Identifying network communities with a high resolution

Community structure is an important property of complex networks. An automatic discovery of such structure is a fundamental task in many disciplines, including sociology, biology, engineering, and computer science. Recently, several community discovery algorithms have been proposed based on the optimization of a quantity called modularity (Q). However, the problem of modularity optimization is NP-hard, and the existing approaches often suffer from prohibitively long running time or poor quality. Furthermore, it has been recently pointed out that algorithms based on optimizing Q will have a resolution limit, i.e., communities below a certain scale may not be detected. In this research, we first propose an efficient heuristic algorithm, Qcut, which combines spectral graph partitioning and local search to optimize Q. Using both synthetic and real networks, we show that Qcut can find higher modularities and is more scalable than the existing algorithms. Furthermore, using Qcut as an essential component, we propose a recursive algorithm, HQcut, to solve the resolution limit problem. We show that HQcut can successfully detect communities at a much finer scale and with a higher accuracy than the existing algorithms. Finally, we apply Qcut and HQcut to study a protein-protein interaction network, and show that the combination of the two algorithms can reveal interesting biological results that may be otherwise undetectable.Comment: 14 pages, 5 figures. 1 supplemental file at http://cic.cs.wustl.edu/qcut/supplemental.pd

Advancing Systems Biology in the International Conference on Intelligent Biology and Medicine (ICIBM) 2015

The 2015 International Conference on Intelligent Biology and Medicine (ICIBM 2015) was held on November 13-15, 2015 in Indianapolis, Indiana, USA. ICIBM 2015 included eight scientific sessions, three tutorial sessions, one poster session, and four keynote presentations that covered the frontier research in broad areas related to bioinformatics, systems biology, big data science, biomedical informatics, pharmacogenomics, and intelligent computing. Here, we present a summary of the 10 research articles that were selected from ICIBM 2015 and included in the supplement to BMC Systems Biology

Intelligent biology and medicine in 2015: advancing interdisciplinary education, collaboration, and data science

We summarize the 2015 International Conference on Intelligent Biology and Medicine (ICIBM 2015) and the editorial report of the supplement to BMC Genomics. The supplement includes 20 research articles selected from the manuscripts submitted to ICIBM 2015. The conference was held on November 13-15, 2015 at Indianapolis, Indiana, USA. It included eight scientific sessions, three tutorials, four keynote presentations, three highlight talks, and a poster session that covered current research in bioinformatics, systems biology, computational biology, biotechnologies, and computational medicine

Structural and biochemical insights into small RNA 3' end trimming by Arabidopsis SDN1.

A family of DEDDh 3'→5' exonucleases known as Small RNA Degrading Nucleases (SDNs) initiates the turnover of ARGONAUTE1 (AGO1)-bound microRNAs in Arabidopsis by trimming their 3' ends. Here, we report the crystal structure of Arabidopsis SDN1 (residues 2-300) in complex with a 9 nucleotide single-stranded RNA substrate, revealing that the DEDDh domain forms rigid interactions with the N-terminal domain and binds 4 nucleotides from the 3' end of the RNA via its catalytic pocket. Structural and biochemical results suggest that the SDN1 C-terminal domain adopts an RNA Recognition Motif (RRM) fold and is critical for substrate binding and enzymatic processivity of SDN1. In addition, SDN1 interacts with the AGO1 PAZ domain in an RNA-independent manner in vitro, enabling it to act on AGO1-bound microRNAs. These extensive structural and biochemical studies may shed light on a common 3' end trimming mechanism for 3'→5' exonucleases in the metabolism of small non-coding RNAs

An Iterative Loop Matching Approach to the Prediction of RNA Secondary Structures with Pseudoknots

Motivation: Pseudoknots have generally been excluded from the prediction of RNA secondary structures due to the difficulty in modeling and complexity in computing. Although several dynamic programming algorithms exist for the prediction of pseudoknots using thermodynamic approaches, they are neither reliable nor efficient. On the other hand, comparative methods are more reliable, but are often done in an ad hoc manner and require expert intervention. Maximum weighted matching (Tabaska et. al, Bioinformatics, 14:691-9, 1998), an algorithm for pseudoknot prediction with comparative analysis, suffers from low prediction accuracy in many cases. Here we present an algorithm, iterative loop matching, for predict-ing RNA secondary structures including pseudoknots reliably and efficiently. The method can utilize either thermodynamic or comparative information or both, thus is able to predict for both aligned sequences and individual sequences. Results: We have tested the algorithm on a number of RNA families, including both structures with and without pseudoknots. Using 8–12 homologous sequences, the algorithm correctly identifies more than 90% of base-pairs for short sequences and 80% overall. It correctly predicts nearly all pseudoknots. Furthermore, it produces very few spurious base-pairs for sequences without pseudoknots. Comparisons show that our algorithm is both more sensitive and more specific than the maximum weighted matching method. In addition, our algorithm has high prediction accuracy on individual sequences, comparable to the PKNOTS algorithm (Rivas & Eddy, J Mol Biol, 285:2053-68, 1999), while using much less computational resources. Availability: The program has been implemented in ANSI C and is freely available for academic use at http://www.cse.wustl.edu/˜zhang/projects/rna/ilm/

