Stage M2: protein annotation, comparative genomics, and metabolic modeling for uncultivated bacteria

SFBI; Talence; On-site; EUR 12,000 - 17,000; Part time

Job summary

SFBI in Talence, France seeks a Stage M2 candidate to work on protein annotation, comparative genomics, and metabolic modeling for uncultivated bacteria. The intern will be supervised by bioinformatics, genomics, and metabolic modelling experts, collaborating with Mollicutes biology teams.

The role focuses on reviewing annotation tools, applying ML to hypothetical proteins, and examining how new annotations alter GSMNs and cultivability.

Qualifications

• Proficiency in Python.
• Good level in English (written and spoken).
• Interest in microbiology/biochemistry.

Responsibilities

• Review computational tools for annotation of unannotated genes/proteins.
• Identify potential functions of hypothetical proteins using machine learning.
• Track unannotated proteins across 36 mollicutes genomes to assess sharing among cultivable/uncultivable strains.
• Assess impact of novel annotations on microbial metabolism and cultivability using GSMNs.
• Summarize and present findings to the team.

Skills

Python

English

Microbiology interest

Bash

High-performance computing

Education

MSc in computational biology

MSc in computer science or AI with biology interest

Tools

Bash

Job description

Stage M2: protein annotation, comparative genomics, and metabolic modeling for uncultivated bacteria

Protein structure/function Gene annotation Machine learning Comparative genomics Minimal bacteria Metabolic modelling

Description

Huge advances in microbiology have been made since the advent of genome sequencing, largely due to the prediction of metabolic “function” of a given microbe from the genes in its genome. These genes are traditionally annotated by sequence comparison to large databases of characterized genes. Despite ever-improving genome sequencing technology, many individual genes (and their respective proteins) remain unannotated by traditional sequence homology methods. These unannotated, or “hypothetical” genes, are referred to as “microbial dark matter”, and are estimated to comprise ~14% of all protein-coding genes (1). With the development of AI and large language models, a range of new methods have come available for structural and functional inference from protein sequences, such as AlphaFOLD (2), RoseTTAfold (3), and CLEAN (4). These methods can be used to shed light on the potential functions of genes/proteins classified as microbial dark matter.
Mollicutes are a group of small bacteria with reduced genomes (5-10x smaller than most classical bacteria) that cause disease in a wide range of hosts- both plant and animal.
Our team has selected 36 mollicutes genomes for comparative genomics analysis, and produced a genome-scale metabolic network (GSMN) from each genome. GMSNs represent every metabolic reaction that could occur in a given microbe- based on the functional annotation of its genome, and are used to predict growth of a microbe in a certain environment when provided certain nutrients. Because these networks directly depend on a genome’s functional annotation, improved annotation could drastically improve the applicability of GSMNs, which would ultimately help cultivation efforts.
The intern will be directly supervised by experts in bioinformatics, genomics, and metabolic modelling, in close collaboration with experts in Mollicutes biology and genomics.

Objectives of the internship include:

• Review and compare computational tools for annotation of unannotated genes/proteins
• Identify potential functions of hypothetical proteins in two highly curated genomes by implementing various machine learning algorithms
• Track and compare unannotated proteins across 36 mollicutes genomes to determine whether they are shared amongst cultivable/ uncultivable strains
• Assess the impact of these novel annotations on microbial metabolism and cultivability using GSMNs
• Summarize and present findings

Expected skills and profile

Expected:

• Proficiency in Python
• Good level in English (written and spoken)
• Interest in microbiology/ biochemistry

Appreciated:

• High-performance computing
• Bash programming language

Working language:

We are seeking a student with one of the following profiles:

• Master’s degree in computational biology
• or a Master’s degree in computer science or artificial intelligence and an interest in biology
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Contacts

Olivia Bulka
[e-mail]