DNA Sequencing technology: Sanger to NGS. George S. Watts, Ph.D.

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1 DNA Sequencing technology: Sanger to NGS George S. Watts, Ph.D.

2 Overview Sequencing Terminology NGS technologies File formats Use case: diagnosing clinical infections

3 9/8/2016 3

5 9/8/2016 5

6 9/8/2016 6

7 Maxam-Gilbert Sequencing (invented 1977): 9/8/2016 7

8 (invented 1977): 9/8/2016 8

9 9/8/2016 9

10 9/8/

11 ABI prism sequencing: Sanger takes off (1986) 4 color sequencing with automated capillary electrophoresis 9/8/

12 9/8/

13 9/8/

14 9/8/

15 NGS Terminology

16 Some terminology Depth: For any given base, the number of reads aligned at that positon Coverage: the proportion of your target genome that is covered by some mapped sequence Depth of coverage : number of reads * read length / assembly size E.g, 20x coverage means each base has been sequenced an average of 20 times

17 Most NGS technologies produce short reads Sanger NGS genomic DNA DNA fragments long reads ~800bp short reads ~50-300bp

18 More terminology Data volume the data (in Gb) that can be obtained from one run of the machine. Multiple samples can be loaded into different lanes within a flow cell or molecular barcoding. Run The sequencing that you can do with the machine in one cycle (turning it on and off once) Illumina flow cell with 8 lanes. Ion Torrent chip.

19 NGS Technologies: better mouse traps A variety of platforms available Differ in: Library preparation Sequencing chemistry All use a method to isolate many DNA molecules and then sequence them in parallel

21 Pyrosequencing (invented 1996): Roche 454 9/8/

22 9/8/

Library preparation Sequencing chemistry Features

length, only available until 2016 Illumina HiSeq

output to cost ratio, low error rates Applied

Highest accuracy, very short reads Pacific

23 Library preparation Sequencing chemistry Features Roche 454 Emulsion PCR Pyrosequencing Longer read length, only available until 2016 Illumina HiSeq Solid phase amplification Reversible terminator Best output to cost ratio, low error rates Applied Biosciences SOLiD Emulsion PCR Sequencing by ligation Highest accuracy, very short reads Pacific Biosciences RS II Single molecule Real time Very long read lengths, highest error rates

24 Roche 454 sequencing reaction Flow cycle is TACG. In this example, two T s are incorporated during the first cycle, and one C during the third cycle.

25 Roche 454 signal output Where 2 identical nucleotides have been incorporated, the detected signal is ~twice as strong With longer homopolymer stretches, the determination of the exact number of identical bases becomes more error prone. TTCACTCGAACT

26 Purchased by the Genomics Shared Service: Jan 2011 Installation: Feb 28-Mar 1, 2011 Training: March 7-9, 2011 First mutation detected April 13, 2011

27 The chip is the machine Sequencing chips produce from 10Mb to greater than 1Gb of highly accurate sequence. The difference between the chips is the number of transistors. The Ion 314 chip has 1.3M, the Ion 316 chip 6M, and the Ion 318 chip 12M. 316 chip run Feb 15, 2012 by the Genomics Core:

The Ion Torrent PGM uses a high-density array of micro-machined wells sitting on top of a standard semiconductor. Each well holds a different DNA template.

28 The Ion Torrent PGM uses a high-density array of micro-machined wells sitting on top of a standard semiconductor. Each well holds a different DNA template. Beneath the wells are the transistors. No moving parts, CCD cameras, lasers, fluorescent dyes, modified nucleotides. Load the beads onto the chip and sequence.

30 Ion Torrent sequencing reaction Nucleotide incorporation measured by a change in ph from H + ion release during reaction

31 An NGS experimental workflow (Illumina) 1. Library Preparation 2. Hybridisation and Amplification 3. Sequencing 4. Data Analyses

32 1. Illumina library preparation 1. Fragmentation and size selection 2. Adaptor ligation genomic DNA

33 2. Hybridisation and Amplification

34 2. Hybridisation and Amplification

reaction Nucleotides modified Reservible terminators

35 3. Illumina sequencing reaction DNA fragments attached to slide via adapters and amplified (PCR) prior to sequencing reaction Nucleotides modified Reservible terminators Flourophore Incoporated one at a time Image taken, and terminators cleaved

36 3. Illumina sequencing reaction cycle 1 cycle 2 cycle 3, etc

37 3. Illumina sequencing reaction

38 Other sequencing technologies PacBio single molecule sequencing, long reads, high error rate Applications include whole genome shotgun, amplicon, transcriptomics as Oxford Nanopore single molecule sequencing

39 Factors to consider when selecting a sequencing technology Read length: longer better More specific, less ambigious More accurate alignments, classifications Produce longer contigs when assembling Accuracy: higher better (especially relevant for amplicon) Data volume: higher better More reads reduces the impact of errors Necessary for WGS Cost: lower better

40 Summary of Platforms Sequencer Chemistry Read Length 454 GS FLX+ (Roche) Pyro- mode 700 up to 1 kb Volume Run Time Advantage Disadvantage Metagenomics? 700 Mb 23 hours Long reads High error rate Maybe HiSeq (Illumina) Reversible terminator bp hrs days High- Low cost Short reads Long run time Yes MiSeq (Illumina) SOLiD (Life) Ion Proton (Life) Ion PGM (Life) PacBio RS Reversible terminator bp hrs Short run time Short reads No Ligation bp days Low error rate Short reads Long run time Proton <200 bp 10 Gb 2 4 hr Short run time Chimeras, homopolymer Proton Mb Gb Real time sequencing mean 4.6 kb to 23 kb 3GB 30 min No Yes 2 7 hr Short run time Homopolymer No No PCR Very long reads High error rate No

