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1.  A Scalable, Flexible Workflow for MethylCap-Seq Data Analysis 
Advances in whole genome profiling have revolutionized the cancer research field, but at the same time have raised new bioinformatics challenges. For next generation sequencing (NGS), these include data storage, computational costs, sequence processing and alignment, delineating appropriate statistical measures, and data visualization. The NGS application MethylCap-seq involves the in vitro capture of methylated DNA and subsequent analysis of enriched fragments by massively parallel sequencing. Here, we present a scalable, flexible workflow for MethylCap-seq Quality Control, secondary data analysis, tertiary analysis of multiple experimental groups, and data visualization. This workflow and its suite of features will assist biologists in conducting methylation profiling projects and facilitate meaningful biological interpretation.
PMCID: PMC3320741  PMID: 22484542
next generation sequencing; DNA methylation; epigenetics; cancer; data analysis; data visualization
2.  Enrichment-based DNA methylation analysis using next-generation sequencing: sample exclusion, estimating changes in global methylation, and the contribution of replicate lanes 
BMC Genomics  2012;13(Suppl 8):S6.
DNA methylation is an important epigenetic mark and dysregulation of DNA methylation is associated with many diseases including cancer. Advances in next-generation sequencing now allow unbiased methylome profiling of entire patient cohorts, greatly facilitating biomarker discovery and presenting new opportunities to understand the biological mechanisms by which changes in methylation contribute to disease. Enrichment-based sequencing assays such as MethylCap-seq are a cost effective solution for genome-wide determination of methylation status, but the technical reliability of methylation reconstruction from raw sequencing data has not been well characterized.
We analyze three MethylCap-seq data sets and perform two different analyses to assess data quality. First, we investigate how data quality is affected by excluding samples that do not meet quality control cutoff requirements. Second, we consider the effect of additional reads on enrichment score, saturation, and coverage. Lastly, we verify a method for the determination of the global amount of methylation from MethylCap-seq data by comparing to a spiked-in control DNA of known methylation status.
We show that rejection of samples based on our quality control parameters leads to a significant improvement of methylation calling. Additional reads beyond ~13 million unique aligned reads improved coverage, modestly improved saturation, and did not impact enrichment score. Lastly, we find that a global methylation indicator calculated from MethylCap-seq data correlates well with the global methylation level of a sample as obtained from a spike-in DNA of known methylation level.
We show that with appropriate quality control MethylCap-seq is a reliable tool, suitable for cohorts of hundreds of patients, that provides reproducible methylation information on a feature by feature basis as well as information about the global level of methylation.
PMCID: PMC3535705  PMID: 23281662
3.  Genome-wide DNA methylation profiling of non-small cell lung carcinomas 
Non-small cell lung carcinoma (NSCLC) is a complex malignancy that owing to its heterogeneity and poor prognosis poses many challenges to diagnosis, prognosis and patient treatment. DNA methylation is an important mechanism of epigenetic regulation involved in normal development and cancer. It is a very stable and specific modification and therefore in principle a very suitable marker for epigenetic phenotyping of tumors. Here we present a genome-wide DNA methylation analysis of NSCLC samples and paired lung tissues, where we combine MethylCap and next generation sequencing (MethylCap-seq) to provide comprehensive DNA methylation maps of the tumor and paired lung samples. The MethylCap-seq data were validated by bisulfite sequencing and methyl-specific polymerase chain reaction of selected regions.
Analysis of the MethylCap-seq data revealed a strong positive correlation between replicate experiments and between paired tumor/lung samples. We identified 57 differentially methylated regions (DMRs) present in all NSCLC tumors analyzed by MethylCap-seq. While hypomethylated DMRs did not correlate to any particular functional category of genes, the hypermethylated DMRs were strongly associated with genes encoding transcriptional regulators. Furthermore, subtelomeric regions and satellite repeats were hypomethylated in the NSCLC samples. We also identified DMRs that were specific to two of the major subtypes of NSCLC, adenocarcinomas and squamous cell carcinomas.
Collectively, we provide a resource containing genome-wide DNA methylation maps of NSCLC and their paired lung tissues, and comprehensive lists of known and novel DMRs and associated genes in NSCLC.
PMCID: PMC3407794  PMID: 22726460
DNA Methylation; Epigenetics; MethylCap; Next generation sequencing; Non-small cell lung Cancer
4.  Global Analysis of DNA Methylation by Methyl-Capture Sequencing Reveals Epigenetic Control of Cisplatin Resistance in Ovarian Cancer Cell 
PLoS ONE  2011;6(12):e29450.
Cisplatin resistance is one of the major reasons leading to the high death rate of ovarian cancer. Methyl-Capture sequencing (MethylCap-seq), which combines precipitation of methylated DNA by recombinant methyl-CpG binding domain of MBD2 protein with NGS, global and unbiased analysis of global DNA methylation patterns. We applied MethylCap-seq to analyze genome-wide DNA methylation profile of cisplatin sensitive ovarian cancer cell line A2780 and its isogenic derivative resistant line A2780CP. We obtained 21,763,035 raw reads for the drug resistant cell line A2780CP and 18,821,061reads for the sensitive cell line A2780. We identified 1224 hyper-methylated and 1216 hypomethylated DMRs (differentially methylated region) in A2780CP compared to A2780. Our MethylCap-seq data on this ovarian cancer cisplatin resistant model provided a good resource for the research community. We also found that A2780CP, compared to A2780, has lower observed to expected methylated CpG ratios, suggesting a lower global CpG methylation in A2780CP cells. Methylation specific PCR and bisulfite sequencing confirmed hypermethylation of PTK6, PRKCE and BCL2L1 in A2780 compared with A2780CP. Furthermore, treatment with the demethylation reagent 5-aza-dC in A2780 cells demethylated the promoters and restored the expression of PTK6, PRKCE and BCL2L1.
