Predicting tissue specific cis-regulatory modules in the human genome using pairs of co-occurring motifs

EnhancerTracker: Comparing cell-type-specific enhancer activity of DNA sequence triplets via an ensemble of deep convolutional neural networks

Motivation

Transcriptional enhancers - unlike promoters - are unrestrained by distance or strand orientation with respect to their target genes, making their computational identification a challenge. Further, there are insufficient numbers of confirmed enhancers for many cell types, preventing robust training of machine-learning-based models for enhancer prediction for such cell types.

Results

We present EnhancerTracker , a novel tool that leverages an ensemble of deep separable convolutional neural networks to identify cell-type-specific enhancers with the need of only two confirmed enhancers. EnhancerTracker is trained, validated, and tested on 52,789 putative enhancers obtained from the FANTOM5 Project and control sequences derived from the human genome. Unlike available tools, which accept one sequence at a time, the input to our tool is three sequences; the first two are enhancers active in the same cell type. EnhancerTracker outputs 1 if the third sequence is an enhancer active in the same cell type(s) where the first two enhancers are active. It outputs 0 otherwise. On a held-out set (15%), EnhancerTracker achieved an accuracy of 64%, a specificity of 93%, a recall of 35%, a precision of 84%, and an F1 score of 49%.

Availability and implementation

https://github.com/BioinformaticsToolsmith/EnhancerTracker.

Contact

hani.girgis@tamuk.edu.

Structural characterization of a novel luciferase-like-monooxygenase fromPseudomonas meliae– an in-silico approach.. [DOI: 10.1101/2023.03.27.534437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background

Luciferase is a well-known oxidative enzyme that produces bioluminescence. ThePseudomonas meliaeis a plant pathogen that causes wood rot on nectarine and peach and possesses a luciferase-like monooxygenase. After activation, it produces bioluminescence, and the pathogen’s bioluminescence is a visual indicator of diseased plants.

Methods

The present study aims to model and characterize the luciferase-like monooxygenase protein inP. meliaefor its similarity to well-established luciferase. In this study, the luciferase-like monooxygenase fromP. meliaeinfects chinaberry plants has been modeled first and then studied by comparing it with existing known luciferase. Also, the similarities between uncharacterized luciferase fromP. meliaeand template fromGeobacillus thermodenitrificanswere analyzed to find the novelty ofP. meliae.

Results

The results suggest that the absence of bioluminescence inP. meliaecould be due to the evolutionary mutation in positions 138 and 311. The active site remains identical except for two amino acids;P. meliaeTyr138 instead of His138 and Leu311 instead of His311. Therefore, theP. meliaewill have a potential future application, and mutation of the residues 138 and 311 can be restored luciferase light-emitting ability.

Conclusions

This study will help further improve, activate, and repurpose the luciferase fromP. meliaeas a reporter for gene expression.

Identity: rapid alignment-free prediction of sequence alignment identity scores using self-supervised general linear models

Pairwise global alignment is a fundamental step in sequence analysis. Optimal alignment algorithms are quadratic-slow especially on long sequences. In many applications that involve large sequence datasets, all what is needed is calculating the identity scores (percentage of identical nucleotides in an optimal alignment-including gaps-of two sequences); there is no need for visualizing how every two sequences are aligned. For these applications, we propose Identity, which produces global identity scores for a large number of pairs of DNA sequences using alignment-free methods and self-supervised general linear models. For the first time, the new tool can predict pairwise identity scores in linear time and space. On two large-scale sequence databases, Identity provided the best compromise between sensitivity and precision while being faster than BLAST, Mash, MUMmer4 and USEARCH by 2-80 times. Identity was the best performing tool when searching for low-identity matches. While constructing phylogenetic trees from about 6000 transcripts, the tree due to the scores reported by Identity was the closest to the reference tree (in contrast to andi, FSWM and Mash). Identity is capable of producing pairwise identity scores of millions-of-nucleotides-long bacterial genomes; this task cannot be accomplished by any global-alignment-based tool. Availability: https://github.com/BioinformaticsToolsmith/Identity.

