Detecting clustering and ordering binding patterns among transcription factors via point process models

Detecting clusters of transcription factors based on a nonhomogeneous poisson process model

BACKGROUND

Rapidly growing genome-wide ChIP-seq data have provided unprecedented opportunities to explore transcription factor (TF) binding under various cellular conditions. Despite the rich resources, development of analytical methods for studying the interaction among TFs in gene regulation still lags behind.

RESULTS

In order to address cooperative TF binding and detect TF clusters with coordinative functions, we have developed novel computational methods based on clustering the sample paths of nonhomogeneous Poisson processes. Simulation studies demonstrated the capability of these methods to accurately detect TF clusters and uncover the hierarchy of TF interactions. A further application to the multiple-TF ChIP-seq data in mouse embryonic stem cells (ESCs) showed that our methods identified the cluster of core ESC regulators reported in the literature and provided new insights on functional implications of transcrisptional regulatory modules.

CONCLUSIONS

Effective analytical tools are essential for studying protein-DNA relations. Information derived from this research will help us better understand the orchestration of transcription factors in gene regulation processes.

Identifying Transcriptional Regulatory Modules Among Different Chromatin States in Mouse Neural Stem Cells

Gene expression regulation is a complex process involving the interplay between transcription factors and chromatin states. Significant progress has been made toward understanding the impact of chromatin states on gene expression. Nevertheless, the mechanism of transcription factors binding combinatorially in different chromatin states to enable selective regulation of gene expression remains an interesting research area. We introduce a nonparametric Bayesian clustering method for inhomogeneous Poisson processes to detect heterogeneous binding patterns of multiple proteins including transcription factors to form regulatory modules in different chromatin states. We applied this approach on ChIP-seq data for mouse neural stem cells containing 21 proteins and observed different groups or modules of proteins clustered within different chromatin states. These chromatin-state-specific regulatory modules were found to have significant influence on gene expression. We also observed different motif preferences for certain TFs between different chromatin states. Our results reveal a degree of interdependency between chromatin states and combinatorial binding of proteins in the complex transcriptional regulatory process. The software package is available on Github at - https://github.com/BSharmi/DPM-LGCP.

Nucleotide Interdependency in Transcription Factor Binding Sites in the Drosophila Genome

A long-standing objective in modern biology is to characterize the molecular components that drive the development of an organism. At the heart of eukaryotic development lies gene regulation. On the molecular level, much of the research in this field has focused on the binding of transcription factors (TFs) to regulatory regions in the genome known as cis-regulatory modules (CRMs). However, relatively little is known about the sequence-specific binding preferences of many TFs, especially with respect to the possible interdependencies between the nucleotides that make up binding sites. A particular limitation of many existing algorithms that aim to predict binding site sequences is that they do not allow for dependencies between nonadjacent nucleotides. In this study, we use a recently developed computational algorithm, MARZ, to compare binding site sequences using 32 distinct models in a systematic and unbiased approach to explore nucleotide dependencies within binding sites for 15 distinct TFs known to be critical to Drosophila development. Our results indicate that many of these proteins have varying levels of nucleotide interdependencies within their DNA recognition sequences, and that, in some cases, models that account for these dependencies greatly outperform traditional models that are used to predict binding sites. We also directly compare the ability of different models to identify the known KRUPPEL TF binding sites in CRMs and demonstrate that a more complex model that accounts for nucleotide interdependencies performs better when compared with simple models. This ability to identify TFs with critical nucleotide interdependencies in their binding sites will lead to a deeper understanding of how these molecular characteristics contribute to the architecture of CRMs and the precise regulation of transcription during organismal development.

CMStalker: A Combinatorial Tool for Composite Motif Discovery

Controlling the differential expression of many thousands different genes at any given time is a fundamental task of metazoan organisms and this complex orchestration is controlled by the so-called regulatory genome encoding complex regulatory networks: several Transcription Factors bind to precise DNA regions, so to perform in a cooperative manner a specific regulation task for nearby genes. The in silico prediction of these binding sites is still an open problem, notwithstanding continuous progress and activity in the last two decades. In this paper, we describe a new efficient combinatorial approach to the problem of detecting sets of cooperating binding sites in promoter sequences, given in input a database of Transcription Factor Binding Sites encoded as Position Weight Matrices. We present CMStalker, a software tool for composite motif discovery which embodies a new approach that combines a constraint satisfaction formulation with a parameter relaxation technique to explore efficiently the space of possible solutions. Extensive experiments with 12 data sets and 11 state-of-the-art tools are reported, showing an average value of the correlation coefficient of 0.54 (against a value 0.41 of the closest competitor). This improvements in output quality due to CMStalker is statistically significant.

The Populus ARBORKNOX1 homeodomain transcription factor regulates woody growth through binding to evolutionarily conserved target genes of diverse function

The class I KNOX homeodomain transcription factor ARBORKNOX1 (ARK1) is a key regulator of vascular cambium maintenance and cell differentiation in Populus. Currently, basic information is lacking concerning the distribution, functional characteristics, and evolution of ARK1 binding in the Populus genome. Here, we used chromatin immunoprecipitation sequencing (ChIP-seq) technology to identify ARK1 binding loci genome-wide in Populus. Computational analyses evaluated the distribution of ARK1 binding loci, the function of genes associated with bound loci, the effect of ARK1 binding on transcript levels, and evolutionary conservation of ARK1 binding loci. ARK1 binds to thousands of loci which are highly enriched proximal to the transcriptional start sites of genes of diverse functions. ARK1 target genes are significantly enriched in paralogs derived from the whole-genome salicoid duplication event. Both ARK1 and a maize (Zea mays) homolog, KNOTTED1, preferentially target evolutionarily conserved genes. However, only a small portion of ARK1 target genes are significantly differentially expressed in an ARK1 over-expression mutant. This study describes the functional characteristics and evolution of DNA binding by a transcription factor in an undomesticated tree, revealing complexities similar to those shown for transcription factors in model animal species.