PMCID: PMC3245283  PMID: 22216282
5.  SNP-guided identification of monoallelic DNA-methylation events from enrichment-based sequencing data 
Nucleic Acids Research  2014;42(20):e157.
Monoallelic gene expression is typically initiated early in the development of an organism. Dysregulation of monoallelic gene expression has already been linked to several non-Mendelian inherited genetic disorders. In humans, DNA-methylation is deemed to be an important regulator of monoallelic gene expression, but only few examples are known. One important reason is that current, cost-affordable truly genome-wide methods to assess DNA-methylation are based on sequencing post-enrichment. Here, we present a new methodology based on classical population genetic theory, i.e. the Hardy–Weinberg theorem, that combines methylomic data from MethylCap-seq with associated SNP profiles to identify monoallelically methylated loci. Applied on 334 MethylCap-seq samples of very diverse origin, this resulted in the identification of 80 genomic regions featured by monoallelic DNA-methylation. Of these 80 loci, 49 are located in genic regions of which 25 have already been linked to imprinting. Further analysis revealed statistically significant enrichment of these loci in promoter regions, further establishing the relevance and usefulness of the method. Additional validation was done using both 14 whole-genome bisulfite sequencing data sets and 16 mRNA-seq data sets. Importantly, the developed approach can be easily applied to other enrichment-based sequencing technologies, like the ChIP-seq-based identification of monoallelic histone modifications.
PMCID: PMC4227762  PMID: 25237057
6.  Quality Evaluation of Methyl Binding Domain Based Kits for Enrichment DNA-Methylation Sequencing 
PLoS ONE  2013;8(3):e59068.
DNA-methylation is an important epigenetic feature in health and disease. Methylated sequence capturing by Methyl Binding Domain (MBD) based enrichment followed by second-generation sequencing provides the best combination of sensitivity and cost-efficiency for genome-wide DNA-methylation profiling. However, existing implementations are numerous, and quality control and optimization require expensive external validation. Therefore, this study has two aims: 1) to identify a best performing kit for MBD-based enrichment using independent validation data, and 2) to evaluate whether quality evaluation can also be performed solely based on the characteristics of the generated sequences. Five commercially available kits for MBD enrichment were combined with Illumina GAIIx sequencing for three cell lines (HCT15, DU145, PC3). Reduced representation bisulfite sequencing data (all three cell lines) and publicly available Illumina Infinium BeadChip data (DU145 and PC3) were used for benchmarking. Consistent large-scale differences in yield, sensitivity and specificity between the different kits could be identified, with Diagenode's MethylCap kit as overall best performing kit under the tested conditions. This kit could also be identified with the Fragment CpG-plot, which summarizes the CpG content of the captured fragments, implying that the latter can be used as a tool to monitor data quality. In conclusion, there are major quality differences between kits for MBD-based capturing of methylated DNA, with the MethylCap kit performing best under the used settings. The Fragment CpG-plot is able to monitor data quality based on inherent sequence data characteristics, and is therefore a cost-efficient tool for experimental optimization, but also to monitor quality throughout routine applications.
PMCID: PMC3598902  PMID: 23554971
7.  ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data 
BMC Bioinformatics  2010;11:237.
Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) or ChIP followed by genome tiling array analysis (ChIP-chip) have become standard technologies for genome-wide identification of DNA-binding protein target sites. A number of algorithms have been developed in parallel that allow identification of binding sites from ChIP-seq or ChIP-chip datasets and subsequent visualization in the University of California Santa Cruz (UCSC) Genome Browser as custom annotation tracks. However, summarizing these tracks can be a daunting task, particularly if there are a large number of binding sites or the binding sites are distributed widely across the genome.
We have developed ChIPpeakAnno as a Bioconductor package within the statistical programming environment R to facilitate batch annotation of enriched peaks identified from ChIP-seq, ChIP-chip, cap analysis of gene expression (CAGE) or any experiments resulting in a large number of enriched genomic regions. The binding sites annotated with ChIPpeakAnno can be viewed easily as a table, a pie chart or plotted in histogram form, i.e., the distribution of distances to the nearest genes for each set of peaks. In addition, we have implemented functionalities for determining the significance of overlap between replicates or binding sites among transcription factors within a complex, and for drawing Venn diagrams to visualize the extent of the overlap between replicates. Furthermore, the package includes functionalities to retrieve sequences flanking putative binding sites for PCR amplification, cloning, or motif discovery, and to identify Gene Ontology (GO) terms associated with adjacent genes.
ChIPpeakAnno enables batch annotation of the binding sites identified from ChIP-seq, ChIP-chip, CAGE or any technology that results in a large number of enriched genomic regions within the statistical programming environment R. Allowing users to pass their own annotation data such as a different Chromatin immunoprecipitation (ChIP) preparation and a dataset from literature, or existing annotation packages, such as GenomicFeatures and BSgenome, provides flexibility. Tight integration to the biomaRt package enables up-to-date annotation retrieval from the BioMart database.
PMCID: PMC3098059  PMID: 20459804
8.  Genome-wide mapping of DNA methylation: a quantitative technology comparison 
Nature biotechnology  2010;28(10):1106-1114.
DNA methylation is a key component of mammalian gene regulation and the most classical example of an epigenetic mark. DNA methylation patterns are mitotically heritable and stable over time, but they undergo considerable changes in response to cell differentiation, diseases and environmental influences. Several methods have been developed for DNA methylation profiling on a genomic scale. Here, we benchmark four of these methods on two sample pairs, comparing their accuracy and power to detect DNA methylation differences. The results show that all evaluated methods (MeDIP-seq: methylated DNA immunoprecipitation, MethylCap-seq: methylated DNA capture by affinity purification, RRBS: reduced representation bisulfite sequencing, and the Infinium HumanMethylation27 assay) produce accurate DNA methylation data. However, these methods differ in their ability to detect differentially methylated regions between pairs of samples. We highlight strengths and weaknesses of the four methods and give practical recommendations for the design of epigenomic case-control studies.
