Fast, accurate and automatic ancient nucleosome and methylation maps with epiPALEOMIX

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The first epigenomes from archaic hominins (AH) and ancient anatomically modern humans (AMH) have recently been characterized, based, however, on a limited number of samples. The extent to which ancient genome-wide epigenetic landscapes can be reconstructed thus remains contentious. Here, we present epiPALEOMIX, an open-source and user-friendly pipeline that exploits post-mortem DNA degradation patterns to reconstruct ancient methylomes and nucleosome maps from shotgun and/or capture-enrichment data. Applying epiPALEOMIX to the sequence data underlying 35 ancient genomes including AMH, AH, equids and aurochs, we investigate the temporal, geographical and preservation range of ancient epigenetic signatures. We first assess the quality of inferred ancient epigenetic signatures within well-characterized genomic regions. We find that tissue-specific methylation signatures can be obtained across a wider range of DNA preparation types than previously thought, including when no particular experimental procedures have been used to remove deaminated cytosines prior to sequencing. We identify a large subset of samples for which DNA associated with nucleosomes is protected from post-mortem degradation, and nucleosome positioning patterns can be reconstructed. Finally, we describe parameters and conditions such as DNA damage levels and sequencing depth that limit the preservation of epigenetic signatures in ancient samples. When such conditions are met, we propose that epigenetic profiles of CTCF binding regions can be used to help data authentication. Our work, including epiPALEOMIX, opens for further investigations of ancient epigenomes through time especially aimed at tracking possible epigenetic changes during major evolutionary, environmental, socioeconomic, and cultural shifts.

OriginalsprogEngelsk
TidsskriftMolecular Biology and Evolution
Vol/bind33
Udgave nummer12
Sider (fra-til)3284-3298
Antal sider15
ISSN0737-4038
DOI
StatusUdgivet - 2016

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