A. tonsa 25 gen evolve and resequence

This repository hold scripts used to analyse the genomic data from a 25 generation selection experiment of Acartia tonsa.

Data availability

Made public once preprint is posted

Note that there were 2 lanes of sequencing. Data processing code below is provided for only one lane, but the methods were identical. Data were merged after aligning.

plotting details

For my own sanity, here are the colors I'm using for each treatment:

• Founding population: #D3DDDC, pch= 21
• Ambient: #6699CC, pch= 21
• Acidic: #F2AD00, pch=22
• Warm: #00A08A, pch= 23
• Greenhouse: #CC3333, pch=24

description of scripts

Below here is a description of scripts used in the analyses.

Genome annotation

Followed GAWN.

Details of the annotation are in genome_annotation.md

Data processing

• Check quality of sequence data: fastqc.sh
• Trim the minor adapter presence from samples: trim.sh
• re-check quality to make sure trimming was good: fastqc_posttrim.sh

Align, call snps

• align with bwa mem: align.sh
• alignment stats: align_stats.sh
• merge resulting bams from each lane/sample: merge_bams.sh
• plot resulting stats: plot_alignment_stats.R
• Call snps with varscan: varscan.sh
• Filter raw SNPs for depth, missingnesss, etc: filter_raw_snps.R

identify candidate SNPs

• convert af file to sync file: to_sync.py
• calculate cmh from sync file above: cmh.sh
• annotate all snps: annotate.py

Simulate drift expectations for significance corrections

• simulation function: sim_function.R
• simulate data: sim_af.R
• calc cmh: ~/tonsa_genomics/scripts/sim_af.sh
• run actual sims: sim_af.sh
• then take these sims and the cmh results from above, and combine into table cmh_results.txt: cmh_process.R

and finally, calc_significance.R outputs cmh.fdr.txt

cvtk

relies on the cvtk package from Vince Buffalo. Found here: https://github.com/vsbuffalo/cvtkpy

See tonsa.ipynb for all analyses.

Figures

• Fig. 1: plots.R
• Fig. 2: af_change.R
• Fig. 3: plots.R
• Fig. 4: plots.R

Supplemental figures:

• Fig. S1: af_change.R
• Fig. S2: go_enrich.md
• Fig. S3: plots.R
• Fig. S4: plots.R
• Fig. S5: ld.R
• Fig. S6: pi.R
• Fig. S7: af_change.R
• Fig. S8: af_change.R
• Fig. S9: scripts/mitotypes/mitotypes.md
• Fig. S10: scripts/mitotypes/mitotypes.md