1. There are the following 25 files for PDAL-seq experiment: ATCC_0mM_S1_R1_001.fastq ATCC_0mM_S1_R2_001.fastq ATCC_20mM_A_S2_R1_001.fastq ATCC_20mM_A_S2_R2_001.fastq ATCC_20mM_B_S3_R1_001.fastq ATCC_20mM_B_S3_R2_001.fastq ATCC_40mM_A_S4_R1_001.fastq ATCC_40mM_A_S4_R2_001.fastq ATCC_40mM_B_S5_R1_001.fastq ATCC_40mM_B_S5_R2_001.fastq F_1_0mM_S6_R1_001.fastq F_1_0mM_S6_R2_001.fastq F_1_20mM_A_S7_R1_001.fastq F_1_20mM_A_S7_R2_001.fastq F_1_20mM_B_S8_R1_001.fastq F_1_20mM_B_S8_R2_001.fastq F_1_40mM_A_S9_R1_001.fastq F_1_40mM_A_S9_R2_001.fastq F_1_40mM_B_S10_R1_001.fastq F_1_40mM_B_S10_R2_001.fastq 20mM_A_S2_R1_001 20mM_A_try25_S6_R1_001 20mM_B_S3_R1_001 40mM_A_S4_R1_001 40mM_B_S5_R1_001 2. In the Galaxy history listed below, each fastq file is mapped to hg19 using BWA-MEM (Creates a .bam file), the .bam file is filtered based on mapping quality >= 30 (Creates another .bam file), .bam file's intervals that intersect with a black listed region are removed, and the resulting .bam file is converted to .bigwig. https://usegalaxy.org/u/kaivan/h/pdal-seq-collection-1 3. In the Galaxy history listed below, .bigwig is converted to histogram format. https://usegalaxy.org/u/kaivan/h/pdal-seq-histogram 4. Download bigwig-to-histogram tabular files from Galaxy history in step 3 into ./input folder: 5. For each chromosome in each bigwig-to-histogram file, deduct the number of reads from the maximum value for the number of reads, so we can find valleys by peak calling. Add as a new column to bigwig-to-histogram files by activating the venv and running the following command: . ./venv/bin/activate;./scripts/add_peak_column.sh <InputFolder> <OutputFolder>; Set both <InputFolder> and <OutputFolder> to './input'. This shell script creates a "_peak.bed" file for each tabular file in ./input folder 5. Do MACS2 peak calling by running the following command: ./scripts/run_peak_callers.sh This script calls ./scripts/run_peak_caller.sh with <EXTSIZE> set to 25, 35, 50, 75, 100, 150, and 200. <EXTSIZE> input parameter is needed by MACS2. For each .bed file, this generates a folder in ./output folder (e.g., for "ATCC_0mM_S1_R1_001_peak.bed" and extsize 25 it creates a "./output/ATCC_0mM_S1_R1_001/extsize_25" folder). In each folder, 3 files are created. E.g., in "./output/ATCC_0mM_S1_R1_001/extsize_25" folder, the following files are created: ATCC_0mM_S1_R1_001_peaks.xls: a tabular file which contains information about called peaks ATCC_0mM_S1_R1_001_peaks.narrowPeak: peak locations together with peak summit, p-value, and q-value ATCC_0mM_S1_R1_001_summits.bed: contains the peak summits locations for every peak 6. ATCC_0mM_S1_R1_001_peaks.xls file contains information about called peaks for all chromosomes. Save the peaks information for each chromosome by running the following command: ./scripts/split_peaks_files.sh This script calls ./script/split_peaks_file.sh with <EXTSIZE> set to 25, 35, 50, 75, 100, 150, and 200. The ./scripts/split_peaks_file.sh, in turn, calls ./scripts/split_peaks_file.py for each "_peaks.xls" file and generates 24 .bed files (chromosomes 1 to 22, X and Y), in the same folder as the "_peaks.xls" file. 7. For each chromosome, find the intersection between chromosome peaks and non-b DNA annotations by running thefollowing command: ./scripts/intersect_peaks.sh <PeaksFolder> <NonBDNAFolder> This shell script creates a "_intersect.bed" file for each chromosome in the same random folder as the peaks file. <PeaksFolder> parameter value is "./output". As for <NonBDNAFolder> parameter value, create a directory called './nonb' and copy the non-B DNA annotations files from 'All-Non-B-DNA-Annotations' collection in Galaxy history below. Pass the full path of 'nonb' folder as the second argument. https://usegalaxy.org/u/kaivan/h/pdal-seq-non-b-dna-annotations The shell script finds the intersect in <EXTSIZE> subfolders 25, 35, 50, 75, 100, 150, and 200. 