MOABS: model based analysis of bisulfite sequencing data

Bisulfite sequencing (BS-seq) is the gold standard for studying genome-wide DNA methylation. We developed MOABS to increase the speed, accuracy, statistical power and biological relevance of BS-seq data analysis. MOABS detects differential methylation with 10-fold coverage at single-CpG resolution based on a Beta-Binomial hierarchical model and is capable of processing two billion reads in 24 CPU hours. Here, using simulated and real BS-seq data, we demonstrate that MOABS outperforms other leading algorithms, such as Fisher’s exact test and BSmooth. Furthermore, MOABS analysis can be easily extended to differential 5hmC analysis using RRBS and oxBS-seq. MOABS is available at http://code.google.com/p/moabs/.     

GBSA: a comprehensive software for analysing whole genome bisulfite sequencing data

High-throughput sequencing is increasingly being used in combination with bisulfite (BS) assays to study DNA methylation at nucleotide resolution. Although several programmes provide genome-wide alignment of BS-treated reads, the resulting information is not readily interpretable and often requires further bioinformatic steps for meaningful analysis. Current post-alignment BS-sequencing programmes are generally focused on the gene-specific level, a restrictive feature when analysis in the non-coding regions, such as enhancers and intergenic microRNAs, is required. Here, we present Genome Bisulfite Sequencing Analyser (GBSA—http://ctrad-csi.nus.edu.sg/gbsa), a free open-source software capable of analysing whole-genome bisulfite sequencing data with either a gene-centric or gene-independent focus. Through analysis of the largest published data sets to date, we demonstrate GBSA’s features in providing sequencing quality assessment, methylation scoring, functional data management and visualization of genomic methylation at nucleotide resolution. Additionally, we show that GBSA’s output can be easily integrated with other high-throughput sequencing data, such as RNA-Seq or ChIP-seq, to elucidate the role of methylated intergenic regions in gene regulation. In essence, GBSA allows an investigator to explore not only known loci but also all the genomic regions, for which methylation studies could lead to the discovery of new regulatory mechanisms.     

A Novel Method for Identification and Quantification of Consistently Differentially Methylated Regions

Advances in biotechnology have resulted in large-scale studies of DNA methylation. A differentially methylated region (DMR) is a genomic region with multiple adjacent CpG sites that exhibit different methylation statuses among multiple samples. Many so-called “supervised” methods have been established to identify DMRs between two or more comparison groups. Methods for the identification of DMRs without reference to phenotypic information are, however, less well studied. An alternative “unsupervised” approach was proposed, in which DMRs in studied samples were identified with consideration of nature dependence structure of methylation measurements between neighboring probes from tiling arrays. Through simulation study, we investigated effects of dependencies between neighboring probes on determining DMRs where a lot of spurious signals would be produced if the methylation data were analyzed independently of the probe. In contrast, our newly proposed method could successfully correct for this effect with a well-controlled false positive rate and a comparable sensitivity. By applying to two real datasets, we demonstrated that our method could provide a global picture of methylation variation in studied samples. R source codes to implement the proposed method were freely available at http://www.csjfann.ibms.sinica.edu.tw/eag/programlist/ICDMR/ICDMR.html.     

coMET: visualisation of regional epigenome-wide association scan results and DNA co-methylation patterns

Background

Epigenome-wide association scans (EWAS) are an increasingly powerful and widely-used approach to assess the role of epigenetic variation in human complex traits. However, this rapidly emerging field lacks dedicated visualisation tools that can display features specific to epigenetic datasets.

Result

We developed coMET, an R package and online tool for visualisation of EWAS results in a genomic region of interest. coMET generates a regional plot of epigenetic-phenotype association results and the estimated DNA methylation correlation between CpG sites (co-methylation), with further options to visualise genomic annotations based on ENCODE data, gene tracks, reference CpG-sites, and user-defined features. The tool can be used to display phenotype association signals and correlation patterns of microarray or sequencing-based DNA methylation data, such as Illumina Infinium 450k, WGBS, or MeDIP-seq, as well as other types of genomic data, such as gene expression profiles. The software is available as a user-friendly online tool from http://epigen.kcl.ac.uk/cometand as an R Bioconductor package. Source code, examples, and full documentation are also available from GitHub.

Conclusion

Our new software allows visualisation of EWAS results with functional genomic annotations and with estimation of co-methylation patterns. coMET is available to a wide audience as an online tool and R package, and can be a valuable resource to interpret results in the fast growing field of epigenetics. The software is designed for epigenetic data, but can also be applied to genomic and functional genomic datasets in any species.     

