DOI: 10.5061/dryad.34tmpg4qn
title: Predicting genome-wide tissue-specific enhancers via combinatorial transcription factor genomic occupancy analysis

description: Background Enhancers belong to the class of non-coding
 cis-regulatory elements that play a vital role in
 transcriptional regulation. Mutations in enhancers effect gene regulation
 and can lead to various disease phenotypes. This has led to an increased
 interest in identifying enhancers and evaluating the impact of mutations
 on the enhancers’ activities. However, in contrast to protein-coding
 intervals, enhancers lack a stereotyped sequence composition. Therefore,
 the computational prediction of enhancers and their tissue-specificity has
 remained challenging. Consequently, enhancers are typically predicted
 based on certain chromatin features, including DNA accessibility,
 post-translational modifications of histones, and transcription factor
 (TF) binding.  Although these features correlate with enhancer
 regions, they are only imperfect predictors. Results The present study
 reports a sequence-based computational model that employs combinatorial TF
 genomic occupancy as principal determinant to predict tissue-specific
 enhancers. This model was trained on different data sets including the
 Encyclopedia of DNA Elements (ENCODE) based DNA accessibility data, Vista
 enhancer browser based in vivo experimental data, and phylogenetic
 foot-printing of binding motifs. The application
 of this novel computational scheme has
 enabled the prediction of 25,000 forebrain specific cis-regulatory modules
 (CRMs) in human genome. These predicted CRMs were subjected to validation
 phase by using ENCODE based enhancer-associated biochemical
 features, GWAS-based disease associated SNPs and in vivo
 analysis in zebrafish.  Conclusion
 Validation data revealed that this new
 computational model is suitable for predicting
 less well-conserved tissue-specific enhancers regions
 that are devoid of characterized chromatin
 features,
 and therefore is able to complement and facilitate experimental approaches in tissue-specific enhancer discovery.
description: Based on heterotypic cooperativity of transcriptional factors
 (TFs), in this work we present a high-throughput workflow for the
 prediction of tissue specific enhancers at a genome-wide scale. The study
 is composed of two phases. Phase 1: Prediction of key
 TFs likely to have a role in forebrain development.
 Phase 2: Devising a pipeline for the prediction of tissue
 specific enhancers by employing the heterotypic cooperativity among TFs
 (curated from phase 1). In phase 1, we aimed to
 pinpoint a set of TFs that have combinatorial genomic occupancy and play a
 significant role during human forebrain development and differentiation
 processes. In initial scrutiny, through extensive literature survey, we
 manually curated a library of 93 TFs relevant to human forebrain-tissue.
 The library of 93 TFs was then subjected to two different strategies
 namely (I) Motif discovery through statistical over-representation and
 continuous tag sequence density estimation through DNase hypersensitive
 sites (DHSs) map, (II) Regular expression based algorithm. In strategy-1,
 first step was to predict candidate transcription factor binding sites
 (TFBSs) of these 93 TFs computationally.  Hence, we used two different
 programs called CLOVER and F-seq. CLOVER is a method to screen a set of
 DNA sequences against a precompiled library of motifs and select the
 motifs which are statistically over-represented in the sequences. In
 Clover, binding profiles of 93 TFs (collected from JASPAR) were searched
 on 104 forebrain specific human enhancers (FSHEs) (positive control) from
 Vista enhancer browser, and a set of 100 human non-coding non-conserved
 sequences (NCNCSs) (negative control) from UCSC genome browser. In
 addition to that, F-Seq is a software package that generates a continuous
 tag sequence density estimation to predict binding sites. In F-seq, DNase
 I hypersensitive sites (DHSs) data of three independent cell lines from
 ENCODE namely GM12878 (Lymphocytes), Cerebrum_Frontal_OC (Cerebrum), and
 Frontal_Cortex_OC (Cortex) were used to predict DNase I hypersensitive
 region of chromatin on FSHEs (positive control) and NCNCSs (negative
 control). CLOVER predicted binding sites and F-seq predicted binding sites
 regions were then overlapped for the accurate prediction of TFBSs.
