[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.

[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]

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]

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]

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]

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.

[jweger1988]

Hi Brian,

I'm wondering if there is any way you can get bbmap (or another of your tools) to give a consensus sequence of an alignment? I went to map to a ref sequence and then use that to create a consensus sequence and then use that to map again and then call variants.

Thanks!

[Brian Bushnell]

Hi Jweger,

Sorry, I don't have anything that will do that. Clumpify allows something sort of similar; you can create consensus sequence from raw reads, and map those. But that loses per-base depth information which is important for variant-calling, so I don't think it's what you want.

[sdriscoll]

I was testing something with BBMap today and I realized I had forgotten something about how to use it and couldn't figure it out. I was mapping RNA-Seq and I thought that it would report spliced alignment cigars (using sam 1.3) as /[0-9]+M[0-9]+N[0-9]+M/ but it was reporting the introns as deletions with the [D] cigar value. Is that right or is there a way to get it to not do that?

Thanks-

[Brian Bushnell]

You can use the flag "intronlen" to control this. For example, "intronlen=100" will change the reporting so that all deletions of at least 100bp will reported using 'N' instead of 'D' in the cigar string.

[sdriscoll]

Oh yes! Thanks Brian.

[jweger1988]

Hi Brian,

Is there anyway to use bbmap (or any other of your tools) to map a read to a reference file and then trim anything to the left of the reference sequence?

For example

My reference is
XXXXXXXXXXXXXXXXXX

And my reads are

NNNNXXXXXXXXXXXXXXXXXX
NNNXXXXXXXXXXXXXXXXXX
NNXXXXXXXXXXXXXXXXXX

I want them to be
XXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXX
XXXXXXXXXXXXXXXXXX

I basically want to just trim anything of the left of the reads that doesn't match my reference? Thanks in advance.

[Brian Bushnell]

To do that, you'd need to trim all soft-clipped bases. I don't have any programs that will do so, but it looks like I could add it to Reformat without too much difficulty.

[darthsequencer]

Hi Brian,
Is there a way to set a minimum input fragment length when mapping with BBMap?

[GenoMax]

You could pre-filter input data with

reformat.sh in=file.fq out=filt.fq minlength=N