Genomics_ATAC

1. PAN genome assembly

Uses the seq-seq-pan software (https://gitlab.com/rki_bioinformatics/seq-seq-pan) to generate an .xmfa multiple genome alignment file. Genomes and gff files are available through lepbase.org.

Run_seq-seq-pan.sh

2. PAN genome processing

Python script to create .bed files with sequence blocks present in pan genome for each genome.

python seq-seq-pan_blocks_intervals.py -I SeqSeqPan_erato_melp_noNewline.xmfa -g 1,2

Bash commands for intersecting block .bed with bedtools.

Intersect_bed.sh

Transform .xmfa file to fasta.

python seq-seq-pan_toFasta.py -I SeqSeqPan_erato_melp_noNewline.xmfa -g 1,2

3. ATAC-seq read mapping, peak calling and counting.

Note that this bash code is written for a slurm array job and would still require to add slurm job submission parameters.

Clean reads using Trimmomatic.

Run_ATAC_trimmomatic.sh

Read mapping.

Run_ATAC_mapping.sh

Peak calling MACS2.

Run_ATAC_MACS2.sh

Combine MACS peaks to creat peak reference set

# Retain peaks present in 2 samples with at least 1 bp overlap
Run_ATAC_combine_peaks_2samples_1bp_per_tissue_stage.sh

# Retain peaks present in all samples with at least 1 bp overlap
Run_ATAC_combine_peaks_ALLsamples_1bp_per_tissue_stage.sh

# Retain peaks present in 2 samples with at least 50% reciprocal overlap
Run_ATAC_combine_peaks_2samples_50perc_per_tissue_stage.sh

# Retain peaks present in all samples with at least 50% reciprocal overlap
Run_ATAC_combine_peaks_ALLsamples_50perc_per_tissue_stage.sh

4. ATAC-seq QC

Count number of reads in .bam file.

samtools view -c SAMPLE.bam

Calculate fraction of reads in peaks (FRIP).

Run_ATAC_Fraction_reads_in_peaks.sh

Calculate Transcription Start Site enrichment score.

TSS_enrichment.R

5. Counting reads in peaks

Counting.

# Count read number in reference peak set in H. erato
Run_ATAC_count_H_erato.sh

# Count read number in reference peak set in H. melpomene
Run_ATAC_count_H_melpomene.sh

Map ATAC-seq peak intervals to PAN genome.

map_peak_intervals_to_pan.sh

6. Map bedgraphs and other features to PAN genome

Python code to transform scaffold positions to genome positions for mapping with seq-seq-pan map.

seq-seq-pan_bedgraph_chrompos.py

Map MACS2 peaks sets to PAN genome coordinates and intersect.

Run_ATAC_map_peaks_to_PAN.sh

Create bedgraph files from .bam files.

Run_bedgraphs.sh

Scale and average bedgraphs for tissue/time.

Run_bedgraphs_scale.sh

Map bedgraphs to PAN genome assembly.

# Extract positions from bedgraph files for mapping with seq-seq-pan map
Run_bedgraphs_map_to_PAN_preprocess.sh

# Map position to pan genome
Run_bedgraphs_map_to_PAN_seqseqpan_mapping.sh

# Combine start and end positions of intervals mapped to pan genome
Run_bedgraphs_map_to_PAN_postprocessing.sh

7. Differential accessibility analyses

Calculate size factors (used when scaling and combining bedgraphs).

ATAC_sizefactor.R

R code for differential accessibility (DA) analyses. Includes:

• Code to match H. erato and H. melpomene peak counts
• code for DA between developmental time points, wings and sections
• Code for PCA
• Merging of DA peaks with DNA sequence conservation
• Foldchange correlation analysis
• Code to output DA peak sets

ATAC_DA_erato_melp_development.R
ATAC_DA_erato_melp_FWHW.R
ATAC_DA_erato_melp_sections.R

8. Differential expression analyses

Download data

Download_RNAseq_data.sh

Map RNA-seq reads and count gene expression.

Run_mapping.sh
Run_count.sh

Create counts table from individual sample mappings.

Create_counts_tables.R

Differential expression forewing versus hindwing.

diff_expression_analysis_FWHW.R

Volcano plots with highlighted genes near DA peaks.

diff_expression_volcano_FWHW.R

Identify genes with shared expression patterns forewing versus hindwing.

shared_genes_with_peaks.R

Differential expression forewing sections.

diff_expression_analysis_sections.R

Identify genes close to DA ATAC-seq peak.

Run_homer_development.sh
Run_homer_FWHW.sh
Run_homer_sections.sh

Correlate ATAC-seq accessibility with gene expression.

# Over development
shared_unique_development_expression.R

# between forewing and hindwing
shared_unique_FWHW_expression.R

9. ATAC-seq peak conservation

Python script to calculate conservation of intervals between pair of genomes.

seq-seq-pan_bedfile_conservation.py

Calculate conservation for different interval sets.

Run_interval_conservation.sh

Summaries and visualisation in R.

calculate_IDY_mean.R

10. TF enrichment

Run meme-chip to find TF enrichment patterns between tissues and time points.

Run_meme_development.sh
Run_meme_FWHW.sh
Run_meme_sections.sh

Parse meme-chip html outputs and extract TFs and enrichment values.

parse_MEME.py

11. Color pattern analysis of optix CRE mutants

R script to extract color and compare with wild type phentypes.

patternize_optix4.R

12. Visualize a PAN genome segment with ATAC-seq data

Ubx

Plot_PAN_ATAC_Ubx.R

optix

Plot_PAN_ATAC_optix.R