PMCID: PMC3066564  PMID: 20852634
Epigenome profiling; epigenetics; sequencing; differentially methylated regions; molecular diagnostics; biomarker discovery; cancer
9.  Methylcap-Seq Reveals Novel DNA Methylation Markers for the Diagnosis and Recurrence Prediction of Bladder Cancer in a Chinese Population 
PLoS ONE  2012;7(4):e35175.
There is a need to supplement or supplant the conventional diagnostic tools, namely, cystoscopy and B-type ultrasound, for bladder cancer (BC). We aimed to identify novel DNA methylation markers for BC through genome-wide profiling of BC cell lines and subsequent methylation-specific PCR (MSP) screening of clinical urine samples.
Experimental Design
The methyl-DNA binding domain (MBD) capture technique, methylCap/seq, was performed to screen for specific hypermethylated CpG islands in two BC cell lines (5637 and T24). The top one hundred hypermethylated targets were sequentially screened by MSP in urine samples to gradually narrow the target number and optimize the composition of the diagnostic panel. The diagnostic performance of the obtained panel was evaluated in different clinical scenarios.
A total of 1,627 hypermethylated promoter targets in the BC cell lines was identified by Illumina sequencing. The top 104 hypermethylated targets were reduced to eight genes (VAX1, KCNV1, ECEL1, TMEM26, TAL1, PROX1, SLC6A20, and LMX1A) after the urine DNA screening in a small sample size of 8 normal control and 18 BC subjects. Validation in an independent sample of 212 BC patients enabled the optimization of five methylation targets, including VAX1, KCNV1, TAL1, PPOX1, and CFTR, which was obtained in our previous study, for BC diagnosis with a sensitivity and specificity of 88.68% and 87.25%, respectively. In addition, the methylation of VAX1 and LMX1A was found to be associated with BC recurrence.
We identified a promising diagnostic marker panel for early non-invasive detection and subsequent BC surveillance.
PMCID: PMC3328468  PMID: 22529986
10.  Next-generation sequencing facilitates quantitative analysis of wild-type and Nrl−/− retinal transcriptomes 
Molecular Vision  2011;17:3034-3054.
Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived retinal transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis.
Retinal mRNA profiles of 21-day-old wild-type (WT) and neural retina leucine zipper knockout (Nrl−/−) mice were generated by deep sequencing, in triplicate, using Illumina GAIIx. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays.
Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts in the retinas of WT and Nrl−/− mice with BWA workflow and 34,115 transcripts with TopHat workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8798. Approximately 10% of the transcripts showed differential expression between the WT and Nrl−/− retina, with a fold change ≥1.5 and p value <0.05. Altered expression of 25 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling.
Our study represents the first detailed analysis of retinal transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
PMCID: PMC3233386  PMID: 22162623
11.  SeqAssist: a novel toolkit for preliminary analysis of next-generation sequencing data 
BMC Bioinformatics  2014;15(Suppl 11):S10.
While next-generation sequencing (NGS) technologies are rapidly advancing, an area that lags behind is the development of efficient and user-friendly tools for preliminary analysis of massive NGS data. As an effort to fill this gap to keep up with the fast pace of technological advancement and to accelerate data-to-results turnaround, we developed a novel software package named SeqAssist ("Sequencing Assistant" or SA).
SeqAssist takes NGS-generated FASTQ files as the input, employs the BWA-MEM aligner for sequence alignment, and aims to provide a quick overview and basic statistics of NGS data. It consists of three separate workflows: (1) the SA_RunStats workflow generates basic statistics about an NGS dataset, including numbers of raw, cleaned, redundant and unique reads, redundancy rate, and a list of unique sequences with length and read count; (2) the SA_Run2Ref workflow estimates the breadth, depth and evenness of genome-wide coverage of the NGS dataset at a nucleotide resolution; and (3) the SA_Run2Run workflow compares two NGS datasets to determine the redundancy (overlapping rate) between the two NGS runs. Statistics produced by SeqAssist or derived from SeqAssist output files are designed to inform the user: whether, what percentage, how many times and how evenly a genomic locus (i.e., gene, scaffold, chromosome or genome) is covered by sequencing reads, how redundant the sequencing reads are in a single run or between two runs. These statistics can guide the user in evaluating the quality of a DNA library prepared for RNA-Seq or genome (re-)sequencing and in deciding the number of sequencing runs required for the library. We have tested SeqAssist using a synthetic dataset and demonstrated its main features using multiple NGS datasets generated from genome re-sequencing experiments.
SeqAssist is a useful and informative tool that can serve as a valuable "assistant" to a broad range of investigators who conduct genome re-sequencing, RNA-Seq, or de novo genome sequencing and assembly experiments.
PMCID: PMC4251038  PMID: 25349885
SeqAssist; next generation sequencing (NGS); sequencing data analysis; genome-wide coverage; breadth; depth; evenness; genome (re-)sequencing; RNA-Seq; FASTQ; BWA-MEM.
12.  High-frequency aberrantly methylated targets in pancreatic adenocarcinoma identified via global DNA methylation analysis using methylCap-seq 
Clinical Epigenetics  2014;6(1):18.
Extensive reprogramming and dysregulation of DNA methylation is an important characteristic of pancreatic cancer (PC). Our study aimed to characterize the genomic methylation patterns in various genomic contexts of PC. The methyl capture sequencing (methylCap-seq) method was used to map differently methylated regions (DMRs) in pooled samples from ten PC tissues and ten adjacent non-tumor (PN) tissues. A selection of DMRs was validated in an independent set of PC and PN samples using methylation-specific PCR (MSP), bisulfite sequencing PCR (BSP), and methylation sensitive restriction enzyme-based qPCR (MSRE-qPCR). The mRNA and expressed sequence tag (EST) expression of the corresponding genes was investigated using RT-qPCR.
