Ancient Biomolecules and Evolutionary Inference

Enrico Cappellini, Ana Prohaska, Fernando Racimo, Frido Welker, Mikkel Winther Pedersen, Morten E. Allentoft, Peter De Barros Damgaard, Petra Gutenbrunner, Julie Dunne, Simon Hammann, Mélanie Roffet-Salque, Melissa Ilardo, J. Víctor Moreno-Mayar, Yucheng Wang, Martin Sikora, Lasse Vinner, Jürgen Cox, Richard P. Evershed, Eske Willerslev

Research output: Contribution to journalReview article (Academic Journal)peer-review

78 Citations (Scopus)
451 Downloads (Pure)

Abstract

Over the past three decades, studies of ancient biomolecules - particularly ancient DNA, proteins, and lipids - have revolutionized our understanding of evolutionary history. Though initially fraught with many challenges, today the field stands on firm foundations. Researchers now successfully retrieve nucleotide and amino acid sequences, as well as lipid signatures, from progressively older samples, originating from geographic areas and depositional environments that, until recently, were regarded as hostile to long-term preservation of biomolecules. Sampling frequencies and the spatial and temporal scope of studies have also increased markedly, and with them the size and quality of the data sets generated. This progress has been made possible by continuous technical innovations in analytical methods, enhanced criteria for the selection of ancient samples, integrated experimental methods, and advanced computational approaches. Here, we discuss the history and current state of ancient biomolecule research, its applications to evolutionary inference, and future directions for this young and exciting field.

Original languageEnglish
Pages (from-to)1029-1060
Number of pages32
JournalAnnual Review of Biochemistry
Volume87
Early online date25 Apr 2018
DOIs
Publication statusPublished - 20 Jun 2018

Keywords

  • ancient DNA
  • ancient genomics
  • ancient lipids
  • ancient proteins
  • paleogenomics
  • paleoproteomics

Fingerprint

Dive into the research topics of 'Ancient Biomolecules and Evolutionary Inference'. Together they form a unique fingerprint.

Cite this