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

BigMPI4py: Python Module for Parallelization of Big Data Objects Discloses Germ Layer Specific DNA Demethylation Motifs

Parallelization in Python integrates Message Passing Interface via the mpi4py module. Since mpi4py does not support parallelization of objects greater than 2³¹ bytes, we developed BigMPI4py, a Python module that wraps mpi4py, supporting object sizes beyond this boundary. BigMPI4py automatically determines the optimal object distribution strategy, and uses vectorized methods, achieving higher parallelization efficiency. BigMPI4py facilitates the implementation of Python for Big Data applications in multicore workstations and High Performance Computer systems. We use BigMPI4py to speed-up the search for germ line specific de novo DNA methylated/unmethylated motifs from the 59 whole genome bisulfite sequencing DNA methylation samples from 27 human tissues of the ENCODE project. We developed a parallel implementation of the Kruskall-Wallis test to find CpGs with differential methylation across germ layers. The parallel evaluation of the significance of 55 million CpG achieved a 22x speedup with 25 cores allowing us an efficient identification of a set of hypermethylated genes in ectoderm and mesoderm-related tissues, and another set in endoderm-related tissues and finally, the discovery of germ layer specific DNA demethylation motifs. Our results point out that DNA methylation signal provide a higher degree of information for the demethylated state than for the methylated state. BigMPI4py is available at https://https://www.arauzolab.org/tools/bigmpi4py and https://gitlab.com/alexmascension/bigmpi4py and the Jupyter Notebook with WGBS analysis at https://gitlab.com/alexmascension/wgbs-analysis.

NaviSE: superenhancer navigator integrating epigenomics signal algebra

Background

Superenhancers are crucial structural genomic elements determining cell fate, and they are also involved in the determination of several diseases, such as cancer or neurodegeneration. Although there are pipelines which use independent pieces of software to predict the presence of superenhancers from genome-wide chromatin marks or DNA-interaction protein binding sites, there is not yet an integrated software tool that processes automatically algebra combinations of raw data sequencing into a comprehensive final annotated report of predicted superenhancers.

Results

We have developed NaviSE, a user-friendly streamlined tool which performs a fully-automated parallel processing of genome-wide epigenomics data from sequencing files into a final report, built with a comprehensive set of annotated files that are navigated through a graphic user interface dynamically generated by NaviSE. NaviSE also implements an ‘epigenomics signal algebra’ that allows the combination of multiple activation and repression epigenomics signals. NaviSE provides an interactive chromosomal landscaping of the locations of superenhancers, which can be navigated to obtain annotated information about superenhancer signal profile, associated genes, gene ontology enrichment analysis, motifs of transcription factor binding sites enriched in superenhancers, graphs of the metrics evaluating the superenhancers quality, protein-protein interaction networks and enriched metabolic pathways among other features. We have parallelised the most time-consuming tasks achieving a reduction up to 30% for a 15 CPUs machine. We have optimized the default parameters of NaviSE to facilitate its use. NaviSE allows different entry levels of data processing, from sra-fastq files to bed files; and unifies the processing of multiple replicates. NaviSE outperforms the more time-consuming processes required in a non-integrated pipeline. Alongside its high performance, NaviSE is able to provide biological insights, predicting cell type specific markers, such as SOX2 and ZIC3 in embryonic stem cells, CDK5R1 and REST in neurons and CD86 and TLR2 in monocytes.

Conclusions

NaviSE is a user-friendly streamlined solution for superenhancer analysis, annotation and navigation, requiring only basic computer and next generation sequencing knowledge. NaviSE binaries and documentation are available at: https://sourceforge.net/projects/navise-superenhancer/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1698-5) contains supplementary material, which is available to authorized users.

Does mouse embryo primordial germ cell activation start before implantation as suggested by single-cell transcriptomics dynamics?

STUDY HYPOTHESIS

Does primordial germ cell (PGC) activation start before mouse embryo implantation, and does the possible regulation of the DNA (cytosine-5-)-methyltransferase 3-like (Dnmt3l) by transcription factor AP-2, gamma (TCFAP2C) have a role in this activation and in the primitive endoderm (PE)-epiblast (EPI) lineage specification?