A total of 1,131 PC-specific and 727 PN-specific hypermethylated DMRs were identified in association with CpG islands (CGIs), including gene-associated CGIs and orphan CGIs; 2,955 PC-specific and 2,386 PN-specific hypermethylated DMRs were associated with gene promoters, including promoters containing or lacking CGIs. Moreover, 1,744 PC-specific and 1,488 PN-specific hypermethylated DMRs were found to be associated with CGIs or CGI shores. These results suggested that aberrant hypermethylation in PC typically occurs in regions surrounding the transcription start site (TSS). The BSP, MSP, MSRE-qPCR, and RT-qPCR data indicated that the aberrant DNA methylation in PC tissue and in PC cell lines was associated with gene (or corresponding EST) expression.
Our study characterized the genome-wide DNA methylation patterns in PC and identified DMRs that were distributed among various genomic contexts that might influence the expression of corresponding genes or transcripts to promote PC. These DMRs might serve as diagnostic biomarkers or therapeutic targets for PC.
PMCID: PMC4177372  PMID: 25276247
CGI shore; DNA methylation; genome-wide; methyl capture sequencing; orphan CGI; pancreatic adenocarcinoma
13.  WASP: Wiki-based Automated Sequence Processor for Epigenomics and Genomics Applications 
The advent of massively parallel sequencing (MPS) technology has lead to the development of assays which facilitate the study of epigenomics and genomics at the genome-wide level. However, the computational burden resulting from the need to store and process the gigbytes of data streaming from sequencing machines, in addition to collecting metadata and returning data to users, is becoming a major issue for both sequencing cores and users alike. We present WASP, a LIMS system designed to automate MPS data pre-processing and analysis. WASP integrates a user-friendly MediaWiki front end, a network file system (NFS) and MySQL database for recording experimental data and metadata, plus a multi-node cluster for data processing. The workflow includes capture of sample submission information to the database using web forms on the wiki, recording of core facility operations on samples and linking of samples to flowcells in the database followed by automatic processing of sequence data and running of data analysis pipelines following the sequence run. WASP currently supports MPS using the Illumina GaIIx. For epigenomics applications we provide a pipeline for our novel HpaII-tiny fragment enrichment by ligation-mediated PCR (HELP)-tag method which enables us to quantify the methylation status of ∼1.8 million CpGs located in 70% of the HpaII sites (CCGG) in the human genome. We also provide ChIP-seq analysis using MACS, which is also applicable for methylated DNA immunoprecipitation (MeDIP) assays, in addition to miRNA and mRNA analyses using custom pipelines. Output from the analysis pipelines is automatically linked to a users wiki-space and the data generated can be immediately viewed as tracks in a local mirror of the UCSC genome browser. WASP also provides capabilities for automated billing and keeping track of facility costs. We believe WASP represents a suitable model on which to develop LIMS systems for supporting MPS applications.
PMCID: PMC2918104
14.  Comparative genome-wide DNA methylation analysis of colorectal tumor and matched normal tissues 
Epigenetics  2012;7(12):1355-1367.
Aberrant DNA methylation often occurs in colorectal cancer (CRC). In our study we applied a genome-wide DNA methylation analysis approach, MethylCap-seq, to map the differentially methylated regions (DMRs) in 24 tumors and matched normal colon samples. In total, 2687 frequently hypermethylated and 468 frequently hypomethylated regions were identified, which include potential biomarkers for CRC diagnosis. Hypermethylation in the tumor samples was enriched at CpG islands and gene promoters, while hypomethylation was distributed throughout the genome. Using epigenetic data from human embryonic stem cells, we show that frequently hypermethylated regions coincide with bivalent loci in human embryonic stem cells. DNA methylation is commonly thought to lead to gene silencing; however, integration of publically available gene expression data indicates that 75% of the frequently hypermethylated genes were most likely already lowly or not expressed in normal tissue. Collectively, our study provides genome-wide DNA methylation maps of CRC, comprehensive lists of DMRs, and gives insights into the role of aberrant DNA methylation in CRC formation.
PMCID: PMC3528691  PMID: 23079744
DNA methylation; colorectal cancer; biomarkers; H3K27me3; gene expression; Illumina sequencing
15.  MetMap Enables Genome-Scale Methyltyping for Determining Methylation States in Populations 
PLoS Computational Biology  2010;6(8):e1000888.
The ability to assay genome-scale methylation patterns using high-throughput sequencing makes it possible to carry out association studies to determine the relationship between epigenetic variation and phenotype. While bisulfite sequencing can determine a methylome at high resolution, cost inhibits its use in comparative and population studies. MethylSeq, based on sequencing of fragment ends produced by a methylation-sensitive restriction enzyme, is a method for methyltyping (survey of methylation states) and is a site-specific and cost-effective alternative to whole-genome bisulfite sequencing. Despite its advantages, the use of MethylSeq has been restricted by biases in MethylSeq data that complicate the determination of methyltypes. Here we introduce a statistical method, MetMap, that produces corrected site-specific methylation states from MethylSeq experiments and annotates unmethylated islands across the genome. MetMap integrates genome sequence information with experimental data, in a statistically sound and cohesive Bayesian Network. It infers the extent of methylation at individual CGs and across regions, and serves as a framework for comparative methylation analysis within and among species. We validated MetMap's inferences with direct bisulfite sequencing, showing that the methylation status of sites and islands is accurately inferred. We used MetMap to analyze MethylSeq data from four human neutrophil samples, identifying novel, highly unmethylated islands that are invisible to sequence-based annotation strategies. The combination of MethylSeq and MetMap is a powerful and cost-effective tool for determining genome-scale methyltypes suitable for comparative and association studies.
