Introducing BBMap, a new short-read aligner for DNA and RNA

[Corydoras]

Hi Brian,

That worked like a charm, thank you! The normalization also greatly improved the assemblies and the kmer-coverage distribution looks much nicer. I was just wondering: by default, bbnorm will use a kmer of 31. But for my assembly I am using 41. The assembly works fine, but is it advisable to normalize the coverage using a kmer of 41?

Thanks,

Sarah

[Brian Bushnell]

Sarah,

Yes, BBNorm is the correct tool.

I'm not sure, but I suspect that your shell is not bash. You could retry the command with "bash" instead of "sh", which may work. But the easier thing is just to skip the shellscript and invoke java manually:

java -ea -Xmx14g -cp /home/martin/Downloads/bbmap/current/ jgi.KmerNormalize bits=32 in=Fowleri_combined.fastq out=normFowleri.fastq target=15

That command would work if you had 16g of RAM. Just set the -Xmx parameter (highlighted in purple) to about 85% of however much RAM is on the machine. If you don't know, you should be able to find out like this on a Linux system:

cat /proc/meminfo

...then look at the first line, "MemTotal".

However, 15x is a fairly low target depth. For velvet I would suggest at least 30x for an optimal assembly, unless you just don't have enough data.

-Brian

[Corydoras]

Hi Brian,

I got a file with cleaned sequence data and I want to assemble this de-novo using velvet. Due to the nature of the sequencing and the library protocol, my kmer coverage is quite variable and I wanted to use BBnorm to normalize the coverage a bit to aid the assembly. Am I correct that BBnorm is the right thing to use for this?

Anyway, currently trying to give it a go and I got this error message:

bbmap$ sh bbnorm.sh in=Fowleri_combined.fastq out=normFowleri.fastq target=15
bbnorm.sh: 104: bbnorm.sh: Bad substitution
bbnorm.sh: 112: bbnorm.sh: [[: not found
bbnorm.sh: 112: bbnorm.sh: [[: not found
bbnorm.sh: 118: bbnorm.sh: source: not found
bbnorm.sh: 119: bbnorm.sh: parseXmx: not found
bbnorm.sh: 120: bbnorm.sh: [[: not found
bbnorm.sh: 123: bbnorm.sh: freeRam: not found
java -ea -Xmxm -cp /home/martin/Downloads/bbmap/current/ jgi.KmerNormalize bits=32 in=Fowleri_combined.fastq
Invalid maximum heap size: -Xmxm
Could not create the Java virtual machine.

Any ideas?

Many thanks,

Sarah

[dietmar13]

Rum

why is RUM always neglected by comparing RNA-seq mappers?
In my hands RUM outperforms other pipelines, e.g. tophat, in sensitivity, especially for spliced reads...

RUM: RNA Seq Unified Mapper

https://github.com/itmat/rum/wiki

RUM is rather slow, but using multithreaded servers allows mapping in tolerable time (compared to sample and library generation and data interpretation)

dietmar

[Brian Bushnell]

It'd be great if you could get in touch with the authors of this paper and just use their test datasets. That would allow comparisons against most of the popular aligners out there.

Thanks for the suggestion; I'll look into that!

[GenoMax]

It'd be great if you could get in touch with the authors of this paper and just use their test datasets. That would allow comparisons against most of the popular aligners out there.

Data is available here:

http://www.ebi.ac.uk/arrayexpress/ex...s/E-MTAB-1728/
http://www.ebi.ac.uk/arrayexpress/ex...-1728/samples/

[dpryan]

It'd be great if you could get in touch with the authors of this paper and just use their test datasets. That would allow comparisons against most of the popular aligners out there.

[Brian Bushnell]

I have compared it to tophat, which it greatly outperforms in speed and has higher sensitivity on real RNA-seq data. I have not yet compared it to STAR - I tried to but was unable to get STAR to run without core-dumping so I gave up. I may have compiled it wrong; I'll try again eventually.

However, I don't have a really good tool for generating and evaluating synthetic RNA-seq data, so it's harder to quantify. The closest I can get is to generate synthetic DNA reads with very large deletions, which is not quite the same thing since RNA-seq data has other strange artifacts and the introns are not distributed randomly.

[kopi-o]

Looks very impressive! Can it beat STAR (speed and accuracy wise) for RNA-seq though? (RNA-seq is listed as one of the use cases towards the end)

[Brian Bushnell]

BBMap will be publicly released soon, pending confirmation with LBL's legal department.

In the meantime feel free to look at these graphs of its performance:

https://drive.google.com/file/d/0B3llHR93L14wbks0ZURFcFhFR1E/edit?usp=sharing

Note that this is a 50MB powerpoint file. It contains graphs of relative performance of BBMap and other short read aligners (bwa, bowtie2, gsnap, smalt) mapping synthetic data.

EDIT:
This thread is now closed; please use this one to post questions.