[Kristian]

Hi Brian,

Is this a Nextera long-mate pair library?

This is Nextera XT prep, and the adapters/linker were trimmed along with demultplexing directly through Illumina RTA pipeline. On my end the bbduk2 left and right trimming had almost no effect, and the splitnextera also had only a few hits (0.1%) to the adapter, so I'd guess the dataset is (for the most part) trimmed.

[Brian Bushnell]

Hi Kristian,

Is this a Nextera long-mate pair library? Those need special processing before they can be mapped. Or... can you give me any more information about the library construction, and the trimming methodology? The library has an extremely high error rate (particularly with read 2), less than half of the reads map to the mito, and it appears that both adapters and transposase are still present.... also, I'm measuring the median insert size as 159 (BBMap) or 133 (BBMerge), so there are a lot of pairs with insert size shorter than the sequenced read length; those might be displayed differently in IGV depending on whether the adapter portion was soft-clipped (which bwa would do by default) or not (bbmap does not soft-clip by default).

I adapter-trimmed the reads and error-corrected them, but still under 50% map. I'm not really sure what's wrong with the library. But, I don't see anything unusual about the pairing orientations. I get 45.5670% properly paired with "rcs=f" (require correct strand = false) and 45.5481% with "rcs=t", so only 0.02% map in the wrong orientation.

[Kristian]

I noticed this line:
Anyway, please feel free to email me both bam files and the reference, and I'll look at them.

Thanks Brian, I sent you a PM with the details. I'm using samtools 1.3.1 (and the above is the result of samtools stats

[Brian Bushnell]

I noticed this line:

BBM: bases mapped (cigar): 29307 # more accurate

I'm guessing you are not counting "=" and "X" symbols as mapped, just "M"... generally, I would classify "M", "=", "X", and "I" as mapped bases. What version of samtools are you using?

Also, I find it odd that the insert size average and stdev differ so much. Often the mode or median is more stable as it is less influenced by outliers.

By inverted pairs, do you mean both reads map to the same side of the reference? If not, I wonder if the fact that the reference is very small and circular could play some role in the differences.

Anyway, please feel free to email me both bam files and the reference, and I'll look at them. It's possible that the circularity, or the fact that bwa looks for local rather than global alignments, is the primary factor behind the difference.

[Kristian]

High error rate with BBmap?

Hi Brian,

while trying to call variants on a human MT DNA I noticed the unusually high error rate in mapping with bbmap (37.10 and 36.92 tested) compared to bwa (0.7.12-r1039). Both were run with the default parameters, and below is the output of samtools stats (after sorting and indexing bams). IGV shows many more inverted pairs with bbmap mapping, and this came as a surprise.

I can provide details, however I prefer to share them offline due to the sensitivity of some of the data. Any hints on how to track down the issue?

Many thanks!

BBmap

BBM: raw total sequences:       537764
BBM: filtered sequences:        0
BBM: sequences: 537764
BBM: is sorted: 1
BBM: 1st fragments:     268882
BBM: last fragments:    268882
BBM: reads mapped:      256366
BBM: reads mapped and paired:   230042  # paired-end technology bit set + both mates mapped
BBM: reads unmapped:    281398
BBM: reads properly paired:     228748  # proper-pair bit set
BBM: reads paired:      537764  # paired-end technology bit set
BBM: reads duplicated:  0       # PCR or optical duplicate bit set
BBM: reads MQ0: 0       # mapped and MQ=0
BBM: reads QC failed:   4282
BBM: non-primary alignments:    0
BBM: total length:      67353071        # ignores clipping
BBM: bases mapped:      32229667        # ignores clipping
BBM: bases mapped (cigar):      29307   # more accurate
BBM: bases trimmed:     0
BBM: bases duplicated:  0
BBM: mismatches:        1672477 # from NM fields
BBM: error rate:        5.706749e+01    # mismatches / bases mapped (cigar)
BBM: average length:    125
BBM: maximum length:    151
BBM: average quality:   30.4
BBM: insert size average:       255.7
BBM: insert size standard deviation:    117.0
BBM: inward oriented pairs:     60121
BBM: outward oriented pairs:    2977
BBM: pairs with other orientation:      48
BBM: pairs on different chromosomes:    0