Author Summary
In the vertebrates, methylation of cytosine residues in DNA regulates gene activity in concert with proteins that associate with DNA. Large-scale genomewide comparative studies that seek to link specific methylation patterns to disease will require hundreds or thousands of samples, and thus economical methods that assay genomewide methylation. One such method is MethylSeq, which samples cytosine methylation at site-specific resolution by high-throughput sequencing of the ends of DNA fragments generated by methylation-sensitive restriction enzymes. MethylSeq's low cost and simplicity of implementation enable its use in large-scale comparative studies, but biases inherent to the method inhibit interpretation of the data it produces. Here we present MetMap, a statistical framework that first accounts for the biases in MethylSeq data and then generates an analysis of the data that is suitable for use in comparative studies. We show that MethylSeq and MetMap can be used together to determine methylation profiles across the genome, and to identify novel unmethylated regions that are likely to be involved in gene regulation. The ability to conduct comparative studies of sufficient scale at a reasonable cost promises to reveal new insights into the relationship between cytosine methylation and phenotype.
PMCID: PMC2924245  PMID: 20856582
16.  Long Non-Coding RNA and Alternative Splicing Modulations in Parkinson's Leukocytes Identified by RNA Sequencing 
PLoS Computational Biology  2014;10(3):e1003517.
The continuously prolonged human lifespan is accompanied by increase in neurodegenerative diseases incidence, calling for the development of inexpensive blood-based diagnostics. Analyzing blood cell transcripts by RNA-Seq is a robust means to identify novel biomarkers that rapidly becomes a commonplace. However, there is lack of tools to discover novel exons, junctions and splicing events and to precisely and sensitively assess differential splicing through RNA-Seq data analysis and across RNA-Seq platforms. Here, we present a new and comprehensive computational workflow for whole-transcriptome RNA-Seq analysis, using an updated version of the software AltAnalyze, to identify both known and novel high-confidence alternative splicing events, and to integrate them with both protein-domains and microRNA binding annotations. We applied the novel workflow on RNA-Seq data from Parkinson's disease (PD) patients' leukocytes pre- and post- Deep Brain Stimulation (DBS) treatment and compared to healthy controls. Disease-mediated changes included decreased usage of alternative promoters and N-termini, 5′-end variations and mutually-exclusive exons. The PD regulated FUS and HNRNP A/B included prion-like domains regulated regions. We also present here a workflow to identify and analyze long non-coding RNAs (lncRNAs) via RNA-Seq data. We identified reduced lncRNA expression and selective PD-induced changes in 13 of over 6,000 detected leukocyte lncRNAs, four of which were inversely altered post-DBS. These included the U1 spliceosomal lncRNA and RP11-462G22.1, each entailing sequence complementarity to numerous microRNAs. Analysis of RNA-Seq from PD and unaffected controls brains revealed over 7,000 brain-expressed lncRNAs, of which 3,495 were co-expressed in the leukocytes including U1, which showed both leukocyte and brain increases. Furthermore, qRT-PCR validations confirmed these co-increases in PD leukocytes and two brain regions, the amygdala and substantia-nigra, compared to controls. This novel workflow allows deep multi-level inspection of RNA-Seq datasets and provides a comprehensive new resource for understanding disease transcriptome modifications in PD and other neurodegenerative diseases.
Author Summary
Long non-coding RNAs (lncRNAs) comprise a novel, fascinating class of RNAs with largely unknown biological functions. Parkinson's-disease (PD) is the most frequent motor disorder, and Deep-brain-stimulation (DBS) treatment alleviates the symptoms, but early disease biomarkers are still unknown and new future genetic interference targets are urgently needed. Using RNA-sequencing technology and a novel computational workflow for in-depth exploration of whole-transcriptome RNA-seq datasets, we detected and analyzed lncRNAs in sequenced libraries from PD patients' leukocytes pre and post-treatment and the brain, adding this full profile resource of over 7,000 lncRNAs to the few human tissues-derived lncRNA datasets that are currently available. Our study includes sample-specific database construction, detecting disease-derived changes in known and novel lncRNAs, exons and junctions and predicting corresponding changes in Polyadenylation choices, protein domains and miRNA binding sites. We report widespread transcript structure variations at the splice junction and exons levels, including novel exons and junctions and alteration of lncRNAs followed by experimental validation in PD leukocytes and two PD brain regions compared with controls. Our results suggest lncRNAs involvement in neurodegenerative diseases, and specifically PD. This comprehensive workflow will be of use to the increasing number of laboratories producing RNA-Seq data in a wide range of biomedical studies.
PMCID: PMC3961179  PMID: 24651478
17.  Integrated analysis of genome-wide DNA methylation and gene expression profiles in molecular subtypes of breast cancer 
Nucleic Acids Research  2013;41(18):8464-8474.
Aberrant DNA methylation of CpG islands, CpG island shores and first exons is known to play a key role in the altered gene expression patterns in all human cancers. To date, a systematic study on the effect of DNA methylation on gene expression using high resolution data has not been reported. In this study, we conducted an integrated analysis of MethylCap-sequencing data and Affymetrix gene expression microarray data for 30 breast cancer cell lines representing different breast tumor phenotypes. As well-developed methods for the integrated analysis do not currently exist, we created a series of four different analysis methods. On the computational side, our goal is to develop methylome data analysis protocols for the integrated analysis of DNA methylation and gene expression data on the genome scale. On the cancer biology side, we present comprehensive genome-wide methylome analysis results for differentially methylated regions and their potential effect on gene expression in 30 breast cancer cell lines representing three molecular phenotypes, luminal, basal A and basal B. Our integrated analysis demonstrates that methylation status of different genomic regions may play a key role in establishing transcriptional patterns in molecular subtypes of human breast cancer.
PMCID: PMC3794600  PMID: 23887935
18.  MethVisual - visualization and exploratory statistical analysis of DNA methylation profiles from bisulfite sequencing 
BMC Research Notes  2010;3:337.
Exploration of DNA methylation and its impact on various regulatory mechanisms has become a very active field of research. Simultaneously there is an arising need for tools to process and analyse the data together with statistical investigation and visualisation.