Bisulfighter: accurate detection of methylated cytosines and differentially methylated regions

Analysis of bisulfite sequencing data usually requires two tasks: to call methylated cytosines (mCs) in a sample, and to detect differentially methylated regions (DMRs) between paired samples. Although numerous tools have been proposed for mC calling, methods for DMR detection have been largely limited. Here, we present Bisulfighter, a new software package for detecting mCs and DMRs from bisulfite sequencing data. Bisulfighter combines the LAST alignment tool for mC calling, and a novel framework for DMR detection based on hidden Markov models (HMMs). Unlike previous attempts that depend on empirical parameters, Bisulfighter can use the expectation-maximization algorithm for HMMs to adjust parameters for each data set. We conduct extensive experiments in which accuracy of mC calling and DMR detection is evaluated on simulated data with various mC contexts, read qualities, sequencing depths and DMR lengths, as well as on real data from a wide range of biological processes. We demonstrate that Bisulfighter consistently achieves better accuracy than other published tools, providing greater sensitivity for mCs with fewer false positives, more precise estimates of mC levels, more exact locations of DMRs and better agreement of DMRs with gene expression and DNase I hypersensitivity. The source code is available at http://epigenome.cbrc.jp/bisulfighter.     

methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles

DNA methylation is a chemical modification of cytosine bases that is pivotal for gene regulation, cellular specification and cancer development. Here, we describe an R package, methylKit, that rapidly analyzes genome-wide cytosine epigenetic profiles from high-throughput methylation and hydroxymethylation sequencing experiments. methylKit includes functions for clustering, sample quality visualization, differential methylation analysis and annotation features, thus automating and simplifying many of the steps for discerning statistically significant bases or regions of DNA methylation. Finally, we demonstrate methylKit on breast cancer data, in which we find statistically significant regions of differential methylation and stratify tumor subtypes. methylKit is available at http://code.google.com/p/methylkit.     

Birds do it,bees do it,worms and ciliates do it too: DNA methylation from unexpected corners of the tree of life

Studies describing intricate patterns of DNA methylation in nematode and ciliate are controversial due to the uncertainty of genomic evolutionary conservation of DNA methylation enzymes.See related research articles http://genomebiology.com/2012/13/10/R99 and http://genomebiology.com/2012/13/10/R100     

Interindividual methylomic variation across blood,cortex, and cerebellum: implications for epigenetic studies of neurological and neuropsychiatric phenotypes

Given the tissue-specific nature of epigenetic processes, the assessment of disease-relevant tissue is an important consideration for epigenome-wide association studies (EWAS). Little is known about whether easily accessible tissues, such as whole blood, can be used to address questions about interindividual epigenomic variation in inaccessible tissues, such as the brain. We quantified DNA methylation in matched DNA samples isolated from whole blood and 4 brain regions (prefrontal cortex, entorhinal cortex, superior temporal gyrus, and cerebellum) from 122 individuals. We explored co-variation between tissues and the extent to which methylomic variation in blood is predictive of interindividual variation identified in the brain. For the majority of DNA methylation sites, interindividual variation in whole blood is not a strong predictor of interindividual variation in the brain, although the relationship with cortical regions is stronger than with the cerebellum. Variation at a subset of probes is strongly correlated across tissues, even in instances when the actual level of DNA methylation is significantly different between them. A substantial proportion of this co-variation, however, is likely to result from genetic influences. Our data suggest that for the majority of the genome, a blood-based EWAS for disorders where brain is presumed to be the primary tissue of interest will give limited information relating to underlying pathological processes. These results do not, however, discount the utility of using a blood-based EWAS to identify biomarkers of disease phenotypes manifest in the brain. We have generated a searchable database for the interpretation of data from blood-based EWAS analyses (http://epigenetics.essex.ac.uk/bloodbrain/).     

Using high-density DNA methylation arrays to profile copy number alterations

The integration of genomic and epigenomic data is an increasingly popular approach for studying the complex mechanisms driving cancer development. We have developed a method for evaluating both methylation and copy number from high-density DNA methylation arrays. Comparing copy number data from Infinium HumanMethylation450 BeadChips and SNP arrays, we demonstrate that Infinium arrays detect copy number alterations with the sensitivity of SNP platforms. These results show that high-density methylation arrays provide a robust and economic platform for detecting copy number and methylation changes in a single experiment. Our method is available in the ChAMP Bioconductor package: http://www.bioconductor.org/packages/2.13/bioc/html/ChAMP.html.     

NNvPDB: Neural Network based Protein Secondary Structure Prediction with PDB Validation

Availabilityhttp://bit.srmuniv.ac.in/cgi-bin/bit/cfpdb/nnsecstruct.pl     

BSmooth: from whole genome bisulfite sequencing reads to differentially methylated regions

DNA methylation is an important epigenetic modification involved in gene regulation, which can now be measured using whole-genome bisulfite sequencing. However, cost, complexity of the data, and lack of comprehensive analytical tools are major challenges that keep this technology from becoming widely applied. Here we present BSmooth, an alignment, quality control and analysis pipeline that provides accurate and precise results even with low coverage data, appropriately handling biological replicates. BSmooth is open source software, and can be downloaded from http://rafalab.jhsph.edu/bsmooth.     

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.     

