Stochastic dynamics pervades gene regulation. Despite being random, the dynamics displays a kind of innate structure. In fact, two stochastic forces combine driving efforts: one force originates from the gradient of the underlying stochastic potential, and the other originates from the mathematical curl of the probability flux. The curl force gives rise to rotation. The gradient force gives rise to drift. Together they give rise to helical behavior. Here, it is shown that around and about the vicinity of attractive fixed points, the gradient force naturally wanes but the curl force is found to remain high. This leads to a locally noticeably different type of stochastic track near and about attractive fixed points, compared to tracks in regions where drift dominates. The consistency of this observation with the experimental fact that, in biology, fate commitment appears to not be a-priory locked-in, but rather necessitating active maintenance, is discussed. Hence attractive fixed-points are not only fuzzy, but may effectively be, locally, “more free”.

Living systems are impacted by randomness rooted in the paucity of molecular regulators. Mathematical stochastic analysis thereof is more subtle than a mere blur of determinism. Stochasticity splits into distinct origins: a gradient source and a curl source. Herein, consequences among a family of prototypical gene regulation circuits are investigated. It is shown that, very near resting points of the dynamics, those effects sourced in the gradient vanish, whereas the effects sourced in the curl do not. Thus, the randomness in the vicinity of biologically stable states differs from that afar. Interestingly, locally, this difference may assist in destabilization.

The sequencing revolution driven by high-throughput technologies has generated a huge amount of marine microbial sequences which hide the interaction patterns among microbial species and environment factors. Exploring these patterns is helpful for exploiting the marine resources. In this paper, we use the complex network approach to mine and analyze the interaction patterns of marine taxa and environments in spring, summer, fall and winter seasons. With the 16S rRNA pyrosequencing data of 76 time point taken monthly over 6 years, we first use our MtHc clustering algorithm to generate the operational taxonomic units (OTUs). Then, employ the k-means method to divide 76 time point samples into four seasonal groups, and utilize mutual information (MI) to construct the four correlation networks among microbial species and environment factors. Finally, we adopt the symmetrical non-negative matrix factorization method to detect the interaction patterns, and analysis the relationship between marine species and environment factors. The results show that the four seasonal microbial interaction networks have the characters of complex networks, and interaction patterns are related with the seasonal variability; the same environmental factor influences different species in the four seasons; the four environmental factors of day length, photosynthetically active radiation, NO₂+NO₃ and silicate may have stronger influences on microbes than other environment factors.

Exploring microbial functions and roles plays a key role in the research of environmental and ecological system biology. High-throughput metagenomic technologies can produce massive sequencing data, which make it possible to analyze the structure of microbial communities and changes across environmental factors. Here we use the complex network approach to mine and analyze the interaction patterns of marine taxa and environments in four seasons at the West English Channel from 16S rRNA data. It is shown that marine microbial interaction patterns changes with the seasons. We also analyze the interactions among different species within a community and their relationship with environment factors.

Reconstruction of transcriptome by de novo assembly from next generation sequencing (NGS) short-sequence reads provides an essential mean to catalog expressed genes, identify splicing isoforms, and capture the expression detail of transcripts for organisms with no reference genome available. De novo transcriptome assembly faces many unique challenges, including alternative splicing, variable expression level covering a dynamic range of several orders of magnitude, artifacts introduced by reverse transcription, etc. In the current review, we illustrate the grand strategy in applying De Bruijn Graph (DBG) approach in de novo transcriptome assembly. We further analyze many parameters proven critical in transcriptome assembly using DBG. Among them, k-mer length, coverage depth of reads, genome complexity, performance of different programs are addressed in greater details. A multi-k-mer strategy balancing efficiency and sensitivity is discussed and highly recommended for de novo transcriptome assembly. Future direction points to the combination of NGS and third generation sequencing technology that would greatly enhance the power of de novo transcriptomics study.

RNA-seq has emerged as a powerful way to study the transcriptome of organisms without reference genome available. In this scenario, de novo transcriptome assembly provides an essential mean to catalog expressed genes, identify splicing isoforms, and capture the expression detail of transcripts. However, de novo transcriptome assembly faces many unique challenges, including alternative splicing, dynamic gene expression level, etc. In this review, we focus on the development of algorithms and computation details in the de novo assembly of transcriptome, through which we hope to provide some useful guidelines that help direct future transcriptomics studies.

