UNLABELLED: The studies of chromatin 3D structure help us to understand its formation and function. Techniques combining chromosome conformation capture and next generation sequencing can capture chromatin structure information and has been applied to several different species and cell lines. We built 3DGD (3D Genome Database), a database that currently collected Hi-C data on four species, for easy accessing and visualization of chromatin 3D structure data. With the integration of other omics data such as genome-wide protein-DNA-binding data, this data source would be useful for researchers interested in chromatin structure and its biological functions. AVAILABILITY AND IMPLEMENTATION: The 3DGD v1.1, data browser, downloadable files and documentation are available at: http://3dgd.biosino.org/.

BACKGROUND: With the rapid development of the next generation sequencing (NGS) technology, large quantities of genome sequencing data have been generated. Because of repetitive regions of genomes and some other factors, assembly of very short reads is still a challenging issue. RESULTS: A novel strategy for improving genome assembly from very short reads is proposed. It can increase accuracies of assemblies by integrating de novo contigs, and produce comparative contigs by allowing multiple references without limiting to genomes of closely related strains. Comparative contigs are used to scaffold de novo contigs. Using simulated and real datasets, it is shown that our strategy can effectively improve qualities of assemblies of isolated microbial genomes and metagenomes. CONCLUSIONS: With more and more reference genomes available, our strategy will be useful to improve qualities of genome assemblies from very short reads. Some scripts are provided to make our strategy applicable at http://code.google.com/p/cd-hybrid/.

BACKGROUND: Inference of causal regulators responsible for gene expression changes under different conditions is of great importance but remains rather challenging. To date, most approaches use direct binding targets of transcription factors (TFs) to associate TFs with expression profiles. However, the low overlap between binding targets of a TF and the affected genes of the TF knockout limits the power of those methods. RESULTS: We developed a TF-centered downstream gene set enrichment analysis approach to identify potential causal regulators responsible for expression changes. We constructed hierarchical and multi-layer regulation models to derive possible downstream gene sets of a TF using not only TF-DNA interactions, but also, for the first time, post-translational modifications (PTM) information. We verified our method in one expression dataset of large-scale TF knockout and another dataset involving both TF knockout and TF overexpression. Compared with the flat model using TF-DNA interactions alone, our method correctly identified five more actual perturbed TFs in large-scale TF knockout data and six more perturbed TFs in overexpression data. Potential regulatory pathways downstream of three perturbed regulators- SNF1, AFT1 and SUT1 -were given to demonstrate the power of multilayer regulation models integrating TF-DNA interactions and PTM information. Additionally, our method successfully identified known important TFs and inferred some novel potential TFs involved in the transition from fermentative to glycerol-based respiratory growth and in the pheromone response. Downstream regulation pathways of SUT1 and AFT1 were also supported by the mRNA and/or phosphorylation changes of their mediating TFs and/or "modulator" proteins. CONCLUSIONS: The results suggest that in addition to direct transcription, indirect transcription and post-translational regulation are also responsible for the effects of TFs perturbation, especially for TFs overexpression. Many TFs inferred by our method are supported by literature. Multiple TF regulation models could lead to new hypotheses for future experiments. Our method provides a valuable framework for analyzing gene expression data to identify causal regulators in the context of TF-DNA interactions and PTM information.

Abstract Summary: PAnnBuilder is an R package to automatically assemble protein annotation information from public resources to provide uniform annotation data for large-scale proteomic studies. Sixteen public databases have been parsed and 54 annotation packages have been constructed based on R environment or SQLite database. These ready-to-use packages cover most frequently needed protein annotation for three model species including human, mouse and rat. Several extended applications such as annotation based on protein sequence similarity are also provided. Sophisticated users can develop their own packages using PAnnBuilder. PAnnBuilder may become an important tool for proteomic research. Availability: PAnnBuilder and example annotation packages are freely available from http://www.biosino.org/PAnnBuilder/ Contact: [email protected]; [email protected]