GobyWeb: Simplified Management and Analysis of Gene Expression and DNA Methylation Sequencing Data

We present GobyWeb, a web-based system that facilitates the management and analysis of high-throughput sequencing (HTS) projects. The software provides integrated support for a broad set of HTS analyses and offers a simple plugin extension mechanism. Analyses currently supported include quantification of gene expression for messenger and small RNA sequencing, estimation of DNA methylation (i.e., reduced bisulfite sequencing and whole genome methyl-seq), or the detection of pathogens in sequenced data. In contrast to previous analysis pipelines developed for analysis of HTS data, GobyWeb requires significantly less storage space, runs analyses efficiently on a parallel grid, scales gracefully to process tens or hundreds of multi-gigabyte samples, yet can be used effectively by researchers who are comfortable using a web browser. We conducted performance evaluations of the software and found it to either outperform or have similar performance to analysis programs developed for specialized analyses of HTS data. We found that most biologists who took a one-hour GobyWeb training session were readily able to analyze RNA-Seq data with state of the art analysis tools. GobyWeb can be obtained at http://gobyweb.campagnelab.org and is freely available for non-commercial use. GobyWeb plugins are distributed in source code and licensed under the open source LGPL3 license to facilitate code inspection, reuse and independent extensions http://github.com/CampagneLaboratory/gobyweb2-plugins.     

16S Classifier: A Tool for Fast and Accurate Taxonomic Classification of 16S rRNA Hypervariable Regions in Metagenomic Datasets

The diversity of microbial species in a metagenomic study is commonly assessed using 16S rRNA gene sequencing. With the rapid developments in genome sequencing technologies, the focus has shifted towards the sequencing of hypervariable regions of 16S rRNA gene instead of full length gene sequencing. Therefore, 16S Classifier is developed using a machine learning method, Random Forest, for faster and accurate taxonomic classification of short hypervariable regions of 16S rRNA sequence. It displayed precision values of up to 0.91 on training datasets and the precision values of up to 0.98 on the test dataset. On real metagenomic datasets, it showed up to 99.7% accuracy at the phylum level and up to 99.0% accuracy at the genus level. 16S Classifier is available freely at http://metagenomics.iiserb.ac.in/16Sclassifier and http://metabiosys.iiserb.ac.in/16Sclassifier.     

Predicting the spatial organization of chromosomes using epigenetic data

Chromosome folding can reinforce the demarcation between euchromatin and heterochromatin. Two new studies show how epigenetic data, including DNA methylation, can accurately predict chromosome folding in three dimensions. Such computational approaches reinforce the idea of a linkage between epigenetically marked chromatin domains and their segregation into distinct compartments at the megabase scale or topological domains at a higher resolution.Please see related articles: http://dx.doi.org/10.1186/s13059-015-0741-y and http://dx.doi.org/10.1186/s13059-015-0740-z     

CyanoPhyChe: A Database for Physico-Chemical Properties,Structure and Biochemical Pathway Information of Cyanobacterial Proteins

CyanoPhyChe is a user friendly database that one can browse through for physico-chemical properties, structure and biochemical pathway information of cyanobacterial proteins. We downloaded all the protein sequences from the cyanobacterial genome database for calculating the physico-chemical properties, such as molecular weight, net charge of protein, isoelectric point, molar extinction coefficient, canonical variable for solubility, grand average hydropathy, aliphatic index, and number of charged residues. Based on the physico-chemical properties, we provide the polarity, structural stability and probability of a protein entering in to an inclusion body (PEPIB). We used the data generated on physico-chemical properties, structure and biochemical pathway information of all cyanobacterial proteins to construct CyanoPhyChe. The data can be used for optimizing methods of expression and characterization of cyanobacterial proteins. Moreover, the ‘Search’ and data export options provided will be useful for proteome analysis. Secondary structure was predicted for all the cyanobacterial proteins using PSIPRED tool and the data generated is made accessible to researchers working on cyanobacteria. In addition, external links are provided to biological databases such as PDB and KEGG for molecular structure and biochemical pathway information, respectively. External links are also provided to different cyanobacterial databases. CyanoPhyChe can be accessed from the following URL: http://bif.uohyd.ac.in/cpc.     

MICO: A meta-tool for prediction of the effects of non-synonymous mutations

The Next Generation Sequencing (NGS) is a state-of-the-art technology that produces high throughput data with high resolution mutation information in the genome. Numerous methods with different efficiencies have been developed to predict mutational effects in the genome. The challenge is to present the results in a balanced manner for better biological insights and interpretation. Hence, we describe a meta-tool named Mutation Information Collector (MICO) for automatically querying and collecting related information from multiple biology/bioinformatics enabled web servers with prediction capabilities. The predicted mutational results for the proteins of interest are returned and presented as an easy-to-read summary table in this service. MICO also allows for navigating the result from each website for further analysis.

Availability

http: //mico.ggc.org /MICO     

Mammalian Mitochondrial ncRNA Database

Mammalian Mitochondrial ncRNA is a web-based database, which provides specific information on non-coding RNA in mammals. This database includes easy searching, comparing with BLAST and retrieving information on predicted structure and its function about mammalian ncRNAs.

Availability

The database is available for free at http://www.iitm.ac.in/bioinfo/mmndb/