SNAP: Fast, accurate sequence alignment enabling biological

applicationsRavi Pandya, Microsoft Research

ASHG 10/19/2014

SNAP

SNAP is fast *Align 50x genome in 1.2 hours(BWA-MEM = 11.75 hours)Sort + index + markdup BAM in 2 hours(samtools+sambamba = 4.25 hours)

SNAP is as accurate as BWA-MEM, Bowtie2, etc.ROC on simulated data% aligned on real dataVariant calls on real data

* NA12878:ERR194147, Azure D14 (16 cores, 112GB RAM, 800GB SSD)

Sequence alignment

The problem:Given a read R and a reference genome GFind the position in p in G that minimizesEditDistance(R, G[p .. p + |R|])

SNAP solves this quickly and accurately because of:Efficient system architectureReducing the number of comparisonsReducing the cost of comparisons

System architecturefull

align sort

async read async write

emptytemp file

mergesort

markduplicates

index

compress

The sequence alignment problemThe easy part:

97% of 20-mersin the human genomeoccur only oncebut at only 75% of locations

The hard part:

The other 3% of 20-mersand 25% of locations

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Single Equally Weighted

Paired Equally Weighted

Single Time Weighted

Paired Time Weighted

10% of reads

95% of time

CDF of per-read/pair alignment time, NA18705 169M pairs(using deeper search parameters than current defaults)

Bill Bolosky, MSR

Hash table lookup

Build a multi-valued map (~30GB for hg19)from all seeds S in G all locations of S in G

330 reads/s

14k reads/s

For all seeds in read, all locations of seed in genome,Score implied alignment of read, keep the best

Ignore frequent seeds (>300 occurrences)Only use a few seeds/read

42x

Bill Bolosky, MSR

Fast scoring

113k reads/s

154k reads/s(470x overall)

Sort candidates by # of seed hits

Skip locations with #seed misses > limit

1.4x

92k reads/s O(n2) Ukkonen O(nd), n=len, d=min(limit, actual)Use limit = best score so far + 2 (for MAPQ)

1.2x

6.6x

Bill Bolosky, MSR

Paired-end alignment

Find & score candidate location pairsC(R1:R2) = C(R1) ∩ C(R2) {± insert size}Enumerate in O(h log n) h = |C(R1) ∩ C(R2)| n = |C(R1)| + |C(R2)|Increases accuracy by allowingmuch higher limit on seed occurrences(e.g. 4k vs 300)

Bill Bolosky, MSR

Results: simulated data

Mason-generated paired-end 100bp reads

Results: real data

NA18507 (Illumina HiSeq 50x)

* AWS cr1.8xlarge (32 cores, 244GB RAM, 2x120GB SSD)

Results: GATK variant calls

Broad GATK pipeline, curated NA12878 variant calls

Results: NIST Genome-in-a-BottleAppistry GATK pipeline, GIAB highly confident callsLonger seeds are much faster, similar precision/recall

11.75

ERR194147*.fastq.gz, Azure D14 (16 cores, 112GB RAM, 800GB SSD)

Results: NIST Genome-in-a-BottleLower confidence calls (qual>20, 2 platforms)

Highly confident indel snp Aligner Recall Precision Recall Precisionbwa-mem 97.24% 97.15% 99.57% 99.65%snap-20 97.04% 97.48% 99.51% 99.57%snap-24 97.04% 97.46% 99.52% 99.57%snap-28 97.04% 97.45% 99.53% 99.57%snap-32 97.00% 97.41% 99.51% 99.57%

Lower confidence indel snp Aligner Recall Precision Recall Precisionbwa-mem 96.38% 96.30% 99.00% 99.32%snap-20 96.17% 96.68% 98.94% 99.25%snap-24 96.17% 96.67% 98.95% 99.23%snap-28 96.16% 96.62% 98.96% 99.21%snap-32 96.11% 96.55% 98.94% 99.17%

Pathogen ID: SURPI (Charles Chiu, UCSF)

“This analysis of DNA sequences required just 96 minutes. A similar analysis conducted with the use of previous generations of computational software on the same hardware platform would have taken 24 hours or more to complete, Chiu said.”

SURPI

SNAP enables SURPI with:Fast filtering mode64-bit index for >40GB ntDBSecondary mapping output

Charles Chiu, UCSF

Acknowledgements

Microsoft ResearchBill BoloskyRavi PandyaUC San FranciscoTaylor SittlerBroad InstituteChristopher Hartl

UC Berkeley AMPLabMatei ZahariaKristal CurtisArmando FoxScott ShenkerIon StoicaDavid Patterson

Binaries, source, documentation (Apache 2.0 licensed)http://snap.cs.berkeley.edu