How to remove the abnomal copy-number (CN) gains or losses regions

[pangxueyu233]

Hi Tan,

it's really a interesting tool to analyse the 3D genome on single-cell level. Recently, we also implemented this platform in our projects. But we met some problems. As you can see in some cells of our data, there were obvious copy-number (CN) gains or losses regions. Although you mentioned .reg files in README.md file, I'm not sure where and how should we do to remove them.

Now, I have two plans to achive it: 1) remove the CN regions by samtools and bedtools from *.aln.sam.gz files; 2) remove them by dip-c reg3. But I'm not sure which way is better, because the RMSD is the important standard for the structure. And the RMSD scores would be calculated based on *3dg files. So, I prefer to remove these region by the first way to before genome structure reconstruction. Do you think whether it's resonbale to do?

Sincerely Xiangyu Pan West China hospital Sichuan University Chengdu, China

[pangxueyu233]

Hi Tan, I found the chromosome name of my data had 'chr', so I modified the source data of reg3.py to make it possible to remove the CN regions as following:

......
    # presets
    presets = {
        "hm":[Reg("chr" + str(i + 1)) for i in range(22)] + [Reg("X"), Reg("Y")],
        "hf":[Reg("chr" + str(i + 1)) for i in range(22)] + [Reg("X")],
        "mm":[Reg("chr" + str(i + 1)) for i in range(19)] + [Reg("chrX"), Reg("chrY")],
        "mf":[Reg("chr" + str(i + 1)) for i in range(19)] + [Reg("chrX")]}
    preset_descriptions = {
        "hm":"human male",
        "hf":"human female",
        "mm":"mouse male",
        "mf":"mouse female"}
......

But when I used the dip-c reg3 to exclude the CN regions in .3dg files, it would return errors. The codes I used are as following:

DIP_C=./programme/dip-c-master/dip-c
sample=MLL3_RS2_1
rep=1
$DIP_C reg3 -e $sample.filter.haplotype.bed -p hf $sample.20k.${rep}.3dg > $sample.20k.${rep}.filter.3dg

And it returns:

Traceback (most recent call last):
  File "/mnt/data/user_data/xiangyu/programme/dip-c-master/dip-c", line 130, in <module>
    main()
  File "/mnt/data/user_data/xiangyu/programme/dip-c-master/dip-c", line 69, in main
    return_value = reg3.reg3(sys.argv[1:])
  File "/mnt/data/user_data/xiangyu/programme/dip-c-master/reg3.py", line 89, in reg3
    g3d_data = file_to_g3d_data(open(args[0], "rb"))
  File "/mnt/data/user_data/xiangyu/programme/dip-c-master/classes.py", line 1427, in file_to_g3d_data
    g3d_data.add_g3d_particle(string_to_g3d_particle(g3d_file_line.strip()))
  File "/mnt/data/user_data/xiangyu/programme/dip-c-master/classes.py", line 1214, in string_to_g3d_particle
    hom_name, ref_locus, x, y, z = g3d_particle_string.split("\t")
ValueError: need more than 1 value to unpack

And I also checked the source data of reg3.py, the error happed in this function file_to_g3d_data. And when I execute the following codes, it would reported same errors:

import sys
import getopt
import gzip
from classes import Reg, file_to_reg_list, file_to_g3d_data, G3dData

g3d_data = file_to_g3d_data(open("./MLL3_RS2_1.20k.1.3dg", "rb"))

it returns:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "classes.py", line 1427, in file_to_g3d_data
    g3d_data.add_g3d_particle(string_to_g3d_particle(g3d_file_line.strip()))
  File "classes.py", line 1214, in string_to_g3d_particle
    hom_name, ref_locus, x, y, z = g3d_particle_string.split("\t")
ValueError: need more than 1 value to unpack

Can you help me work it out?

And I had submitted my .3dg file and abnomal CN region data. mydata.zip

Thanks Xiangyu Pan

[pangxueyu233]

I have figrued it out, I found the wrong .3dg files were used as input. When I used the .20k.${rep}.dip-c.3dg files, produced by hickit_3dg_to_3dg_rescale_unit.sh, to run $DIP_C reg3, the right output had been generated.

