Bioinformatics in Public Health Laboratories Duncan MacCannell PhD
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1 Bioinformatics in Public Health Laboratories Duncan MacCannell PhD National Center for Emerging and Zoonotic Infectious Diseases Office of Advanced Molecular Detection
2 What is bioinformatics? 2 The use of computer systems to analyze and interpret complex biological datasets in order to better inform public health action.
3 Why is it necessary? 3
4 NGS: A Disruptive Technology Cost to sequence 1 million basepairs VOLUME OF RAW DATA Maximum output per instrument 4 kilobytes (10 3 bytes) terabytes (10 12 bytes)
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6 NGS Sequencing Technologies Illumina MiSeq//NextSeq/HiSeq IonTorrent PGM/Proton/S5 SHORT READ SEQUENCING 10X Genomics GemCode Illumina TruSeq SLR (moleculo) SYNTHETIC LONG READ 75 to 400bp readlengths* Millions to billions of reads Various error models Relatively inexpensive (~$60/isolate) Issues: Resolving complex structures, phasing Pacific BioSciences RS II Pacific BioSciences Sequel* Oxford Nanopore MinION LONG READ SEQUENCING >3 20kbp readlengths* Hundreds of thousands of reads* High error rates have presented challenges* Roughly $600/isolate (RSII; Nanopore TBD) Other adv: SMRT; methyl; phasing; closing 6
7 NGS Applications in Microbiology Library Data Info. ACAATTTGTGCATAACATGTGGACAGTTTTAATCACATGTGGGTAAATAGTTGTCCACATTTGCTTTTTT TGTCGAAAACCCTATCTCATATACAAACGACGTTTTTAGGTTTTAAAATACGTTTCGTATAAATATACAT TTTATATTTATTAGGTTGTACATTTGTTGCGCAACCTTATTCTTTTACCATCTTAGTAAAGGAGGGACAC CTTTGGAAAATATCTCTGATTTATGGAATAGTGCCTTAAAAGAATTAGAAAAAAAGGTAAGCAAGCCTAG TTATGAAACATGGTTAAAATCAACAACGGCTCATAACTTGAAGAAAGACGTATTAACGATTACAGCTCCA AATGAATTTGCTCGTGACTGGCTAGAATCTCATTACTCAGAACTTATTTCGGAAACACTATACGATTTAA CAGGGGCAAAATTAGCAATTCGCTTTATTATTCCCCAAAGTCAATCGGAAGAGGACATTGATCTTCCTCC AGTTAAGCGGAATCCAGCACAAGATGATTCAGCTCATTTACCACAGAGCATGTTAAATCCAAAATATACA TTTGATACATTTGTTATCGGCTCTGGTAACCGTTTTGCCCATGCAGCTTCATTAGCTGTAGCCGAGGCGC CAGCTAAAGCGTATAATCCACTCTTTATTTATGGGGGAGTTGGGCTTGGAAAGACGCATTTAATGCACGC AATTGGTCATTATGTAATTGAACATAATCCAAATGCAAAAGTTGTATATTTATCATCAGAAAAATTCACG AATGAATTTATTAACTCTATTCGTGATAATAAAGCTGTTGATTTTCGTAATAAATATCGCAACGTAGATG 7 Input: DNA/RNA Source: Genomic Amplicon Whole sample Host/vector/ pathogen/ environment NGS Workflow: Platforms Chemistry Perf. char. Labor/TaT Expertise Cost Increasingly Universal Workflows Working to establish standardized sequencing workflows for a wide range of pathogens. Sample intake Prep/staging Extraction Conversion Library prep Sequencing Bioinformatics Workflow: Hardware/software Specialized skillsets Algorithms/pipelines Pathogen databases Data analysis/interpret/ Integration/visualization A Moving Target Rapidly evolving technology space. Changing hardware and COTS/OSS capabilities. Lots of choice, but lack of consistent standards. BIG DATA. New workforce and skillset is required. File hashes/versioning QA/QC Security Validated methods/databases Skills/proficiency Process logging/audit Standards Reporting Pathogen and application specific, standard and/or compliant assays Output: Information From Sequence Data Comparative Genomics Identification High res Straintyping/Subtyping Cluster identification Molecular evolution Genotypic characterization Virulence, AR, signatures Functional annotation Diagnostic dev/validation Minor populations, quasispecies Host/pathogen expression Metagenomics Pathogen identification/discovery Culture independent diagnostics Microbial ecology/diversity Many results from a single dataset. Faster and cheaper than serial tests.
