Managing Genomes at Scale: Big Data Lessons Learned
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This document summarizes lessons learned from managing large genomic datasets at Monsanto. It discusses how Monsanto uses big data technologies like Hadoop, HBase, and Solr to store and query genomic data at scale. Key lessons include using HBase more like a hashmap than a relational database, denormalizing HBase schemas, and using distributed search technologies like SolrCloud rather than rebuilding Solr indexes. The document provides examples of genomic data formats and architectures used to store, index, and retrieve genomic feature data from petabytes of sequence data.
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Managing Genomes at Scale: Big Data Lessons Learned
- 1. Managing Genomes at Scale: What We Learned Rob Long Monsanto: Big Data Engineer Twitter: @plantimals 5-29-14
- 2. Big Data At Monsanto What do we mean when we say “big data”?
- 3. Monsanto is a (Big) Data Company
- 4. A-Maizing
- 6. A Reference
- 8. Architecture Application Layer Compute Farm Query Genomic Data Hadoop Cluster Genomic Data HbaseHadoop Map Reduce Engine Data Services Edge Node Edge Node … Unstructured Query Solr Indexes Lineage Graph DB
- 9. VCF – Variant Call Format ##fileformat=VCFv4.1 ##fileDate=20090805 ##source=myImputationProgramV3.1 ##reference=file:///seq/references/1000GenomesPilot-NCBI36.fasta ##contig=<ID=20,length=62435964,assembly=B36,md5=f126cdf8a6e0c7f379d618ff66beb2da,species="Homo sapiens",taxonomy=x> ##phasing=partial ##INFO=<ID=NS,Number=1,Type=Integer,Description="Number of Samples With Data"> ##INFO=<ID=DP,Number=1,Type=Integer,Description="Total Depth"> ##INFO=<ID=AF,Number=A,Type=Float,Description="Allele Frequency"> ##INFO=<ID=AA,Number=1,Type=String,Description="Ancestral Allele"> ##INFO=<ID=DB,Number=0,Type=Flag,Description="dbSNP membership, build 129"> ##INFO=<ID=H2,Number=0,Type=Flag,Description="HapMap2 membership"> ##FILTER=<ID=q10,Description="Quality below 10"> ##FILTER=<ID=s50,Description="Less than 50% of samples have data"> ##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype"> ##FORMAT=<ID=GQ,Number=1,Type=Integer,Description="Genotype Quality"> ##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Read Depth"> ##FORMAT=<ID=HQ,Number=2,Type=Integer,Description="Haplotype Quality"> #CHROM POS ID REF ALT QUAL FILTER INFO FORMAT NA00001 20 14370 rs6054257 G A 29 PASS NS=3;DP=14;AF=0.5;DB;H2 GT:GQ:DP:HQ 0|0:48:1:51,51 20 17330 . T A 3 q10 NS=3;DP=11;AF=0.017 GT:GQ:DP:HQ 0|0:49:3:58,50 20 1110696 rs6040355 A G,T 67 PASS NS=2;DP=10;AF=0.333,0.667;AA=T;DB GT:GQ:DP:HQ 1|2:21:6:23,27 20 1230237 . T . 47 PASS NS=3;DP=13;AA=T GT:GQ:DP:HQ 0|0:54:7:56,60 20 1234567 microsat1 GTC G,GTCT 50 PASS NS=3;DP=9;AA=G GT:GQ:DP 0/1:35:4 20 14370 rs6054257 G A 29 PASS …
- 10. Let’s Try HBase
- 11. HBase Layout C1 C2 Cn V1 Vn V2 Vn Rowkey1 -> Rowkey2 -> ColumnFamily1
- 12. Tall Narrow Ind:chr:pos Id Ref SNP rs1243 A true rs321 C true rs1243 A true 1a2b3c:chr1:1001 -> 1a2b3c:chr1:1000 -> ColumnFamily:V 456def:chr1:1000 ->
- 13. Matrix Report Use Case Pos VCF1 VCF2 VCF3 VCF4 chr1:100 A A . . chr1:101 . C C C chr1:102 . . . A chr1:103 T T . T chr1:104 . . . .
- 14. First Try Mapper Individual 1 Region 1 Mapper Individual n Region m … Reduce chr1:1000 Chrom:Pos Reduce chr1:1001 Reduce chrN:M … intermediate result intermediate result intermediate result Mapper Individual 1 Region 1
- 15. Mind the gaps 8 12 24
- 16. Check For Gaps Mapper Individual 1 chr 1 Mapper Individual 2 chr 1 Mapper Individual n chr m … Reduce Abc123:chr1 Reduce abc124:chr1 Reduce n:m … intermediate result intermediate result intermediate result intermediate result intermediate result intermediate result Check Gaps against coverage table
- 17. This Takes Some Time 0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 1 2 3 4 5 6 7 8 9 10 11 Matrix Report Running Time time in minutes # of VCFs Minutes
- 18. Flat Wide genome:chr:pos 123:alt 123:qual 456:alt 456:qual A 30 C 50 T 35 T 45 b73:chr1:1000 -> b73:chr1:1001 ->
- 20. Feature Search Find genomic features like promoter regions, UTR, exons, genes, etc.
