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Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

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publications

MungeSumstats: a Bioconductor package for the standardization and quality control of many GWAS summary statistics

Published in Bioinformatics, 2021

we have developed MungeSumstats, a Bioconductor R package for the standardization and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardized, tabular summary statistic file, VCF or R native data object.

Recommended citation: Murphy, A. E., Schilder, B. M. & Skene, N. G. Bioinformatics 37, 4593–4596 (2021)
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Predicting cell type-specific epigenomic profiles accounting for distal genetic effects

Published in Nature Communications, 2024

Enformer Celltyping outperforms current best-in-class approaches and generalises across cell types and biological regions. Moreover, we propose a framework for evaluating models on genetic variant effect prediction using regulatory quantitative trait loci mapping studies, highlighting current limitations in genomic deep learning models. Despite this, Enformer Celltyping can also be used to study cell type-specific genetic enrichment of complex traits.

Recommended citation: Murphy, A.E., Beardall, W., Rei, M. et al. Nat. Commun. 15, 9951 (2024)
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Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states

Published in Nucleic Acids Research (NAR), 2024

No individual histone mark is consistently the strongest predictor of gene expression across all genomic and cellular contexts. This highlights the need to consider all three factors when determining the effect of histone mark activity on transcriptional state. Furthermore, we conducted in silico histone mark perturbation assays, uncovering functional and disease related loci and highlighting frameworks for the use of chromatin deep learning models to uncover new biological insight.

Recommended citation: Murphy, A.E., Askarova,A. , Lenhard, B. et al. Nucleic Acids Research, gkae1212, (2024).
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