene regulation. To address this drawback, we propose a computational method that exploits never used TF properties to discover the missing TFBMs and their sites in all human gene promoters. The method starts by predicting a dictionary of regulatory "DNA words." From this dictionary, it distills 4098 novel predictions. To disclose the crosstalk between motifs, an additional algorithm extracts TF combinatorial binding patterns creating a collection of TF regulatory syntactic rules. Using these rules, we narrowed down a list of 504 novel motifs that appear frequently in syntax patterns. We tested the predictions against 509 known motifs confirming that our system can reliably predict ab initio motifs with an accuracy of 81%-far higher than previous approaches. We found that on average, 90% of the discovered combinatorial binding patterns target at least 10 genes, suggesting that to control in an independent manner smaller gene sets, supplementary regulatory mechanisms are required. Additionally, we discovered that the new TFBMs and their combinatorial patterns convey biological meaning, targeting TFs and genes related to developmental functions. Thus, among all the possible available targets in the genome, the TFs tend to regulate other TFs and genes involved in developmental functions. We provide a comprehensive resource for regulation analysis that includes a dictionary of "DNA words," newly predicted motifs and their corresponding combinatorial patterns. Combinatorial patterns are a useful filter to discover TFBMs that play a major role in orchestrating other factors and thus, are likely to lock/unlock cellular functional clusters.

CLIP-seq of eIF4AIII reveals transcriptome-wide mapping of the human exon junction complex

The exon junction complex (EJC) is a central effector of the fate of mRNAs, linking nuclear processing to mRNA transport, translation and surveillance. However, little is known about its transcriptome-wide targets. We used cross-linking and immunoprecipitation methods coupled to high-throughput sequencing (CLIP-seq) in human cells to identify the binding sites of the DEAD-box helicase eIF4AIII, an EJC core component. CLIP reads form peaks that are located mainly in spliced mRNAs. Most expressed exons harbor peaks either in the canonical EJC region, located ~24 nucleotides upstream of exonic junctions, or in other noncanonical regions. Notably, both of these types of peaks are preferentially associated with unstructured and purine-rich sequences containing the motif GAAGA, which is a potential binding site for EJC-associated factors. Therefore, EJC positions vary spatially and quantitatively between exons. This transcriptome-wide mapping of human eIF4AIII reveals unanticipated aspects of the EJC and broadens its potential impact on post-transcriptional regulation.