Studying Local Adaptations in American Pikas

Author: Tien Ly
Updated: March 2025

🚧 Under Construction 🚧

This repository includes scripts and analyses for investigating local adaptations in American pikas as part of my MS Bioinformatics thesis project at San Jose State University. The project is structured into three main components: variant calling, population genomics analyses, and gene ontology (GO) enrichment analysis.

Project Overview

Part 1: Variant Calling

In this section, I develop a variant calling pipeline. Key steps include:

1. Quality Control:

   • fastqc.sh: Assess the quality of raw sequencing data
   • multiqc.sh: Summarize quality reports from multiple fastqc.sh outputs

2. Data Trimming:

   • fastp.sh: Trim low-quality bases and adapters from the reads
   • trimmed-fastqc.sh: Re-assess quality after trimming
   • trimmed-multiqc.sh: Summarize the quality of trimmed reads

3. Read Mapping:

   • bwa-map.sh: Align reads to the reference genome

4. Post-Processing:

   • bam-trans.sh: Convert SAM files to BAM format
   • query-sort.sh: Sort the BAM files by query name
   • read-groups.sh: Add read group information
   • mark-dup.sh: Mark duplicate reads
   • coord-sort.sh: Sort the BAM files by coordinates

5. Haplotype Calling:

   • haplotype.sh: Call haplotypes from the sorted BAM files to identify variant sites

6. Genotyping and Variant Filtering:

   • genotype.sh: Aggregate per-sample variant data and genotype the samples
   • filter-vcf.sh: Apply quality-based filtering to retain high-confidence variants

These scripts can be found in the wgs-pika folder.

Additional Files

• Create .dict and .fai files for the reference genome
  • samtools faidx GCA_014633375.1_OchPri4.0_genomic.fna

Nextflow Pipeline

In addition to the individual scripts, a Nextflow pipeline is available to streamline the entire variant calling process. You can find it in the wgs-pika-nextflow folder.

Part 2: Population Genomics Analyses

Once the variant calling is complete, I perform downstream statistical analyses on the VCF files, including:

• Outlier Detection: Identify genetic loci under selection using pcadapt and BayeScan
• Genotype-Environment Associations: Explore environmental drivers of selection and adaptation using RDA and BayPass

The scripts can be found in the selection-analysis folder.

Part 3: GO Enrichment Analysis

In this section, I investigate potential functional annotations of genes associated with local adaptation. Scripts for this analysis can be found in the go-analysis folder.