Simulate 100 Reads from .fastq file | FASTQC

Question

[Anonymous]

I am totally new to .fastq files and have just installed FASTQC. I can see all of the analyses available ok.

I am following along with this TDS Article on Cluster Analysis of 100 Simulated Reads, from each .fastq file. However, it generates them using different software called SimLord, which I was unsuccessful at using.

My question; how can I generate 100 simulated reads, each of a length >10,000bc? Is there a link to a tutorial that is good?

Update: I am now going to use Linux as many tutorials do

Answer 1

[GenoMax]

Title: Simulate 100 Reads from .fastq file | FASTQC

My question; how can I generate 100 simulated reads, each of a length 10,000bc?

FastQC is a quality control program. It can't be used for simulation or downstream analysis.

If you are looking to generate 10kbp reads then you will want to start with a source that is of much longer length (it would not make sense to use short illumina fastq reads e.g.) Since you are looking for very long reads you also want a simulator that can mimic longer read technologies like PacBio/ONT. This was the reason you were trying to use SimLord.

Note: You need to clarify for us if you are working on Windows command prompt. If you are then many things being shown here will simply not work. It appears that you were using git bash (from your other posts) which is not adequate for these programs to work. You will want to at least use Windows Subsystem for Linux (LINK) which allows you to use Ubuntu (and other) linux OS on a windows machine.

Coming back to simulation. You can use randomreads.sh from BBMap suite like this. genome.fa can be human genome. You will need to create an index first.

bbmap.sh -Xmx30g ref=genome.fa

then run the simulation

randomreads.sh -Xmx10g in=genome.fa pacbio=t length=10000 out=test.fq reads=100

[Anonymous]

Update: I have now spun-up Debian Linux. Will get back to trying this at 7pm BST

Answer 2

[Istvan Albert]

use wgsim

• https://github.com/lh3/wgsim

or an modified version of the code above called pywgsim (disclaimer I developed this latter):

• https://github.com/ialbert/pywgsim