A method for comparing multiple bacterial community structures from 16S rDNA clone library sequences

Culture-independent approaches, based on 16S rDNA sequences, are extensively used in modern microbial ecology. Sequencing of the clone library generated from environmental DNA has advantages over fingerprint-based methods, such as denaturing gradient gel electrophoresis, as it provides precise identification and quantification of the phylotypes present in samples. However, to date, no method exists for comparing multiple bacterial community structures using clone library sequences. In this study, an automated method to achieve this has been developed, by applying pair wise alignment, hierarchical clustering and principle component analysis. The method has been demonstrated to be successful in comparing samples from various environments. The program, named CommCluster, was written in JAVA, and is now freely available, at http://chunlab.snu.ac.kr/commcluster/.     

GxGrare: gene-gene interaction analysis method for rare variants from high-throughput sequencing data

Background

With the rapid advancement of array-based genotyping techniques, genome-wide association studies (GWAS) have successfully identified common genetic variants associated with common complex diseases. However, it has been shown that only a small proportion of the genetic etiology of complex diseases could be explained by the genetic factors identified from GWAS. This missing heritability could possibly be explained by gene-gene interaction (epistasis) and rare variants. There has been an exponential growth of gene-gene interaction analysis for common variants in terms of methodological developments and practical applications. Also, the recent advancement of high-throughput sequencing technologies makes it possible to conduct rare variant analysis. However, little progress has been made in gene-gene interaction analysis for rare variants.

Results

Here, we propose GxGrare which is a new gene-gene interaction method for the rare variants in the framework of the multifactor dimensionality reduction (MDR) analysis. The proposed method consists of three steps; 1) collapsing the rare variants, 2) MDR analysis for the collapsed rare variants, and 3) detect top candidate interaction pairs. GxGrare can be used for the detection of not only gene-gene interactions, but also interactions within a single gene. The proposed method is illustrated with 1080 whole exome sequencing data of the Korean population in order to identify causal gene-gene interaction for rare variants for type 2 diabetes.

Conclusion

The proposed GxGrare performs well for gene-gene interaction detection with collapsing of rare variants. GxGrare is available at http://bibs.snu.ac.kr/software/gxgrare which contains simulation data and documentation. Supported operating systems include Linux and OS X.

     

Bis-class: a new classification tool of methylation status using bayes classifier and local methylation information

Background

Whole genome sequencing of bisulfite converted DNA (‘methylC-seq’) method provides comprehensive information of DNA methylation. An important application of these whole genome methylation maps is classifying each position as a methylated versus non-methylated nucleotide. A widely used current method for this purpose, the so-called binomial method, is intuitive and straightforward, but lacks power when the sequence coverage and the genome-wide methylation level are low. These problems present a particular challenge when analyzing sparsely methylated genomes, such as those of many invertebrates and plants.

Results

We demonstrate that the number of sequence reads per position from methylC-seq data displays a large variance and can be modeled as a shifted negative binomial distribution. We also show that DNA methylation levels of adjacent CpG sites are correlated, and this similarity in local DNA methylation levels extends several kilobases. Taking these observations into account, we propose a new method based on Bayesian classification to infer DNA methylation status while considering the neighborhood DNA methylation levels of a specific site. We show that our approach has higher sensitivity and better classification performance than the binomial method via multiple analyses, including computational simulations, Area Under Curve (AUC) analyses, and improved consistencies across biological replicates. This method is especially advantageous in the analyses of sparsely methylated genomes with low coverage.

Conclusions

Our method improves the existing binomial method for binary methylation calls by utilizing a posterior odds framework and incorporating local methylation information. This method should be widely applicable to the analyses of methylC-seq data from diverse sparsely methylated genomes. Bis-Class and example data are provided at a dedicated website (http://bibs.snu.ac.kr/software/Bisclass).

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-608) contains supplementary material, which is available to authorized users.     