MethVisual is a new application that enables exploratory analysis and intuitive visualization of DNA methylation data as is typically generated by bisulfite sequencing. The package allows the import of DNA methylation sequences, aligns them and performs quality control comparison. It comprises basic analysis steps as lollipop visualization, co-occurrence display of methylation of neighbouring and distant CpG sites, summary statistics on methylation status, clustering and correspondence analysis. The package has been developed for methylation data but can be also used for other data types for which binary coding can be inferred. The application of the package, as well as a comparison to existing DNA methylation analysis tools and its workflow based on two datasets is presented in this paper.
The R package MethVisual offers various analysis procedures for data that can be binarized, in particular for bisulfite sequenced methylation data. R/Bioconductor has become one of the most important environments for statistical analysis of various types of biological and medical data. Therefore, any data analysis within R that allows the integration of various data types as provided from different technological platforms is convenient. It is the first and so far the only specific package for DNA methylation analysis, in particular for bisulfite sequenced data available in R/Bioconductor enviroment. The package is available for free at and from the Bioconductor Consortium
PMCID: PMC3012040  PMID: 21159174
19.  MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing 
BMC Bioinformatics  2014;15:224.
Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome.
For optimization of tools and parameters, MAP-RSeq was validated using both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients.
Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from
PMCID: PMC4228501  PMID: 24972667
Transcriptomic sequencing; RNA-Seq; Bioinformatics workflow; Gene expression; Exon counts; Fusion transcripts; Expressed single nucleotide variants; RNA-Seq reports
20.  A Genome-Wide Screen for Promoter Methylation in Lung Cancer Identifies Novel Methylation Markers for Multiple Malignancies  
PLoS Medicine  2006;3(12):e486.
Promoter hypermethylation coupled with loss of heterozygosity at the same locus results in loss of gene function in many tumor cells. The “rules” governing which genes are methylated during the pathogenesis of individual cancers, how specific methylation profiles are initially established, or what determines tumor type-specific methylation are unknown. However, DNA methylation markers that are highly specific and sensitive for common tumors would be useful for the early detection of cancer, and those required for the malignant phenotype would identify pathways important as therapeutic targets.
Methods and Findings
In an effort to identify new cancer-specific methylation markers, we employed a high-throughput global expression profiling approach in lung cancer cells. We identified 132 genes that have 5′ CpG islands, are induced from undetectable levels by 5-aza-2′-deoxycytidine in multiple non-small cell lung cancer cell lines, and are expressed in immortalized human bronchial epithelial cells. As expected, these genes were also expressed in normal lung, but often not in companion primary lung cancers. Methylation analysis of a subset (45/132) of these promoter regions in primary lung cancer (n = 20) and adjacent nonmalignant tissue (n = 20) showed that 31 genes had acquired methylation in the tumors, but did not show methylation in normal lung or peripheral blood cells. We studied the eight most frequently and specifically methylated genes from our lung cancer dataset in breast cancer (n = 37), colon cancer (n = 24), and prostate cancer (n = 24) along with counterpart nonmalignant tissues. We found that seven loci were frequently methylated in both breast and lung cancers, with four showing extensive methylation in all four epithelial tumors.
By using a systematic biological screen we identified multiple genes that are methylated with high penetrance in primary lung, breast, colon, and prostate cancers. The cross-tumor methylation pattern we observed for these novel markers suggests that we have identified a partial promoter hypermethylation signature for these common malignancies. These data suggest that while tumors in different tissues vary substantially with respect to gene expression, there may be commonalities in their promoter methylation profiles that represent targets for early detection screening or therapeutic intervention.
John Minna and colleagues report that a group of genes are commonly methylated in primary lung, breast, colon, and prostate cancer.
Editors' Summary
Tumors or cancers contain cells that have lost many of the control mechanisms that normally regulate their behavior. Unlike normal cells, which only divide to repair damaged tissues, cancer cells divide uncontrollably. They also gain the ability to move round the body and start metastases in secondary locations. These changes in behavior result from alterations in their genetic material. For example, mutations (permanent changes in the sequence of nucleotides in the cell's DNA) in genes known as oncogenes stimulate cells to divide constantly. Mutations in another group of genes—tumor suppressor genes—disable their ability to restrain cell growth. Key tumor suppressor genes are often completely lost in cancer cells. But not all the genetic changes in cancer cells are mutations. Some are “epigenetic” changes—chemical modifications of genes that affect the amount of protein made from them. In cancer cells, methyl groups are often added to CG-rich regions—this is called hypermethylation. These “CpG islands” lie near gene promoters—sequences that control the transcription of DNA into RNA, the template for protein production—and their methylation switches off the promoter. Methylation of the promoter of one copy of a tumor suppressor gene, which often coincides with the loss of the other copy of the gene, is thought to be involved in cancer development.
Why Was This Study Done?
The rules that govern which genes are hypermethylated during the development of different cancer types are not known, but it would be useful to identify any DNA methylation events that occur regularly in common cancers for two reasons. First, specific DNA methylation markers might be useful for the early detection of cancer. Second, identifying these epigenetic changes might reveal cellular pathways that are changed during cancer development and so identify new therapeutic targets. In this study, the researchers have used a systematic biological screen to identify genes that are methylated in many lung, breast, colon, and prostate cancers—all cancers that form in “epithelial” tissues.
What Did the Researchers Do and Find?
The researchers used microarray expression profiling to examine gene expression patterns in several lung cancer and normal lung cell lines. In this technique, labeled RNA molecules isolated from cells are applied to a “chip” carrying an array of gene fragments. Here, they stick to the fragment that represents the gene from which they were made, which allows the genes that the cells express to be catalogued. By comparing the expression profiles of lung cancer cells and normal lung cells before and after treatment with a chemical that inhibits DNA methylation, the researchers identified genes that were methylated in the cancer cells—that is, genes that were expressed in normal cells but not in cancer cells unless methylation was inhibited. 132 of these genes contained CpG islands. The researchers examined the promoters of 45 of these genes in lung cancer cells taken straight from patients and found that 31 of the promoters were methylated in tumor tissues but not in adjacent normal tissues. Finally, the researchers looked at promoter methylation of the eight genes most frequently and specifically methylated in the lung cancer samples in breast, colon, and prostate cancers. Seven of the genes were frequently methylated in both lung and breast cancers; four were extensively methylated in all the tumor types.
