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Hadoop ecosystem for life sciencesUri Laserson30 September 2013

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About the speaker

• Currently “Data Scientist” at Cloudera

• PhD in Biomedical Engineering at MIT/Harvard (2005-2012)

• Focused on next-generation DNA sequencing technology in George Church’s lab

• Co-founded Good Start Genetics (2007-)• First application of next-gen sequencing to genetic

carrier screening

• laserson@cloudera.com

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Agenda

• Historical context• Introduction to Hadoop ecosystem• Genomics on Hadoop• Other use cases in life sciences

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Historical Context

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1999!

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Indexing the Web

• Web is Huge• Hundreds of millions of pages in 1999

• How do you index it?• Crawl all the pages• Rank pages based on relevance metrics• Build search index of keywords to pages• Do it in real time!

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Databases in 1999

1. Buy a really big machine2. Install expensive DBMS on it3. Point your workload at it4. Hope it doesn’t fail5. Ambitious: buy another big machine as backup

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Database Limitations

• Didn’t scale horizontally• High marginal cost ($$$)

• No real fault-tolerance story• Vendor lock-in ($$$)• SQL unsuited for search ranking

• Complex analysis (PageRank)• Unstructured data

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Google does something different

• Designed their own storage and processing infrastructure

• Google File System (GFS) and MapReduce (MR)• Goals: KISS

• Cheap• Scalable• Reliable

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Google does something different

• It worked!• Powered Google Search for many years• General framework for large-scale batch computation

tasks• Still used internally at Google to this day

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Google benevolent enough to publish

2003 2004

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Birth of Hadoop at Yahoo!

• 2004-2006: Doug Cutting and Mike Cafarella implement GFS/MR.

• 2006: Spun out as Apache Hadoop• Named after Doug’s son’s yellow stuffed elephant

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Industry strategy: Copy Google

Google Open-source Function

GFS HDFS Distributed file system

MapReduce MapReduce Batch distributed data processing

Bigtable HBase Distributed DB/key-value store

Protobuf/Stubby Thrift or Avro Data serialization/RPC

Pregel Giraph Distributed graph processing

Dremel/F1 Cloudera Impala Scalable interactive SQL (MPP)

FlumeJava Crunch Abstracted data pipelines on Hadoop

Hadoop

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Overview of core technology

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HDFS design assumptions

• Based on Google File System• Files are large (GBs to TBs)• Failures are common

• Massive scale means failures very likely• Disk, node, or network failures

• Accesses are large and sequential• Files are append-only

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HDFS properties

• Fault-tolerant• Gracefully responds to node/disk/network failures

• Horizontally scalable• Low marginal cost

• High-bandwidth

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Input File

HDFS storage distributionNode A Node B Node C Node D Node E

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MapReduce computation

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MapReduce

• Structured as1. Embarrassingly parallel “map stage”2. Cluster-wide distributed sort (“shuffle”)3. Aggregation “reduce stage”

• Data-locality: process the data where it is stored• Fault-tolerance: failed tasks automatically detected

and restarted• Schema-on-read: data must not be stored conforming

to rigid schema

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WordCount example

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HPC separates compute from storage

Storage infrastructure Compute cluster

• Proprietary, distributed file system

• Expensive

• High-performance hardware

• Low failure rate• Expensive

Big network pipe ($$$)

User typically works by manually submitting jobs to scheduler

e.g., LSF, Grid Engine, etc.

HPC is about compute.Hadoop is about data.

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Hadoop colocates compute and storage

Compute clusterStorage infrastructure

• Commodity hardware• Data-locality• Reduced networking

needs

User typically works by manually submitting jobs to scheduler

e.g., LSF, Grid Engine, etc.

HPC is about compute.Hadoop is about data.

