A

Andrew E. Teschendorff

Shanghai Institute of Nutrition and Health

ORCID: 0000-0001-7410-6527

Publishes on Epigenetics and DNA Methylation, Single-cell and spatial transcriptomics, Cancer Genomics and Diagnostics. 323 papers and 30.7k citations.

323Publications
30.7kTotal Citations

Is this you? Claim your profile.

Add your photo, update your bio, and get notified when your ranking changes.

Top publicationsby citations

A beta-mixture quantile normalization method for correcting probe design bias in Illumina Infinium 450 k DNA methylation data
Cited by 1.9kOpen Access

MOTIVATION: The Illumina Infinium 450 k DNA Methylation Beadchip is a prime candidate technology for Epigenome-Wide Association Studies (EWAS). However, a difficulty associated with these beadarrays is that probes come in two different designs, characterized by widely different DNA methylation distributions and dynamic range, which may bias downstream analyses. A key statistical issue is therefore how best to adjust for the two different probe designs. RESULTS: Here we propose a novel model-based intra-array normalization strategy for 450 k data, called BMIQ (Beta MIxture Quantile dilation), to adjust the beta-values of type2 design probes into a statistical distribution characteristic of type1 probes. The strategy involves application of a three-state beta-mixture model to assign probes to methylation states, subsequent transformation of probabilities into quantiles and finally a methylation-dependent dilation transformation to preserve the monotonicity and continuity of the data. We validate our method on cell-line data, fresh frozen and paraffin-embedded tumour tissue samples and demonstrate that BMIQ compares favourably with two competing methods. Specifically, we show that BMIQ improves the robustness of the normalization procedure, reduces the technical variation and bias of type2 probe values and successfully eliminates the type1 enrichment bias caused by the lower dynamic range of type2 probes. BMIQ will be useful as a preprocessing step for any study using the Illumina Infinium 450 k platform. AVAILABILITY: BMIQ is freely available from http://code.google.com/p/bmiq/. CONTACT: a.teschendorff@ucl.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ChAMP: updated methylation analysis pipeline for Illumina BeadChips
Yuan Tian, Tiffany Morris, Amy P. Webster et al.|Bioinformatics|2017
Cited by 1.2kOpen Access

SUMMARY: The Illumina Infinium HumanMethylationEPIC BeadChip is the new platform for high-throughput DNA methylation analysis, effectively doubling the coverage compared to the older 450 K array. Here we present a significantly updated and improved version of the Bioconductor package ChAMP, which can be used to analyze EPIC and 450k data. Many enhanced functionalities have been added, including correction for cell-type heterogeneity, network analysis and a series of interactive graphical user interfaces. AVAILABILITY AND IMPLEMENTATION: ChAMP is a BioC package available from https://bioconductor.org/packages/release/bioc/html/ChAMP.html. CONTACT: a.teschendorff@ucl.ac.uk or s.beck@ucl.ac.uk or a.feber@ucl.ac.uk. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ChAMP: 450k Chip Analysis Methylation Pipeline
Tiffany Morris, Lee M Butcher, Andrew Feber et al.|Bioinformatics|2013
Cited by 1.1kOpen Access

UNLABELLED: The Illumina Infinium HumanMethylation450 BeadChip is a new platform for high-throughput DNA methylation analysis. Several methods for normalization and processing of these data have been published recently. Here we present an integrated analysis pipeline offering a choice of the most popular normalization methods while also introducing new methods for calling differentially methylated regions and detecting copy number aberrations. AVAILABILITY AND IMPLEMENTATION: ChAMP is implemented as a Bioconductor package in R. The package and the vignette can be downloaded at bioconductor.org

DNA methylation aging clocks: challenges and recommendations
Christopher G. Bell, Robert Lowe, Peter D. Adams et al.|Genome biology|2019
Cited by 1.1kOpen Access

Epigenetic clocks comprise a set of CpG sites whose DNA methylation levels measure subject age. These clocks are acknowledged as a highly accurate molecular correlate of chronological age in humans and other vertebrates. Also, extensive research is aimed at their potential to quantify biological aging rates and test longevity or rejuvenating interventions. Here, we discuss key challenges to understand clock mechanisms and biomarker utility. This requires dissecting the drivers and regulators of age-related changes in single-cell, tissue- and disease-specific models, as well as exploring other epigenomic marks, longitudinal and diverse population studies, and non-human models. We also highlight important ethical issues in forensic age determination and predicting the trajectory of biological aging in an individual.