PRI-Modeler: extracting RNA structural elements from PDB files of protein-RNA complexes

A complete understanding of protein and RNA structures and their interactions is important for determining the binding sites in protein-RNA complexes. Computational approaches exist for identifying secondary structural elements in proteins from atomic coordinates. However, similar methods have not been developed for RNA, due in part to the very limited structural data so far available. We have developed a set of algorithms for extracting and visualizing secondary and tertiary structures of RNA and for analyzing protein-RNA complexes. These algorithms have been implemented in a web-based program called PRI-Modeler (protein-RNA interaction modeler). Given one or more protein data bank files of protein-RNA complexes, PRI-Modeler analyzes the conformation of the RNA, calculates the hydrogen bond (H bond) and van der Waals interactions between amino acids and nucleotides, extracts secondary and tertiary RNA structure elements, and identifies the patterns of interactions between the proteins and RNAs. This paper presents PRI-Modeler and its application to the hydrogen bond and van der Waals interactions in the most representative set of protein-RNA complexes. The analysis reveals several interesting interaction patterns at various levels. The information provided by PRI-Modeler should prove useful for determining the binding sites in protein-RNA complexes. PRI-Modeler is accessible at http://wilab.inha.ac.kr/primodeler/, and supplementary materials are available in the analysis results section at http://wilab.inha.ac.kr/primodeler/.     

ArrayCyGHt: a web application for analysis and visualization of array-CGH data

ArrayCyGHt is a web-based application tool for analysis and visualization of microarray-comparative genomic hybridization (array-CGH) data. Full process of array-CGH data analysis, from normalization of raw data to the final visualization of copy number gain or loss, can be straightforwardly achieved on this arrayCyGHt system without the use of any further software. ArrayCyGHt, therefore, provides an easy and fast tool for the analysis of copy number aberrations in any kinds of data format. AVAILABILITY: ArrayCyGHt can be accessed at http://genomics.catholic.ac.kr/arrayCGH/     

BcSNPdb: bovine coding region single nucleotide polymorphisms located proximal to quantitative trait loci

Bovine coding region single nucleotide polymorphisms located proximal to quantitative trait loci were identified to facilitate bovine QTL fine mapping research. A total of 692,763 bovine SNPs was extracted from 39,432 UniGene clusters, and 53,446 candidate SNPs were found to be a depth >3. In order to validate the in silico SNPs experimentally, 186 animals representing 14 breeds and 100 mixed breeds were analyzed. Genotyping of 40 randomly selected candidate SNPs revealed that 43% of these SNPs ranged in frequency from 0.009 to 0.498. To identify non-synonymous SNPs and to correct for possible frameshift errors in the ESTs at the predicted SNP positions, we designed a program that determines coding regions by protein-sequence referencing, and identified 17,735 nsSNPs. The SNPs and bovine quantitative traits loci informations were integrated into a bovine SNP data: BcSNPdb (http://snugenome.snu.ac.kr/BtcSNP/). Currently there are 43 different kinds of quantitative traits available. Thus, these SNPs would serve as valuable resources for exploiting genomic variation that influence economically and agriculturally important traits in cows.     

Q-omics: Smart Software for Assisting Oncology and Cancer Research

The rapid increase in collateral omics and phenotypic data has enabled data-driven studies for the fast discovery of cancer targets and biomarkers. Thus, it is necessary to develop convenient tools for general oncologists and cancer scientists to carry out customized data mining without computational expertise. For this purpose, we developed innovative software that enables user-driven analyses assisted by knowledge-based smart systems. Publicly available data on mutations, gene expression, patient survival, immune score, drug screening and RNAi screening were integrated from the TCGA, GDSC, CCLE, NCI, and DepMap databases. The optimal selection of samples and other filtering options were guided by the smart function of the software for data mining and visualization on Kaplan-Meier plots, box plots and scatter plots of publication quality. We implemented unique algorithms for both data mining and visualization, thus simplifying and accelerating user-driven discovery activities on large multiomics datasets. The present Q-omics software program (v0.95) is available at http://qomics.sookmyung.ac.kr.     

Genotype transposer: automated genotype manipulation for linkage disequilibrium analysis

SUMMARY: The purpose of this work is to provide the modern molecular geneticist with tools to perform more efficient and more accurate analysis of the genotype data they produce. By using Microsoft Excel macros written in Visual Basic, we can translate genotype data into a form readable by the versatile software 'Arlequin', read the Arlequin output, calculate statistics of linkage disequilibrium, and put the results in a format for viewing with the software 'GOLD'. AVAILABILITY: The software is available by FTP at: ftp://xcsg.iarc.fr/cox/Genotype_Transposer/. SUPPLEMENTARY INFORMATION: Detailed instruction and examples are available at: ftp://xcsg.iarc.fr/cox/Genotype&_Transposer/. Arlequin is available at: http://lgb.unige.ch/arlequin/. GOLD is available at: http://www.well.ox.ac.uk/asthma/GOLD/.     

