This repository contains data and analysis files for a comprehensive genetic diversity study of six Plethodon salamander species across multiple temporal and geographic sampling strategies.

Project Overview

This study examines genetic diversity patterns in Plethodon salamanders using targeted sequence capture data. The research compares genetic diversity estimates across:

• 6 species: P. cinereus, P. cylindraceus, P. glutinosus, P. montanus, P. welleri, P. yonahlossee
• 2 localities: IGG (Indian Grave Gap) and SG (Skull's Gap)
• 3 sample types/timepoints: FFS (formalin-fixed specimens from the 1960s and 1970s), frozen blood (1980s), and fresh liver (2018/2019)
• 141 total samples across all combinations

Directory Structure

/Final_Baits/

Contains bait design files and coverage statistics for the targeted sequence capture approach.

Key Files:

• recommended_coverage_summary_table.txt: Coverage statistics for all 1,200+ target loci
  • Columns: Target name, Bait coverage %, coverage within 100nt/300nt/500nt windows
  • Six target sets representing different gene categories:
    • Set 1: Immune genes (ILF2, MHC, TLR-2, chemokine receptors)
    • Set 2: Phylogenetically informative genes (RAG1, SLC8A3, RHO, etc.)
    • Set 3: Developmental genes (OTX2, KLF9, ALDH1A3, dlx2, etc.)
    • Set 4: Vision genes (RHO, ARR3, GNAT1/2, GRK1/7, etc.)
    • Set 5: Ultraconserved elements (UCEs) - 80 conserved genomic regions
    • Set 6: Additional coding genes (~600 genes from various functional categories)

Bait Design Sequence Files:

• Salamander-baits-all.fas: Complete set of all bait sequences in FASTA format
• Salamander-baits-all_Recommended_15791.fas: Final recommended bait set (15,791 baits) after filtering and optimization
• Salamander-baits-all-filtration.txt: Documentation of filtering criteria and statistics applied to bait selection
• Salamander-input-seq.fas: Input sequences used for bait design (target loci sequences)

Bait Design Processing Scripts:

• 0_annotate_trans.py: Initial annotation of transcript sequences
• 1_IDexon_single_genelist.py: Identifies exons for single-gene target lists
• 1_IDexon_multi_genelist_same_names.py: Identifies exons for multi-gene lists with shared nomenclature
• 2.1_splice_ref_exons.py: Splices reference exon sequences for bait design
• 3_blast_Transcripts_to_Ref.py: BLAST alignment of transcripts to reference sequences
• 4_filter_blastout_splice_CDS.py: Filters BLAST output and splices coding sequences
• 6.1_splice_immune_transcripts.py: Specialized processing for immune gene transcripts

/scripts/

Analysis and processing scripts organized by function:

Consensus Sequence Generation:

• generate_consensus_align_from_phased_pleth_v1.py: First version of consensus alignment generation from phased data
• generate_consensus_align_from_phased_pleth_v2.py: Updated version of consensus alignment generation with improved algorithms
• generate_consensus_align_from_phased.py: Consensus alignment generation from phased sequences
• create_sp_consensus.py: Creates species-level consensus sequences
• fill_gaps_consensus.py: Fills gaps in consensus sequences using reference data
• fill_gaps_consensus_pleth_ref.py: Plethodon-specific gap filling using reference sequences

Sequence Processing Utilities:

• concatenate_fasta.py: Concatenates multiple FASTA files into a single output
• calc_ref_aln_length.py: Calculates reference alignment lengths for quality control
• remove_fails.py: Removes failed sequences or low-quality samples from datasets
• santity_check.py: Quality control and validation checks for processed sequences

Bootstrap Analysis (/Bootstrap_genetic_diversity_Pleth/)

• bootstrap_populations.py: Bootstrap resampling for genetic diversity estimation
• calc_summary_stats_bootstrap.py: Statistical calculations for bootstrap results
• Additional supporting scripts for population genetic analyses

Summary Statistics (/Generate_summary_statistics/)