- 21. Original Indexing Workflow HBase Feature Table Table Mapper Solr Index Reducer Solr Index Reducer Index zip Index zip Table Mapper Table Mapper
- 22. Solr Pulls In Updates HDFS Solr Server cron
- 23. SolrCloud
- 24. Cloudera Search HBase Feature Table HDFS HBase MapReduce Indexer Tool Morphline file
- 25. Morphline File { extractAvroPaths { flatten : true paths : { feature_name : /featureName feature_type : /type start : /start stop : /stop } } }
- 26. Lessons Learned • Use HBase like a HashMap, not like a relational database • Denormalize HBase schemas, no foreign keys • To scale solr indexes past one server, use SolrCloud/Cloudera Search, don’t rebuild it on your own
- 27. Questions
Editor's Notes
- Good Afternoon Rob Long Today, talk about big data at monsanto And some of the lessons we’ve learned. Started > year ago Little knowledge Something with plants But then, so cool
- Been doing big data since 2009 It wasn’t so big then, but… Big data not size, but handling Mutliple machines Denormalized nosql Mapreduce Also variety, unstructured and semistructured integration
- Monsanto not prev. assoc. w/ big data Sell seeds Recipes for soil air water into food We need to understand: Genomics, agronomy, breeding, chemistry, Data feedback for breeding Year over year improvement
- US average corn yields 1863 - 2002 Axes: x- year, y-bushels per acre ( 0 – 160 ) Up to 1930’s, yield was 30 bushels Increasing yields, due to breeding, treatments, automation, weather prediction, etc Now > 140 bshls/acre 5x increase Goal of 300 Bshls/acre by 2030 How?
- Knowledge of the molecular machinery, answers “how?” Highschool biology, applied Squiggles are important – called organelles Smaller scale ACGT’s Genes as recipes
- How many people familiar w/ hum gen proj? Some done on the east campus Actual books on shelves Map for a genome Shred a book Need a guide A reference, just like the library. Many references, one per species
- Store individual genomes Warehouse / data mart Archipelago of sources Bureaucratic friction Solution: GenRe
- <point out the parts> App servers are web logic, Using Cloudera, CDH4.6 hadoop cluster has 30 data nodes, 3 edge nodes Edge nodes host services Compute farm - blast to find sequences Graph db for lineage
- Header – standard/custom data 1 variant per line Variant ind. != ref Address, chrom/pos Chrom = file, pos = offset Count from 1 1 line per pos Maize 2.3B bases 1/1000 variants Keep variants only 1/1000th storage requirement Pay a price, explain later Three main access patterns. Dump ind. Matrix, sites passing filters Regions or whole genome Aligned to same ref Flanking regions Not real-time
- How many familiar w/ HBase? watch: “Introduction to NoSQL” by Martin Fowler on youtube HBase, big table Distributed persistent hashmap Keyspace partitioned into regions Region servers host the regions CAP, CP
- Rows, cf rowkeys Column families, similar datas Sparse columns, 1 c1 vs 2 c1
- Our approach Tall/narrow schema Fixed # cols Many rows 1 row / variant / VCF Id:chr:pos, groups indiv. data Good for indiv. And flanking (explain) But…
- Review matrix use case Filter: Pos w/ => 1 variant More joins in tall/narrow M * N scanners, worst case
- TableMapper, region/individual Join this on chrom:pos, get individuals at a pos Now we can filter Emit if needed Intermediate result, map files
- Blue: ref Green: data vert.boxes, 8 variant, 12 ref, 24 no data 24 complicates, no row Either ref or no data? Solved: store gaps
- Group by individual/chrom Gaps addressed this way Intersect gaps Then w/gaps, back to chr:pos orientation Dump results to n files
- X: num individuals Y: minutes Exponential growth Too many joins
- Swap individual with genome build One row per pos per genome One get HBase used correctly
- Single pass for M individ.
- Variants have a context Location, in a gene, known effects Search on any field Jbrowse, open source visualization
- An established pattern Features dumped from hbase Embedded solr in reducers Zips in hdfs
- Solr server runs cron Pulls zipped indexes Merges Incremental updates skip MR phase Problems: Brittle, lots of code to maintain Not distributed, a single solr server Must move data off HDFS
- Ramping up to billions of features The old solr server was choking. Enter, solrcloud. Indexes in HDFS, Coordinates through zookeeper Collection concept Same REST interface
- Cloudera search lily hbase indexer service (real time) MR indexer Morphline file defines mappings between inputs and solr docs Foreign key problem Add a step
- Mapping from avro to solr Can have multi-level records
- Hbase like hashmap Denormalize Scale with solrcloud, don’t rebuild
- Credit to: Jeff GenRe team Amandeep Khurana