Skip to content

Latest commit

 

History

History
175 lines (119 loc) · 7.18 KB

README.md

File metadata and controls

175 lines (119 loc) · 7.18 KB

2µ project

Codes and scripts to reproduce the analyses and plots.

Dependencies

Scripts and codes can be run on OS X and other Unix-based systems. It basically requires to have Python installed on your machine which is commonly installed on Unix-based systems. For windows, you can have a look to https://www.python.org/downloads/windows/. Then, a few python modules are necessary for diverses operations on arrays and vizualisation.

Python (>=2.7)

  • Numpy
  • Matplotlib (>=1.0)
  • Scipy
  • Biopython

External programs

Raw data extraction and alignment

Data extraction

Data can be dowloaded on Short Read Archive server at the following address http://www.ncbi.nlm.nih.gov/sra.

A SRA executable called fastq-dump from SRA toolkit can be used to download and split both mates of a NGS library:

./fastq-dump SRR639031 --split-3 -O /home/data/

Alignment of the Hi-C libraries

To align the reads and generate the contact files in cool format, we used hicstuff pipeline:

hicstuff pipeline -t 18 --mapping="iterative" -D -a bowtie2 --matfmt bg2 --no-cleanup -F -p -o out_Micro-C_WT_log_classic_genome  -g SC288_with_micron SRR7939017.1_1.fastq SRR7939017.1_2.fastq

and convert into cool file:

cooler cload pairs -c1 2 -p1 3 -c2 4 -p2 5 sacCer3.chr_sizes.txt:200 valid_idx_pcrfree.pairs valid_idx_pcrfree.pairs.cool

Processing of genomic data like Mnase-seq, RNA-seq or ChIP-seq

We used tinyMapper:

./tinyMapper.sh -m MNase -s SRR5399542.1 -g S288C_with_micron -o results_H3_CC 

./tinyMapper.sh -m RNA -s SRR7692240.1 -g S288C_with_micron -o results_RNAseq

./tinyMapper.sh -m MNase -s SRR11235539.1 -g S288C_with_micron -o results_ATAC-seq

./tinyMapper.sh -m ChIP -s SRR7175393.1 -i SRR7175394.1 -g S288C_with_micron -o results_CHIP_Rpb3

Visualisation of contact maps with plasmid signal

We used home python code to plot contact maps and signal of 2 micron plasmid.

Visualisation of contact maps

python3 plasmid_micron2_hot_spots_ARG1.py out2_pKan-STB-P/tmp/valid_idx_pcrfree.pairs.cool  pKan-STB-P pKan-STB-P
python3 plasmid_micron_its_Map.py  valid_idx_pcrfree.pairs.cool.200 plasmid_p2-micron micro-C-log

To have the 1D enrichment plot for contact signal of the plasmid:

python3 plasmid_HSC_1D_agglo_norm3.py  valid_idx_pcrfree.pairs.cool plasmid_p2-micron topo2 HSC_plasmids_in_Micro-C_WT_log_SC288_genome.txt.sort.formated

To automatically detect the peaks of contact between plasmid and yeast chromosomes, we use plasmid_micron2_hot_spots_ARG1_fig_sup1_norm3.py

Averaged plot of genomic signals around hot spots of contact:

python plasmid_micron2_Chip-seq_ARG5.py SRR13736589.bis.fastq.sam.MQ0 SRR13736587.bis.fastq.sam.MQ0 H3_log H3_log

python plasmid_micron2_Chip-seq_ARG5_1Mnase.py SRR7692240.1_1.fastq.gz.sam.MQ0 RNAseq RNAseq # with log 

python plasmid_micron2_Chip-seq_ARG5_1Mnase.py SRR6246290.1.bis_1.fastq.sam.MQ0 ATAC_WT

