Summer 2019

Rules of Conduct

1. No eating, drinking, or loud talking in the lab. Socialize in the lobby only.
2. Be respectful to each other, regardless of level of study
3. Be on time & responsible. Communicate in advance with the PI if late or absent
4. No use of phone or laptop during lab meetings

Schedule

• June 19 (Wed). Summer research kickoff. Papers assigned. To prepare for Python tutorial, install the jupyter notebook in one of two following ways (by Edgar):
  • Installing the Anaconda Distribution (https://www.anaconda.com/distribution/#download-section): This is the easiest way to install Python on your machine. It also comes with a lot of packages for data science. However, it is quite heavy (~3GB), so if space is an issue you can try installing Miniconda. If you choose to install Anaconda, you don't need to install any additional packages since they are going to be installed automatically. Make sure you download Python 3.
  • Installing Miniconda3 (https://docs.conda.io/en/latest/miniconda.html): This is like a mini version of Anaconda that comes with the Conda package manager and Python. It doesn't include any packages so it requires less space.
    • Installing on MacOs: https://docs.conda.io/projects/conda/en/latest/user-guide/install/macos.html
    • Installing on Windows: https://docs.conda.io/projects/conda/en/latest/user-guide/install/windows.html
    • Once you install Miniconda, you can use the conda command on your terminal to install other packages:

conda install numpy
conda install pandas
conda install matplotlib
conda install jupyter

• June 21 (Fri). Python Tutorial I. Jupyter notebook, string, list, conditions, loops (by Edgar)
  • A Python Tutorial
  • Exercises
  • Iris data set
• June 24 (Mon). Python Tutorial II. string functions, function, dictionary, modules (by Edgar)
• June 26 (Wed). Python Tutorial III. BioPython (Edgar & Muhammud)
  • BioPython
  • Alignment file
  • FASTA file
• June 27 (Thur). Paper presentations

Participants

1. Dr Oliver Attie, Research Associate
2. Brian Sulkow, Research Associate
3. Saymon Akther, CUNY Graduate Center, EEB Program
4. Lily Li, CUNY Graduate Center, EEB Program
5. Christopher Panlasigui, Hunter Biology
6. Summer Interns: Muhammad, Radhika Mohan, Oscar Eng, Oliver Cai

Journal Club

1. a Unix & Perl tutorial
2. A short introduction to molecular phylogenetics: http://www.ncbi.nlm.nih.gov/pubmed/12801728
3. A review on Borrelia genomics: https://www.ncbi.nlm.nih.gov/pubmed/24704760
4. A model of immune selection: He et al (2018). https://www.nature.com/articles/s41467-018-04219-3
5. A model of flu evolution: Neher et al (2016). https://www.pnas.org/content/113/12/E1701?ijkey=72c6025e999dd043d32f6822dc06c7356d8494b2&keytype2=tf_ipsecsha
6. Reading on Dengue virus
   1. Bäck & Lundkvist 2013
   2. Overview (from Scitable)

Projects

Project	Description/Goal	Participants	Leader	Status/Notes/Weekly report (7/12, 7/19, 7/26)
Lyme genomics	phylogeography & genome intragression	Saymon, Chris	Saymon
Lyme ecology & population genetics	host identification; SIR model; coalescence	Lily, Chris	Lily
Borrelia peptide library	Compile ORFs from genome database & send to Mt Sinai team N=17 north american strains selected query script: get_pepseq_anno_for_each_genome.pl -p "B31" or strain ID; -a to get patric annotation Expected outputs: 17 FASTA files; Excel workbook with 17 sheets	Chris, Saymon	Chris
Origin of genetic code	manuscript revision Response letter done Track-change in progress To do: Reference update; Figure 6 (and legend/text)	Oliver & Brian	Oliver
Pseudomonas metabolomics	Shiny Web portal development	Chris, Edgar	Chris
Dengue antigenic variations	Parse Dengue sequences	Oliver Cai, Muhammad/Benjamin, Che	Muhammad
OspC design	Optimization by GA Identified a sequence with d<=43 to all 16 alleles (using DEAP) next, write a fitness function to minimize the maximum distance to any of the 16 alleles; run ~10 times output in FASTA file and do a tree	Edgar, Lia	Brian
OspC antigenecity model	Develop model with mice data by Invanova et al	Nevila, Brian	Brian
OspC per-site model	Quantify per-site importance with likelihood, i.e., Prob{fit->0, given that CW50, site=i} For allele A, file has been generated at CW50=50 Radhika has turned strings into {0,1} To do: plot fitness ~ pos CW50 values: given by Mohammad, estimated by using CW50=-(intercept/slope) Estimate importance using GA: fit = sum(1-p[i]), with the fitness/error function: error = |fit[obs] - fit[simulated]|; using GA to minimize the error; output p[i] as results (importance)	Radhika, Muhammad, Brian	Brian
flu	implement paper algorithm plot HI vs Seq.diff, one for each of 15 "refv" plot HI vs SNP, colored by 0 or 1, with boxplot + jitter implement Neher et al	Oscar, Brian	Brian