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Scaling up genomic analysis with ADAM
1. Scaling up genomic
analysis with ADAM
Frank Austin Nothaft, UC Berkeley AMPLab
fnothaft@berkeley.edu, @fnothaft
12/8/2014
2. Data Intensive Genomics
• Scale of genomic analyses is growing rapidly:
• New experiments sequence 10-100k samples
• Use high coverage, WGS for variant analyses
• 100k samples @ 60x WGS will generate ~20PB of
read data and ~300TB of genotype data
3. Petabytes Cause Problems
1. Analysis systems must be horizontally scalable
without substantial programmer overhead
2. Data storage format must compress well while
providing good read performance
3. Need to efficiently slice and dice dataset: not all
users want the same views or subsets of data
4. Analysis Characteristics
• Current genomics pipelines are limited by I/O
• Most genomics algorithms can be formulated as a
data or graph parallel computation
• Analysis algorithms use iteration and pipelining
• Reference genome/experiment metadata access
must be cheap! —> impacts analysis performance
5. What is ADAM?
• An open source, high performance, distributed
platform for genomic analysis
• ADAM defines a:
1. Data schema and layout on disk*
2. A Scala API
3. A command line interface
* Via Avro and Parquet
6. Principles for Scalable
Design in ADAM
• Reuse commodity horizontally scalable systems
• Parallel FS and data representation (HDFS +
Parquet) combined with in-memory computing
eliminates disk bandwidth bottleneck
• Spark provides horizontally scalable iterative/
pipelined Map-Reduce
• Minimize data movement: send code to data,
efficiently encode metadata
7. • An in-memory data parallel computing framework
• Optimized for iterative jobs —> unlike Hadoop
• Data maintained in memory unless inter-node
movement needed (e.g., on repartitioning)
• Presents a functional programing API, along with support
for iterative programming via REPL
• Set Daytona Greysort record (100TB in 23 min, 206 nodes)
8. Data Format
• Avro schema encoded by Parquet
• Schema can be updated without
breaking backwards compatibility
• Normalize metadata fields into
schema for O(1) metadata access
• Genotype schema is strictly
biallelic, a “cell in the matrix”
record AlignmentRecord {
union { null, Contig } contig = null;
union { null, long } start = null;
union { null, long } end = null;
union { null, int } mapq = null;
union { null, string } readName = null;
union { null, string } sequence = null;
union { null, string } mateReference = null;
union { null, long } mateAlignmentStart = null;
union { null, string } cigar = null;
union { null, string } qual = null;
union { null, string } recordGroupName = null;
union { int, null } basesTrimmedFromStart = 0;
union { int, null } basesTrimmedFromEnd = 0;
union { boolean, null } readPaired = false;
union { boolean, null } properPair = false;
union { boolean, null } readMapped = false;
union { boolean, null } mateMapped = false;
union { boolean, null } firstOfPair = false;
union { boolean, null } secondOfPair = false;
union { boolean, null } failedVendorQualityChecks = false;
union { boolean, null } duplicateRead = false;
union { boolean, null } readNegativeStrand = false;
union { boolean, null } mateNegativeStrand = false;
union { boolean, null } primaryAlignment = false;
union { boolean, null } secondaryAlignment = false;
union { boolean, null } supplementaryAlignment = false;
union { null, string } mismatchingPositions = null;
union { null, string } origQual = null;
union { null, string } attributes = null;
union { null, string } recordGroupSequencingCenter = null;
union { null, string } recordGroupDescription = null;
union { null, long } recordGroupRunDateEpoch = null;
union { null, string } recordGroupFlowOrder = null;
union { null, string } recordGroupKeySequence = null;
union { null, string } recordGroupLibrary = null;
union { null, int } recordGroupPredictedMedianInsertSize = null;
union { null, string } recordGroupPlatform = null;
union { null, string } recordGroupPlatformUnit = null;
union { null, string } recordGroupSample = null;
union { null, Contig} mateContig = null;
}
9. Parquet
• ASF Incubator project, based on
Google Dremel
• http://www.parquet.io
• High performance columnar
store with support for projections
and push-down predicates
• 3 layers of parallelism:
• File/row group
• Column chunk
• Page
Image from Parquet format definition: https://github.com/Parquet/parquet-format
10. Big Data in Parquet
• ADAM in Parquet provides a 25% improvement over
compressed BAM
• Enables efficient slice-and-dice:
• Can select column projections —> reduce I/O
• Support pushdown predicates for efficient filtering
• Have Parquet/S3 integration to push computing
down into remote block stores for cold data
11. Scalability
• Evaluated on 1000G WGS
NA12878, 234GB dataset
• Used 32-128 m2.4xlarge, 1
cr1.8xlarge from AWS
• Achieve linear scalability out
to 128 nodes for most tasks
• 2-4x improvement vs {GATK,
samtools/Picard} on single
machine for most tasks
13. The State of Analysis
• Conventional short-read alignment based pipelines
are really good at calling SNPs
• Need improvement at calling INDELs and SVs
• And are slow: 2 weeks to sequence, 1 week to
analyze. Not fast enough.