LtrDetector: A tool-suite for detecting long terminal repeat retrotransposons de-novo

BACKGROUND

Long terminal repeat retrotransposons are the most abundant transposons in plants. They play important roles in alternative splicing, recombination, gene regulation, and defense mechanisms. Large-scale sequencing projects for plant genomes are currently underway. Software tools are important for annotating long terminal repeat retrotransposons in these newly available genomes. However, the available tools are not very sensitive to known elements and perform inconsistently on different genomes. Some are hard to install or obsolete. They may struggle to process large plant genomes. None can be executed in parallel out of the box and very few have features to support visual review of new elements. To overcome these limitations, we developed LtrDetector, which uses techniques inspired by signal-processing.

RESULTS

We compared LtrDetector to LTR_Finder and LTRharvest, the two most successful predecessor tools, on six plant genomes. For each organism, we constructed a ground truth data set based on queries from a consensus sequence database. According to this evaluation, LtrDetector was the most sensitive tool, achieving 16-23% improvement in sensitivity over LTRharvest and 21% improvement over LTR_Finder. All three tools had low false positive rates, with LtrDetector achieving 98.2% precision, in between its two competitors. Overall, LtrDetector provides the best compromise between high sensitivity and low false positive rate while requiring moderate time and utilizing memory available on personal computers.

CONCLUSIONS

LtrDetector uses a novel methodology revolving around k-mer distributions, which allows it to produce high-quality results using relatively lightweight procedures. It is easy to install and use. It is not species specific, performing well using its default parameters on genomes of varying size and repeat content. It is automatically configured for parallel execution and runs efficiently on an ordinary personal computer. It includes a k-mer scores visualization tool to facilitate manual review of the identified elements. These features make LtrDetector an attractive tool for future annotation projects involving long terminal repeat retrotransposons.

Computational identification of tissue-specific transcription factor cooperation in ten cattle tissues

Transcription factors (TFs) are a special class of DNA-binding proteins that orchestrate gene transcription by recruiting other TFs, co-activators or co-repressors. Their combinatorial interplay in higher organisms maintains homeostasis and governs cell identity by finely controlling and regulating tissue-specific gene expression. Despite the rich literature on the importance of cooperative TFs for deciphering the mechanisms of individual regulatory programs that control tissue specificity in several organisms such as human, mouse, or Drosophila melanogaster, to date, there is still need for a comprehensive study to detect specific TF cooperations in regulatory processes of cattle tissues. To address the needs of knowledge about specific combinatorial gene regulation in cattle tissues, we made use of three publicly available RNA-seq datasets and obtained tissue-specific gene (TSG) sets for ten tissues (heart, lung, liver, kidney, duodenum, muscle tissue, adipose tissue, colon, spleen and testis). By analyzing these TSG-sets, tissue-specific TF cooperations of each tissue have been identified. The results reveal that similar to the combinatorial regulatory events of model organisms, TFs change their partners depending on their biological functions in different tissues. Particularly with regard to preferential partner choice of the transcription factors STAT3 and NR2C2, this phenomenon has been highlighted with their five different specific cooperation partners in multiple tissues. The information about cooperative TFs could be promising: i) to understand the molecular mechanisms of regulating processes; and ii) to extend the existing knowledge on the importance of single TFs in cattle tissues.

HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures

BACKGROUND

Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions.

RESULTS

To this end, we developed a software tool, HebbPlot, which utilizes a Hebbian neural network in learning a general chromatin signature from regions with a common function. Hebbian networks can learn the associations between tens of marks and thousands of regions. HebbPlot presents a signature as a digital image, which can be easily interpreted. Moreover, signatures produced by HebbPlot can be compared quantitatively. We validated HebbPlot in six case studies. The results of these case studies are novel or validating results already reported in the literature, indicating the accuracy of HebbPlot. Our results indicate that promoters have a directional chromatin signature; several marks tend to stretch downstream or upstream. H3K4me3 and H3K79me2 have clear directional distributions around active promoters. In addition, the signatures of high- and low-CpG promoters are different; H3K4me3, H3K9ac, and H3K27ac are the most different marks. When we studied the signatures of enhancers active in eight tissues, we observed that these signatures are similar, but not identical. Further, we identified some histone modifications - H3K36me3, H3K79me1, H3K79me2, and H4K8ac - that are associated with coding regions of active genes. Other marks - H4K12ac, H3K14ac, H3K27me3, and H2AK5ac - were found to be weakly associated with coding regions of inactive genes.