8 Impact of NGS on Infectious Disease Research Bacteria, fungi and parasites Genomics revolutionary: phylogenetics, molecular evolution and transmission dynamics; broad based characterization; open ended inquiry; insight into poorly characterized genus/species; AR/virulence. Metagenomics (discovery, diagnostics, ecology/population, non culturable) Transcriptomics (host/pathogen/vector) Viruses Deep sequencing (minor var, quasispec, diversity, AR) Metagenomics (discovery, diagnostics, ecology/population, non culturable) Transcriptomics (host/pathogen/vector) 8
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11 Run QC & Metrics Demultiplexing Read QC FastQC, CLC, FastXToolkit, etc 11 PAN GENOME, wgmlst, AMR PREDICTION, FUNC ANNOTATION Trimming/Filtering Trimmomatic, CLC, kraken, bowtie2,... Reference Mapping Alignment bwa Variant Calling samtools, gatk, varscan, freebayes, Variant Filter/Annot MLST/AMR srst2 Tree Building RAxML, PHYML, Fasttree, ksnp De Novo Assembly WG Alignment mauve, parsnp Annotation/FP prokka, pgaap, etc Tree Building Harvest, ksnp, MLST/AMR abricate, mlst Comparative Genomics Hat tip: N. Loman
12 What does the data actually look like? 3 SAMPLES Illumina Paired End ~47GB compressed ID,flowcell,barcode,lane,pair SEQUENCE DATA Identifier for each read. (Syntax varies Illumina) INSTRUMENT: HISEQ RUN ID: 165 FLOWCELL ID: C1CKRACXX FLOWCELL LANE: 2 TILE: 2201 X,Y: 1257,1980 PAIR: 1 FILTERING: N BARCODE: GAGTGG 1 2 Per base quality score: ASCII 64 or 94 levels LOWEST! (HEX 21) HIGHEST ~ (HEX7E) M 100bp reads in CDD5 Further reading:
13 CLCbio Genomics Workbench 13 For demonstration; does not imply endorsement.
14 NGS Quality Assessment: FastQC 14
15 Genome Assembly and Mapping 15 INCLUDES: Sequence(s) of interest, Contaminants MAY NOT INCLUDE: Poorly sequenceable regions MAY NOT RESOLVE: repeats, rearrangements, complex structure
16 16 Mapped assembly For demonstration purposes only. All copyrights belong to their respective owners.
17 Reference Guided (Mapped) Assembly Contig 1 Contig 2 1X Reference Sequence/Genome 17 Coverage 18X Low sequence coverage UNMAPPED READS 1. Sequences not present in the reference. 2. Plasmids or other extrachromosomal. 3. DNA Structural Variation/Rearrangement ADVANTAGES: Relatively fast, well suited to highly conserved genomes. DISADVANTAGES: Issues with high diversity, mobile elements, linear reference Example software: BWA ( breseq (
18 Mapping Example: MTB (ref: h37rv) 18 Mapping parameters to consider: mismatch, insertion, deletion, extension, cutoffs, algorithm
19 Example Application: WG SNP Typing Reference Sequence/Core Genome 1 A C T AG A A T C C T T AG AC Advantages: adaptable, highly discriminatory, good for cluster investigations. Disadvantages: not well suited for surveillance or studies where reference or allele set may shift over time; limited additional data. T T ACTAGA ACTAGT TCTACT Example software: SNIPPY ( LYVE SET ( SET), SNP Pipeline ( Biostatistics/snp pipeline), others.
20 20 De Novo Assembly For demonstration purposes only. All copyrights belong to their respective owners.
21 De Novo Assembly Contig 1 Contig 2 Contig 3 21 Contig 4 Contig 5 Contig 6 Contig 7 PLASMID ADVANTAGES: Reference agnostic: assembles all the reads it can. Various algorithms. DISADVANTAGES: Doesn t always get things right. Particularly with complex repeats. Example software: SPAdes ( List:
22 Example De Novo: MTB contigs; 126 contigs >1.5kbases in length 4.35Mbases (h37rv=4.41mbases); N50: 59kbases
23 Example application: wgmlst 23 Conventional MLST Sequencing and assignment of alleles for 6 or 7 bacterial housekeeping genes. Sequence type on the basis of allelic differences. wgmlst (PulseNet, others) Expanded scheme: Typically includes all relevant open reading frames across core or pangenome Denovo assembly, sequence typing of >1000 loci Advantages: discriminatory; reproducible; results in consistent, hierarchical nomenclature; corollary information; data are reasonably portable. Disadvantages: may have decreased discriminatory power, particularly with clustered genomic variations; requires initial dev/curation of allele db; computationally intensive. Example software: SRST2 (
24 Getting Started with Bioinformatics For demonstration purposes only. 24
25 Setting the Stage: Scientific Computing 25 General requirements: High performance computing: o Scientific workstation (8+ cores, >16GB RAM) Linux or OSX pref o Access to high performance computing cluster(s) and private/public cloud resources helpful Storage capacity (10TB+) o Backup & data management plan o Access for instruments & users High speed networking (>1Gb) o Instruments, storage, compute Bare bones: workstation, VM. Bioinformatics, sysadmin and software engineering expertise
