Summer 2021

Project 1. Borrelia genomics

• Participants
• Questions & Goals:
  • Upgrade database, genome pipeline, and website (Lia)
  • Phylogeography & evolutionary maintenance of divided genome (Saymon)
  • vls evolution (with simulation) & development of immunoflorescence microsopy methods(Lily)
• Reading list
  • Schward et al (2021). Multipartite Genome of Lyme Disease Borrelia: Structure, Variation and Prophages
  • Stevenson & Seshu (2018). Regulation of Gene and Protein Expression in the Lyme Disease Spirochete

Project 2. Design algorithms for vaccines

• Participants:
  • Dr Saad Mneimneih (CS Department)
  • Brian
• Questions & Goals:
  • Generalized algorithms for antigen with arbitrary tree shape
  • Combination algorithms
  • Naive Bayes models to integrate immunogenicity data
  • Natural language models to improve structural stability (see Project 4 below)
• Reading list
  • Di et al (2021). Maximum antigen divergence in Lyme bacterial population

Project 3. HIV compartmentalized evolution

• Participants
  • Lily
• Questions and goals
  • Do HIV evolve cell type tropisms within the host? Specifically, the Neural(N)-tropism vs T-cell(T)-tropism?
  • Build a classifier of N-tropism HIV subtypes
  • A presentation for an HIV conference in October
• Reading list
  • HIV compartmentalized evolution: Evering et al (2014)
• Data sets
  • ~500 sequences of env genes from 15 patients
  • 2nd time point single-cell genome sequences for some of the patients
  • Experimentally verified N-tropism subtypes
• Approach
  • Evolutionary mechanisms: mutation, recombination, and test of adaptive selection
  • Evolutionary rates & signature (BEAST)

Project 4. Natural Language models of proteins

• Participants
• Questions & Goals

1. Learn, implement, and compare the existing tools
2. Fine-tuning for OspC, to be integrated with the centroid algorithm
3. 2nd-generation centroid design: k-means algorithm (with applications to vls, Dengue, flu B)

• Reading list
  • Strodthoff et al (2020). Bioinformatics. UDSMProt: universal deep sequence models for protein classification. Source code on Github
  • Rives et al (2021). PNAS. Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences. Github repository