Learning Bioinformatics

 Week 12:  Creating an R Markdown File for Alignment Score Visualization Creating the R Markdown file "Alignment Score Visualization" was a great learning experience. It allowed me to combine R code, text, and visuals in one document, which is both easy to read and share. Here’s what I learned and how it went: Getting Started At first, R Markdown felt a bit confusing because it combines text and code in one file. But once I understood the structure, it became clear how powerful it is. The key was using code chunks, which are sections of the file that run the R code. Each chunk starts with ```{r} and ends with ``` . For my project, I wanted to display alignment scores for V and J regions in a table and a bar chart. Writing and organizing the code into chunks made it easy to run and test each part. Challenges I Faced Code Showing as Plain Text : When I first tried knitting the file, the HTML output only showed the code as plain text. I realized this was because I had included...

Week: 11 Debugging Journey- Fixing the tukey_multiple Function Introduction In this post, I’ll explain how I fixed a bug in the tukey_multiple function in R. The goal of this function is to find rows in a dataset where all the values are outliers. Initially, I ran into errors, but I eventually found the problem and fixed it. Here’s how I did it. Understanding the tukey_multiple Function The function tukey_multiple is supposed to: Check each column in a dataset to see if values are outliers. Return a list that tells us whether each row has only outliers across all columns. Here’s the original code I started with: Step 1: Initial Problem - Missing Function When I tried to run the code, I got an error saying tukey.outlier wasn’t found. After checking, I realized that tukey.outlier isn’t a built-in function in R and wasn’t defined anywhere else in the code. This function is supposed to check if values in a column are outliers, so I knew I needed to write my own function to do that. Wh...

 Week : 10  "VDJ_Analysis: Package for Immune Receptor Alignment and Functional Junction Analysis" Introduction to the VDJ_Analysis   Package In bioinformatics and immunology, studying immune receptor sequences—like T-cell receptors (TCRs) and antibodies—is key to understanding how our immune system detects different pathogens. The immune system creates diversity through recombination of gene segments called V (variable), D (diversity), and J (joining) regions. Analyzing these segments and identifying functional (productive) junctions helps us better understand immune responses and diseases. To make this analysis easier in R, I am proposing the VDJ_Analysis package. This package would align immune receptor sequences, match them to known V and J regions, and evaluate junction productivity. It aims to provide researchers with an R-based tool that consolidates alignment scores, matched regions, and productivity assessments, simplifying immune receptor analysis. Objectiv...