BACKGROUND: In the post-genomic era, the development of high-throughput gene expression detection technology provides huge amounts of experimental data, which challenges the traditional pipelines for data processing and analyzing in scientific researches. RESULTS: In our work, we integrated gene expression information from Gene Expression Omnibus (GEO), biomedical ontology from Medical Subject Headings (MeSH) and signaling pathway knowledge from sigPathway entries to develop a context mining tool for gene expression analysis - GEOGLE. GEOGLE offers a rapid and convenient way for searching relevant experimental datasets, pathways and biological terms according to multiple types of queries: including biomedical vocabularies, GDS IDs, gene IDs, pathway names and signature list. Moreover, GEOGLE summarizes the signature genes from a subset of GDSes and estimates the correlation between gene expression and the phenotypic distinction with an integrated p value. CONCLUSION: This approach performing global searching of expression data may expand the traditional way of collecting heterogeneous gene expression experiment data. GEOGLE is a novel tool that provides researchers a quantitative way to understand the correlation between gene expression and phenotypic distinction through meta-analysis of gene expression datasets from different experiments, as well as the biological meaning behind. The web site and user guide of GEOGLE are available at: http://omics.biosino.org:14000/kweb/workflow.jsp?id=00020.

BACKGROUND: Although many genomic features have been used in the prediction of protein-protein interactions (PPIs), frequently only one is used in a computational method. After realizing the limited power in the prediction using only one genomic feature, investigators are now moving toward integration. So far, there have been few integration studies for PPI prediction; one failed to yield appreciable improvement of prediction and the others did not conduct performance comparison. It remains unclear whether an integration of multiple genomic features can improve the PPI prediction and, if it can, how to integrate these features. RESULTS: In this study, we first performed a systematic evaluation on the PPI prediction in Escherichia coli (E. coli) by four genomic context based methods: the phylogenetic profile method, the gene cluster method, the gene fusion method, and the gene neighbor method. The number of predicted PPIs and the average degree in the predicted PPI networks varied greatly among the four methods. Further, no method outperformed the others when we tested using three well-defined positive datasets from the KEGG, EcoCyc, and DIP databases. Based on these comparisons, we developed a novel integrated method, named InPrePPI. InPrePPI first normalizes the AC value (an integrated value of the accuracy and coverage) of each method using three positive datasets, then calculates a weight for each method, and finally uses the weight to calculate an integrated score for each protein pair predicted by the four genomic context based methods. We demonstrate that InPrePPI outperforms each of the four individual methods and, in general, the other two existing integrated methods: the joint observation method and the integrated prediction method in STRING. These four methods and InPrePPI are implemented in a user-friendly web interface. CONCLUSION: This study evaluated the PPI prediction by four genomic context based methods, and presents an integrated evaluation method that shows better performance in E. coli.

BACKGROUND: The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear. RESULTS: In this work, the functional and evolutionary modularity of Homo sapiens (H. sapiens) metabolic network were investigated from a topological point of view. Network decomposition shows that the metabolic network is organized in a highly modular core-periphery way, in which the core modules are tightly linked together and perform basic metabolism functions, whereas the periphery modules only interact with few modules and accomplish relatively independent and specialized functions. Moreover, over half of the modules exhibit co-evolutionary feature and belong to specific evolutionary ages. Peripheral modules tend to evolve more cohesively and faster than core modules do. CONCLUSION: The correlation between functional, evolutionary and topological modularity suggests that the evolutionary history and functional requirements of metabolic systems have been imprinted in the architecture of metabolic networks. Such systems level analysis could demonstrate how the evolution of genes may be placed in a genome-scale network context, giving a novel perspective on molecular evolution.

The evolution of new gene families subsequent to gene duplication may be coupled to the fluctuation of population and environment variables. Based upon that, we presented a systematic analysis of the animal transmembrane gene duplication events on a macroevolutionary scale by integrating the palaeontology repository. The age of duplication events was calculated by maximum likelihood method, and the age distribution was estimated by density histogram and normal kernel density estimation. We showed that the density of the duplicates displays a positive correlation with the estimates of maximum number of cell types of common ancestors, and the oxidation events played a key role in the major transitions of this density trace. Next, we focused on the Phanerozoic phase, during which more macroevolution data are available. The pulse mass extinction timepoints coincide with the local peaks of the age distribution, suggesting that the transmembrane gene duplicates fixed frequently when the environment changed dramatically. Moreover, a 61-million-year cycle is the most possible cycle in this phase by spectral analysis, which is consistent with the cycles recently detected in biodiversity. Our data thus elucidate a strong coupling of duplication events and macroevolution; furthermore, our method also provides a new way to address these questions.