Computation of aggregated plots around the 73 loci contacted by 2u plasmid:

computeMatrix scale-regions  -S  Micro-C_WT_log_interpolated.bw -R HSC_73.bed2  --beforeRegionStartLength 20000  --regionBodyLength 10  --afterRegionStartLength 20000  --sortRegions keep -o heatmap.gz

plotHeatmap -m heatmap.gz -out heat_map_contact_best.pdf --colorMap afmhot_r  --missingDataColor white --sortRegions keep



computeMatrix scale-regions -S SRR14693235.bis^mapped_SC288_with_micron_SC88^ZW95QD.unstranded.CPM.bw -R HSC_plasmids_in_Micro-C_WT_log_SC288_genome.txt.sort.formated.bed --beforeRegionStartLength 20000  --regionBodyLength 10  --afterRegionStartLength 20000  --skipZeros -o heatmap.gz

plotHeatmap -m heatmap.gz -out heat_map_pol2_test4.pdf --colorMap Blues --missingDataColor grey  --sortRegions keep  --zMin 0. --zMax 20.0 --interpolationMethod  nearest

Plot for the gene boxes:

Use home made python code gene_boxes.py

pdfjam $(ls -v  window7_gene_*.pdf) --nup 1x73 --landscape --outfile Page12_win_genes7.pdf

Computation of pileup plot according the gene structure:

First, we create of bw file for the contact signal with the python code create_big_wig.py then

computeMatrix scale-regions -S Micro-C_WT_log_redone_plasmid_contact_signal.bw   -R long_genes_only_host_chrm.txt2  --beforeRegionStartLength 7000 --regionBodyLength 7000 --afterRegionStartLength  7000 --outFileName signal_2u_genes.gz 

plotProfile -m signal_2u_genes.gz -out Profile_contact_long_genes.pdf --numPlotsPerRow 2  --plotTitle "Contact signal at long gene (size>7b)"

To plot the agglomerated plots of contact signal around centromeres:

python3 plasmid_HSC_1D_agglo_norm3.py  valid_idx_pcrfree.pairs.cool  plasmid_p2-micron log_centros centro1.dat55

Generation and comparison of agglomerated plot of contact signal of 2 micron:

To automate the visualization and comparison of the average contact signal between 2 biological conditions, we use a home made script in bash.

file_set_positions="HSC_plasmids_in_Micro-C_WT_log_SC288_genome.txt.sort.formated"
cool_file1="out_FG01/tmp/valid_idx_pcrfree.pairs.2000.cool"
cool_file2="out_FG02_4/tmp/valid_idx_pcrfree.pairs.2000.cool"

name1="FG1"
name2="FG2"

python3 plasmid_HSC_1D_agglo_norm2.py $cool_file1 plasmid_p2-micron $name1  $file_set_positions
python3 plasmid_HSC_1D_agglo_norm2.py $cool_file2 plasmid_p2-micron $name2  $file_set_positions

file1='/home/axel/Bureau/compare_agglo_redone_ylim/'$name1'_files/agglomerated_signal_on_HSC__'$name1'_2.0kb_norm2.txt'
file2='/home/axel/Bureau/compare_agglo_redone_ylim/'$name2'_files/agglomerated_signal_on_HSC__'$name2'_2.0kb_norm2.txt'

python3 plot_agglo_HSC2_norm2_arg_n.py  2 $file1 $file2 $name1 $name2

Generation and visualisation of scatter plot for the ChIP-exo libraries

After alignment with bowtie2 of the 1251 libraries, we filtered the reads with MQ>0 and then process all the files:

#!/bin/bash

for i in *.fastq.sam.MQ0 ; 
do j=$(echo $i | grep -E -o  SRR[0-9]+) ; 
if [ ! -f "1D_ENRICHMENT_HSC_"$j"_73Hot_spots_plasmid.pdf" ];
then 
echo $j;
python3 plasmid_micron2_Chip-seq_ARG3.py $i $i; 
fi; done

repo: /media/axel/RSG4/diverse_yeast_data/CHip_seq_2018

We use the code density_scatter_plot5.py to generate the plot.

Computation of GC content for regions contacted by the plasmid and the whole genome

seqkit fx2tab sequences_73HSC.txt -n --length --gc --gc-skew --header-line > gc_content_73HSC.txt

dnaglider-linux -window 10000 -threads 8 -fields "GC,GCSKEW" -fasta SC288_with_micron.fa  -out gc_stats.tsv