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HPC is lower-level than Hadoop

• HPC only exposes job scheduling• Parallelization typically occurs through MPI

• Very low-level communication primitives• Difficult to horizontally scale by simply adding nodes

• Large data sets must be manually split• Failures must be dealt with manually

• Hadoop has fault-tolerance, data locality, horizontal scalability

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Sqoop

Bidirectional data transfer between Hadoop and almost any SQL database with a JDBC driver

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Flume

A streaming data collection and aggregation system for massive volumes of data, such as RPC services, Log4J, Syslog, etc.

Client

Client

Client

Client

Agent

Agent

Agent

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Cloudera Impala

Modern MPP database built on top of HDFS

Designed for interactive queries on terabyte-scale data sets.

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Cloudera Search

• Interactive search queries on top of HDFS

• Built on Solr and SolrCloud• Near-realtime indexing of new documents

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Benefits of Hadoop ecosystem

• Inexpensive commodity compute/storage• Tolerates random hardware failure

• Decreased need for high-bandwidth network pipes• Co-locate compute and storage• Exploit data locality

• Simple horizontal scalability by adding nodes• MapReduce jobs effectively guaranteed to scale

• Fault-tolerance/replication built-in. Data is durable• Large ecosystem of tools• Flexible data storage. Schema-on-read. Unstructured

data.

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Scaling Genomics

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NCBI Sequence Read Archive (SRA)

Today…1.14 petabytes

One year ago…609 terabytes

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Every ‘ome has a -seq

Genome DNA-seq

TranscriptomeRNA-seqFRT-seqNET-seq

Methylome Bisulfite-seq

Immunome Immune-seq

ProteomePhIP-seqBind-n-seq

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Genomics ETL

GATK best practices

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Genomics ETL

.fastq .bam .vcf

short read alignment

genotype calling

• Short read alignment is embarrassingly parallel• Pileup/variant calling requires distributed sort• GATK is a reimplementation of MapReduce; could run on Hadoop• Already available Hadoop tools

• Crossbow: short read alignment/variant calling• Hadoop-BAM: distributed bamtools• BioPig: manipulating large fasta/q• SEAL: Hadoop-enabled BWA• Contrail: de-novo assembly

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Use case 1: Scaling a genome center pipeline

• Currently at 5k genomes (150 TB incl. raw), looking to scale to 25k now (1 PB) and eventually 100k (requiring 4 PB)

• Current throughput• >1300 samples per month• >12 TB raw data per month

• Data ultimately served from MySQL database• 750 GB of processed variant data• 25k genomes requires >3.5 TB in MySQL

• Complex 4-tier storage system, including tape, filer, and RDMBS

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Use case 1: Scaling a genome center pipeline

• Database serves population genetics applications and case/control studies

• Unify all data processing into HDFS• Replace MySQL with Impala on Hadoop for increased

scalability• Possibly move raw data processing into MapReduce

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Use case 2: Querying large, integrated data sets

• Biotech client has thousands of genomes• Want to expose ad hoc querying functionality on large

scale• e.g., vcftools/PLINK-SEQ on terabyte-scale data sets

• Integrating data with public data sets (e.g., ENCODE, UCSC browser)

• Terabyte-scale annotation sets• Currently, these capabilities (e.g., data joins) are often

manually implemented

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Use case 2: Querying large, integrated data sets

• Hadoop allows all data to be centrally stored and accessible

• Impala exposes a SQL query interface to data sets in Hadoop

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Variant-filtering example

• “Give me all SNPs that are:• on chromosome 5• absent from dbSNP• present in COSMIC• observed in breast cancer samples• absent from prostate cancer samples• overlap a DNase hypersensitivity site• overlap a ChIP-seq site for a particular TF”

• On full 1000 genome data set (~37 billion variants), query finishes in a couple seconds

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All-vs-all eQTL

• Possible to generate trillions of hypothesis tests• 107 loci x 104 phenotypes x 10s of tissues = 1012 p-values• Tested below on 120 billion associations

• Example queries:• “Given 5 genes of interest, find top 20 most significant

eQTLs (cis and/or trans)”• Finishes in several seconds

• “Find all cis-eQTLs across the entire genome”• Finishes in a couple of minutes• Limited by disk throughput