CONCLUSIONS

This study resulted in a novel software tool, HebbPlot, for learning and visualizing the chromatin signature of a genetic element. Using HebbPlot, we produced a visual catalog of the signatures of multiple genetic elements in 57 cell types available through the Roadmap Epigenomics Project. Furthermore, we made a progress toward a functional catalog consisting of 22 histone marks. In sum, HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code.

Detection of cooperatively bound transcription factor pairs using ChIP-seq peak intensities and expectation maximization

Transcription factors (TFs) often work cooperatively, where the binding of one TF to DNA enhances the binding affinity of a second TF to a nearby location. Such cooperative binding is important for activating gene expression from promoters and enhancers in both prokaryotic and eukaryotic cells. Existing methods to detect cooperative binding of a TF pair rely on analyzing the sequence that is bound. We propose a method that uses, instead, only ChIP-seq peak intensities and an expectation maximization (CPI-EM) algorithm. We validate our method using ChIP-seq data from cells where one of a pair of TFs under consideration has been genetically knocked out. Our algorithm relies on our observation that cooperative TF-TF binding is correlated with weak binding of one of the TFs, which we demonstrate in a variety of cell types, including E. coli, S. cerevisiae and M. musculus cells. We show that this method performs significantly better than a predictor based only on the ChIP-seq peak distance of the TFs under consideration. This suggests that peak intensities contain information that can help detect the cooperative binding of a TF pair. CPI-EM also outperforms an existing sequence-based algorithm in detecting cooperative binding. The CPI-EM algorithm is available at https://github.com/vishakad/cpi-em.

Removing Background Co-occurrences of Transcription Factor Binding Sites Greatly Improves the Prediction of Specific Transcription Factor Cooperations

Today, it is well-known that in eukaryotic cells the complex interplay of transcription factors (TFs) bound to the DNA of promoters and enhancers is the basis for precise and specific control of transcription. Computational methods have been developed for the identification of potentially cooperating TFs through the co-occurrence of their binding sites (TFBSs). One challenge of these methods is the differentiation of TFBS pairs that are specific for a given sequence set from those that are ubiquitously appearing, rendering the results highly dependent on the choice of a proper background set. Here, we present an extension of our previous PC-TraFF approach that estimates the background co-occurrence of any TF pair by preserving the (oligo-) nucleotide composition and, thus, the core of TFBSs in the sequences of interest. Applying our approach to a simulated data set with implanted TFBS pairs, we could successfully identify them as sequence-set specific under a variety of conditions. When we analyzed the gene expression data sets of five breast cancer associated subtypes, the number of overlapping pairs could be dramatically reduced in comparison to our previous approach. As a result, we could identify potentially cooperating transcriptional regulators that are characteristic for each of the five breast cancer subtypes. This indicates that our approach is able to discriminate specific potential TF cooperations against ubiquitously occurring combinations. The results obtained with our method may help to understand the genetic programs governing specific biological processes such as the development of different tumor types.

rAAV-compatible MiniPromoters for restricted expression in the brain and eye

BACKGROUND

Small promoters that recapitulate endogenous gene expression patterns are important for basic, preclinical, and now clinical research. Recently, there has been a promising revival of gene therapy for diseases with unmet therapeutic needs. To date, most gene therapies have used viral-based ubiquitous promoters-however, promoters that restrict expression to target cells will minimize off-target side effects, broaden the palette of deliverable therapeutics, and thereby improve safety and efficacy. Here, we take steps towards filling the need for such promoters by developing a high-throughput pipeline that goes from genome-based bioinformatic design to rapid testing in vivo.