What Do These Findings Mean?
These results identify several new genes that are often methylated in four types of epithelial tumor. The observation that these genes are methylated in multiple independent tumors strongly suggests, but does not prove, that loss of expression of the proteins that they encode helps to convert normal cells into cancer cells. The frequency and diverse patterning of promoter methylation in different tumor types also indicates that methylation is not a random event, although what controls the patterns of methylation is not yet known. The identification of these genes is a step toward building a promoter hypermethylation profile for the early detection of human cancer. Furthermore, although tumors in different tissues vary greatly with respect to gene expression patterns, the similarities seen in this study in promoter methylation profiles might help to identify new therapeutic targets common to several cancer types.
Additional Information.
Please access these Web sites via the online version of this summary at
US National Cancer Institute, information for patients on understanding cancer
CancerQuest, information provided by Emory University about how cancer develops
Cancer Research UK, information for patients on cancer biology
Wikipedia pages on epigenetics (note that Wikipedia is a free online encyclopedia that anyone can edit)
The Epigenome Network of Excellence, background information and latest news about epigenetics
PMCID: PMC1716188  PMID: 17194187
21.  Comprehensive methylome analysis of ovarian tumors reveals hedgehog signaling pathway regulators as prognostic DNA methylation biomarkers 
Epigenetics  2013;8(6):624-634.
Women with advanced stage ovarian cancer (OC) have a five-year survival rate of less than 25%. OC progression is associated with accumulation of epigenetic alterations and aberrant DNA methylation in gene promoters acts as an inactivating ?hit? during OC initiation and progression. Abnormal DNA methylation in OC has been used to predict disease outcome and therapy response. To globally examine DNA methylation in OC, we used next-generation sequencing technology, MethylCap-sequencing, to screen 75 malignant and 26 normal or benign ovarian tissues. Differential DNA methylation regions (DMRs) were identified, and the Kaplan?Meier method and Cox proportional hazard model were used to correlate methylation with clinical endpoints. Functional role of specific genes identified by MethylCap-sequencing was examined in in vitro assays. We identified 577 DMRs that distinguished (p < 0.001) malignant from non-malignant ovarian tissues; of these, 63 DMRs correlated (p < 0.001) with poor progression free survival (PFS). Concordant hypermethylation and corresponding gene silencing of sonic hedgehog pathway members ZIC1 and ZIC4 in OC tumors was confirmed in a panel of OC cell lines, and ZIC1 and ZIC4 repression correlated with increased proliferation, migration and invasion. ZIC1 promoter hypermethylation correlated (p < 0.01) with poor PFS. In summary, we identified functional DNA methylation biomarkers significantly associated with clinical outcome in OC and suggest our comprehensive methylome analysis has significant translational potential for guiding the design of future clinical investigations targeting the OC epigenome. Methylation of ZIC1, a putative tumor suppressor, may be a novel determinant of OC outcome.
PMCID: PMC3857342  PMID: 23774800
DNA methylation; Hedgehog pathway; ZIC1; ZIC4; ovarian cancer
22.  CMS: A Web-Based System for Visualization and Analysis of Genome-Wide Methylation Data of Human Cancers 
PLoS ONE  2013;8(4):e60980.
DNA methylation of promoter CpG islands is associated with gene suppression, and its unique genome-wide profiles have been linked to tumor progression. Coupled with high-throughput sequencing technologies, it can now efficiently determine genome-wide methylation profiles in cancer cells. Also, experimental and computational technologies make it possible to find the functional relationship between cancer-specific methylation patterns and their clinicopathological parameters.
Methodology/Principal Findings
Cancer methylome system (CMS) is a web-based database application designed for the visualization, comparison and statistical analysis of human cancer-specific DNA methylation. Methylation intensities were obtained from MBDCap-sequencing, pre-processed and stored in the database. 191 patient samples (169 tumor and 22 normal specimen) and 41 breast cancer cell-lines are deposited in the database, comprising about 6.6 billion uniquely mapped sequence reads. This provides comprehensive and genome-wide epigenetic portraits of human breast cancer and endometrial cancer to date. Two views are proposed for users to better understand methylation structure at the genomic level or systemic methylation alteration at the gene level. In addition, a variety of annotation tracks are provided to cover genomic information. CMS includes important analytic functions for interpretation of methylation data, such as the detection of differentially methylated regions, statistical calculation of global methylation intensities, multiple gene sets of biologically significant categories, interactivity with UCSC via custom-track data. We also present examples of discoveries utilizing the framework.
CMS provides visualization and analytic functions for cancer methylome datasets. A comprehensive collection of datasets, a variety of embedded analytic functions and extensive applications with biological and translational significance make this system powerful and unique in cancer methylation research. CMS is freely accessible at:
PMCID: PMC3632540  PMID: 23630576
23.  dPeak: High Resolution Identification of Transcription Factor Binding Sites from PET and SET ChIP-Seq Data 
PLoS Computational Biology  2013;9(10):e1003246.
Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) has been successfully used for genome-wide profiling of transcription factor binding sites, histone modifications, and nucleosome occupancy in many model organisms and humans. Because the compact genomes of prokaryotes harbor many binding sites separated by only few base pairs, applications of ChIP-Seq in this domain have not reached their full potential. Applications in prokaryotic genomes are further hampered by the fact that well studied data analysis methods for ChIP-Seq do not result in a resolution required for deciphering the locations of nearby binding events. We generated single-end tag (SET) and paired-end tag (PET) ChIP-Seq data for factor in Escherichia coli (E. coli). Direct comparison of these datasets revealed that although PET assay enables higher resolution identification of binding events, standard ChIP-Seq analysis methods are not equipped to utilize PET-specific features of the data. To address this problem, we developed dPeak as a high resolution binding site identification (deconvolution) algorithm. dPeak implements a probabilistic model that accurately describes ChIP-Seq data generation process for both the SET and PET assays. For SET data, dPeak outperforms or performs comparably to the state-of-the-art high-resolution ChIP-Seq peak deconvolution algorithms such as PICS, GPS, and GEM. When coupled with PET data, dPeak significantly outperforms SET-based analysis with any of the current state-of-the-art methods. Experimental validations of a subset of dPeak predictions from PET ChIP-Seq data indicate that dPeak can estimate locations of binding events with as high as to resolution. Applications of dPeak to ChIP-Seq data in E. coli under aerobic and anaerobic conditions reveal closely located promoters that are differentially occupied and further illustrate the importance of high resolution analysis of ChIP-Seq data.
Author Summary
Chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq) is widely used for studying in vivo protein-DNA interactions genome-wide. Current state-of-the-art ChIP-Seq protocols utilize single-end tag (SET) assay which only sequences ends of DNA fragments in the library. Although paired-end tag (PET) sequencing is routinely used in other applications of next generation sequencing, it has not been much adapted to ChIP-Seq. We illustrate both experimentally and computationally that PET sequencing significantly improves the resolution of ChIP-Seq experiments and enables ChIP-Seq applications in compact genomes like Escherichia coli (E. coli). To enable efficient identification using PET ChIP-Seq data, we develop dPeak as a high resolution binding site identification algorithm. dPeak implements probabilistic models for both SET and PET data and facilitates efficient analysis of both data types. Applications of dPeak to deeply sequenced E. coli PET and SET ChIP-Seq data establish significantly better resolution of PET compared to SET sequencing.
PMCID: PMC3798280  PMID: 24146601
24.  Statistical Quantification of Methylation Levels by Next-Generation Sequencing 
PLoS ONE  2011;6(6):e21034.
Recently, next-generation sequencing-based technologies have enabled DNA methylation profiling at high resolution and low cost. Methyl-Seq and Reduced Representation Bisulfite Sequencing (RRBS) are two such technologies that interrogate methylation levels at CpG sites throughout the entire human genome. With rapid reduction of sequencing costs, these technologies will enable epigenotyping of large cohorts for phenotypic association studies. Existing quantification methods for sequencing-based methylation profiling are simplistic and do not deal with the noise due to the random sampling nature of sequencing and various experimental artifacts. Therefore, there is a need to investigate the statistical issues related to the quantification of methylation levels for these emerging technologies, with the goal of developing an accurate quantification method.
In this paper, we propose two methods for Methyl-Seq quantification. The first method, the Maximum Likelihood estimate, is both conceptually intuitive and computationally simple. However, this estimate is biased at extreme methylation levels and does not provide variance estimation. The second method, based on Bayesian hierarchical model, allows variance estimation of methylation levels, and provides a flexible framework to adjust technical bias in the sequencing process.
We compare the previously proposed binary method, the Maximum Likelihood (ML) method, and the Bayesian method. In both simulation and real data analysis of Methyl-Seq data, the Bayesian method offers the most accurate quantification. The ML method is slightly less accurate than the Bayesian method. But both our proposed methods outperform the original binary method in Methyl-Seq. In addition, we applied these quantification methods to simulation data and show that, with sequencing depth above 40–300 (which varies with different tissue samples) per cleavage site, Methyl-Seq offers a comparable quantification consistency as microarrays.
PMCID: PMC3115964  PMID: 21698242
25.  An automated proteomic data analysis workflow for mass spectrometry 
BMC Bioinformatics  2009;10(Suppl 11):S17.
Mass spectrometry-based protein identification methods are fundamental to proteomics. Biological experiments are usually performed in replicates and proteomic analyses generate huge datasets which need to be integrated and quantitatively analyzed. The Sequest™ search algorithm is a commonly used algorithm for identifying peptides and proteins from two dimensional liquid chromatography electrospray ionization tandem mass spectrometry (2-D LC ESI MS2) data. A number of proteomic pipelines that facilitate high throughput 'post data acquisition analysis' are described in the literature. However, these pipelines need to be updated to accommodate the rapidly evolving data analysis methods. Here, we describe a proteomic data analysis pipeline that specifically addresses two main issues pertinent to protein identification and differential expression analysis: 1) estimation of the probability of peptide and protein identifications and 2) non-parametric statistics for protein differential expression analysis. Our proteomic analysis workflow analyzes replicate datasets from a single experimental paradigm to generate a list of identified proteins with their probabilities and significant changes in protein expression using parametric and non-parametric statistics.
The input for our workflow is Bioworks™ 3.2 Sequest (or a later version, including cluster) output in XML format. We use a decoy database approach to assign probability to peptide identifications. The user has the option to select "quality thresholds" on peptide identifications based on the P value. We also estimate probability for protein identification. Proteins identified with peptides at a user-specified threshold value from biological experiments are grouped as either control or treatment for further analysis in ProtQuant. ProtQuant utilizes a parametric (ANOVA) method, for calculating differences in protein expression based on the quantitative measure ΣXcorr. Alternatively ProtQuant output can be further processed using non-parametric Monte-Carlo resampling statistics to calculate P values for differential expression. Correction for multiple testing of ANOVA and resampling P values is done using Benjamini and Hochberg's method. The results of these statistical analyses are then combined into a single output file containing a comprehensive protein list with probabilities and differential expression analysis, associated P values, and resampling statistics.
For biologists carrying out proteomics by mass spectrometry, our workflow facilitates automated, easy to use analyses of Bioworks (3.2 or later versions) data. All the methods used in the workflow are peer-reviewed and as such the results of our workflow are compliant with proteomic data submission guidelines to public proteomic data repositories including PRIDE. Our workflow is a necessary intermediate step that is required to link proteomics data to biological knowledge for generating testable hypotheses.
PMCID: PMC3226188  PMID: 19811682

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