Why does cell type correction matter in DNA methylation analysis for obesity studies?

Cell type correction revealed that natural killer and B cells were higher in obese women, and significantly reduced false-positive CpG hits from 43,463 to 3,329 significant sites, ensuring observed methylation changes reflect true biological differences rather than cellular heterogeneity.

How does bisulfite conversion enable accurate methylation detection in this workflow?

Bisulfite treatment converts unmethylated cytosines to uracil while preserving methylated cytosines, allowing subsequent amplification and hybridization to distinguish methylation states at single-CpG resolution across the genome.

What quantitative output enables comparison of methylation levels between obese and non-obese groups?

Beta-value distribution from processed IDAT files provides a continuous measure of methylation density (0 to 1), allowing statistical comparison of CpG sites between groups after normalization and filtering.

Why are replication requirements critical for cross-functional collaboration in epigenetic studies?

Replication across 32 participants (15 obese, 17 non-obese) ensured technical robustness and biological validity, enabling bioinformaticians and biologists to trust downstream pathway enrichment results for decision-making.

What statistical analysis is required before identifying trait-associated CpG sites in this protocol?

Singular value decomposition (SVD) analysis of normalized data identified BMI, double C, and fat mass as key influencers of methylation variability, guiding the selection of 13,222 fat mass-related CpG sites for downstream association testing.

הכנת מדגם לניתוח ביואינפורמטיקה של מתילציה DNA: אסטרטגיית האגודה להשמנת יתר ומחקרי תכונות נלוות

6.2K צפיות

צוטט 9 פעמים

14:56 דק'

6 במאי 2022

10.3791/62598-v

6 במאי 2022

6.2K צפיות

Natália Yumi Noronha^*¹ , Guilherme da Silva Rodrigues^*¹ , Marcela Augusta de Souza Pinhel¹ , Jean-Baptiste Cazier²^,³ , Lígia Moriguchi Watanabe¹ , Albert Nobre Menezes⁴ , Carlos Roberto Bueno⁵ , Carolina Ferreira Nicoletti¹ , Bruno Affonso Parenti de Oliveira¹ , Isabelle Mello Schineider⁶ , Isabella Harumi Yonehara Noma⁷ , Igor Caetano Dias Alcarás⁸ , Fernando Barbosa⁹ , Carla Barbosa Nonino⁶

¹Department of Internal Medicine, Ribeirão Preto Medical School, University of São Paulo, ²Centre for Computational Biology, University of Birmingham, ³Institute of Cancer and Genomic Sciences, College of Medical and Dental Sciences, University of Birmingham, ⁴Cancer Genetics and Evolution Laboratory, Cancer Research UK, Institute of Genetics & Molecular Medicine, The University of Edinburgh, ⁵Ribeirão Preto School of Physical Education and Sport, University of São Paulo, ⁶Health Sciences Department, Ribeirao Preto Medical School, University of Sao Paulo, ⁷Faculty of Pharmaceutical Sciences, University of São Paulo, ⁸Department of Genetics, Ribeirão Preto Medical School, University of São Paulo, ⁹Department of Clinical, Bromatological and Toxicological Analysis, Faculty of Pharmaceutical Sciences of Ribeirao Preto, University of São Paulo

^* These authors contributed equally

המחקר הנוכחי מתאר את זרימת העבודה לניהול נתוני מתילציה DNA המתקבלים על ידי טכנולוגיות microarray. הפרוטוקול מדגים שלבים מהכנה מדגמית לניתוח נתונים. כל ההליכים מתוארים בפירוט, והסרטון מציג את השלבים המשמעותיים.