Discovering weak community structures in large biological networks

Identifying intrinsic structures in large networks is a fundamental problem in many fields, such as biology, engineering and social sciences. Motivated by biology applications, in this paper we are concerned with identifying community structures, which are densely connected sub-graphs, in large biological networks. We address several critical issues for finding community structures. First, biological networks directly constructed from experimental data often contain spurious edges and may also miss genuine connections. As a result, community structures in biological networks are often weak. We introduce simple operations to capture local neighborhood structures for identifying weak communities. Second, we consider the issue of automatically determining the most appropriate number of communities, a crucial problem for all clustering methods. This requires to properly evaluate the quality of community structures. We extend an existing work of a modularity function for evaluating community structures to weighted graphs. Third, we propose a spectral clustering algorithm to optimize the modularity function, and a greedy partitioning method to approximate the first algorithm with much reduced running time. We evaluate our methods on many networks of known structures, and apply them to three real-world networks that have different types of network communities: a yeast protein-protein interaction network, a co-expression network of yeast cell-cycle genes, and a collaboration network of bioinformaticians. The results show that our methods can find superb community structures and the correct numbers of communities. Our results reveal several interesting network structures that have not been reported previously

Discovering Functional Modules by Clustering Gene Co-expression Networks

Identification of groups of functionally related genes from high throughput gene expression data is an important step towards elucidating gene functions at a global scale. Most existing approaches treat gene expression data as points in a metric space, and apply conventional clustering algorithms to identify sets of genes that are close to each other in the metric space. However, they usually ignore the topology of the underlying biological networks. In this paper, we propose a network-based clustering method that is biologically more realistic. Given a gene expression data set, we apply a rank-based transformation to obtain a sparse co-expression network, and use a novel spectral clustering algorithm to identify natural community structures in the network, which correspond to gene functional modules. We have tested the method on two large-scale gene expression data sets in yeast and Arabidopsis, respectively. The results show that the clusters identified by our method on these datasets are functionally richer and more coherent than the clusters from the standard k-means clustering algorithm

A Top-Performing Algorithm for the DREAM3 Gene Expression Prediction Challenge

A wealth of computational methods has been developed to address problems in systems biology, such as modeling gene expression. However, to objectively evaluate and compare such methods is notoriously difficult. The DREAM (Dialogue on Reverse Engineering Assessments and Methods) project is a community-wide effort to assess the relative strengths and weaknesses of different computational methods for a set of core problems in systems biology. This article presents a top-performing algorithm for one of the challenge problems in the third annual DREAM (DREAM3), namely the gene expression prediction challenge. In this challenge, participants are asked to predict the expression levels of a small set of genes in a yeast deletion strain, given the expression levels of all other genes in the same strain and complete gene expression data for several other yeast strains. I propose a simple -nearest-neighbor (KNN) method to solve this problem. Despite its simplicity, this method works well for this challenge, sharing the “top performer” honor with a much more sophisticated method. I also describe several alternative, simple strategies, including a modified KNN algorithm that further improves the performance of the standard KNN method. The success of these methods suggests that complex methods attempting to integrate multiple data sets do not necessarily lead to better performance than simple yet robust methods. Furthermore, none of these top-performing methods, including the one by a different team, are based on gene regulatory networks, which seems to suggest that accurately modeling gene expression using gene regulatory networks is unfortunately still a difficult task

CAGER: classification analysis of gene expression regulation using multiple information sources

BACKGROUND: Many classification approaches have been applied to analyzing transcriptional regulation of gene expressions. These methods build models that can explain a gene's expression level from the regulatory elements (features) on its promoter sequence. Different types of features, such as experimentally verified binding motifs, motifs discovered by computer programs, or transcription factor binding data measured with Chromatin Immunoprecipitation (ChIP) assays, have been used towards this goal. Each type of features has been shown successful in modeling gene transcriptional regulation under certain conditions. However, no comparison has been made to evaluate the relative merit of these features. Furthermore, most publicly available classification tools were not designed specifically for modeling transcriptional regulation, and do not allow the user to combine different types of features. RESULTS: In this study, we use a specific classification method, decision trees, to model transcriptional regulation in yeast with features based on predefined motifs, automatically identified motifs, ChlP-chip data, or their combinations. We compare the accuracies and stability of these models, and analyze their capabilities in identifying functionally related genes. Furthermore, we design and implement a user-friendly web server called CAGER (Classification Analysis of Gene Expression Regulation) that integrates several software components for automated analysis of transcriptional regulation using decision trees. Finally, we use CAGER to study the transcriptional regulation of Arabidopsis genes in response to abscisic acid, and report some interesting new results. CONCLUSION: Models built with ChlP-chip data suffer from low accuracies when the condition under which gene expressions are measured is significantly different from the condition under which the ChIP experiment is conducted. Models built with automatically identified motifs can sometimes discover new features, but their modeling accuracies may have been over-estimated in previous studies. Furthermore, models built with automatically identified motifs are not stable with respect to noises. A combination of ChlP-chip data and predefined motifs can substantially improve modeling accuracies, and is effective in identifying true regulons. The CAGER web server, which is freely available at , allows the user to select combinations of different feature types for building decision trees, and interact with the models graphically. We believe that it will be a useful tool to facilitate the discovery of gene transcriptional regulatory networks