# Toward a Species Search Engine: KISSE Offers a Rigorous Statistical Framework for Bone Collagen Tandem Mass Spectrometry Data

## TL;DR

KISSE is a new statistical method for identifying species from ancient or degraded samples using collagen peptides and a curated database.

## Contribution

KISSE introduces a rigorous statistical framework for species identification from collagen data, extending methods from proteomics.

## Key findings

- KISSE uses a species-specific library of collagen peptides and their abundances for species identification.
- The method provides a probability of correct identification and can detect when a species is not in the library.
- The approach can be extended to other tissues beyond bone collagen.

## Abstract

DNA and bone collagen are two key sources of resilient molecular markers used to identify species from their remains. Collagen is more stable than DNA, and thus it is preferred for ancient and degraded samples. Current mass spectrometry‐based collagen sequencing approaches are empirical and lack a rigorous statistical framework. Based on the well‐developed approaches to protein identification in shotgun proteomics, a first approximation of the species search engine (SSE) is introduced. SSE named KISSE is based on a species‐specific library of collagenous peptides that uses both peptide sequences and their relative abundances. The developed statistical model can identify the species and the probability of correct identification, as well as determine the likelihood of the analyzed species not being in the library. The advantages and limitations of the proposed approach, and the possibility of extending it to other tissues is discussed.

The Species Search Engine (KISSE) is a novel statistical approach for identifying species from collagen peptides, using a curated library of sequences and their relative abundances derived from shotgun proteomics.