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All-vs-all eQTL

• “Find all SNPs that are:• in LD with some lead SNP

or eQTL of interest• align with some functional

annotation of interest”• Still in testing, but likely

finishes in seconds

Schaub et al, Genome Research, 2012

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Genomics summary

• ETL (raw data to analysis-ready data)• Data integration

• e.g., interactively queryable UCSC genome browser• De novo assembly• NLP on scientific literature

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Clinical dataManufacturing

Other use cases

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Use case 3: Clinical document queries for EHR company

• EHR wants to expose query functionality to clinicians• >16 million clinical documents with free text; processed

through NLP pipeline• >500 million lab results

• Perform subject expansion on search queries via ontologies

• e.g., “myocardial infarction” will match “heart disease”• Search functionality implemented with Lucene

(serving) on top of Hbase (processing/storage/indexing)

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Use case 3: Clinical document queries for EHR company

• Interested in recommendation engine-enabled queries, like:

• Clinician searches “diabetes” and has relevant lab results already highlighted when opening a patient’s record

• Clinician wants to know what other conditions might be correlated with a finding of interest

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Use case 3: Clinical document queries for EHR company

“Find other patients similar to mine”

• The Stanford system is limited to search

• Recommendation engines allow a button “find similar”

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Use case 4: Insurance company

• Data from 30 different EHRs across multiple business units

• High variance in ICD9 coding between locales.• Use NLP and machine learning to improve ICD9 coding

to reduce variance in diagnosis

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Use case 5: Pharma company variance in yields

• Pharma company performs large batch fermentations of their product

• Find high levels of variance in their yield• Fermentations are automated and highly

instrumented• e.g., dissolved oxygen, nutrients, COAs, temperature, etc.

• Perform time series analysis on fermentation runs to predict yields and determine which variables control variance.

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Use case 6: AgTech company integrating data sources

• Multiple reference genome sequences• Genotyping on thousands of samples• Weather data• Soil data• Microbiome data• Yield data• Geo data• All integrated in HBase

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Use case 6: AgTech company integrating data sources

• Can increase crop yields ~15% by “printing” seeds onto a field

• Support search queries by name, ontology concepts, protein families, creation dates, assembly/chromsome positions, SNPs

• Import any annotation data in CSV/GFF• Integration with cloning tools• Supports a web front-end for easy access

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Conclusions

Highly heterogeneous data

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COMMUNICATIONSLocation-based advertising

HEALTH CAREPatient sensors, monitoring, EHRs Quality of care

LAW ENFORCEMENT & DEFENSEThreat analysis,Social media monitoring, Photo analysis

EDUCATION& RESEARCHExperimentsensor analysis

FINANCIAL SERVICESRisk & portfolioanalysisNew products

ON-LINE ERVICES / SOCIAL MEDIAPeople & career matching

Websiteoptimization

UTILITIESSmart Meter analysis for network capacity

CONSUMER PACKAGED GOODSSentiment analysis of what’s hot,customer service

MEDIA /ENTERTAINMENTViewers /advertising effectiveness

TRAVEL &TRANSPORTATIONSensor analysis for optimal traffic flowsCustomer sentiment

LIFE SCIENCESClinical trialsGenomics

RETAILConsumer sentimentOptimized marketing

AUTOMOTIVEAuto sensors reporting location, problems

HIGH TECH / INDUSTRIAL MFG.Mfg. quality

Warranty analysis

OIL & GASDrilling exploration sensor analysis

©2013 Cloudera, Inc. All Rights Reserved.

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Flexibility• Store any data• Run any analysis and processing• Keeps pace with the rate of change of incoming data

Scalability• Proven growth to PBs/1,000s of nodes• No need to rewrite queries, automatically scales• Keeps pace with the rate of growth of incoming data

Efficiency• Cost per TB at a fraction of other options• Keep all of your data alive in an active archive• Powering the data beats algorithm movement

The Cloudera Enterprise Platform for Big Data

©2013 Cloudera, Inc. All Rights Reserved.

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Cloudera Hadoop Stack

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