Welcome to my website showcasing my work at the HackBio Internship 2024, a collaborative and immersive experience aimed at advancing bioinformatics skills through projects using real-world data.

About HackBio

Link to the official website

HackBio is a bioinformatics internship founded to bridge the gap between knowledge and application in the field of computational biology. With a strong focus on mentorship, collaboration, and hands-on learning, HackBio provided us, aspiring scientists and bioinformaticians, the opportunity to work on significant projects with real-life implications.

Track: AMR in Cancer

For this project, my teammates and I were working on the Antimicrobial Resistance (AMR) in Cancer track.

Antimicrobial resistance roughly refers to the ability of microbes (such as bacteria, fungi, viruses, and parasites) to evolve and resist the effects of drugs that were once effective against them. This phenomenon is challenging in cancer treatment, particularly for immunocompromised patients undergoing chemotherapy, where infections caused by resistant pathogens can lead to complications.

Tools and Software Used

Overview

Stage 0

Goal: Write an essay (400 words) on Antimicrobial Resistance (AMR) in cancer using references.

Implication: This step involves understanding the intersection between cancer treatment and AMR, where resistance to antibiotics affects treatment outcomes, particularly in cancer patients. For example, AMR in gut microbiomes can influence immunotherapy efficiency. This stage focuses on research synthesis and scientific communication, explaining the challenges of managing infections in cancer patients while balancing antibiotic use.

Stage 1

Goal: Analyze the paper "Gut Resistome of NSCLC Patients Treated with Immunotherapy" & make a short video about it.

Implication: The purpose here is to understand how gut microbiomes contribute to AMR in cancer treatments, particularly for non-small cell lung cancer (NSCLC) patients using immunotherapy. The task helps highlight how AMR genes can reduce the effectiveness of cancer treatments, providing insights into future personalized cancer therapies by managing resistance factors in the gut microbiome. Collaborative team engagement and research presentation are crucial here.

Stage 2

Goal: Download and clean the AMR Products Dataset, perform statistical analysis, visualize data, and generate key insights about AMR trends.

Implication: The task is aimed at developing data analysis skills by cleaning, processing, and interpreting an AMR dataset. By visualizing trends in AMR product development, it’s possible to identify promising drug candidates and understand AMR dynamics in different regions. This stage focuses on technical skills in data science (e.g., using Python or R), and critical thinking to extract insights that can influence public health strategies.

Stage 3

Goal: Introduce bioinformatics using Bash scripting for a simulated wet-lab biologist, alongside analyzing a WHO dataset about cholera outbreaks, using R.

Implication: This phase provides foundational bioinformatics tasks with an emphasis on Bash scripting for sequencing data analysis and file navigation. By offering simple, yet powerful, scripts, wet-lab biologists can automate tasks like genome assembly, variant calling, and data preprocessing. The parallel analysis of the cholera outbreak dataset exemplifies the importance of bioinformatics tools in handling global health data. Through this, we demonstrate how key insights from large datasets like cholera outbreaks can inform public health decisions and antimicrobial resistance (AMR) strategies.

Stage 4

Goal: Build an NGS analysis pipeline using bash or another workflow manager (e.g., Nextflow, Snakemake) for whole genome sequencing datasets.

Implication: Creating a next-generation sequencing (NGS) analysis pipeline addresses the complexity of genomic data processing and analysis. The workflow enhances reproducibility, efficiency, and accuracy in analyzing genomic data, particularly related to AMR. By using established tools and frameworks, it allows teams to quickly adapt to emerging research questions and respond to AMR outbreaks with precision.