Recombination analysis

ddRAD-Seq is a cheap, high-depth sequencing technique that generates genotyping data across the whole genome. The goal of this project is to quantify and map recombination events using ddRAD-Seq data, or any genomic data of progeny from two parents that can be characterized with markers

Ploidy analysis an filtration

For ploidy analysis:

We used Ymap for ploidy analysis.

ddRAD-Seq demultiplexing and alignment

Sequencing libraries were prepared using ddRad-Seq methods and sequenced with Illumina MiSeq using 50bp single-end sequencing

A tool called barcode_splitter (found here)is employed for out single-end reads:

barcode_splitter --bcfile barcode_file.txt lotsa_samples.fastq --idxread 1

• barcode_file.txt is a tab-separated file containing the sample names and respective barcodes

Sample Name	Sample Barcode
Sample_1	TCGAT
Sample_2	TGCAT
Sample_3	CAACC
Sample_4	AAGGA
Sample_5	AGCTA
Sample_6	ACACA

Alignments and .bam file processing

Alignments were performed using bowtie2

bowtie2 -x reference_directory/ -q single_end_reads.fastq -S output_file.sam -p 8

STACKS

Manual for v1 STACKS: STACKS V1 (Data as of 6/7/21 has been analyzed and prepared using this version)

pstacks:

To generate markers for each individual progeny, use pstacks_automator_9.py

python3 pstacks_automator_9.py ../alignments/filamentation/pstacks_input.txt ../alignments/filamentation/ filamentation/ python3 pstacks_automator_9.py ../alignments/fluconazole/pstacks_input.txt ../alignments/fluconazole/ fluconazole/

cstacks:

To generate a catalog with individual parents, use this command:

cstacks -b 2 -o filamentation -p 8 -s filamentation/529L_ddRAD-Seq_parent -s filamentation/SC5314_ddRAD-Seq_parent cstacks -b 2 -o fluconazole/ -p 8 -s fluconazole/P60002_ddRAD-Seq_parent -s fluconazole/SC5314_ddRAD-Seq_parent

To generate a catalog with both parents and every progeny use the cstacks_automator_10.py program:

python3 cstacks_automator_10.py filamentation/pstacks_input.txt ../stacks_output/filamentation/ ../stacks_output/filamentation/ 2 python3 cstacks_automator_10.py fluconazole/pstacks_input.txt ../stacks_output/fluconazole/ ../stacks_output/fluconazole/ 2

sstacks:

The 1 at the end of the command indicates the first batch number which uses the parents-only catalog I made with cstacks. Replacing this 1 with a 2 will

python3 sstacks_automator_11.py ../alignments/filamentation/pstacks_input.txt filamentation/ filamentation/ 1 python3 sstacks_automator_11.py ../alignments/fluconazole/pstacks_input.txt fluconazole/ fluconazole/ 1

Genotypes:

Genotypes command used to obtain files:

genotypes -b 1 -P ./genotype_input_files/ -c -t GEN --min_hom_seqs 10 --min_het_seqs 0.4 --max_het_seqs 0.05

genotypes is the Stacks tool being employed here. It takes p-stacks and c-stacks (two tools used in the Stacks pipeline) and combines them into a single file.

-b denotes the "batch" of files being used. In this case, the first batch is used to track all the 529L cross SC5314 files previously made in Stacks.

-P is the location of the directory containing all of your Stacks files you would like to input into genotypes.

-o is the format of the output file you would like to receive from Stacks. In this case, I want to use output data from genotypes in the r/qtl pipeline, so I've typed in rqtl.

-t --This is optional-- is the format of the genotypes that you would like to have returned to you. Here, I've input F2 meaning I wish my data to be represented as an F2 intercross file in the r/qtl format.

-c tells genotypes to use your input to decide how to call each marker.

--min_hom_seqs is the minimum number of reads required at a stack to call a homozygous genotype. The default is 5 reads, I've required a minimum of 10 reads to call a stack for a sample.

--min_het_seqs below this minor allele frequency a stack is called a homozygote, above it (but below --max_het_seqs) it is called unknown (default 0.05).

--max_het_seqs minimum frequency of minor allele to call a heterozygote (default 0.1)

Retrieving and formatting your data

Data output from stacks is in a .tsv or "tab separated value" format in long tables. The orientation of the files needs to be transposed onto its side in order to be used in the next step. There are two files that need to be transposed: a file marked .genotypes.tsv and a file marked .catalog.tags.tsv

To transpose the data, open the file with Microsoft Excel, copy all the data onto your clipboard and paste it using the transposition function.

Once the .catalog.tags.tsv and .genotypes.tsv files have been transposed, save both of them as a .csv instead of a .tsv file to in order to use them in the allele_switch_finder.py program.