METHODS

For much of this work, therapeutically interesting Pleiades MiniPromoters (MiniPs; ~4 kb human DNA regulatory elements), previously tested in knock-in mice, were "cut down" to ~2.5 kb and tested in recombinant adeno-associated virus (rAAV), the virus of choice for gene therapy of the central nervous system. To evaluate our methods, we generated 29 experimental rAAV2/9 viruses carrying 19 different MiniPs, which were injected intravenously into neonatal mice to allow broad unbiased distribution, and characterized in neural tissues by X-gal immunohistochemistry for icre, or immunofluorescent detection of GFP.

RESULTS

The data showed that 16 of the 19 (84 %) MiniPs recapitulated the expression pattern of their design source. This included expression of: Ple67 in brain raphe nuclei; Ple155 in Purkinje cells of the cerebellum, and retinal bipolar ON cells; Ple261 in endothelial cells of brain blood vessels; and Ple264 in retinal Müller glia.

CONCLUSIONS

Overall, the methodology and MiniPs presented here represent important advances for basic and preclinical research, and may enable a paradigm shift in gene therapy.

Combinatorial Cis-regulation in Saccharomyces Species

Transcriptional control of gene expression requires interactions between the cis-regulatory elements (CREs) controlling gene promoters. We developed a sensitive computational method to identify CRE combinations with conserved spacing that does not require genome alignments. When applied to seven sensu stricto and sensu lato Saccharomyces species, 80% of the predicted interactions displayed some evidence of combinatorial transcriptional behavior in several existing datasets including: (1) chromatin immunoprecipitation data for colocalization of transcription factors, (2) gene expression data for coexpression of predicted regulatory targets, and (3) gene ontology databases for common pathway membership of predicted regulatory targets. We tested several predicted CRE interactions with chromatin immunoprecipitation experiments in a wild-type strain and strains in which a predicted cofactor was deleted. Our experiments confirmed that transcription factor (TF) occupancy at the promoters of the CRE combination target genes depends on the predicted cofactor while occupancy of other promoters is independent of the predicted cofactor. Our method has the additional advantage of identifying regulatory differences between species. By analyzing the S. cerevisiae and S. bayanus genomes, we identified differences in combinatorial cis-regulation between the species and showed that the predicted changes in gene regulation explain several of the species-specific differences seen in gene expression datasets. In some instances, the same CRE combinations appear to regulate genes involved in distinct biological processes in the two different species. The results of this research demonstrate that (1) combinatorial cis-regulation can be inferred by multi-genome analysis and (2) combinatorial cis-regulation can explain differences in gene expression between species.

PC-TraFF: identification of potentially collaborating transcription factors using pointwise mutual information

Background

Transcription factors (TFs) are important regulatory proteins that govern transcriptional regulation. Today, it is known that in higher organisms different TFs have to cooperate rather than acting individually in order to control complex genetic programs. The identification of these interactions is an important challenge for understanding the molecular mechanisms of regulating biological processes. In this study, we present a new method based on pointwise mutual information, PC-TraFF, which considers the genome as a document, the sequences as sentences, and TF binding sites (TFBSs) as words to identify interacting TFs in a set of sequences.

Results

To demonstrate the effectiveness of PC-TraFF, we performed a genome-wide analysis and a breast cancer-associated sequence set analysis for protein coding and miRNA genes. Our results show that in any of these sequence sets, PC-TraFF is able to identify important interacting TF pairs, for most of which we found support by previously published experimental results. Further, we made a pairwise comparison between PC-TraFF and three conventional methods. The outcome of this comparison study strongly suggests that all these methods focus on different important aspects of interaction between TFs and thus the pairwise overlap between any of them is only marginal.

Conclusions

In this study, adopting the idea from the field of linguistics in the field of bioinformatics, we develop a new information theoretic method, PC-TraFF, for the identification of potentially collaborating transcription factors based on the idiosyncrasy of their binding site distributions on the genome. The results of our study show that PC-TraFF can succesfully identify known interacting TF pairs and thus its currently biologically uncorfirmed predictions could provide new hypotheses for further experimental validation. Additionally, the comparison of the results of PC-TraFF with the results of previous methods demonstrates that different methods with their specific scopes can perfectly supplement each other. Overall, our analyses indicate that PC-TraFF is a time-efficient method where its algorithm has a tractable computational time and memory consumption.