Thanks Xiangyu Pan

[tanlongzhi]

Hi Xiangyu,

Thank you for your interest in our work, and sorry about the delay in my response. Because 3D modeling is challenging for abnormal CNs (e.g. CN gain or LOH; CN loss is actually fine), I recommend removing these regions at a much earlier stage: using dip-c reg or hickit to remove contacts from these regions before any subsequent analysis.

Best, Tan

[pangxueyu233]

Hi Tan,

Recently, I use dip-c for subsequent analysis on the GM12878 data. I found the structure reconstruction were different by aligning with different version of reference (hg38 and/or hg19). The RMSD values are also changed a lot. Here is my results you could check:

1. I thought the SNP_file would be important file for imputaion, my SNP_file were downloaded and processed by following codes:

mwget -n 50 -c 50 ftp://platgene_ro:''@ussd-ftp.illumina.com/2017-1.0/hg38/small_variants/NA12878/NA12878.vcf.gz
mv NA12878.vcf.gz NA12878_hg38.vcf.gz
hickit.js vcf2tsv NA12878_hg38.vcf.gz > phased_SNP_hg38.tsv

mwget -n 50 -c 50 ftp://platgene_ro:''@ussd-ftp.illumina.com/2017-1.0/hg19/small_variants/NA12878/NA12878.vcf.gz
mv NA12878.vcf.gz NA12878_hg19.vcf.gz
hickit.js vcf2tsv NA12878_hg19.vcf.gz > phased_SNP_hg19.tsv

2. And I also found that l thought the structure generated from hickit_3dg_to_3dg_rescale_backbone.sh and hickit_3dg_to_3dg_rescale_unit.sh are similar, the RMSD values are different as following:

Because I didn't familiar the internal formula and codes in dip-c, did you think whether it has something would affect the RMSD values and structure files .clean.3dg by aligning with different version of reference (especially, the hg19 and hg38)

If you need the raw data, I could provide them for you.

And here is my codes for runing the sample. If you find somewhere wrong, please tell me.

cd /mnt/data/sequencedata/3D_Genome/tmp
# GENOME=/mnt/data/user_data/xiangyu/programme/genome_index/bwa_index/bwa_hg19_index/hg19.tmp
GENOME=/mnt/data/user_data/xiangyu/programme/genome_index/bwa_index/bwa_hg38_index/hg38.fa
SNP_file=/mnt/data/user_data/xiangyu/programme/dip-c-master/snps/phased_SNP_hg38.tsv
# SNP_file=/mnt/data/user_data/xiangyu/programme/dip-c-master/snps/phased_SNP_hg19.tsv
scripts=/mnt/data/user_data/xiangyu/programme/dip-c-master/scripts
output=/mnt/data/sequencedata/3D_Genome/tmp/
DIP_C=/mnt/data/user_data/xiangyu/programme/dip-c-master/dip-c
COLOR=/mnt/data/user_data/xiangyu/programme/dip-c-master/color
sample=PD10.hg38

bwa mem -5SP -t 25 -R "@RG\tID:PD10.hg38\tSM:PD10.hg38\tLB:Genome3D\tPL:Illumina" $GENOME \
/mnt/data/sequencedata/3D_Genome/tmp/PD10_XXL_pa-2_S101_L002_R1_001.fastq.gz \
/mnt/data/sequencedata/3D_Genome/tmp/PD10_XXL_pa-2_S101_L002_R2_001.fastq.gz | gzip > PD10.hg38.aln.sam.gz

mkdir /mnt/data/sequencedata/3D_Genome/tmp/sam_all
mv *.sam.gz ./sam_all 

hickit.js sam2seg -v $SNP_file $output/sam_all/$sample.aln.sam.gz 2> $sample.raw.contacts.seg.log | hickit.js chronly -y - | gzip > $sample.raw.contacts.seg.gz
hickit -i $sample.raw.contacts.seg.gz  -o - 2> $sample.raw.contacts.pairs.log | bgzip > $sample.raw.contacts.pairs.gz
hickit -i $sample.raw.contacts.pairs.gz -u -o - 2> $sample.raw.impute.pairs.log | bgzip > $sample.raw.impute.pairs.gz 