• Scripts for calculating diversity metrics (π, heterozygosity, Fis, etc.)
• Population-level and species-level statistical summaries

VCF Processing (/vcf_processing/)

• VCF file manipulation and quality filtering scripts
• Format conversion utilities

/summary_statistics/

Contains all genetic diversity summary statistics files:

File Naming Convention:
{dataset}_{snp_type}_{site_type}_{num_loci}_{num_samples}sumstats_combined_all_samples.txt

Dataset Types:

• allreads: All sequencing reads included
• downsamp: Downsampled to equal coverage depth

SNP Types:

• allsnps: All SNPs per locus
• singlesnps: One SNP per locus (to reduce linkage)

Site Types:

• allsites: All targeted sites
• coding: Coding regions only
• noncoding: Non-coding regions only

Summary Statistics Columns:

• Population: Species_Locality_Timepoint combination
• Species: Salamander species name
• Locality: IGG or SG
• Replicate: Sample type (Blood/FFS/Fresh)
• Sites: Number of loci analyzed
• Variant_Sites: Sites with variation
• Polymorphic_Sites: Polymorphic sites within the population
• %Polymorphic_Loci: Percentage of polymorphic loci
• Num_Indv: Number of individuals
• Pi: Nucleotide diversity (π)
• Obs_Het: Observed heterozygosity
• Exp_Het: Expected heterozygosity
• Fis: Inbreeding coefficient
• Various variance and standard error estimates

/vcfs/

Variant Call Format (VCF) files containing SNP data:

File Naming:
{dataset}_{snp_type}_{site_type}_{num_loci}_{num_samples}.vcf

Key Files:

• allreads_allsnps_allsites_31049_141.vcf: Complete dataset (31,049 loci, 141 samples)
• downsamp_*: Coverage-standardized versions
• Coding vs. non-coding region subsets
• Single SNP per locus versions for population genetic analyses

Sample Information

Species Abbreviations:

• cinereus: Plethodon cinereus (red-backed salamander)
• cylindraceus: Plethodon cylindraceus (white-spotted slimy salamander)
• glutinosus: Plethodon glutinosus (northern slimy salamander)
• montanus: Plethodon montanus (mountain salamander)
• welleri: Plethodon welleri (Weller's salamander)
• yonahlossee: Plethodon yonahlossee (Yonahlossee salamander)

Locality Codes:

• IGG: Indian Grave Gap
• SG: Skull's Gap

Sample Types:

• Blood: Recently collected blood samples (DNA extracted from fresh blood)
• FFS: Formalin-fixed specimens (museum specimens preserved in formalin)
• Fresh: Fresh tissue samples (recently collected tissue samples)

Key Results Files

• populations.tsv: Master sample metadata linking sample IDs to species, locality, and timepoint
• combined_sumstats_*.txt: Aggregated results across all analyses
• *_bootstrap.txt: Bootstrap confidence intervals for diversity estimates
• *_summary.txt: Final summary statistics and comparisons

Analysis Pipeline

1. Raw sequence processing: Quality filtering and adapter removal
2. Target enrichment: Alignment to reference target sequences
3. Variant calling: SNP identification using VCF format
4. Quality filtering: Coverage and genotype quality thresholds
5. Population genetics: Diversity calculation using VCFtools/custom scripts
6. Bootstrap analysis: Resampling for confidence intervals
7. Comparative analysis: Temporal and geographic comparisons

Data Specifications

• Sequencing platform: Illumina paired-end sequencing
• Target enrichment: Custom bait design (1,200+ loci)
• Coverage: Variable across samples (standardized in downsampled analyses)
• Reference: Custom reference sequences for each target locus
• Quality thresholds: Minimum coverage and genotype quality filters applied

Code/Software

Required Software for Data Analysis

Core Bioinformatics Tools:

