path2max
How to apply: Write a cover letter (email) about yourself and your interest to Professor Rahnavard at rahnavard@gwu.edu and CC PhD Student Ranojoy Chatterjee ranojoy@gwu.edu
When to apply: Anytime, preferred: between November – May.
Program Details
Time of the internship: approximately the last week of July
Time of the internship: approximately the last week of July
Address: 800 22nd Street, NW, Washington, DC 20052 (GW Science and Engineering Hall: SEH). MAP
Arrival: Please plan to arrive before 10 am. Use the elevator to reach the 7th floor, then proceed to room #7000, our front desk. They will let you into our lab area. Ranojoy and other lab members will be there to welcome you, and we will have a brief orientation at that time. You will be in a group with 3-4 other high schoolers. On your first day, email us 5 minutes before your arrival so we can let you into the building and coordinate your access.
Lunch and a one-week prepaid metro card will be provided to assist with your transportation. Please let me know if you need it so we can prepare it for you on your first day at GW.
Topics/sessions by day:
During your time with us, you will meet our lab members and learn about their research projects and daily work. You will also learn computing and data analyzing techniques (e.g., longitudinal data analysis, visualization), data processing (e.g., COVID data), attend our lab meetings, etc.
Monday:
- Orientation of our research lab
- Intro to Bioinformatics and Computational Biology (Chiraag Gohel, PhD Student)
- Paper review (Biology, Bioinformatics/ML, Immunology and Infectious Diseases)
- What is the message
- Figures and caption
Tuesday:
- Learn the basics of Python programming, including objects, assigning values to objects, lists, sets, dictionaries, for loops, and creating functions.
- Learn how to extract a whole genome or part of a genome from the NCBI database.
- Learn the basics of sequence analysis and how to compare sequences, including nucleotide counts, GC-content, finding k-mers, counting k-mers, calculating frequencies, and comparing genome results.
- Understand the application of deepBreaks in Python with examples.
- Explore how machine learning and deep learning can help in understanding DNA sequences.
Wednesday:
- A Hands-on Introduction to Data Science using R (Dan Kerchner, PhD Student)
- Local versus cloud computing
- Open science, reproducibility, and coding
- Introduction to coding using R: Data types and formats
- Data wrangling with tidyverse
- Statistical data analysis in R
- Publication-quality data visualization in R using ggplot2
Thursday:
- seqSight(Xinyang Zhang, PhD Student)
- Meta-analysis of the gut microbiota in response to cancer immunotherapy.
- Introduction of Biosynthesis gene clusters and their functions.
- Different bioinformatics tools(e.g., taxa, Biosynthetic Gene Clusters, metabolic pathway profiling).
- Applications of metagenomics in the human gut microbiome.
- Introduction to Genomics and Transcriptomics (Erika Hubbard, PhD Student)
- Introductions & Background
- Terminology, applications of bioinformatics
- What is bioinformatics? Genomics? Transcriptomics?
- Central dogma
- Why is bioinformatics relevant? How is it used?
- Transcriptomics Research Examples
- Comparing inflammatory mechanisms underlying SLE, RA, and OA
- Relationships between inflammatory biomarkers in SLE
- Precision medicine: subsetting SLE patients into “endotypes” based on gene expression
- Metagenomics Research Example
- Characterization of the gut microbiome in HIV-exposed infants
- Hands-on Genomics Activity
- Asked the students for a gene of interest or disease of interest
- Explored a relevant gene using NCBI tools, UCSC Genome Browser
- Shared advice & recommendations based on my experiences
- Q & A
- Discussed how to get started doing research!
- path2max program dinner
Friday:
- Ali Reza Taheriyoun (Postdoc Associate)
- How to formulate an interesting scientific question; mathematical vs statistical models
- Treatment/drug efficiency within longitudinal studies; cross-sectional vs longitudinal studies and the concept of subject effect
- Introduction to R package lme4 to analyze continuous longitudinal studies.
- Introduction to Python package waveome to discover the dynamics of omics data.