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Fagny, Maud

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Fagny

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Maud

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Fagny, Maud
Author's Publications: search.filters.isAuthorOfPublication.610ea721-084c-410e-b641-57097309b37b

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    Regulatory network changes between cell lines and their tissues of origin
    (BioMed Central, 2017) Lopes-Ramos, Camila; Paulson, Joseph; Chen, Cho-Yi; Kuijjer, Marieke; Fagny, Maud; Platig, John; Sonawane, Abhijeet; Demeo, Dawn; Quackenbush, John; Glass, Kimberly
    Background: Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin. Results: We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE. Conclusions: Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues. Electronic supplementary material The online version of this article (10.1186/s12864-017-4111-x) contains supplementary material, which is available to authorized users.
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    An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease
    (BioMed Central, 2016) Horvath, Steve; Gurven, Michael; Levine, Morgan E.; Trumble, Benjamin C.; Kaplan, Hillard; Allayee, Hooman; Ritz, Beate R.; Chen, Brian; Lu, Ake T.; Rickabaugh, Tammy M.; Jamieson, Beth D.; Sun, Dianjianyi; Li, Shengxu; Chen, Wei; Quintana-Murci, Lluis; Fagny, Maud; Kobor, Michael S.; Tsao, Philip S.; Reiner, Alexander P.; Edlefsen, Kerstin L.; Absher, Devin; Assimes, Themistocles L.
    Background: Epigenetic biomarkers of aging (the “epigenetic clock”) have the potential to address puzzling findings surrounding mortality rates and incidence of cardio-metabolic disease such as: (1) women consistently exhibiting lower mortality than men despite having higher levels of morbidity; (2) racial/ethnic groups having different mortality rates even after adjusting for socioeconomic differences; (3) the black/white mortality cross-over effect in late adulthood; and (4) Hispanics in the United States having a longer life expectancy than Caucasians despite having a higher burden of traditional cardio-metabolic risk factors. Results: We analyzed blood, saliva, and brain samples from seven different racial/ethnic groups. We assessed the intrinsic epigenetic age acceleration of blood (independent of blood cell counts) and the extrinsic epigenetic aging rates of blood (dependent on blood cell counts and tracks the age of the immune system). In blood, Hispanics and Tsimane Amerindians have lower intrinsic but higher extrinsic epigenetic aging rates than Caucasians. African-Americans have lower extrinsic epigenetic aging rates than Caucasians and Hispanics but no differences were found for the intrinsic measure. Men have higher epigenetic aging rates than women in blood, saliva, and brain tissue. Conclusions: Epigenetic aging rates are significantly associated with sex, race/ethnicity, and to a lesser extent with CHD risk factors, but not with incident CHD outcomes. These results may help elucidate lower than expected mortality rates observed in Hispanics, older African-Americans, and women. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1030-0) contains supplementary material, which is available to authorized users.
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    Tissue-aware RNA-Seq processing and normalization for heterogeneous and sparse data
    (BioMed Central, 2017) Paulson, Joseph; Chen, Cho-Yi; Lopes-Ramos, Camila; Kuijjer, Marieke; Platig, John; Sonawane, Abhijeet; Fagny, Maud; Glass, Kimberly; Quackenbush, John
    Background: Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data – critical first steps for any subsequent analysis. Results: We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project. Conclusions: An R package instantiating YARN is available at http://bioconductor.org/packages/yarn. Electronic supplementary material The online version of this article (10.1186/s12859-017-1847-x) contains supplementary material, which is available to authorized users.