 Furthermore, to categorize cluster of co-occurring TFBSs in human
 forebrain specific enhancers, we applied principal component analysis
 (PCA). In the second strategy, we employed phylogenetic
 foot-printing based approach to predict the evolutionary conserved binding
 sites of 93 forebrain TFs (curated from literature) in FSHEs (positive
 control) and NCNCSs (negative control). For this purpose, we designed a
 regular expression based algorithm, that aligns human and mouse FSHEs
 (positive control) and NCNCSs (negative control) orthologue sequences. The
 designed algorithm pinpoints TRANSFAC based binding sites (for 93
 forebrain TFs) that are conserved among human-mouse orthologous sequences.
 Evolutionary conserved predicted binding sites were then subjected to PCA
 to identify the cluster of conserved TFBSs in each of the FSHEs (positive
 control) and NCNCs (negative control). PCA results from strategy 1 and
 strategy II were compared and as a result 23 TFs were found common and
 shortlisted for further analyses. The 23 shortlisted TFs were then
 employed to predict the clusters of co-occurring motifs that might serve
 as forebrain cis-regulatory modules. For this purpose, we used three human
 genomic regions that are enriched of forebrain related
 (development/expression) protein coding genes. These genomic segments were
 scanned for 23 TFs by using purposely designed sliding window brute force
 search algorithm (SDBFSA). Conclusively, we found that a subset of 6/93
 TFs namely FOXP2, OTX1, OTX2, GATA3, HES5 and NGN2 share a relatively
 higher heterotypic as well as homotypic binding sites
 preferences. The set of 6TFs were subjected to phase 2
 where we adopted the combinatorial code to predict the clusters of these 6
 TFs at genome wide-scale. For this purpose, a specialized Perl script was
 designed and applied on repeat and exon masked sequence of human genome
 (GRCh37).  As a result of it, we identified the clusters of TFBSs that
 were closely spaced at the spacer distance of 250bps.  Each of the
 identified cluster contained binding sites for at least 5 of the 6
 distinct TFs. We termed each such cluster as an independent cis-regulatory
 module (CRMs) and assigned a unique identifier called “crm_id” to it.
 Resultantly, we compiled a catalogue of 25,000 forebrain-specific CRMs
 which were composed of concentrated clusters of recognition motifs for 6
 core TFs (details mentioned in dataset and README file). These predictions
 were validated by using ENCODE based enhancer-associated biochemical
 features, GWAS-based disease- or trait-associated SNPs and in vivo
 functional analysis in zebrafish. Our devised computational workflow is
 suitable for predicting less well-conserved tissue-specific enhancers that
 are devoid of characterized chromatin features, and therefore is able to
 complement and facilitate experimental approaches in tissue-specific
 enhancer discovery.
description: There are two files in the dataset: File 1:
 Human_forebrain_enhancers_coordinates_TFclusters.xlsx: This file can be
 opened using any one of the following programs  
 Office Editing for Docs, Sheets & Slides
 Google Sheets LibreOffice Calc
 Apache OpenOffice Aspose Excel
 Viewer Zoho Sheet 
 File 2: Human_forebrain_enhancers_sequences.txt: Microsoft
 Notepad and WordPad can open TXT files in Windows. The TextEdit app on
 macOS can also open TXT files.
description: ### Title: Predicting genome-wide tissue-specific enhancers via
 combinatorial transcription factor genomic occupancy analysis From
 cellular differentiation to development, the spatiotemporal expression and
 regulation of tissue-specific genes is mediated by DNA regulatory
 elements. Enhancers, also known as distal-acting cis-regulatory modules
 (CRMs), are defined as cis-acting DNA sequences responsible for increasing
 the level of gene expression. Their functionality is
 orientation-independent, and their distance from the target gene body is
 highly variable. Given the significant role of tissue-specific enhancers
 in disease and development, there is currently a great deal of interest in
 annotating the human genome for tissue/cell-type-specific enhancers.
 However, this poses a serious challenge for several reasons: * Vast Search
 Space: Enhancers are scattered across the 98.5% of the human genome that
 is non-protein-coding, creating a vast search space (billions of base
 pairs). * Variable Positioning: Although enhancers regulate genes in cis,
 their positions relative to target genes are highly variable—they can be
 upstream, downstream, or within introns. They may bypass neighboring genes
 to regulate distant genes. * Complex Gene Regulation: Some enhancers
 regulate multiple genes, adding further complexity to their annotation. *
 Lack of a Well-defined Sequence Code: Unlike protein-coding genes with
 well-defined sequence codes, the sequence code of enhancers, if it exists,
 remains poorly understood. Over the last couple of decades, significant
 progress has been made in enhancer prediction using evolutionary and
 biochemical methods. However, both approaches have limitations.