26 Bioinformatics Software: F/OSS versus COTS 26 Free and Open Source Software (F/OSS) The source code for many bioinformatic tools are publicly released & linked to publication. o Various licenses, govern use/modification/attribution o Use of DOIs for sourcecode and software Multiple public repositories: o Github, SourceForge, project/laboratory websites. Most tools written for Linux/OSX command line, some windows and crossplatform Friendliness of the code varies; ongoing support. Commercial off the shelf (COTS) BioNumerics, CLCBio, DNAStar NGEN, Geneious Professional, MATLAB, SAS JMP Genomics, Vendor supported, many provide a GUI for F/OSS algorithms Much more user friendly than most CLI tools, features Some software has hybrid model (eg: non commercial use)
27 Bioinformatic Software Packages YES YES YES 27 Wyres et al. Pathogens 2014, 3(2), ; doi: /pathogens
28 Run QC & Metrics Demultiplexing Read QC FastQC, CLC, FastXToolkit, etc 28 PAN GENOME, wgmlst, AMR PREDICTION, FUNC ANNOTATION Trimming/Filtering Trimmomatic, CLC, kraken, bowtie2,... Reference Mapping Alignment bwa Variant Calling samtools, gatk, varscan, freebayes, Variant Filter/Annot MLST/AMR srst2 Tree Building RAxML, PHYML, Fasttree, ksnp De Novo WG Alignment mauve, parsnp Annotation/FP prokka, pgaap, etc Tree Building Harvest, ksnp, MLST/AMR abricate, mlst Comparative Genomics Hat tip: N. Loman
29 Pipelines: Automating Workflows 29
30 Pipelines: Automating Workflows 30 Development considerations: OS and hardware environment, dependencies, parallization, flexibility, modularity, documentation, support,
31 CLCbio Genomics: Workflows 31
32 BioNumerics: Workflows 32
33 Geneious: Workflows 33
34 GALAXY 34
35 Common Bioinformatics Platforms: Next Generation PulseNet LIMS Database managers and end users Sequencer Raw sequences PH databases Closed to the public Nomenclature server Allele databases Currently CDC (Ideally public domain) Genus/species Serotype Pathotype Virulence Resistance 7 gene MLST emlst cgmlst wgmlst Data pathway Analysis request External storage NCBI, ENA, BaseSpace Calculation engine Trimming, mapping, de novo assembly, SNP detection, allele detection Currently CDC (Ideally public domain) A number of other CDC programs are evaluating the use of BioNumerics infrastructure: mening, TB, GC, legionella, strep, HAIs, malaria, HIV 35
36 AMD Portal: Standardizing Data Flow STATE PHL PHL USER COMMUNITY AMD PORTAL INFLUENZA TB Etc. HPC CLUSTERS STORAGE ARRAY (3.6PB) CDC High Performance Computing CoE CDC USER COMMUNITY 36
37 Developing A Bioinformatics Workforce for Public Health 37 Like microbiology and epidemiology, good bioinformatics is increasingly critical to good PH. Challenges with recruitment and retention Skilled bioinformaticians are a highly sought after resource. Career bioinformaticians with relevant public health/microbiology experience are an extremely rare breed. It is more common to find microbiologists with on the job bioinformatics experience expand/enable through training. Limited state/federal workforce support (job series, competencies, training ) for new technical labor categories Technical and informatics challenges (compute/storage/net) Interdisciplinary communication is vital: translating between program scientific needs and bioinformatics technical specialists. Team based approaches, hub and spoke model. AMD training and capacity building for PHL: stay tuned!
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39 39 Application deadline for class: April 1,
40 Links and Further Reading 40 Further reading Loman NJ, Pallen M. Twenty years of bacterial genome sequencing. Nat Rev Microbiol Dec;13(12): doi: /nrmicro3565 Kwong JC, McCallum N, Sintchenko V, Howden BP. Whole genome sequencing in clinical and public health microbiology. Pathology Apr;47(3): doi: /PAT Free and open source software: NCBI Genome Workbench ( SRST2 ( Prokka/snippy/arbricate ( BWA ( GATK ( SPAdes ( HARVEST ( ksnp ( PhyloViz ( Samtools ( FreeBayes ( BIGSdb ( RAxML ( RAxML); Mauve ( GEPHI ( BioPerl ( BioPython ( R ( project.org) Commercial software: BioNumerics ( maths.com); CLCbio Genomics Workbench ( DNAStar Lasergene NGEN ( Geneious Professional (
41 Advanced Molecular Detection FOLLOW US ON THE WEB: FOLLOW US ON 41
42 42
43 Questions? For more information please contact Centers for Disease Control and Prevention 1600 Clifton Road NE, Atlanta, GA Telephone: CDC INFO ( )/TTY: E mail: fms2@cdc.gov Web: The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention. National Center for Emerging and Zoonotic Infectious Diseases Office of Advanced Molecular Detection
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