Preparing STACKS files for recombination analysis

allele_switch_finder.py is a python program that combines the .catalog.tags.csv and .genotypes.csv files together to get them ready for r/qtl.

To use allele_switch_finder.py, use the following command:

python3 ./allele_switch_finder.py ./genotypes.csv ./catalogs.tags.csv parent_string_1 parent_string_2 output_name.csv

python3 tells the command line that you want to access the python3 library. This is needed to run the program that is written in python3.

./allele_switch_finder.py is the path to the allele_switch_finder.py program. This tells the command line where to find the program file in order to execute the program. The path will likely not be as simple as the current directory: ./. Instead, you will probably need to know how to write paths into the command line in order to execute this command.

./genotypes.csv is the location of your transposed genotypes file along a file path. This is so the program knows where to find the file to use it.

./catalogs.tags.csv is the location of the catalogs file.

parent_1_string is a series of characters exclusive to any parental samples that were processed by STACKS. This tells the program to find any genotyped samples in your library and combine them into one sample parent that is a consensus of all your parental samples. It is important that no other progeny sample names contain this particular string or else they will get lumped into the consensus parental strain.

parent_2_string and parent_1_string are the respective names on the parents

output_name.csv names an output for the file which can have a path added to it if you need the output written to a special location.

Required input files for recombination analysis with recombination_analyzer.py

calbicans_chromosomes.csv: A file containing chromosomal information on the name of the chromosome (as it appears in the marker file, probably what it is in the organism's .fasta assembly), length of the chromosome (this doesn't have to be perfect, if anything, slightly larger is better than slightly too short), the start and end of the centromere (this allows us to split the markers into arms of each chromosome) and a shortened name, or alias of the chromosome (used for labelling some graphs so the names don't run over eachother).

Chromosome Name	Chromosome Length	Centromere Start	Centromere End	Alias
Ca21chr1_C_albicans_SC5314	3190000	1563038	1
Ca21chr2_C_albicans_SC5314	2230000	1927255	2
Ca21chr3_C_albicans_SC5314	1800000	826481	3
Ca21chr4_C_albicans_SC5314	1600000	996216	4
Ca21chr5_C_albicans_SC5314	1190000	471745	5
Ca21chr6_C_albicans_SC5314	1030000	983792	6
Ca21chr7_C_albicans_SC5314	950000	428712	7
Ca21chrR_C_albicans_SC5314	2290000	1747664	R

progeny_ploidy_blacklist.csv: File with two columns containing progeny name and any chromosomes that need to be removed due to aneuploidy or other reasons. If the second column is left blank, all markers from that progeny will be removed! Multiple chromosomes can be removed if entered into the second column and separated by commas.

Recombination analysis with marker data

Run this program by using the following command:

python3 recombination_analyzer.py SCx529L/529LxSC_4way.csv

Removed 1890 markers ( 73.6 % ) Removed using remove_markers.csv: 4 Markers replaced or removed using strange_markers.csv: 8475 Total progeny: 75 Total recombinations: 2625

python3 recombination_analyzer.py SCxP60002/P60002xSC_4way.csv

Removed 1725 markers ( 70.0 % ) Removed using remove_markers.csv: 0 Markers replaced or removed using strange_markers.csv: 5380 Total progeny: 70 Total recombinations: 274

Recombination and SNP analysis less-than-diploid progeny WGS data:

I found inconsistencies with the P60002 parental; there are far fewer SNPs in this data set (35 distinguishable SNPs) than those in our original sequencing of the 21 clinical isolates (30,000), so we are leaving these data out of the analysis.

In LTD_Progeny_Markers, I convert SNP data or progeny that have some haploid chromosomes to 4way data so their recombination landscapes can be run with recombination_analyzer.py to see definitively whether or not there is recombination in euploid chromosomes.

Start this pipeline by running:

python3 ltd_wgs_converter.py WGS_outputSNPs.csv

Then run it through the pipeline:

python3 recombination_analyzer.py LTD_Progeny_Markers/ltd_529L_4way.csv

Removed 82816 markers ( 81.6 % ) Removed using remove_markers.csv: 174 Markers replaced or removed using strange_markers.csv: 211 Total progeny: 5 Total recombinations: 2287

Output files from recombination_analyzer.py

undesirables_XXX.csv: File containing markers that have been deemed "undesirable" - parental genotypes share an allele or missing alleles. Generated by marker_cleaner function in recombination_analyzer.py. These markers are removed from downstream analysis.

