introgression_AWT

Almost all the scripts for the introgression AWT paper.

The scripts for the exome capture part (assembly, annotation, etc) is split over:

• https://github.com/singhal/exomeCapture
• https://github.com/CGRL-QB3-UCBerkeley/denovoTargetCapturePopGen

Exome Capture Scripts

Pipeline 1: https://github.com/CGRL-QB3-UCBerkeley/denovoTargetCapturePopGen

• 2-ScrubReads: used to scrub reads
• 3-GenerateAssemblies: used to assemble reads
• 4-FinalAssembly: used to assemble across assemblies

Pipeline 2: https://github.com/singhal/exomeCapture

• 7annotateContigs.pl: used to match assembles to original targets
• 8initialSNPset.pl: used to map reads and call SNPs in transcriptome data
• 9clineSNPset.pl: used to map reads and call SNPs in clinal populations
• 10benchmarking.pl: used to get data for loci used in benchmarking.
• 11clineAFfiles.pl: used to get allele frequency data from the cline SNP set

Probe Design Scripts

• divPoly.pl: calculate divergence & polymorphism for transcriptome data to identify highly-differentiated transcripts
• dnds.pl: calculate a (rough) version of dn/ds for transcriptome data to identify transcripts putatively under positive selection
• exomeCapture.pl: the script that actually does most of the work; identifies possible exons, filters for GC content etc.
• finalExons_species.pl: picks some exons and adds in the other targeted sequence (i.e., mtDNA, UTRs, etc)
• finalFile_makeShorter.pl: makes the exon capture array shorter because the original files were overshoots
• fst.pl: calculate a (rough) version of Fst for transcriptome data to identify highly-differentiated transcripts
• initialExonFile.pl: generated the exon list to be used for downstream selection
• utrExtract.pl: extracts UTR sequences to be used in benchtruthing

Analysis Scripts

• aliquotDNA.pl: figured out amounts of DNA to spike into tube to get pooled libraries
• averageExonMtCoverage.R: calculated average coverage across each population in each lineage-pair
• benchtruthing_af.R: determines how much variance there is in mtDNA SNPs
• cline_fitting.py: fits clines to variants; takes in allele frequencies at each variant in each population
• divergence_contig.py: estimates divergence & polymorphism for transcriptome data on a per contig (transcript) basis
• divergence_full.py: estimates divergence & polymorphism for transcriptome data on a per exon basis
• dnds.py: estimates dn/ds for each transcript using PAML
• fst_contig.py: estimates Fst for transcriptome data on a per contig (transcript) basis
• fst_full.py: estimates Fst for transcriptome data on a per exon basis
• makeLDfiles.pl: looks at how cline centers and widths change over genomic space -- allows Anolis to be used as a reference
• moransI_bootstrap.py: takes in the files generated by makeLDfiles.pl and generates Moran's I estimates as well as doing some bootstrapping
• pooled_discrepancy.R: runs the simple R simulations to see how pooling might affect our ability to infer allele frequency accurately

Figure Scripts

• paper_figures.ipynb: includes Python code for most of the figures in the manuscript and some figures that didn't survive
• correlationCoverage.R: produces Figure S5 in Supplementary Information
• coverageAcrossContacts.R: produces Figure S6 in Supplementary Information
• cumulativeCoveragePlot.R: produces Figure S7 in Supplementary Information
• divpolyCoverage.R: produces Figure S8 in Supplementary Information
• gcContentCoverage.R: produces Figure S9 in Supplementary Information
• specificity.R: produces Figure S4 in Supplementary Information