例如deletion 的定义位置pysim362
引用位置：pysim838

Hi everyone,

I'm trying to simulate variants running pysim_main.py but I keep getting key error in script pysim1.py in line 194:

if len(snp_dic[key])>=num:
KeyError: '1'

Is anyone able to solve this issue?

Generating a fasta file with the specify_ploidy.py does not result in a fasta file in the same order. Although this is not a huge issue, it seems like it should be a quick fix and would result in more consistent data.

Input:

>chrM
>chr1
>chr2
>chr3
>chr4
>chr5
>chr6
>chr7
>chr8
>chr9
>chr10
>chr11
>chr12
>chr13
>chr14
>chr15
>chr16
>chr17
>chr18
>chr19
>chr20
>chr21
>chr22
>chrX
>chrY

Result:

>chr1-hap-1
>chr1-hap-2
>chr10-hap-1
>chr10-hap-2
>chr11-hap-1
>chr11-hap-2
>chr12-hap-1
>chr12-hap-2
>chr13-hap-1
>chr13-hap-2
>chr14-hap-1
>chr14-hap-2
>chr15-hap-1
>chr15-hap-2
>chr16-hap-1
>chr16-hap-2
>chr17-hap-1
>chr17-hap-2
>chr18-hap-1
>chr18-hap-2
>chr19-hap-1
>chr19-hap-2
>chr2-hap-1
>chr2-hap-2
>chr20-hap-1
>chr20-hap-2
>chr21-hap-1
>chr21-hap-2
>chr22-hap-1
>chr22-hap-2
>chr3-hap-1
>chr3-hap-2
>chr4-hap-1
>chr4-hap-2
>chr5-hap-1
>chr5-hap-2
>chr6-hap-1
>chr6-hap-2
>chr7-hap-1
>chr7-hap-2
>chr8-hap-1
>chr8-hap-2
>chr9-hap-1
>chr9-hap-2
>chrM-hap-1
>chrM-hap-2
>chrX-hap-1
>chrX-hap-2
>chrY-hap-1
>chrY-hap-2

Hello,

I am following the tutorial in the manual, and I see that for the, simulate_SV.py, stage, we supply a ini.config file containing:

[pysim_settings]
SV_config_file = /projects/bioinformatics/mattk_mgc/pysim_test/config_test
ref_fasta=/projects/bioinformatics/mattk_mgc/pysim_test/hg19_chr1_chr2-2.1.0.fa
dbsnp=/data/qfxing/sim/snp/dbsnp_138.hg19.vcf
chrome=chr22
somatic=Y
germline_num=10
somatic_num=10
db=N
somatic_SNP_db=''
hyp_rate=0.5
up_down_stream=50
snp_rate=0.1
indel_prob=1
min_indel_length=5
max_indel_length=15
out_prex=test_chr22_new_
sub_clone=2
sub_sub_clone=0
germ_ratio=0

I am just using chromosome1 from the human reference, and simulated the ploidy for my reference as 3-ploid. Do I use the output file generated from the specify_ploidy.py step as my reference if I did that?

Could you post a manual indicating how to generate SVs such as deletion and translocation?

pysim_main fails for sub_sub_clone != 0. Pretty sure it's because you use variable ref_somatic_new in a function call but never define it.

config_test 文件在哪里？

I kept getting key errors when running specifyploidy. I fixed the issues with the addition of the following commands. I would recommend changing your script to be more generalizable, the FASTA I am using is the one provided by 1000 Genomes.

Add this command under def read_config

newline=line.rstrip().split('\t')
# added command below ensures the keys from the cofig file and FASTA will match
if 'chr' not in newline[0]: newline[0] = 'chr'+str(newline[0])

In read_fasta

chr_name=newline.split('>')[1].split(' ').pop(0)
# previously it was chr_name=newline.split('>')[1]
# This is the pattern of the FASTA header I am using ">1 dna:chromosome chromosome:GRCh37:1:1:249250621:1" 

Thanks!

Hi,
I am not a python specialist, but I tried to simulate germline SV using this code. Although the program is promising, it didn't run very smoothly in my case using python 3. What I struggled with was

1. I had to update some scripts from python 2 to 3 (e.g. pysim1.py)
2. I had to install ART function art_illumina from https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm (make sure path to art_illumina in run_art.py is correct)
3. I had to change the way GC_bias.py reads the options for -g (GC file) to make GC_v1.0.?.py create the GC_content.txt file. The main code indicates that if no file is provided ("opts.input_GC is None") input_GC should be "GC_content.txt", however the GC_v1.0.?.py file expect input_GC to be None, need to make it matching. (I changed line 295 in GC_v1.0.2.py from " if opts.input_GC == None: "
   into " if opts.input_GC == 'GC_content.txt': ")

Now it finally seems to work well, so I hope this will help future users in running this (in python 3). Very pleased with a program to simulate SV from reference genomes. So many thanks to the developers!