STUDY FINDING

A burst of expression of PGC markers, such as Dppa3/Stella, Ifitm2/Fragilis, Fkbp6 and Prdm4, is observed from embryonic day (E) 3.25, and some of them, together with the late germ cell markers Zp3, Mcf2 and Morc1, become restricted to the EPI subpopulation at E4.5, while the dynamics analysis of the PE-EPI transitions in the single-cell data suggests that TCFAP2C transitorily represses Dnmt3l in EPI cells at E3.5 and such repression is withdrawn with reactivation of Dnmt3l expression in PE and EPI cells at E4.5.

WHAT IS KNOWN ALREADY

In the mouse preimplantation embryo, cells with the same phenotype take different fates based on the orchestration between topological clues (cell polarity, positional history and division orientation) and gene regulatory rules (at transcriptomics and epigenomics level), prompting the proposal of positional, stochastic and combined models explaining the specification mechanism. PGC specification starts at E6.0-6.5 post-implantation. In view of the important role of DNA methylation in developmental events, the cross-talk between some transcription factors and DNA methyltransferases is of particular relevance. TCFAP2C has a CpG DNA methylation motif that is not methylated in pluripotent cells and that could potentially bind on DNMT3L, the stimulatory DNA methyltransferase co-factor that assists in the process of de novo DNA methylation. Chromatin-immunoprecipitation analysis has demonstrated that Dnmt3l is indeed a target of TCFAP2C.

STUDY DESIGN, SAMPLES/MATERIALS, METHODS

We aimed to assess the timing of early preimplantation events and to understand better the segregation of the inner cell mass (ICM) into PE and EPI. We designed a single-cell transcriptomics dynamics computational study to identify markers of the PE-EPI bifurcation in ICM cells through searching for statistically significant (using the Student's t-test method) differently expressed genes (DEGs) between PE and EPI cells from E3.5 to E4.5. The DEGs common for E3.5 and E4.5 were used as the markers defining the steady states. We collected microarray and next-generation sequencing transcriptomics data from public databases from bulk populations and single cells from mice at E3.25, E3.5 and E4.5. The results are based on three independent single-cell transcriptomics data sets, with a fold change of 3 and P-value <0.01 for the DEG selection.

MAIN RESULTS AND THE ROLE OF CHANCE

The dynamics analysis revealed new transitory E3.5 and steady PE and EPI markers. Among the transitory E3.5 PE markers (Dnmt3l, Dusp4, Cpne8, Akap13, Dcaf12l1, Aaed1, B4galt6, BC100530, Rnpc3, Tfpi, Lgalsl, Ckap4 and Fbxl20), several (Dusp4, Akap13, Cpn8, Dcaf12l1 and Tfpi) are related to the extracellular regulated kinase pathway. We also identified new transitory E3.5 EPI markers (Sgk1, Mal, Ubxn2a, Atg16l2, Gm13102, Tcfap2c, Hexb, Slc1a1, Svip, Liph and Mier3), six new stable PE markers (Sdc4, Cpn1, Dkk1, Havcr1, F2r/Par1 and Slc7a6os) as well as three new stable EPI markers (Zp3, Mcf2 and Hexb), which are known to be late stage germ cell markers. We found that mouse PGC marker activation starts at least at E3.25 preimplantation. The transcriptomics dynamics analyses support the regulation of Dnmt3l expression by TCFAP2C.

LIMITATIONS, REASONS FOR CAUTION

Since the regulation of Dnmt3l by TCFAP2C is based on computational prediction of DNA methylation motifs, Chip-Seq and transcriptomics data, functional studies are required to validate this result.