strangers_XXX.csv: File contain markers with 3 or 4 alleles in two markers. Generated in deploidy function in recombination_analyzer.py. These markers can be mismarked in ddRAD-Seq data, probably due to multiple-aligned reads. These markers are kept for downstream analysis, though it is suggested that you manually curate them due to stated mismarking. Manual curation can be done in the strange_markers.csv file.

cleaned_XXX.csv: File containing remaining markers after removing undesirables. Markers in this file still retain alleles with full genotype data. Generated by marker_cleaner function in recombination_analyzer.py

f2_cleaned_XXX.csv: cleaned_XXX.csv file that has had the marker data converted from full genotype data to a simplified genotype data that denotes the parental alleles inherited by the progeny (1 if the progeny marker is homozygous for parent 1, 2 for a homozygous parent 2 alleles, or n (short for neither because there were originally some markers that were not easily defined as heterozygous) for heterozygous alleles from both parents).

f2_recleaned_XXX.csv: Simplified markers (the same found in f2_cleaned_XXX.csv) are cleared of aneuploid chromosomes in progeny (while maintaining chromosomes with clear diploidy) and whole progeny if their karyotype is not easily recognizable or very aneuploid (more than 3 chromosomes at higher ploidy). Generated by deploidy and replace_mkrs functions (there isn't an intermediate file, though there could be!) in recombination_analyzer.py.

stackbar_XXX.csv: Parental contribution marker data (1, 2, or n) count and proportion data for both parent and all progeny. Columns a_prop (proportion of parent 1 markers in this progeny), b_prop (proportion of parent 2 markers in this progeny), and h_prop (proportion of heterozygous markers in this progeny) are independent of other columns, and are there for convenience while doing other analyses. This file is generated by marker_tally_ho function in recombination_analysis.py

stackbar_XXX.png: Stacked bar charts showing genotype proportion for each progeny generated using data in stackbar_XXX.csv processed by geno_graphs function in recombination_bins.R

statistics_XXX.csv: Information on each individual recombination event, progeny, chromosome, chromosomal location (starts at the centromere) and the distance between events. Generated by recombination_analysis function in recombination_analyzer.py.

statistics_XXX.png: Graph showing distribution of detected recombination break points. Generated by track_lengths function in recombination_bins.R.

recom-count_XXX.csv: File associating progeny names with the total number of recombinations in the genome. Generated by recombination_analysis function in recombination_analyzer.py

a/b/n_genotype_tally.png: Bargraphs showing raw marker count in 100,000bp bins across the genome. Data is generated by marker_tally_ho function in recombiantion_analyzer.py and the graphs themselves are generated by recombination_bins.R

recom_XXX.csv: Information on marker identity (columns a,b, and h; missing and s columns are not utilized; s stands for "strange" markers that are manually removed from the analysis) and recombination information (recoms and recom.by.mkr which is the number of recombinations in a 100,000bp bin divided by the total number of informative markers in that bin; recom.by.col is not utilized). This file is generated by recombination_analysis function in recombination_analyzer.py.

recom_XXX.png: Bargraphs showing recombinations/informative marker in 100,000bp bins (from recom_XXX.csv) across the genome.

recom_XXX_NORMALIZED_RECOMs.png: Graph showing recombination counts for the relative length (number of 100,000bp bins) of each chromosome in the cross. Generated by recom_graphs function in recombination_bins.R

loh_deploided_XXX.csv: Strange markers (strange markers are markers that have 3 or more alleles between the parents and are the only markers where LOH (loss or heterozygosity) can be detected with any accuracy, making LOH events appear rather rare) with individual aneuploid chromosomes removed as well as whole progeny with difficult-to-assess karyotypes. Generated with deploidy function in recombination_analyzer.py

f2_loh_events_XXX.csv: Same data as loh_deploided_XXX.csv but with genotype representation (1, 2, and n) instead of allele-based marker representation. Generated by loh_reco_f2_converter_mapper function in recombination_analysis.py

loh_events_XXX.csv: Looks for LOH-specific recombination events in f2_loh_events_XXX.csv. Generated by loh_reco function in recombination_analyzer.py

LOH_STACKAROONEY_XXX.png: Graph showing location (100,000bp bins across all chromosome) of LOH events across all progeny. Generated by loh_reco function in recombination_bins.R

centro_XXX.csv: contains information on cumulative recombination count from centromere to telomere. Generated by centromerely_math function in recombination_analyzer.py.

centro_XXX.png: graph of information in centro_XXX.csv generated by centro_graphs function in recombination_bins.R

CENTO_two_XXX.csv: information on recombination break's normalized proximity to centromere, generated by centro_func_two in recombination_analyzer.py

Unutilized files (that haven't been removed from the pipeline yet because I am just a humble man)

missing_genotype_tally.png

RECO_histo_XXX.png