Lengyel, E. | bioRxiv Science

bioRxiv2019

RADAR: Differential analysis of MeRIP-seq data with a random effect model

Epitranscriptome profiling using MeRIP-seq is a powerful technique for in vivo functional studies of reversible RNA modifications. We develop RADAR, a comprehensive analytical tool for detecting differentially methylated loci in MeRIP-seq data. RADAR enables accurate identification of altered methylation sites by accommodating variability of pre-immunoprecipitation expression level and post-immunoprecipitation count using different strategies. In addition, it is compatible with complex study design when covariates need to be incorporated in the analysis. Through simulation and real datasets analyses, we show that RADAR leads to more accurate and reproducible differential methylation analysis results than alternatives, which is available at https://github.com/scottzijiezhang/RADAR.

bioinformatics↗

bioRxiv2019

A streamlined mass spectrometry-based proteomics workflow forlarge scale FFPE tissue analysis

Formalin fixation and paraffin-embedding (FFPE) is the most common method to preserve human tissue for clinical diagnosis and FFPE archives represent an invaluable resource for biomedical research. Proteins in FFPE material are stable over decades but their efficient extraction and streamlined analysis by mass spectrometry (MS)-based proteomics has so far proven challenging. Here, we describe an MS-based proteomic workflow for quantitative profiling of large FFPE tissue cohorts directly from pathology glass slides. We demonstrate broad applicability of the workflow to clinical pathology specimens and variable sample amounts, including less than 10,000 cancer cells isolated by laser-capture microdissection. Using state-of-the-art data dependent acquisition (DDA) and data independent (DIA) MS workflows, we consistently quantify a large part of the proteome in 100 min single-run analyses. In an adenoma cohort comprising more than 100 samples, total work up took less than a day. We observed a moderate trend towards lower protein identifications in long-term stored samples (>15 years) but clustering into distinct proteomic subtypes was independent of archival time. Our results underline the great promise of FFPE tissues for patient phenotyping using unbiased proteomics and prove the feasibility of analyzing large tissue cohorts in a robust, timely and streamlined manner.

systems biology↗