FastMotif: spectral sequence motif discovery

Prediction of cooperative homeodomain DNA binding sites from high-throughput-SELEX data

Homeodomain proteins constitute one of the largest families of metazoan transcription factors. Genetic studies have demonstrated that homeodomain proteins regulate many developmental processes. Yet, biochemical data reveal that most bind highly similar DNA sequences. Defining how homeodomain proteins achieve DNA binding specificity has therefore been a long-standing goal. Here, we developed a novel computational approach to predict cooperative dimeric binding of homeodomain proteins using High-Throughput (HT) SELEX data. Importantly, we found that 15 of 88 homeodomain factors form cooperative homodimer complexes on DNA sites with precise spacing requirements. Approximately one third of the paired-like homeodomain proteins cooperatively bind palindromic sequences spaced 3 bp apart, whereas other homeodomain proteins cooperatively bind sites with distinct orientation and spacing requirements. Combining structural models of a paired-like factor with our cooperativity predictions identified key amino acid differences that help differentiate between cooperative and non-cooperative factors. Finally, we confirmed predicted cooperative dimer sites in vivo using available genomic data for a subset of factors. These findings demonstrate how HT-SELEX data can be computationally mined to predict cooperativity. In addition, the binding site spacing requirements of select homeodomain proteins provide a mechanism by which seemingly similar AT-rich DNA sequences can preferentially recruit specific homeodomain factors.

ProSampler: an ultrafast and accurate motif finder in large ChIP-seq datasets for combinatory motif discovery

MOTIVATION

The availability of numerous ChIP-seq datasets for transcription factors (TF) has provided an unprecedented opportunity to identify all TF binding sites in genomes. However, the progress has been hindered by the lack of a highly efficient and accurate tool to find not only the target motifs, but also cooperative motifs in very big datasets.

RESULTS

We herein present an ultrafast and accurate motif-finding algorithm, ProSampler, based on a novel numeration method and Gibbs sampler. ProSampler runs orders of magnitude faster than the fastest existing tools while often more accurately identifying motifs of both the target TFs and cooperators. Thus, ProSampler can greatly facilitate the efforts to identify the entire cis-regulatory code in genomes.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries are freely available for download at https://github.com/zhengchangsulab/prosampler. It was implemented in C++ and supported on Linux, macOS and MS Windows platforms.

SUPPLEMENTARY INFORMATION

Supplementary materials are available at Bioinformatics online.

Modular discovery of monomeric and dimeric transcription factor binding motifs for large data sets

In some dimeric cases of transcription factor (TF) binding, the specificity of dimeric motifs has been observed to differ notably from what would be expected were the two factors to bind to DNA independently of each other. Current motif discovery methods are unable to learn monomeric and dimeric motifs in modular fashion such that deviations from the expected motif would become explicit and the noise from dimeric occurrences would not corrupt monomeric models. We propose a novel modeling technique and an expectation maximization algorithm, implemented as software tool MODER, for discovering monomeric TF binding motifs and their dimeric combinations. Given training data and seeds for monomeric motifs, the algorithm learns in the same probabilistic framework a mixture model which represents monomeric motifs as standard position-specific probability matrices (PPMs), and dimeric motifs as pairs of monomeric PPMs, with associated orientation and spacing preferences. For dimers the model represents deviations from pure modular model of two independent monomers, thus making co-operative binding effects explicit. MODER can analyze in reasonable time tens of Mbps of training data. We validated the tool on HT-SELEX and ChIP-seq data. Our findings include some TFs whose expected model has palindromic symmetry but the observed model is directional.

FisherMP: fully parallel algorithm for detecting combinatorial motifs from large ChIP-seq datasets

Detecting binding motifs of combinatorial transcription factors (TFs) from chromatin immunoprecipitation sequencing (ChIP-seq) experiments is an important and challenging computational problem for understanding gene regulations. Although a number of motif-finding algorithms have been presented, most are either time consuming or have sub-optimal accuracy for processing large-scale datasets. In this article, we present a fully parallelized algorithm for detecting combinatorial motifs from ChIP-seq datasets by using Fisher combined method and OpenMP parallel design. Large scale validations on both synthetic data and 350 ChIP-seq datasets from the ENCODE database showed that FisherMP has not only super speeds on large datasets, but also has high accuracy when compared with multiple popular methods. By using FisherMP, we successfully detected combinatorial motifs of CTCF, YY1, MAZ, STAT3 and USF2 in chromosome X, suggesting that they are functional co-players in gene regulation and chromosomal organization. Integrative and statistical analysis of these TF-binding peaks clearly demonstrate that they are not only highly coordinated with each other, but that they are also correlated with histone modifications. FisherMP can be applied for integrative analysis of binding motifs and for predicting cis-regulatory modules from a large number of ChIP-seq datasets.

LMMO: A Large Margin Approach for Refining Regulatory Motifs

Although discriminative motif discovery (DMD) methods are promising for eliciting motifs from high-throughput experimental data, they usually have to sacrifice accuracy and may fail to fully leverage the potential of large datasets. Recently, it has been demonstrated that the motifs identified by DMDs can be significantly improved by maximizing the receiver-operating characteristic curve (AUC) metric, which has been widely used in the literature to rank the performance of elicited motifs. However, existing approaches for motif refinement choose to directly maximize the non-convex and discontinuous AUC itself, which is known to be difficult and may lead to suboptimal solutions. In this paper, we propose Large Margin Motif Optimizer (LMMO), a large-margin-type algorithm for refining regulatory motifs. By relaxing the AUC cost function with the surrogate convex hinge loss, we show that the resultant learning problem can be cast as an instance of difference-of-convex (DC) programs, and solve it iteratively using constrained concave-convex procedure (CCCP). To further save computational time, we combine LMMO with existing techniques for improving the scalability of large-margin-type algorithms, such as cutting plane method. Experimental evaluations on synthetic and real data illustrate the performance of the proposed approach. The code of LMMO is freely available at: https://github.com/ekffar/LMMO.