Regulons are the basic units of the response system in a bacterial cell, and each consists of a set of transcriptionally co-regulated operons. Regulon elucidation is the basis for studying the bacterial global transcriptional regulation network. In this study, we designed a novel co-regulation score between a pair of operons based on accurate operon identification and cis regulatory motif analyses, which can capture their co-regulation relationship much better than other scores. Taking full advantage of this discovery, we developed a new computational framework and built a novel graph model for regulon prediction. This model integrates the motif comparison and clustering and makes the regulon prediction problem substantially more solvable and accurate. To evaluate our prediction, a regulon coverage score was designed based on the documented regulons and their overlap with our prediction; and a modified Fisher Exact test was implemented to measure how well our predictions match the co-expressed modules derived from E. coli microarray gene-expression datasets collected under 466 conditions. The results indicate that our program consistently performed better than others in terms of the prediction accuracy. This suggests that our algorithms substantially improve the state-of-the-art, leading to a computational capability to reliably predict regulons for any bacteria.

/pdf/bacterial-regulon-modeling-and-prediction-based-on-53qwuzod0m.pdf

Bacterial regulon modeling and prediction based on systematic cis regulatory motif analyses.

Citation: Zhu DX, Qin ZS. Systems biology and metagenomics: a showcase of Chinese bioinformatics researchers and their work. It is our great honor to guest-edit this Thematic Issue on Bioinformatics in Science China Life Sciences. In recent years, a strong cohort of Chinese scientists has emerged as leading scholars in the exciting fields of bioinformatics and computational biology. In this issue, we are pleased to present outstanding research work produced from 10 world renowned researchers. System biology and metagenomics are recently revitalized by the inception of next-generation sequencing technologies. The former seeks to understand the function and behavior of complex biology systems through studying the interaction of their components. The latter extends the systems biology concept and employs systems biology approaches to study the organization and interaction among microbes in their original habituate. In this Thematic Issue, we present a showcase of research work conducted by prominent researchers which can be broadly categorized into these two following areas. (i) Systems biology to identify disease genes or bi-omarkers. Chen et al. [1] used graphical model approach to identify disease genes from biological networks. In their approach, they proposed a weighted kernel based Markov random field method and employed three different kernels to describe the overall relationship among the network nodes integrated from five networks. The disease genes were prioritized and selected using the posterior probability obtained from an improved Gibbs sampling procedure. The disease gene identification using topological features represents a promising direction. Given the increasing amount of functional annotation information available, Li et al. [2] developed a new approach to identify disease genes using both topological features and functional similarity measured by gene ontology (GO). In their approach, they used shortest path algorithm to prioritize the disease gene in the protein protein interaction network by integrating the semantic similarity of GO annotations. The idea of incorporating functional annotations is further implemented in a ge-nome-wide association study (GWAS) to identify the disease associated single nucleotide polymorphisms (SNPs). As one of the representative studies, Lin et al. [3] proposed a statistical framework to estimate the extent of the improvement in disease SNP identification by incorporating functional annotation data into GWAS study. Although the improvement is not as significant as expected, it warrants further investigations in this promising direction. The aforementioned methods can be considered as computational approaches that are applicable to the data collected from steady-state disease or homogeneous disease. It is …

/pdf/systems-biology-and-metagenomics-a-showcase-of-chinese-o61cuv7a4n.pdf

Systems biology and metagenomics: a showcase of Chinese bioinformatics researchers and their work

The rapid accumulation of fully sequenced prokaryotic genomes provides unprecedented information for biological studies of bacterial and archaeal organisms in a systematic manner. Operons are the basic functional units for conducting such studies. Here, we review an operon database DOOR (the Database of prOkaryotic OpeRons) that we have previously developed and continue to update. Currently, the database contains 6 975 454 computationally predicted operons in 2072 complete genomes. In addition, the database also contains the following information: (i) transcriptional units for 24 genomes derived using publicly available transcriptomic data; (ii) orthologous gene mapping across genomes; (iii) 6408 cis-regulatory motifs for transcriptional factors of some operons for 203 genomes; (iv) 3 456 718 Rho-independent terminators for 2072 genomes; as well as (v) a suite of tools in support of applications of the predicted operons. In this review, we will explain how such data are computationally derived and demonstrate how they can be used to derive a wide range of higher-level information needed for systems biology studies to tackle complex and fundamental biology questions.

DOOR: a prokaryotic operon database for genome analyses and functional inference.

Bacterial genomes are now recognized as interacting intimately with cellular processes. Uncovering organizational mechanisms of bacterial genomes has been a primary focus of researchers to reveal the potential cellular activities. The advances in both experimental techniques and computational models provide a tremendous opportunity for understanding these mechanisms, and various studies have been proposed to explore the organization rules of bacterial genomes associated with functions recently. This review focuses mainly on the principles that shape the organization of bacterial genomes, both locally and globally. We first illustrate local structures as operons/transcription units for facilitating co-transcription and horizontal transfer of genes. We then clarify the constraints that globally shape bacterial genomes, such as metabolism, transcription and replication. Finally, we highlight challenges and opportunities to advance bacterial genomic studies and provide application perspectives of genome organization, including pathway hole assignment and genome assembly and understanding disease mechanisms.

The functional determinants in the organization of bacterial genomes.

The availability of a large number of sequenced bacterial genomes facilitates in-depth studies about why genes (operons) in a bacterial genome are globally organized the way they are. We have previously discovered that (the relative) transcription- activation frequencies among different biological pathways encoded in a genome have a dominating role in the global arrangement of operons. One complicating factor in such a study is that some operons may be involved in multiple pathways with different activation frequencies. A quantitative model has been developed that captures this information, which tends to be minimized by the current global arrangement of operons in a bacterial (and archaeal) genome compared to possible alternative arrangements. A study is carried out here using this model on a collection of 52 closely related E. coli genomes, which revealed interesting new insights about how bacterial genomes evolve to optimally adapt to their environments through adjusting the (relative) genomic locations of the encoding operons of biological pathways once their utilization and hence transcription activation frequencies change, to maintain the above energy-efficiency property. More specifically we observed that it is the frequencies of the transcription activation of pathways relative to those of the other encoded pathways in an organism as well as the variation in the activation frequencies of a specific pathway across the related genomes that play a key role in the observed commonalities and differences in the genomic organizations of genes (and operons) encoding specific pathways across different genomes.

/pdf/understanding-the-commonalities-and-differences-in-genomic-345tmzln2f.pdf

Understanding the commonalities and differences in genomic organizations across closely related bacteria from an energy perspective

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Understanding the commonalities and differences in genomic organizations across closely related bacteria from an energy perspective