Hi, I have a question about FLASH parameter.
FLASH has a maximum overlap parameter called -M.
And parameter recommend this value that calculated from the read length, fragment size, fragment length standard deviation.
-r, --read-len=LEN
-f, --fragment-len=LEN
-s, --fragment-len-stddev=LEN
Average read length, fragment length, and fragment
standard deviation. These are convenience parameters
only, as they are only used for calculating the
maximum overlap (--max-overlap) parameter.
***The maximum overlap is calculated as the overlap of
average-length reads from an average-size fragment
plus 2.5 times the fragment length standard
deviation.***
The default values are -r 100, -f 180,
and -s 18, so this works out to a maximum overlap of
65 bp. If --max-overlap is specified, then the
specified value overrides the calculated value.
So, I made a python code for calculate read length, average read length, standard deviation read length.
I suddenly found that everything is wrong. Because I can't get fragment size. I just have a forward, reverse fastq file.
------------------------------------- <fragment>
-----------------------> <forward read>
over_lap
<----------------------- <reverse read>
I confused all about that.
I set the -M parameter(max overlap) Avg(forward read length) + 2.5 * (standard deviation(forward read length).
But I think it is wrong. Because FLASH recommended the value like this.
The maximum overlap is calculated as the overlap of
average-length reads from an average-size fragment
plus 2.5 times the fragment length standard
deviation.
I think I should know about fragment size. But I just have forward / reverse fastq file.
what do you think I should do? I really need your advice.
1 answer
If your samples were run through BioAnalyser prior to library preparation (could be some other magical machine, I am terrible at wet-lab stuff), it would spit out a fragment length distribution.
Bioinformatically, you could assemble and map the reads against the assembly to estimate insert (fragment) size, see tadpole and bbmap documentations, they are fast programs to perform these tasks. Hint, use parameter ihist with bbmap to get insert size distribution.
Finally, I would use bbmerge instead of flash to perform pair merging. It also outputs an estimate of fragment size and its standard deviation (again, use parameter ihist) - and do not need this information a priori.
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If you have a reference genome, you can align the reads to the genome to get a bam file, and run
CollectInsertSizeMetricsfrom Picard