Sessile serrated adenomas/polyps (SSA/Ps) are the putative precursors of the ~20% of colon cancers with the CpG island methylator phenotype (CIMP). To investigate the epigenetic phenotype of these precancers, we prospectively collected fresh-tissue samples of 17 SSA/Ps and 15 conventional adenomas (cADNs), each with a matched sample of normal mucosa. Their DNA was subjected to bisulfite next-generation sequencing to assess methylation levels at ~2.7 million CpGs located predominantly in gene regulatory regions and spanning 80.5Mb; RNA was sequenced to define the samples’ transcriptomes. Compared with normal mucosa, SSA/Ps and cADNs exhibited markedly remodeled methylomes. In cADNs, hypomethylated regions were far more numerous (18,417 vs 4288 in SSA/Ps) and rarely affected CpG islands/shores. SSA/Ps seemed to have escaped this wave of demethylation. Cytosine hypermethylation in SSA/Ps was more pervasive (hypermethylated regions: 22,147 vs 15,965 in cADNs; hypermethylated genes: 4938 vs 3443 in cADNs) and more extensive (region for region), and it occurred mainly within CpG islands and shores. Given its resemblance to the CIMP typical of SSA/Ps' putative descendant colon cancers, we refer to the SSA/P methylation phenotype as proto-CIMP. Verification studies of six hypermethylated regions in an independent series of precancers demonstrated DNA methylation markers’ high potential for predicting the diagnosis of SSA/Ps and cADNs. Surprisingly, proto-CIMP in SSA/Ps was associated with upregulated gene expression; downregulation was more common in cADNs. In conclusion, the epigenetic landscape of SSA/Ps differs markedly from that of cADNs. These differences are a potentially rich source of novel tissue-based and noninvasive biomarkers.
The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in a myriad of specialized software for its analysis. Each software module typically targets a specific step within the analysis pipeline, making it necessary to join several of them to get a single cohesive workflow. Multiple software programs automating this procedure have been proposed, but often lack modularity, transparency or flexibility. We present ARMOR, which performs an end-to-end RNA-seq data analysis, from raw read files, via quality checks, alignment and quantification, to differential expression testing, geneset analysis and browser-based exploration of the data. ARMOR is implemented using the Snakemake workflow management system and leverages conda environments; Bioconductor objects are generated to facilitate downstream analysis, ensuring seamless integration with many R packages. The workflow is easily implemented by cloning the GitHub repository, replacing the supplied input and reference files and editing a configuration file. Although we have selected the tools currently included in ARMOR, the setup is modular and alternative tools can be easily integrated.
7 1 These authors contributed equally 8 2 The order of the shared first authors was determined randomly, using the sample() function in R v3.5.2, 9with the random seed 1552397284.Abstract 15 The extensive generation of RNA sequencing (RNA-seq) data in the last decade has resulted in 16 a myriad of specialized software for its analysis. Each software module typically targets a specific 17 step within the analysis pipeline, making it necessary to join several of them to get a single cohesive 18 workflow. Multiple software programs automating this procedure have been proposed, but often lack 19 modularity, transparency or flexibility. We present ARMOR, which performs an end-to-end RNA-seq 20 data analysis, from raw read files, via quality checks, alignment and quantification, to differential ex-21 pression testing, geneset analysis and browser-based exploration of the data. ARMOR is implemented 22 using the Snakemake workflow management system and leverages conda environments; Bioconduc-23 tor objects are generated to facilitate downstream analysis, ensuring seamless integration with many 24 R packages. The workflow is easily implemented by cloning the GitHub repository, replacing the sup-25 plied input and reference files and editing a configuration file. Although we have selected the tools 26 currently included in ARMOR, the setup is modular and alternative tools can be easily integrated. 27 1 Introduction 28 Since the first high-throughput RNA-seq experiments about a decade ago, there has been a tremendous 29 development in the understanding of the characteristic features of the collected data, as well as the meth-30 ods used for the analysis. Today there are vetted, well-established algorithms and tools available for 31 many aspects of RNA-seq data analysis (Conesa et al. 2016; Van Den Berge et al. 2018). In this study, 32 we capitalize on this knowledge and present a modular, light-weight RNA-seq workflow covering the 33 most common parts of a typical end-to-end RNA-seq data analysis. The application is implemented 34 using the Snakemake workflow management system (Köster and Rahmann 2012), and allows the user 35 to easily perform quality assessment, adapter trimming, genome alignment, transcript and gene abun-36 dance quantification, differential expression analysis and geneset analyses with a simple command, after 37 specifying the required reference files and information about the experimental design in a configuration 38 file. Reproducibility is ensured via the use of conda environments, and all relevant log files are retained 39 for transparency. The output is provided in state-of-the-art R/Bioconductor classes, ensuring interoper-40 ability with a broad range of Bioconductor packages. In particular, we provide a template to facilitate 41 browser-based interactive visualization of the quantified abundances and the results of the statistical 42 analyses with iSEE (Rue-Albrecht et al. 2018).43 Among already existing pipelines for automated reference-based RNA-seq analysis, several focus 44 either on the p...
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