Anyone using SNAP from UC Berkeley?

[lh3]

I forgot the version I was trying. It has been a while. SNAP does require a lot of memory, tens of GB for human genome. I don't know if it works for genomes longer than 4GB.

I have talked to the SNAP developers once. They are extremely strong on the technical end.

Bwa works on nt. The index is here (max 20 connections):

ftp://hengli-data:[email protected]/nt/

You need ~110GB RAM for mapping.

[ymc]

I found that later in the pipeline requires indexing the 70GB nt file.

I suppose this might require more than 1TB RAM to run. I don't think I have budget for such a machine. I might as well think about using bwa as a sub...

[ymc]

My last comment was old. Recent snap is good. It is very fast and fairly accurate. What I am not sure is whether it is able to find a bit longer indels.

Are you talking about the 1.0beta or the old 0.15.4?

What about RAM usage?

Thanks

[lh3]

My last comment was old. Recent snap is good. It is very fast and fairly accurate. What I am not sure is whether it is able to find a bit longer indels.

[ymc]

SNAP is incredibly fast but very inaccurate in my testing, which was over a year ago; it may have improved. Also it has (or had, anyway) a hard limit of ~3gbp reference size. Human HG19 barely fit in some versions, and didn't on others.

So - if that file is more than 3 gigabases or so, it won't work no matter how much RAM you have. BBMap is slower than SNAP, but has no upper bound on the number of scaffolds or total reference size; it works on both refseq microbial and nt. It does, however, require ~6 bytes per bp, or roughly 3 bytes per bp in low-memory mode.

Oh I see. Thanks for your quick reply.

I am trying the SURPI pipeline developed by UCSF

I think I will try to understand its script and see if it is possible to substitute snap with bwa...

[Brian Bushnell]

SNAP is incredibly fast but very inaccurate in my testing, which was over a year ago; it may have improved. Also it has (or had, anyway) a hard limit of ~3gbp reference size. Human HG19 barely fit in some versions, and didn't on others.

So - if that file is more than 3 gigabases or so, it won't work no matter how much RAM you have. BBMap is slower than SNAP, but has no upper bound on the number of scaffolds or total reference size; it works on both refseq microbial and nt. It does, however, require ~6 bytes per bp, or roughly 3 bytes per bp in low-memory mode.

[ymc]

Hi I tried to index this fasta with "-s 16" parameter but I couldn't do it with 64GB RAM. Can someone give it a try and tell me how much RAM I need to run this??

http://chiulab.ucsf.edu/SURPI/databases/Bacterial_Refseq_05172012.CLEAN.LenFiltered.uniq.fa.gz

Thanks a lot in advance

The command line looks like:
snap index Bacterial_Refseq_05172012.CLEAN.LenFiltered.uniq.fa snap_index_Bacterial_Refseq_05172012.CLEAN.LenFiltered.uniq_s16 -s 16 -O1000

[apratap]

The current version if SNAP as I understand is mainly supported for human genome...I had a list of trouble getting it to run on other genomes and finally gave it..may need some more time to turn into a mature software.

-Abhi

[lh3]

Hmm... "bwa fastmap" takes 11 seconds. Although "fastmap" does not give the final alignments, it only takes a few more seconds to generate them. The accuracy is about 0.05% as I remember. To this end, snap is only marginally faster than fastmap, while taking 7X more memory.

[lh3]

Didn't know this. Quite impressive. For my simulated 100*2 data set, bwa takes 71 seconds. Snap is indeed much faster. Here is the "time" output:

real 5m27.244s
user 0m3.329s
sys 0m15.825s

Note that the CPU time taken by snap should be between 3.3 and 3.3+15.8 seconds. It is hard to get an accurate timing on my tiny data set (so don't take mine as a good evaluation). For snap, most wall-clock time goes to index loading. My machine cannot cache the entire snap index in memory.

On accuracy, bwa is able to align 93% of reads without a single mismapping (bowtie2 is similar; novoalign can do even better), while for the highest mapQ=60, 0.05% snap mappings are wrong, which means snap does not have enough power to distinguish some good and bad hits. The manuscript chooses 0.05% as the cutoff because snap is unable to achieve higher accuracy while bwa can. That being said, how much 0.05% mismapping matters to variant calling is unknown to me (certainly matters to SV discovery); accuracy on real data may also be different.

The peak memory used by snap is about 37GB, not as bad as 64GB.

In summary, snap trades memory for speed to achieve >10X speedup in comparison to bwa. For 100bp simulated PE reads, it is not as accurate as bwa and novoalign, but its accuracy is arguably sufficient for SNP/indel calling.

[xied75]

I might late for the game, but this thing http://snap.cs.berkeley.edu/ said it's 10-100x faster than BWA etc. With a cost of needing 64GB memory.

Best,

dong