InfarctSizer: computing infarct volume from brain images of a stroke animal model

Many computational methods for determining the infarct volume from the image of 2,3,5-triphenyltetrazolium chloride-stained brain slices rely on the discretion of the user to determine the infarct region by visual inspection. Once the user determines the infarct boundary by visual inspection, the methods compute the area within the boundary with the assumption that all the spots within the boundary have been infarcted at the same level. However, in the same brain image, partially infarcted spots often tend to appear pinkish whereas fully or severely infarcted spots appear white. We developed a program called InfarctSizer, which automatically detects the infarct region and computes the infarct volume proportional to infarction levels. Comparison of InfarctSizer with other methods shows that InfarctSizer computes the infarct volume more accurately and efficiently than other methods. InfarctSizer and sample brain images are available at http://wilab.inha.ac.kr/brainimage.     

LVB: parsimony and simulated annealing in the search for phylogenetic trees

The program LVB seeks parsimonious phylogenies from nucleotide alignments, using the simulated annealing heuristic. LVB runs fast and gives high quality results. AVAILABILITY: The software is available at http://www.rubic.reading.ac.uk/lvb/ Supplementary information: Supplementary information may be downloaded from http://www.rubic.reading.ac.uk/~daniel/     

ModuleSearch: finding functional modules in a protein–protein interaction network

Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein–protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein–protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.     

Drawing phylogenetic trees in LATEX and Microsoft Word

SUMMARY: newicktree is a PSTricks-based LATEX package which enables phylogenetic trees described in the Newick format to be drawn directly into LATEX documents. mswordtree is a macro for producing phylogenetic trees using the drawing elements available in Microsoft Word. AVAILABILITY: Both programs are available free from the John Innes Centre's Bioinformatics Research Group website at http://jic-bioinfo.bbsrc.ac.uk/bioinformatics-research/software/index.html. SUPPLEMENTARY INFORMATION: A full user-guide for newicktree and installation and usage instructions for mswordtree and available at http://jic-bioinfo.bbsrc.ac.uk/bioinformatics-research/software/index.html     

A new algorithm for detecting low-complexity regions in protein sequences

MOTIVATION: Pair-wise alignment of protein sequences and local similarity searches produce many false positives because of compositionally biased regions, also called low-complexity regions (LCRs), of amino acid residues. Masking and filtering such regions significantly improves the reliability of homology searches and, consequently, functional predictions. Most of the available algorithms are based on a statistical approach. We wished to investigate the structural properties of LCRs in biological sequences and develop an algorithm for filtering them. RESULTS: We present an algorithm for detecting and masking LCRs in protein sequences to improve the quality of database searches. We developed the algorithm based on the complexity analysis of subsequences delimited by a pair of identical, repeating subsequences. Given a protein sequence, the algorithm first computes the suffix tree of the sequence. It then collects repeating subsequences from the tree. Finally, the algorithm iteratively tests whether each subsequence delimited by a pair of repeating subsequences meets a given criteria. Test results with 1000 proteins from 20 families in Pfam show that the repeating subsequences are a good indicator for the low-complexity regions, and the algorithm based on such structural information strongly compete with others. AVAILABILITY: http://bioinfo.knu.ac.kr/research/CARD/ CONTACT: swshin@bioinfo.knu.ac.kr     

SelSim: a program to simulate population genetic data with natural selection and recombination

SUMMARY: SelSim is a program for Monte Carlo simulation of DNA polymorphism data for a recombining region within which a single bi-allelic site has experienced natural selection. SelSim allows simulation from either a fully stochastic model of, or deterministic approximations to, natural selection within a coalescent framework. A number of different mutation models are available for simulating surrounding neutral variation. The package enables a detailed exploration of the effects of different models and strengths of selection on patterns of diversity. This provides a tool for the statistical analysis of both empirical data and methods designed to detect natural selection. AVAILABILITY: http://www.stats.ox.ac.uk/mathgen/software.html. SUPPLEMENTARY INFORMATION: http://www.stats.ox.ac.uk/mathgen/software.html.