 Evolutionary approaches lack the strength to detect lineage-specific
 enhancers, while biochemical marks on enhancer-associated chromatin
 regions are not always deterministic of enhancer function and can occur
 stochastically. In the present study, we report a DNA-sequence-based model
 that utilizes tissue-specific transcription factor (TF) occupancy to
 enhance the accuracy of enhancer prediction. Our pipeline heavily relies
 on prior datasets, such as: A known set of transcription factors (TFs)
 relevant to the tissue/cell type of interest Well-characterized binding
 motifs for the known set of TFs A benchmark dataset comprising enhancers
 characterized in vivo or in vitro, relevant to the tissue/cell type of
 interest Based on this principle, using the mammalian forebrain as a model
 organ, we employed this approach and identified approximately 25,000
 distinct forebrain enhancers across the non-coding and non-repetitive
 portions of the human genome. The functional relevance of these predicted
 forebrain enhancer datasets (CRMs) was evaluated by intersecting our
 predictions with: Active enhancer-related chromatin marks relevant to the
 brain DNase hypersensitive (HS) site data from brain cell lines GWAS-based
 brain SNPs Additionally, a subset of our predictions was directly tested
 through in vivo assays using zebrafish transgenic models. Furthermore,
 evolutionary conservation depth analysis of our predictions revealed that
 the majority of our predicted forebrain CRMs (&gt;85%) are conserved only
 in mammals or primates, highlighting the relevance of our approach in
 predicting lineage-specific enhancers. Our integrated approach provides a
 reliable complement to classical computational methods and
 epigenomic-based assays for predicting tissue-specific enhancers. The
 workflow is inherently generalizable and can be adapted to predict
 enhancers for any specific tissue type. The predicted set of 25,000
 forebrain cis-regulatory modules can serve as a valuable resource for: 1\)
 Screening brain-relevant pathological and developmental mechanisms. 2\)
 Exploring the gene-regulatory basis of mammalian/primate brain evolution.
 This README file describes the dataset, which contains genomic features of
 approximately 25,000 forebrain cis-regulatory modules (CRMs/enhancers)
 predicted using the aforementioned workflow/principle. Files: There are
 two files: 1\)
 [Human_forebrain_enhancers_coordinates_TFclusters.xlsx](https://datadryad.org/stash/downloads/file_stream/3501517): The file contains a table that shows the genome-wide catalogue of predicted human forebrain specific cis-regulatory modules (CRMs) or enhancers. Variables used: Crm_id: This unique identifier (Column 1) is assigned to each human forebrain-specific enhancer. The dataset includes a total of 25,000 Crm_ids. Chromosome: Column 2 lists the chromosome names where the predicted CRMs are located. Start_position: Column 3 indicates the start position of the predicted enhancers within the human genome (Assembly version GRCh37). End_position: Column 4 specifies the end position of the predicted enhancers in the human genome (Assembly version GRCh37). Size in base pairs: This column (column 5) shows the length of each of the predicted CRM in base pairs. Transcription factors: Column 6 shows the clustered transciption factors (TFs) residing in each of the predicted CRM. 2\) [Human_forebrain_enhancers_sequences.fasta](https://datadryad.org/stash/downloads/file_stream/3501516): This file contains sequences of 25000 human forebrain enhancers where each of the predicted enhancer is termed as CRM. All sequences in Fasta format are from human genome assembly version GRCh37. Sequence of each CRM starts with a header line. for example &gt;GRCh37|chr1:780200-780944|Homo sapiens|chr1_crm_1|forebrain enhancer|DNA length=745 Here "&gt;" symbol shows the start of new sequence. GRCh37 is the version of human genome assembly used. Genome assembly is followed by the genomic location and name of specie to which each CRM belongs. After the name of specie there is a "crm_id", a unique identifier assigned to each of the predicted CRM. Next to the crm_id is the name of tissue to which the crm belongs. For instance in current study it is forebrain. At the end of header line is the DNA length which depicts the size of each crm in base pairs.