• If we move away from short reads, do we have other
options?
14. Opportunities
• New read technologies are available
• Provide much longer reads (250bp vs. >10kbp)
• Different error model… (15% INDEL errors, vs. 2%
SNP errors)
• Generally, lower sequence specific bias
Left: PacBio homepage, Right: Wired, http://www.wired.com/2012/03/oxford-nanopore-sequencing-usb/
15. If long reads are available…
• We can use conventional methods:
Carneiro et al, Genome Biology 2012
16. But!
• Why not make raw assemblies out of the reads?
Find overlapping reads Find consensus sequence
for all pairs of reads (i,j):
i j
=?
…ACACTGCGACTCATCGACTC…
• Problems:
1. Overlapping is O(n
2
) and single evaluation is expensive anyways
2. Typical algorithms find a single consensus sequence; what if we’ve got
polymorphisms?
17. Fast Overlapping with
MinHashing
• Wonderful realization by Berlin et al1: overlapping is
similar to document similarity problem
• Use MinHashing to approximate similarity:
1: Berlin et al, bioRxiv 2014
Per document/read,
compute signature:!
!
1. Cut into shingles
2. Apply random
hashes to shingles
3. Take min over all
random hashes
Hash into buckets:!
!
Signatures of length l
can be hashed into b
buckets, so we expect
to compare all elements
with similarity
≥ (1/b)^(b/l)
Compare:!
!
For two documents with
signatures of length l,
Jaccard similarity is
estimated by
(# equal hashes) / l
!
• Easy to implement in Spark: map, groupBy, map, filter
18. Overlaps to Assemblies
• Finding pairwise overlaps gives us a directed
graph between reads (lots of edges!)
19. Transitive Reduction
• We can find a consensus between clique members
• Or, we can reduce down:
• Via two iterations of Pregel!
20. Monoallelic Sequence Model
• Traditional probabilistic models assume independence
at each site and a good reference model
• This discards information about local sequence context
• Can consider a different formulation of the problem:
• Per reduced segment, build a graph of the alleles
• Find the allelic copy numbers that maximize
segment probability
21. Allele Graphs
ACACTCG
C
A
TCTCA
G
C
• Edges of graph define conditional probabilities
!
!
TCCACACT
• Can efficiently marginalize probabilities over graph using Eliminate
algorithm1, exactly solve for argmax
1. Jordan, “Probabilistic Graphical Models.”
Notes:!
X = copy number of this allele
Y = copy number of preceding allele
k = number of reads observed
j = number of reads supporting Y —> X transition
Pi = probability that read i supports Y —> X transition
22. Output
• Current assemblers emit FASTA contigs
• We’ll emit “multigs”, which we’ll map back to reference
graph
• Multig = multi-allelic (polymorphic) contig
• Will include a confidence score per base
• Working with UCSC, who’ve done some really neat work1
deriving formalisms & building software for mapping
between sequence graphs, and GA4GH ref. variation team
1. Paten et al, “Mapping to a Reference Genome Structure”, arXiv 2014.
23. Acknowledgements
• UC Berkeley: Matt Massie, André Schumacher,
Jey Kottalam, Christos Kozanitis, Adam Bloniarz!
• Mt. Sinai: Arun Ahuja, Neal Sidhwaney, Michael
Linderman, Jeff Hammerbacher!
• GenomeBridge: Timothy Danford, Carl Yeksigian!
• Cloudera: Uri Laserson!
• Microsoft Research: Jeremy Elson, Ravi Pandya!
• And many other open source contributors: 26
contributors to ADAM/BDG from >11 institutions