8. For each chromosome, summarize the intersect files by dividing the sum of the length of the intersect intervals by the sum of the length of the peak intervals, by running the following command: ./scripts/summarize_intersect.sh <PeaksFolder> <PeaksFolder> parameter value is "./output". This shell scripts calls ./scripts/summarize_intersect.py for each folder and each <EXTSIZE>. For each chromosome, it divides the sum of peaks/non-B DNA intersect lengths by the sum of the peaks lengths, and writes them to a "peaks_summary.txt" file in the same folder. The last line of the txt file calculates the harmonic mean of the intersect ratios for all chromosomes. 9. Aggregate intersect summaries by running the following command: ./scripts/aggregate_intersect_summaries.sh This script calls ./scripts/aggregate_intersect_summaries.py, that reads the last line of 'peaks_summary.txt' file in each folder to get the Harmonic mean value of the intersect ratio between peaks and non-B DNA, and writes the Harmonic mean value to a file named './output/intersect_summaries.tsv. The folder names in this file are sorted based the value of the Harmonic mean, in descending order. 10. To evaluate PDAL-seq, we compare PDAL-seq intervals with random intervals regarding intersection with non-B DNA intervals. To generate random intervals, run the following command: ./scripts/generate_random_peaks.sh The shell script calls ./scripts/generate_random_peaks.py for all folders and all EXTSIZE subfolders. The script creates a file with the same name as .xls file, but ending in '_peaks_random.bed'. 11. Split the random intervals file, for all folders and EXTSIZE subfolders, by running the following command: ./scripts/split_random_peaks_file.sh This script creates a 'random' folder, in each folder and EXTSIZE subfolder, then creates a .bed file for random intervals in each chromosome. 12. For each chromosome in each folder and EXTSIZE subfolder, find the intersection between the chromosome random intervals and non-b DNA annotations, by running thefollowing command: ./scripts/intersect_random_peaks.sh <PeaksFolder> <NonBDNAFolder> This shell script creates a "_intersect.bed" file for each chromosome in the same random folder as the .bed file. <PeaksFolder> parameter value is "./output". As for <NonBDNAFolder> parameter value, set it to './nonb' and copy the non-B DNA Annotations files from 'All-Non-B-DNA-Annotations' collection in Galaxy history below: https://usegalaxy.org/u/kaivan/h/pdal-seq-non-b-dna-annotations 13. For each chromosome in the random folder, summarize the intersect files by dividing the sum of the length of intersect intervals by the sum of the length of random intervals, by running the following command: ./scripts/summarize_random_intersect.sh <PeaksFolder> <PeaksFolder> parameter value is "./output". This shell scripts calls ./scripts/summarize_intersect.py for each folder. For each chromosome, it divides the sum of random/non-B DNA intersect intervals lengths by the sum of the random intervals lengths, and writes them to a "peaks_summary.txt" file in the same folder. The last line of the txt file calculates the harmonic mean of the intersect ratios for all chromosomes. 14. Aggregate random intersect summaries by running the following command: ./scripts/aggregate_random_intersect_summaries.sh This script calls ./scripts/aggregate_intersect_summaries.py, that reads the last line of 'peaks_summary.txt' file in each random folder to get the Harmonic mean value of intersect ratio between random intervals and non-B DNA, and writes the Harmonic mean value to a file named './output/random_intersect_summaries.tsv. The folder names in this file are sorted based the value of the Harmonic mean, in descending order. 15. Plot the samples with best intersect ratio in a bar plot by running the following command: ./scripts/plot_intersect_ratio.sh The shell script calls ./scripts/plot_intersect_ratio.py. Intersect summaries (./output/intersect_summaries.tsv) and random intersect summaries (./output/random_intersect_summaries.tsv) are passed in as input. We also pass a filter value, such that rows in intersect_summaries.tsv whose harmonic mean is less than the filter value are ignored. The bar plot is saved as an .png file. A more advanced plot can be created by using the R scripts in Jacob Sieg's repo at https://github.com/JPSieg/beebar