PaleoProPhyler: a reproducible pipeline for phylogenetic inference using ancient proteins

  1. Ioannis Patramanis
  2. Jazmín Ramos-Madrigal
  3. Enrico Cappellini
  4. Fernando Racimo

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Abstract

Ancient proteins from fossilized or semi-fossilized remains can yield phylogenetic information at broad temporal horizons, in some cases even millions of years into the past. In recent years, peptides extracted from archaic hominins and long-extinct mega-fauna have enabled unprecedented insights into their evolutionary history. In contrast to the field of ancient DNA - where several computational methods exist to process and analyze sequencing data - few tools exist for handling ancient protein sequence data. Instead, most studies rely on loosely combined custom scripts, which makes it difficult to reproduce results or share methodologies across research groups. Here, we present PaleoProPhyler: a new fully reproducible pipeline for aligning ancient peptide data and subsequently performing phylogenetic analyses. The pipeline can not only process various forms of proteomic data, but also easily harness genetic data in different formats (CRAM, BAM, VCF) and translate it, allowing the user to create reference panels for phyloproteomic analyses. We describe the various steps of the pipeline and its many functionalities, and provide some examples of how to use it. PaleoProPhyler allows researchers with little bioinformatics experience to efficiently analyze palaeoproteomic sequences, so as to derive insights from this valuable source of evolutionary data.

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  1. Version published to 10.24072/pcjournal.344
  2. Peer Community in Paleontology

    One of the most recent technological advances in paleontology enables the characterization of ancient proteins, a new discipline known as palaeoproteomics (Ostrom et al., 2000; Warinner et al., 2022). Palaeoproteomics has superficial similarities with ancient DNA, as both work with ancient molecules, however the former focuses on peptides and the latter on nucleotides. While the study of ancient DNA is more established (e.g., Shapiro et al., 2019), palaeoproteomics is experiencing a rapid diversification of application, from deep time paleontology (e.g., Schroeter et al., 2022) to taxonomic identification of bone fragments (e.g., Douka et al., 2019), and determining genetic sex of ancient individuals (e.g., Lugli et al., 2022). However, as Patramanis et al. (2023) note in this manuscript, tools for analyzing protein sequence data are still in the informal stage, making the application of this methodology a challenge for many new-comers to the discipline, especially those with little bioinformatics expertise.

    In the spirit of democratizing the field of palaeoproteomics, Patramanis et al. (2023) developed an open-source pipeline, PaleoProPhyler released under a CC-BY license (https://github.com/johnpatramanis/Proteomic_Pipeline). Here, Patramanis et al. (2023) introduce their workflow designed to facilitate the phylogenetic analysis of ancient proteins. This pipeline is built on the methods from earlier studies probing the phylogenetic relationships of an extinct genus of rhinoceros Stephanorhinus (Cappellini et al., 2019), the large extinct ape Gigantopithecus (Welker et al., 2019), and Homo antecessor (Welker et al., 2020). PaleoProPhyler has three interacting modules that initialize, construct, and analyze an input dataset. The authors provide a demonstration of application, presenting a molecular hominid phyloproteomic tree. 

    In order to run some of the analyses within the pipeline, the authors also generated the Hominid Palaeoproteomic Reference Dataset which includes 10,058 protein sequences per individual translated from publicly available whole genomes of extant hominids (orangutans, gorillas, chimpanzees, and humans) as well as some ancient genomes of Neanderthals and Denisovans. This valuable research resource is also publicly available, on Zenodo (Patramanis et al., 2022). 

    Three reviewers reported positively about the development of this program, noting its importance in advancing the application of palaeoproteomics more broadly in paleontology.

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  3. Version published to 10.1101/2022.12.12.519721 on bioRxiv