ORCID Profile
0000-0002-1508-461X
Current Organisation
University of Zurich
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Publisher: Cold Spring Harbor Laboratory
Date: 25-05-2023
DOI: 10.1101/2023.05.24.542159
Abstract: We previously identified 16,772 colorectal cancer-associated hypermethylated DNA regions that were also detectable in precancerous colorectal lesions (preCRCs) and unrelated to normal mucosal aging. We have now conducted a study to validate 990 of these differently methylated DNA regions in a new series of preCRCs. We used targeted bisulfite sequencing to validate these 990 potential biomarkers in 59 preCRC tissue s les (41 conventional adenomas, 18 sessile serrated lesions), each with a patient-matched normal mucosal s le. Differential DNA methylation tests for each CpG dinucleotide were conducted, with results aggregated at region level, to choose panels of candidate biomarkers that were (cross-)validated with respect to their stratifying potential between preCRCs and normal mucosas as well as on an independent cohort.. Strong differences in methylation level were observed across the full set of 990 investigated DMRs. Among the 100 randomly selected panels of 30 DMRs analyzed with our bioinformatic approach, the best performing panel correctly classified 58/59 tumors (area under the receiver operating curve: 0.998). These validated DNA hypermethylation markers can be exploited to develop more accurate noninvasive colorectal tumor screening assays.
Publisher: Springer Science and Business Media LLC
Date: 06-2020
DOI: 10.1186/S13072-020-00346-8
Abstract: DNA methylation is a highly studied epigenetic signature that is associated with regulation of gene expression, whereby genes with high levels of promoter methylation are generally repressed. Genomic imprinting occurs when one of the parental alleles is methylated, i.e., when there is inherited allele-specific methylation (ASM). A special case of imprinting occurs during X chromosome inactivation in females, where one of the two X chromosomes is silenced, to achieve dosage compensation between the sexes. Another more widespread form of ASM is sequence dependent (SD-ASM), where ASM is linked to a nearby heterozygous single nucleotide polymorphism (SNP). We developed a method to screen for genomic regions that exhibit loss or gain of ASM in s les from two conditions (treatments, diseases, etc.). The method relies on the availability of bisulfite sequencing data from multiple s les of the two conditions. We leverage other established computational methods to screen for these regions within a new R package called DAMEfinder. It calculates an ASM score for all CpG sites or pairs in the genome of each s le, and then quantifies the change in ASM between conditions. It then clusters nearby CpG sites with consistent change into regions. In the absence of SNP information, our method relies only on reads to quantify ASM. This novel ASM score compares favorably to current methods that also screen for ASM. Not only does it easily discern between imprinted and non-imprinted regions, but also females from males based on X chromosome inactivation. We also applied DAMEfinder to a colorectal cancer dataset and observed that colorectal cancer subtypes are distinguishable according to their ASM signature. We also re-discover known cases of loss of imprinting. We have designed DAMEfinder to detect regions of differential ASM (DAMEs), which is a more refined definition of differential methylation, and can therefore help in breaking down the complexity of DNA methylation and its influence in development and disease.
Publisher: Cold Spring Harbor Laboratory
Date: 10-10-2019
DOI: 10.1101/800383
Abstract: DNA methylation is a highly studied epigenetic signature that is associated with regulation of gene expression, whereby genes with high levels of promoter methylation are generally repressed. Genomic imprinting occurs when one of the parental alleles is methylated, i.e, when there is inherited allele-specific methylation (ASM). A special case of imprinting occurs during X chromosome inactivation in females, where one of the two X chromosomes is silenced, in order to achieve dosage compensation between the sexes. Another more widespread form of ASM is sequence dependent (SD-ASM), where ASM is linked to a nearby heterozygous single nucleotide polymorphism (SNP). We developed a method to screen for genomic regions that exhibit loss or gain of ASM in s les from two conditions (treatments, diseases, etc.). The method relies on the availability of bisulfite sequencing data from multiple s les of the two conditions. We leverage other established computational methods to screen for these regions within a new R package called DAMEfinder. It calculates an ASM score for all CpG sites or pairs in the genome of each s le, and then quantifies the change in ASM between conditions. It then clusters nearby CpG sites with consistent change into regions. In the absence of SNP information, our method relies only on reads to quantify ASM. This novel ASM score compares favourably to current methods that also screen for ASM. Not only does it easily discern between imprinted and non-imprinted regions, but also females from males based on X chromosome inactivation. We also applied DAMEfinder to a colorectal cancer dataset and observed that colorectal cancer subtypes are distinguishable according to their ASM signature. We also re-discover known cases of loss of imprinting. We have designed DAMEfinder to detect regions of differential ASM (DAMEs), which is a more refined definition of differential methylation, and can therefore help in breaking down the complexity of DNA methylation and its influence in development and disease.
Publisher: Oxford University Press (OUP)
Date: 07-2019
Abstract: 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.
Publisher: Springer Science and Business Media LLC
Date: 06-04-2020
DOI: 10.1186/S12885-020-06777-6
Abstract: Identifying molecular differences between primary and metastatic colorectal cancers—now possible with the aid of omics technologies—can improve our understanding of the biological mechanisms of cancer progression and facilitate the discovery of novel treatments for late-stage cancer. We compared the DNA methylomes of primary colorectal cancers (CRCs) and CRC metastases to the liver. Laser microdissection was used to obtain epithelial tissue (10 to 25 × 10 6 μm 2 ) from sections of fresh-frozen s les of primary CRCs ( n = 6), CRC liver metastases ( n = 12), and normal colon mucosa ( n = 3). DNA extracted from tissues was enriched for methylated sequences with a methylCpG binding domain (MBD) polypeptide-based protocol and subjected to deep sequencing. The performance of this protocol was compared with that of targeted enrichment for bisulfite sequencing used in a previous study of ours. MBD enrichment captured a total of 322,551 genomic regions (249.5 Mb or ~ 7.8% of the human genome), which included over seven million CpG sites. A few of these regions were differentially methylated at an expected false discovery rate (FDR) of 5% in neoplastic tissues (primaries: 0.67%, i.e., 2155 regions containing 279,441 CpG sites liver metastases: 1%, i.e., 3223 regions containing 312,723 CpG sites) as compared with normal mucosa s les. Most of the differentially methylated regions (DMRs 94% in primaries 70% in metastases) were hyper methylated, and almost 80% of these (1882 of 2396) were present in both lesion types. At 5% FDR, no DMRs were detected in liver metastases vs. primary CRC. However, short regions of low-magnitude hypo methylation were frequent in metastases but rare in primaries. Hypermethylated DMRs were far more abundant in sequences classified as intragenic, gene-regulatory, or CpG shelves-shores-island segments, whereas hypomethylated DMRs were equally represented in extragenic (mainly, open-sea) and intragenic (mainly, gene bodies) sequences of the genome. Compared with targeted enrichment, MBD capture provided a better picture of the extension of CRC-associated DNA hypermethylation but was less powerful for identifying hypomethylation. Our findings demonstrate that the hypermethylation phenotype in CRC liver metastases remains similar to that of the primary tumor, whereas CRC-associated DNA hypomethylation probably undergoes further progression after the cancer cells have migrated to the liver.
Publisher: Cold Spring Harbor Laboratory
Date: 12-03-2019
DOI: 10.1101/575951
Abstract: 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.
Publisher: Informa UK Limited
Date: 02-11-2018
Publisher: Informa UK Limited
Date: 09-08-2021
No related grants have been discovered for Stephany Orjuela.