BWA

BWA: raw total sequences:       537764
BWA: filtered sequences:        0
BWA: sequences: 537764
BWA: is sorted: 1
BWA: 1st fragments:     268882
BWA: last fragments:    268882
BWA: reads mapped:      266324
BWA: reads mapped and paired:   248064  # paired-end technology bit set + both mates mapped
BWA: reads unmapped:    271440
BWA: reads properly paired:     245908  # proper-pair bit set
BWA: reads paired:      537764  # paired-end technology bit set
BWA: reads duplicated:  0       # PCR or optical duplicate bit set
BWA: reads MQ0: 178     # mapped and MQ=0
BWA: reads QC failed:   0
BWA: non-primary alignments:    0
BWA: total length:      67353071        # ignores clipping
BWA: bases mapped:      33736838        # ignores clipping
BWA: bases mapped (cigar):      32160973        # more accurate
BWA: bases trimmed:     0
BWA: bases duplicated:  0
BWA: mismatches:        603462  # from NM fields
BWA: error rate:        1.876380e-02    # mismatches / bases mapped (cigar)
BWA: average length:    125
BWA: maximum length:    151
BWA: average quality:   30.4
BWA: insert size average:       384.2
BWA: insert size standard deviation:    1040.7
BWA: inward oriented pairs:     67383
BWA: outward oriented pairs:    3645
BWA: pairs with other orientation:      112
BWA: pairs on different chromosomes:    0

[Brian Bushnell]

You can reduce kmer length and increase sensitivity - change "k=13" to "k=11" and add "slow". But ultimately, since BBMap is a global aligner, it does not like large structural rearrangements when aligning long queries, since they do not fit into the model of "match/substitution/insertion/deletion" reported in single alignments.

[catagui]

@minja - I replied via email and still have not had a chance to look into that; sorry - I will when I have a chance.

@catagui - BBMap cannot handle query sequences over 600bp. When you feed it a fasta input file, by default, sequences longer than 500bp are broken into 500bp pieces and mapped individually (and not recombined afterward). Some of these pieces could be quite short (say, 15bp) and will usually map if you have a big enough reference. You can discard them with "minlen=40" or so. Or you could simply use MapPacBio for mapping as it can accommodate much longer reads. In that case, though, you should increase "maxindel" which has different defaults between BBMap and MapPacBio, and furthermore, a longer transcript could contain multiple long introns.

Try "mapPacBio.sh k=13 maxindel=200000 maxlen=60000 minlen=40 (other arguments)" and see if that improves its mapping rate. Ultimately, though, longer query sequences will always have lower alignment rates than shorter ones when using a global or glocal aligner, so shredding the sequences (which BBMap does) artificially increases the alignment rate.

Hi Brian thanks for your reply.

I haven't been able to improve the mapping percentage. I tried your suggestion (bbmap/mapPacBio.sh k=13 maxindel=200000 maxlen=60000 minlen=40) and still get 13%.

Is it because the mapping is too stringent? As I am dealing with different species within the same genus. Is there another flag aside from "minid" that I could change to improve the mapping.

Thanks again

[Brian Bushnell]

Normally, the variant quality is the best indicator of whether a variant is real. I think, in this case, it's likely that most of them are real - viruses mutate rapidly, so viral "isolates" often contain multiple different viral genomes (which is why they are hard to assemble). The possibilities are, essentially...

1) Sequencing error randomly matched somewhere distant in the genome, causing the false appearance of a deletion. This is extremely unlikely with small, non-redundant genomes, and would usually only be represented by a single read.
2) Chimeric reads formed from different genomic fragments fusing together during sequencing may look like long deletion events. These should universally be represented by a single read (or pair) with a PCR-free or deduplicated library.
3) Real deletion events. Hopefully these will be represented by multiple reads.

So, you may want to deduplicate your library (if it was PCR'd, otherwise don't) and then call variants using a threshold of, say, 3 reads (minreads=3 flag, placed after "clearfilters").

[jweger1988]

Normally, I would just map the reads with the flag "maxindel=100k" (or whatever is appropriate, given the length of the genome) and then call variants with "callvariants.sh" using the flag "rarity=0.01" to find low-frequency events. But alternatively, you can separate the reads with multiple BBMap passes, like this:

bbmap.sh in=reads.fq outm=mapped.fq maxindel=100k
bbmap.sh in=mapped.fq outm=normal.fq outu=longdeletion.fq maxindel=100k dellenfilter=20
bbmap.sh in=longdeletion.fq out=mapped_longdeletion.sam maxindel=100k

Brian, that's awesome, I didn't even know there was a variant caller in your suite. That worked well and I was able to find a bunch of deletions by using the flag "clearfilters" in callvariants.sh. However, I'm sure a lot of it is probably not real. Is there a particular parameter you suggest looking at as a measure of probability of being real? I'm not sure what some of the readouts in the output from callvariants.sh actually mean.

