PhD thesis in paleoproteomics (M/W)

Organisation/Company

CNRS

Department

Centre de Recherche en Informatique, Signal et Automatique de Lille

Research Field

Biological sciences
Computer science
Mathematics

Researcher Profile

First Stage Researcher (R1)

Country

France

Application Deadline

22 Sep 2023 – 23:59 (UTC)

Type of Contract

Temporary

Job Status

Full-time

Hours Per Week

35

Offer Starting Date

1 Jan 2024

Is the job funded through the EU Research Framework Programme?

Not funded by an EU programme

Is the Job related to staff position within a Research Infrastructure?

No

Offer Description

The project is funded by the CNRS 80vacanciesin.euPRIME initiative and will be developed in an inter-institutional and interdisciplinary collaboration between the UMR CRIStAL and UMR EEP of the CNRS and the University of Lille. Furthermore, this project is realized in close collaboration with the ZooMS platform in Lille (MSAP).

In recent years, the analysis of ancient biological samples has changed our understanding of the evolution of life on Earth, renewing the approaches previously used in paleontology based on the study of fossils or carbon-14 dating. At the forefront of new molecular techniques is paleogenomics (sequencing of ancient DNA), although DNA degrades relatively quickly. More recently, paleoproteomics via ZooArchaeology by mass spectrometry (ZooMS) offers a possibility to identify morphologically ambiguous or unidentifiable bone fragments from bone assemblages. Identification of bones with ZooMS results from the sequencing of a target protein, such as collagen, which is abundant in bone fragments. The collagen present in the samples is digested and the mass of the peptides obtained by spectrometry gives indirect information on the amino acid sequence of the protein studied. To exploit this data, the community works with marker peptides, which serve as a sort of molecular barcode for taxonomic assignment. But the use of these marker peptides suffers from two limitations: it remains manual and it neglects the evolutionary dimension of the data.

There is therefore a real need to formalize and automate the methods in order to obtain robust and reproducible assignments, even on a large scale. This raises multiple questions:
How can the marker peptide approach be generalized towards the combination of marker peptides or consensus marker peptides to take full advantage of the phylogenetic signal contained in the data?
How to infer marker peptides at different taxonomic levels ?
How to measure the phylogenetic signal contained in the target protein and its peptides ?
How to reconstruct ancestral protein sequences from spectra and contemporary sequences to enrich contemporary data sets ?

The methods developed will combine sequence algorithmic approaches and a probabilistic framework using protein sequence evolution models to reconstruct phylogenetic trees and ancestral sequences. The expected results are twofold: to develop a toolbox for data analysis, and to propose a methodological framework for an informed use of marker peptides in ZooMS.

Requirements

Research Field

Biological sciences

Education Level

PhD or equivalent

Research Field

Computer science

Education Level

PhD or equivalent

Research Field

Mathematics

Education Level

PhD or equivalent

Languages

FRENCH

Level

Basic

Research Field

Biological sciences

Years of Research Experience

None

Research Field

Computer science

Years of Research Experience

None

Research Field

Mathematics

Years of Research Experience

None

Additional Information

Website for additional job details

https://emploi.cnrs.fr/Offres/Doctorant/UMR9189-HELTOU-002/Default.aspx

Work Location(s)

Number of offers available

1

Company/Institute

Centre de Recherche en Informatique, Signal et Automatique de Lille

Country

France

City

VILLENEUVE D ASCQ

Where to apply

Website

https://emploi.cnrs.fr/Candidat/Offre/UMR9189-HELTOU-002/Candidater.aspx

Contact

City

VILLENEUVE D ASCQ

Website

http://cristal.univ-lille.fr/

STATUS: EXPIRED