$scripts/hickit_pairs_to_con.sh $sample.raw.contacts.pairs.gz
$scripts/hickit_impute_pairs_to_con.sh $sample.raw.impute.pairs.gz

for rep in `seq 1 3`
do
  hickit -s${rep} -M -i $sample.raw.impute.pairs.gz -Sr1m -c1 -r10m -c2 -b4m \
  -b1m -O $sample.raw.1m.${rep}.3dg \
  -b200k -O $sample.raw.200k.${rep}.3dg \
  -D5 -b50k -O $sample.raw.50k.${rep}.3dg \
  -D5 -b20k -O $sample.raw.20k.${rep}.3dg
done

# hickit_3dg_to_3dg_rescale_backbone.sh
for rep in `seq 1 3`
do
  $scripts/hickit_3dg_to_3dg_rescale_unit.sh $sample.raw.20k.${rep}.3dg
  $DIP_C clean3 -c $sample.raw.impute.con.gz $sample.raw.20k.${rep}.dip-c.3dg > $sample.raw.20k.${rep}.clean.3dg 2> $sample.20k_clean.log # remove repetitive (contact-less) regions
  $scripts/hickit_3dg_to_3dg_rescale_unit.sh $sample.raw.50k.${rep}.3dg
  $DIP_C clean3 -c $sample.raw.impute.con.gz $sample.raw.50k.${rep}.dip-c.3dg > $sample.raw.50k.${rep}.clean.3dg 2> $sample.50k_clean.log # remove repetitive (contact-less) regions
  $scripts/hickit_3dg_to_3dg_rescale_unit.sh $sample.raw.200k.${rep}.3dg
  $DIP_C clean3 -c $sample.raw.impute.con.gz $sample.raw.200k.${rep}.dip-c.3dg > $sample.raw.200k.${rep}.clean.3dg 2> $sample.200k_clean.log # remove repetitive (contact-less) regions
  $scripts/hickit_3dg_to_3dg_rescale_unit.sh $sample.raw.1m.${rep}.3dg
  $DIP_C clean3 -c $sample.raw.impute.con.gz $sample.raw.1m.${rep}.dip-c.3dg > $sample.raw.1m.${rep}.clean.3dg 2> $sample.1m_clean.log # remove repetitive (contact-less) regions
done

$DIP_C align -o $sample.raw.aligned.20k. $sample.raw.20k.[1-3].clean.3dg 2> $sample.raw.20k.align.log > $sample.raw.20k.align.color
$DIP_C align -o $sample.raw.aligned.50k. $sample.raw.50k.[1-3].clean.3dg 2> $sample.raw.50k.align.log > $sample.raw.50k.align.color
$DIP_C align -o $sample.raw.aligned.200k. $sample.raw.200k.[1-3].clean.3dg 2> $sample.raw.200k.align.log > $sample.raw.200k.align.color
$DIP_C align -o $sample.raw.aligned.1m. $sample.raw.1m.[1-3].clean.3dg 2> $sample.raw.1m.align.log > $sample.raw.1m.align.color

$DIP_C color -n $COLOR/hg19.chr_new.txt $sample.raw.20k.1.clean.3dg | $DIP_C vis -c /dev/stdin $sample.raw.20k.1.clean.3dg > $sample.raw.20k.clean.n.cif 
$DIP_C color -n $COLOR/hg19.chr_new.txt $sample.raw.50k.1.clean.3dg | $DIP_C vis -c /dev/stdin $sample.raw.50k.1.clean.3dg > $sample.raw.50k.clean.n.cif 
$DIP_C color -n $COLOR/hg19.chr_new.txt $sample.raw.200k.1.clean.3dg | $DIP_C vis -c /dev/stdin $sample.raw.200k.1.clean.3dg > $sample.raw.200k.clean.n.cif 
$DIP_C color -n $COLOR/hg19.chr_new.txt $sample.raw.1m.1.clean.3dg | $DIP_C vis -c /dev/stdin $sample.raw.1m.1.clean.3dg > $sample.raw.1m.clean.n.cif 

Thanks a lot Xiangyu Pan