The PC-TraFF server is freely accessible at http://pctraff.bioinf.med.uni-goettingen.de/

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0827-2) contains supplementary material, which is available to authorized users.

High resolution mapping of enhancer-promoter interactions

RNA Polymerase II ChIA-PET data has revealed enhancers that are active in a profiled cell type and the genes that the enhancers regulate through chromatin interactions. The most commonly used computational method for analyzing ChIA-PET data, the ChIA-PET Tool, discovers interaction anchors at a spatial resolution that is insufficient to accurately identify individual enhancers. We introduce Germ, a computational method that estimates the likelihood that any two narrowly defined genomic locations are jointly occupied by RNA Polymerase II. Germ takes a blind deconvolution approach to simultaneously estimate the likelihood of RNA Polymerase II occupation as well as a model of the arrangement of read alignments relative to locations occupied by RNA Polymerase II. Both types of information are utilized to estimate the likelihood that RNA Polymerase II jointly occupies any two genomic locations. We apply Germ to RNA Polymerase II ChIA-PET data from embryonic stem cells to identify the genomic locations that are jointly occupied along with transcription start sites. We show that these genomic locations align more closely with features of active enhancers measured by ChIP-Seq than the locations identified using the ChIA-PET Tool. We also apply Germ to RNA Polymerase II ChIA-PET data from motor neuron progenitors. Based on the Germ results, we observe that a combination of cell type specific and cell type independent regulatory interactions are utilized by cells to regulate gene expression.

Automatic and real time recognition of microalgae by means of pigment signature and shape

Microalgae are unicellular photoautotrophic organisms that grow in any habitat such as fresh and salt water bodies, hot springs, ice, air, and in or on other organisms and substrates. Massive growth of microalgae may produce harmful effects on the marine and freshwater ecological environment and fishery resources. Therefore, rapid and accurate recognition and classification of microalgae is one of the most important issues in water resource management. In this paper, a new methodology for automatic and real time identification of microalgae by means of microscopy image analysis is presented. This methodology is based on segmentation, shape features extraction, and characteristic colour (i.e. pigment signature) determination. A classifier algorithm based on the minimum distance criterion was used for microalgae grouping according to the measured features. 96.6% accuracy from a set of 3423 images of 24 different microalgae representing the major algal phyla was achieved by this methodology.

RFECS: a random-forest based algorithm for enhancer identification from chromatin state

Transcriptional enhancers play critical roles in regulation of gene expression, but their identification in the eukaryotic genome has been challenging. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of cell types or chromatin marks have previously been investigated for this purpose, leaving the question unanswered whether there exists an optimal set of histone modifications for enhancer prediction in different cell types. Here, we address this issue by exploring genome-wide profiles of 24 histone modifications in two distinct human cell types, embryonic stem cells and lung fibroblasts. We developed a Random-Forest based algorithm, RFECS (Random Forest based Enhancer identification from Chromatin States) to integrate histone modification profiles for identification of enhancers, and used it to identify enhancers in a number of cell-types. We show that RFECS not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify the most informative and robust set of three chromatin marks for enhancer prediction.

Enhancers are regions in the genome that can activate the expression of a gene irrespective of their location with respect to the gene. Identifying these elements is critical in understanding regulatory differences between different cell-types. Since enhancers lack characteristic sequence features and can be far away from the gene they regulate, their identification is not trivial. Experimentally determining the genome-wide binding sites of transcriptional co-activator p300 is one way of finding enhancers but it can only identify a subset of enhancers. A few years ago, it was observed that the binding sites of p300 are marked by distinctive, post-translational histone modifications. Several groups have exploited this discovery to predict genome-wide enhancers based on their similarity to the histone modification profiles of p300 binding sites. We here report a novel algorithm for this purpose and show that it has much greater accuracy than existing methods. Another unique feature of our algorithm is the ability to automatically deduce the most informative set of histone modifications required for enhancer prediction. We expect that this method will become increasingly useful with the expanding number of known histone modifications and rapid accumulation of epigenomic datasets for various cell types and species.