Thanks again.

[Brian Bushnell]

Normally, I would just map the reads with the flag "maxindel=100k" (or whatever is appropriate, given the length of the genome) and then call variants with "callvariants.sh" using the flag "rarity=0.01" to find low-frequency events. But alternatively, you can separate the reads with multiple BBMap passes, like this:

bbmap.sh in=reads.fq outm=mapped.fq maxindel=100k
bbmap.sh in=mapped.fq outm=normal.fq outu=longdeletion.fq maxindel=100k dellenfilter=20
bbmap.sh in=longdeletion.fq out=mapped_longdeletion.sam maxindel=100k

[jweger1988]

Hi James,

Sorry for the slow reply. You could, in your case, do this:

Assume the barcode is from position 10 to 19 in the read (inclusive, zero-based). Then you would do:

reformat.sh in=reads.fq ftl=10 ftr=19 out=trimmed.fq
kmercountexact.sh in=trimmed.fq k=10 rcomp=f out=kmers.fa

"kmers.fa" will contain all the barcodes and their counts.

Awesome Brian, thanks for the response. It seems like that is going to work. It's a little more complicated since there are actually three amplicons with barcodes in different areas in the same file but if I can pull them out using one of your other tools the kmercountexact.sh should work just fine.

I thought maybe I would ask you a question about another problem I'm currently having. I have been trying to use BBmap to detect large deletions in viral sequencing data (defective genomes) but so far haven't had good luck. Is there a way I can sort out specifically these reads that have "break points" or deletions in the viral genome away from the reads that map normally?

Thanks again for all of your work.

James

[Brian Bushnell]

Hi James,

Sorry for the slow reply. You could, in your case, do this:

Assume the barcode is from position 10 to 19 in the read (inclusive, zero-based). Then you would do:

reformat.sh in=reads.fq ftl=10 ftr=19 out=trimmed.fq
kmercountexact.sh in=trimmed.fq k=10 rcomp=f out=kmers.fa

"kmers.fa" will contain all the barcodes and their counts.

[jweger1988]

Hello Brian,

I made some viruses that have a set of degenerate nucleotides in the genome to look at bottlenecks - we're calling these barodes. I'm wondering if any of your tools have a way of extracting the specific region where the barcodes are and then listing the different sequences that are present and possibly how many times they are present. I've been able to do pretty much everything I've needed to with your tools so I figured I would ask. Thanks for all of the work you've done.

James

[Brian Bushnell]

@minja - I replied via email and still have not had a chance to look into that; sorry - I will when I have a chance.

@catagui - BBMap cannot handle query sequences over 600bp. When you feed it a fasta input file, by default, sequences longer than 500bp are broken into 500bp pieces and mapped individually (and not recombined afterward). Some of these pieces could be quite short (say, 15bp) and will usually map if you have a big enough reference. You can discard them with "minlen=40" or so. Or you could simply use MapPacBio for mapping as it can accommodate much longer reads. In that case, though, you should increase "maxindel" which has different defaults between BBMap and MapPacBio, and furthermore, a longer transcript could contain multiple long introns.

Try "mapPacBio.sh k=13 maxindel=200000 maxlen=60000 minlen=40 (other arguments)" and see if that improves its mapping rate. Ultimately, though, longer query sequences will always have lower alignment rates than shorter ones when using a global or glocal aligner, so shredding the sequences (which BBMap does) artificially increases the alignment rate.

[catagui]

Bbmap for mapping a transcriptome to a genome

Hi Brian,
I would like to use Bbmap to map a transcriptome to a genome.
My first questions would be in the Results it saids:

Reads Used: 165317 (61187186 bases)

but the transcriptome that I am using has 95,389 transcripts. Shouldn't the number of transcripts be the same as the reads used?

2nd. This transcriptome has a N50 of 363 and N75 of 696, the manual saids to use BBMapPacBio for reads longer than 700bp. However, when I use BBMapPacBio instead of bbmap I get a 28% mapping percentage, that is lower than when using bbmap (38%).This transcriptome has transcripts from other organisms and the mapping percentage should be around 50%.

What else should I take into account when mapping a transcriptome to the genome, since is it different than mapping mRNA reads?

Thanks