• VCFtools (v0.1.16+): Primary tool for VCF file manipulation and population genetic calculations
  • Used for: SNP filtering, summary statistics calculation, format conversion
  • Installation: conda install -c bioconda vcftools Or from the source
  • Key functions: --site-pi, --het, --hardy, --freq
• BCFtools (v1.9+): VCF file processing and variant calling
  • Used for: VCF manipulation, filtering, and format conversion
  • Installation: conda install -c bioconda bcftools
• Python (v3.7+) with required packages:
  • pandas (v1.3+): Data manipulation and analysis
  • numpy (v1.20+): Numerical computations
  • subprocess: System command execution
  • glob: File pattern matching
  • argparse: Command-line argument parsing

Data Viewing and Analysis:

• R (v4.0+) for statistical analysis and visualization:
  • ggplot2: Data visualization
  • dplyr: Data manipulation
  • readr: File I/O
  • tidyr: Data tidying
• Text Editor/Spreadsheet Software:
  • Any text editor for viewing .txt and .tsv files
  • Excel, LibreOffice Calc, or R/Python for .txt summary statistics
  • Command line tools: less, head, tail for large files

Software Workflow

1. VCF File Analysis:

# View VCF structure
bcftools view -h input.vcf | head -50

# Calculate population statistics
vcftools --vcf input.vcf --keep population_list.txt --site-pi --out results

2. Summary Statistics Processing:

# Python scripts for bootstrap analysis
python bootstrap_populations.py --vcf input.vcf --populations populations.tsv
python calc_summary_stats_bootstrap.py --input bootstrap_results/

3. Data Visualization:

# R scripts for plotting results
library(ggplot2)
library(dplyr)
data <- read.table("summary_statistics.txt", header=TRUE, sep="\t")

Included Scripts

Bootstrap Analysis Scripts:

• bootstrap_populations.py: Performs bootstrap resampling of individuals within populations
  • Input: VCF files, population metadata
  • Output: Bootstrap replicate summary statistics
  • Dependencies: Python 3.7+, pandas, subprocess
• calc_summary_stats_bootstrap.py: Calculates confidence intervals from bootstrap results
  • Input: Bootstrap replicate files
  • Output: Mean estimates with confidence intervals
  • Dependencies: Python 3.7+, pandas, numpy
• bootstrap_genetic_diveristy_pleth.py:creates keep-files, subsamples a VCF by species/replicate/locality, runs summary stats, and outputs files sorted by species and datatype

**Alignment Processing Scripts: **

• clean_alignments.py: Quality filtering and cleanup of sequence alignments
• concatenate_alignments.py: Combines individual locus alignments for phylogenetic analysis

VCF Processing Scripts:

• Various Python scripts for VCF manipulation, filtering, and format conversion
• Custom functions for population-specific analyses

Installation Instructions

Using Conda (Recommended):

# Create environment with required tools
conda create -n plethodon_analysis python=3.8 vcftools bcftools pandas numpy
conda activate plethodon_analysis

# Install R packages
conda install -c conda-forge r-base r-ggplot2 r-dplyr r-readr r-tidyr

Alternative Installation:

• VCFtools: Download from https://vcftools.github.io/
• BCFtools: Download from http://samtools.github.io/bcftools/
• Python packages: pip install pandas numpy
• R packages: install.packages(c("ggplot2", "dplyr", "readr", "tidyr"))

File Processing Workflow

1. VCF Analysis: Use VCFtools to calculate basic population genetics statistics
2. Bootstrap Resampling: Run Python bootstrap scripts for confidence intervals
3. Data Aggregation: Combine results across populations and datasets
4. Visualization: Use R or Python plotting libraries for figure generation
5. Statistical Testing: Employ R for comparative statistical analyses

Citation and Contact

This dataset supports research on temporal genetic diversity patterns in Appalachian salamanders. Please contact the authors for data usage and collaboration inquiries.

File Format Notes

• VCF files: Standard Variant Call Format (v4.2)
• Summary statistics: Tab-delimited text files
• Sample metadata: Tab-delimited with header row
• Coverage tables: Tab-delimited with percentage values
• All coordinate systems are 1-indexed unless otherwise specified