WIDER IMPLICATIONS OF THE FINDINGS

We identified a collection of previously undescribed E3.5-specific PE and EPI markers, and new steady PE and EPI markers. Identification of these genes, many of which encode cell membrane proteins, will facilitate the isolation and characterization of early PE and EPI populations. Since it is so well established in the literature that mouse PGC specification is a post-implantation event, it was surprising for us to see activation of PGC markers as early as E3.25 preimplantation, and identify the newly found steady EPI markers as late germ cell markers. The discovery of such early activation of PGC markers has important implications in the derivation of germ cells from pluripotent cells (embryonic stem cells or induced pluripotent stem cells), since the initial stages of such derivation resemble early development. The early activation of PGC markers points out the difficulty of separating PGC cells from pluripotent populations. Collectively, our results suggest that the combining of the precision of single-cell omics data with dynamic analysis of time-series data can establish the timing of some developmental stages as earlier than previously thought.

LARGE-SCALE DATA

Not applicable.

STUDY FUNDING AND COMPETING INTERESTS

This work was supported by grants DFG15/14 and DFG15/020 from Diputación Foral de Gipuzkoa (Spain), and grant II14/00016 from I + D + I National Plan 2013-2016 (Spain) and FEDER funds. The authors declare no conflict of interest.

T-KDE: a method for genome-wide identification of constitutive protein binding sites from multiple ChIP-seq data sets

BACKGROUND

A protein may bind to its target DNA sites constitutively, i.e., regardless of cell type. Intuitively, constitutive binding sites should be biologically functional. A prerequisite for understanding their functional relevance is knowing all their locations for a protein of interest. Genome-wide discovery of constitutive binding sites requires robust and efficient computational methods to integrate results from numerous binding experiments. Such methods are lacking, however.

RESULTS

To locate constitutive binding sites for a protein using ChIP-seq data for that protein from multiple cell lines, we developed a method, T-KDE, which combines a binary range tree with a kernel density estimator. Using 132 CTCF (CCCTC-binding factor) ChIP-seq datasets, we showed that the number of constitutive sites identified by T-KDE is robust to the choice of tuning parameter and that T-KDE identifies binding site locations more accurately than a binning approach. Furthermore, T-KDE can identify constitutive sites that are missed by a motif-based approach either because a bound site failed to reach the motif significance cutoff or because the peak sequence scanned was too short. By studying sites declared constitutive by T-KDE but not by the motif-based approach, we discovered two new CTCF motif variants. Using ENCODE data on 22 transcription factors (TF) in 132 cell lines, we identified constitutive binding sites for each TF and provide evidence that, for some TFs, they may be biologically meaningful.

CONCLUSIONS

T-KDE is an efficient and effective method to predict constitutive protein binding sites using ChIP-seq peaks from multiple cell lines. Besides constitutive binding sites for a given protein, T-KDE can identify genomic "hot spots" where several different proteins bind and, conversely, cell-type-specific sites bound by a given protein.

Disclosing the crosstalk among DNA methylation, transcription factors, and histone marks in human pluripotent cells through discovery of DNA methylation motifs

Gene expression regulation is gated by promoter methylation states modulating transcription factor binding. The known DNA methylation/unmethylation mechanisms are sequence unspecific, but different cells with the same genome have different methylomes. Thus, additional processes bringing specificity to the methylation/unmethylation mechanisms are required. Searching for such processes, we demonstrated that CpG methylation states are influenced by the sequence context surrounding the CpGs. We used such a property to develop a CpG methylation motif discovery algorithm. The newly discovered motifs reveal “methylation/unmethylation factors” that could recruit the “methylation/unmethylation machinery” to the loci specified by the motifs. Our methylation motif discovery algorithm provides a synergistic approach to the differently methylated region algorithms. Since our algorithm searches for commonly methylated regions inside the same sample, it requires only a single sample to operate. The motifs that were found discriminate between hypomethylated and hypermethylated regions. The hypomethylation-associated motifs have a high CG content, their targets appear in conserved regions near transcription start sites, they tend to co-occur within transcription factor binding sites, they are involved in breaking the H3K4me3/H3K27me3 bivalent balance, and they transit the enhancers from repressive H3K27me3 to active H3K27ac during ES cell differentiation. The new methylation motifs characterize the pluripotent state shared between ES and iPS cells. Additionally, we found a collection of motifs associated with the somatic memory inherited by the iPS from the initial fibroblast cells, thus revealing the existence of epigenetic somatic memory on a fine methylation scale.