A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry
Abstract Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism’s proteome—even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides...
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Nature Portfolio
2021
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oai:doaj.org-article:f72890fc2a914052b3c97c1744bba3b22021-12-02T15:00:25ZA predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry10.1038/s41598-021-90231-52045-2322https://doaj.org/article/f72890fc2a914052b3c97c1744bba3b22021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90231-5https://doaj.org/toc/2045-2322Abstract Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism’s proteome—even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides detectable in a sample that frequently contains hundreds or thousands of proteins. Species identification is based on detection of a small numbers of species-specific peptides. Genetic analysis of proteins by mass spectrometry, however, is a developing field, and the bone proteome, typically consisting of only two proteins, pushes the limits of this technology. Nearly 20% of highly confident spectra from modern human bone samples identify non-human species when searched against a vertebrate database—as would be necessary with a fragment of unknown bone. These non-human peptides are often the result of current limitations in mass spectrometry or algorithm interpretation errors. Consequently, it is difficult to know if a “species-specific” peptide used to identify a sample is actually present in that sample. Here we evaluate the causes of peptide sequence errors and propose an unbiased, probabilistic approach to determine the likelihood that a species is correctly identified from bone without relying on species-specific peptides.Heyi YangErin R. ButlerSamantha A. MonierJennifer TeublDavid FenyöBeatrix UeberheideDonald SiegelNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021) |
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Medicine R Science Q Heyi Yang Erin R. Butler Samantha A. Monier Jennifer Teubl David Fenyö Beatrix Ueberheide Donald Siegel A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry |
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Abstract Proteogenomics is an increasingly common method for species identification as it allows for rapid and inexpensive interrogation of an unknown organism’s proteome—even when the proteome is partially degraded. The proteomic method typically uses tandem mass spectrometry to survey all peptides detectable in a sample that frequently contains hundreds or thousands of proteins. Species identification is based on detection of a small numbers of species-specific peptides. Genetic analysis of proteins by mass spectrometry, however, is a developing field, and the bone proteome, typically consisting of only two proteins, pushes the limits of this technology. Nearly 20% of highly confident spectra from modern human bone samples identify non-human species when searched against a vertebrate database—as would be necessary with a fragment of unknown bone. These non-human peptides are often the result of current limitations in mass spectrometry or algorithm interpretation errors. Consequently, it is difficult to know if a “species-specific” peptide used to identify a sample is actually present in that sample. Here we evaluate the causes of peptide sequence errors and propose an unbiased, probabilistic approach to determine the likelihood that a species is correctly identified from bone without relying on species-specific peptides. |
format |
article |
author |
Heyi Yang Erin R. Butler Samantha A. Monier Jennifer Teubl David Fenyö Beatrix Ueberheide Donald Siegel |
author_facet |
Heyi Yang Erin R. Butler Samantha A. Monier Jennifer Teubl David Fenyö Beatrix Ueberheide Donald Siegel |
author_sort |
Heyi Yang |
title |
A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry |
title_short |
A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry |
title_full |
A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry |
title_fullStr |
A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry |
title_full_unstemmed |
A predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry |
title_sort |
predictive model for vertebrate bone identification from collagen using proteomic mass spectrometry |
publisher |
Nature Portfolio |
publishDate |
2021 |
url |
https://doaj.org/article/f72890fc2a914052b3c97c1744bba3b2 |
work_keys_str_mv |
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