A data-driven method to learn a jump diffusion process from aggregate biological gene expression data.

Dynamic models of gene expression are urgently required. In this paper, we describe the time evolution of gene expression by learning a jump diffusion process to model the biological process directly. Our algorithm needs aggregate gene expression data as input and outputs the parameters of the jump diffusion process. The learned jump diffusion process can predict population distributions of gene expression at any developmental stage, obtain long-time trajectories for individual cells, and offer a novel approach to computing RNA velocity. Moreover, it studies biological systems from a stochastic dynamic perspective. Gene expression data at a time point, which is a snapshot of a cellular process, is treated as an empirical marginal distribution of a stochastic process. The Wasserstein distance between the empirical distribution and predicted distribution by the jump diffusion process is minimized to learn the dynamics. For the learned jump diffusion process, its trajectories correspond to the development process of cells, the stochasticity determines the heterogeneity of cells, its instantaneous rate of state change can be taken as "RNA velocity", and the changes in scales and orientations of clusters can be noticed too. We demonstrate that our method can recover the underlying nonlinear dynamics better compared to previous parametric models and the diffusion processes driven by Brownian motion for both synthetic and real world datasets. Our method is also robust to perturbations of data because the computation involves only population expectations.

BL-Hi-C is an efficient and sensitive approach for capturing structural and regulatory chromatin interactions.

In human cells, DNA is hierarchically organized and assembled with histones and DNA-binding proteins in three dimensions. Chromatin interactions play important roles in genome architecture and gene regulation, including robustness in the developmental stages and flexibility during the cell cycle. Here we propose in situ Hi-C method named Bridge Linker-Hi-C (BL-Hi-C) for capturing structural and regulatory chromatin interactions by restriction enzyme targeting and two-step proximity ligation. This method improves the sensitivity and specificity of active chromatin loop detection and can reveal the regulatory enhancer-promoter architecture better than conventional methods at a lower sequencing depth and with a simpler protocol. We demonstrate its utility with two well-studied developmental loci: the beta-globin and HOXC cluster regions.

Kinetic behavior of the general modifier mechanism of Botts and Morales with non-equilibrium binding.

In this paper, we perform a complete analysis of the kinetic behavior of the general modifier mechanism of Botts and Morales in both equilibrium steady states and non-equilibrium steady states (NESS). Enlightened by the non-equilibrium theory of Markov chains, we introduce the net flux into discussion and acquire an expression of the rate of product formation in NESS, which has clear biophysical significance. Up till now, it is a general belief that being an activator or an inhibitor is an intrinsic property of the modifier. However, we reveal that this traditional point of view is based on the equilibrium assumption. A modifier may no longer be an overall activator or inhibitor when the reaction system is not in equilibrium. Based on the regulation of enzyme activity by the modifier concentration, we classify the kinetic behavior of the modifier into three categories, which are named hyperbolic behavior, bell-shaped behavior, and switching behavior, respectively. We show that the switching phenomenon, in which a modifier may convert between an activator and an inhibitor when the modifier concentration varies, occurs only in NESS. Effects of drugs on the Pgp ATPase activity, where drugs may convert from activators to inhibitors with the increase of the drug concentration, are taken as a typical example to demonstrate the occurrence of the switching phenomenon.

[Advances on bioinformatic research in transcription factor binding sites].

Transcription regulation is carried out by the interactions between transcription factors and their binding sites in DNA. Transcription factor binding sites (TFBS) are short, degenerate nucleotide sequences, and usually are 6-20 bp long. Accurate prediction of TFBSs is a crucial step towards understanding the regulation mechanism of transcription and constructing transcriptional regulatory networks. Currently, high-throughput technologies, such as ChIP-chip, are producing a large amount of data for TFBS study. TFBS prediction using either ChIP-chip data or multiple genome information is becoming a hot topic in bioinformatics. In this review, we summarize experimental techniques for locating TFBS and databases for TFBS study and further statistical survey models for TFBS description and software for TFBS prediction. As a consequence, we believe that bioinformatical and experimental studies of TFBS will promote each other in the future to understand the nature of transcriptional regulation.

Features of coding and noncoding sequences based on 3-tuple distributions.

The origin of non-coding sequences, especially introns,is an outstanding issue that has been receiving continuous debate for the last two decades. In the current work we use a mathematical model to characterize DNA sequences and find that the 3-tuple distributions in different reading frames of a given coding sequence differ sharply from each other, while they are almost identical to each other in introns or other non-coding sequences. SREs (Symmetric relative entropies) decrease progressively from coding sequences of primitive prokaryotes to those of advanced eukaryotes and from non-coding sequences of low eukaryotes to those of high eukaryotes with a correlation coefficient of 0.86. In silico evolution experiments show that SREs typical of higher eukaryotic introns can be achieved from prokaryotic coding sequences as the mutation ratio reaches 2/100. The fact that (a total of 25 introns) from all three different genomes S. pombe, C. elegans and H. sapiens searched are found to share high sequence identity with coding regions indicates that at least some introns may have come directly from CDS (coding sequences). We suggest that SREs may be a useful feature for evolutionary study.

Predicting polymerase II core promoters by cooperating transcription factor binding sites in eukaryotic genes.

Several discriminate functions for predicting core promoters that based on the potential cooperation between transcription factor binding sites (TFBSs) are discussed. It is demonstrated that the promoter predicting accuracy is improved when the cooperation among TFBSs is taken into consideration. The core promoter region of a newly discovered gene CKLFSF1 is predicted to locate more than 1.5 kb far away from the 5' end of the transcript and in the last intron of its upstream gene, which is experimentally confirmed later. The core promoters of 3402 human RefSeq sequences, obtained by extending the mRNAs in human genome sequences, are predicted by our algorithm, and there are about 60% of the predicted core promoters locating within the +/- 500 bp region relative to the annotated transcription start site.