jellyfish K-mer analysis and genome size estimate

標籤：

jellyfish不能用fq.gz 要先轉為fq才行用gunzip -c *.fq.gz > *.fq  $ jellyfish count -t 30 -C -m 21 -s 150G  --min-quality=20 --quality-start=33 ./*.fastq Assume a haploid genome, for simplicity. In the picture provided, the first peak at depth ~31 indicates amount of 1-copy content (in other words, the genome has exactly 1 copy of that kmer, so it is unique). The weak peak at ~62x indicates the amount of 2-copy content. Everything under ~11x can be assumed to be error kmers, unrelated to genome size.

So, to estimate manually, take the sum of the counts of unique kmers under the first peak and multiply by 1; add the sum of the counts of unique kmers under the peak at 2x the depth of the first peak and multiply by 2; etc, for all peaks. This will give you the haploid genome size. So if your genome is tetraploid, the actual size will be 1/4 of your result, since the first peak will correspond to mutations present on only 1 ploidy (1/0/0/0 genotype).

You can make this more accurate by modelling the peaks as a sum of Gaussian curves, but that probably won‘t change the result much. Of course, this method is subjective because calling peaks is subjective.

Please note - I think 17-mers are too short for this kind of analysis. I prefer 31-mers because they are the longest computationally-efficient kmers. Also, FYI, BBNorm is faster than Jellyfish and can also generate kmer-frequency histograms:

khist.sh in=reads.fq hist=khist.txt

Also, it makes more sense to plot these things as log-log rather than linear-linear; and the Y-axis should be count, not frequency, which is useless for the purpose of genome-size estimation. Outline

1. count k-mer occurence using Jellyfish (jellyfish count)
2. summarize as histogram (jellyfish histo)
3. plot graph with R
4. determine the total number of k-mer analyzed and the peak position
5. compare the peak shape with poisson distribution

Count k-mer occurence

In this example we have 5 pair of fastq files in three different subdirectories. The file to process can be specified with "*/*.qf.fastq" and veriied with ls.

$ ls */*.qf.fastqrun1/s_1_1_sequence.qf.fastq  run2/s_2_2_sequence.qf.fastqrun1/s_1_2_sequence.qf.fastq  run3/s_1_1_sequence.qf.fastqrun2/s_1_1_sequence.qf.fastq  run3/s_1_2_sequence.qf.fastqrun2/s_1_2_sequence.qf.fastq  run3/s_2_1_sequence.qf.fastqrun2/s_2_1_sequence.qf.fastq  run3/s_2_2_sequence.qf.fastq

Next, we issue the jellyfish count command

jellyfish count -t 8 -C -m 25 -s 5G -o spec1_25mer --min-quality=20 --quality-start=33 */*.qf.fastq

-t 8

specifies the number of threads to be used. This value should be equal to the number of cores on the machine or the number of slots you reserved through job management system ($NSLOTS in SGE or UGE).

-C

specifies the both strands are considered. If you do not specify this, the apparent depth would be half, --- that is undesirable

-m 25

specified that now you are counting for 25 mer (i.e., k=25)

-s 5G

is some kind of magical number specification of hash size. This should be as high as the physical memory allows. The higher the faster, but exceeding the available memory leads to failure or extremely slow counting.

-o spec1_25mer

specifies the prefix of output file names.

--quality-start=33

specified that your fastq file have 33 based quality value string. Be careful on the dataformat. There are cases that your data are 64 based depending on the sequending system and software versions. This is relevant only when you specify --min-quality

--min-quality=20

specifies that nucleotide having qv lower than 20 should not included in the count. This selection reduces the k-mers derived from sequence errors and make the peak clearer.

*/*.qf.fastq

will be expanded to the ten filenames explained above by the shell and passed to jellyfish as input files

summarize as histogram (jellyfish histo)

First confirm that you got the output file

$ ls spec1_25mer*spec1_25mer_0

now that there is a single file spec1_25mer_0

$ jellyfish histo -o spec1_25mer.histo spec1_25mer_0

Confirm that you got the output

$ ls spec1_25mer*spec1_25mer_0  spec1_25mer.histo

Examine the numbers by your eyes

$ head -25 spec1_25mer.histo1 4619385832 956060443 192804774 138367545 110184806 95550907 85579358 78632449 731950510 692088011 658972312 632192313 614863814 603612015 597226416 596223417 598769618 605117119 615442920 629737321 648513522 670057923 693257024 721762725 7533211

  運行出錯：
terminate called after throwing an instance of ‘jellyfish::invertible_hash::ErrorAllocation‘
  what():  Failed to allocate 628292358736 bytes of memoryAborted (core dumped)解決：50G should be more than enough. That is the amount of memory I usually am using and I have never had any problems. This also means that you probably do not need the high memory nodes.     freemaoFAFU

jellyfish K-mer analysis and genome size estimate