New paper from #cpgSTHLM published!

In this study, authors @TatianaFireborn and @IndianaDiez et al. present a fast and easy method to identify and exclude human contamination from ancient faunal datasets.

#openaccess paper https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-07229-y

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They sequenced 70 ancient samples, including #woollymammoths and #dogs, to answer one important question: do the alignment files from ancient faunal datasets contain contaminant sequences from modern humans?

Contaminant sequences can cause trouble in downstream analyses!

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Most of the samples contain very low numbers (<0.03%) of human contaminant sequences, but some of them can contain significant amounts.

For example, the alignment files from one #woollymammoth sample contained 1.3% of human sequences!

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So Feuerborn et al. came up with a method to exclude these human contaminant reads:

Performing #CompetitiveMapping on concatenated reference genomes composed by human-elephant for mammoths and human-dog for dogs.

AKA 'ElephantMan' and 'WereWolf'

https://twitter.com/IndianaDiez/status/1333696895450013697

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They think this method could be "(..) an important addition to the selection of tools aimed at computationally reducing the effects of human contamination in ancient faunal DNA research."

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This paper is a very special one for us since it was a joint effort from many people based at #cpgsthlm including @TatianaFireborn @T_vd_Valk @JohannaNystrm @PatriciaChrzan @MaDehasque @ErikErsmark @vkl_vendela @AndersGother @love_dalen @IndianaDiez and more!

Congrats!

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