Although the diploid nature has been observed for over 50 years, phasing the diploid is still a laborious task. The speed and throughput of next generation sequencing have largely increased in the past decades. However, the short read-length remains one of the biggest challenges of haplotype analysis. For instance, reads as short as 150 bp span no more than one variant in most cases. Numerous experimental technologies have been developed to overcome this challenge. Distance, complexity and accuracy of the linkages obtained are the main factors to evaluate the efficiency of whole genome haplotyping methods. Here, we review these experimental technologies, evaluating their efficiency in linkages obtaining and system complexity. The technologies are organized into four categories based on its strategy: (i) chromosomes separation, (ii) dilution pools, (iii) crosslinking and proximity ligation, (ix) long-read technologies. Within each category, several subsections are listed to classify each technology. Innovative experimental strategies are expected to have high-quality performance, low cost and be labor-saving, which will be largely desired in the future.

Although the diploid nature has been observed for over 50 years, phasing the diploid is still a laborious task. The short read-length remains one of the biggest challenges of whole genome haplotyping technologies. Here, we review these experimental haplotyping technologies based on next generation sequencing and evaluate their efficiency in linkages obtaining and system complexity. The technologies are organized into four categories based on its strategy. Innovative experimental strategies are expected to have high-quality performance, low cost and be labor-saving, which will be largely desired in the future.

Fundamental improvement was made for genome sequencing since the next-generation sequencing (NGS) came out in the 2000s. The newer technologies make use of the power of massively-parallel short-read DNA sequencing, genome alignment and assembly methods to digitally and rapidly search the genomes on a revolutionary scale, which enable large-scale whole genome sequencing (WGS) accessible and practical for researchers. Nowadays, whole genome sequencing is more and more prevalent in detecting the genetics of diseases, studying causative relations with cancers, making genome-level comparative analysis, reconstruction of human population history, and giving clinical implications and instructions. In this review, we first give a typical pipeline of whole genome sequencing, including the lab template preparation, sequencing, genome assembling and quality control, variants calling and annotations. We compare the difference between whole genome and whole exome sequencing (WES), and explore a wide range of applications of whole genome sequencing for both mendelian diseases and complex diseases in medical genetics. We highlight the impact of whole genome sequencing in cancer studies, regulatory variant analysis, predictive medicine and precision medicine, as well as discuss the challenges of the whole genome sequencing.

Whole genome sequencing is prevalent in detecting the genetics of diseases, studying causative relations with cancers, making genome-level comparative analysis, and giving clinical implications and instructions. In this review, we give a typical pipeline of whole genome sequencing, compare the difference between whole genome and whole exome sequencing, and explore a wide range of applications of whole genome sequencing in medical genetics. We highlight the impact of whole genome sequencing in cancer studies, regulatory variant analysis, predictive medicine and precision medicine, and discuss the challenges of the whole genome sequencing.

The recent advances in chromosome configuration capture (3C)-based series molecular methods and optical super-resolution (SR) techniques offer powerful tools to investigate three dimensional (3D) genomic structure in prokaryotic and eukaryotic cell nucleus. In this review, we focus on the progress during the last decade in this exciting field. Here we at first introduce briefly genome organization at chromosome, domain and sub-domain level, respectively; then we provide a short introduction to various super-resolution microscopy techniques which can be employed to detect genome 3D structure. We also reviewed the progress of quantitative and visualization tools to evaluate and visualize chromatin interactions in 3D genome derived from Hi-C data. We end up with the discussion that imaging methods and 3C-based molecular methods are not mutually exclusive - - - - actually they are complemental to each other and can be combined together to study 3D genome organization.

There are mainly three kinds of methods to study 3D genome organization: molecular mapping, imaging and computational modeling. Here we focus on reviewing some topics of these methods: (i) the recent developed super-resolution microscopy techniques to image and study chromatin, and (ii) the computational methods to analyze and visualize chromatin interactions derived from chromosome configuration capture (3C)-based molecular mapping data, especially, from Hi-C data. We also show that the strategies developed from these methods are not mutually exclusive —actually they are complemental to each other and can be combined together to study 3D genome organization.