41 Summary of Platforms Sequencer Chemistry Read Length 454 GS FLX+ (Roche) Pyro- mode 700 up to 1 kb Volume Run Time Advantage Disadvantage Metagenomics? 700 Mb 23 hours Long reads High error rate Maybe HiSeq (Illumina) Reversible terminator bp hrs days High- Low cost Short reads Long run time Yes MiSeq (Illumina) SOLiD (Life) Ion Proton (Life) Reversible terminator bp hrs Short run time Short reads Human only Ligation bp days Low error rate Short reads Long run time Proton <200 bp 10 Gb 2 4 hr Short run time Chimeras, homopolymer No No Yes Ion PGM (Life) PacBio RS Proton Mb Gb Real time sequencing mean 4.6 kb to 23 kb 3GB 30 min 2 7 hr Short run time Homopolymer No No PCR Very long reads High error rate No

42 Which NGS platform is best? Considerations when choosing a platform Intended application Run cost Read length # reads per run Error rate Availability For a comparison, see

43 NGS File Formats

44 NGS file formats from the machine Most common file formats: BAM files (~SAM files) FastQ FastA SFF (going extinct) Less common: SRF (Helicos) HDF5 (PacBio, Applied Biosystems, Oxford Nanopore). most widely used

45 SAM = BAM files 9/8/

46 BAM files can be viewed in IGV (integrated genome viewer) 9/8/

47 FastQ format

48 What are Phred quality (Q) scores? Quality scores show the probability of an erroneous base call quality score Q = 10 log 10 P error probability P = 10 Q/10 example: call with Q = 30 has error probability P = 10-3 = 1 in 1000 Score range is from 0 93 (represented by ASCII code e.g. Q score 0 can be represented as!, 10 as +

49 FastA Like FastQ, but with no quality information or + line: indicates new sequence starts, of fastq sequence name >ERR FT9FZH301B6YPS/3 ATCAACACATTAGGACTTACACGAATCAGGCATTCGTTACCATC AGTATGTCGAT sequence >ERR FT9FZH301ARSRC/3 ATGCTTGCTCGGCCGACGTGAGCGTTATTCGAGCAGGGCTCGGA TGGTAGTTAGCGATCCAAAGGGGAGTC

50 Diagnosing infections using next-gen sequencing

51 16s rrna Introduction & background Ribosomes are critical to translating RNA to protein. Ribosomes are composed of a gene-coded RNA core bound by proteins. The ubiquity of ribosomes means the gene coding for ribosomal RNA is present in all prokaryotes.

52 Prokaryotes have 70S ribosomes, each consisting of a small (30S) and a large (50S) subunit. The 30S subunit has a 1540 nucleotide RNA subunit (16S) bound to 21 proteins. The prokaryote 30s subunit is shown below, RNA is shown in yellow, proteins in purple.

53 When the nucleotides of the 16s sequence are replaced with circles indicating variation (larger circles indicate higher sequence diversity across bacterial species), the conserved and variable regions become visible. The hypervariable regions have been numbered V1- V9.

54 Graphing sequence conservation across the 16s gene reveals V1, V3, V6 are the most diverse Conservation Hypervariable regions V1, V3, V6 of the bacterial 16s rrna gene have the highest variability, and thus the greatest discrimination between types of bacteria.

55 Conservation We can take advantage of the alternating variable and conserved regions to create universal primers to the conserved regions that amplify the intervening variable regions. Here V3 is shown PCR Primer PCR product containing V3 variable sequence

Overview of sequence-based bacterial identification Prepare 16s or Whole genome sequencing library Analyze sequence data to identify species present in sample 16s rrna: Align reads to form clusters

56 Overview of sequence-based bacterial identification Prepare 16s or Whole genome sequencing library Analyze sequence data to identify species present in sample 16s rrna: Align reads to form clusters and choose a consensus sequence, blast the consensus against the NCBI 16s database and parse results from all clusters to determine if identity hits are at the species, genus, or family level Whole Genome sequencing: Assemble reads into contigs and blast the contigs against the NCBI nr database.

57 Experimental Plan Samples 41 clinical strain isolates obtained from ATCC. Amplicon Generation Samples were boiled 20 minutes, 1ul was used as template in a PCR reaction for V1, V3, and V6. Amplicons were confirmed by gel electrophoresis and purified using a Qiagen PCR purification kit. Library Preparation Ion Plus Fragment kit with The Ion Xpress Barcode Adapter 1-96 Kit, 1 barcode per amplicon. Ion PGM Template 200 Kit. Ion PGM Sequencing 300 Kit. Sequencing 314 chip on an Ion Torrent Personal Genome Machine.

58 Sequencing Run Summary V6, V1, V3 amplicons can be discriminated by read length

59 Data analysis of 41 strain isolates 1. Reads were separated by barcode =.BAM file 2. TMAP used to align BAM files to known reference sequences for each isolate 3. Samtools mpileup used to create consensus sequence = FASTA file 4. Consensus Sequence blasted against 16s rrna database 5. Best hits filtered and results parsed into a human-friendly list

60 How I came to know love Perl : Hello World! Became...

61 Results for 2 of 41 strains Proteus mirabilis strain HI PG1-88_V1C_barcode_01_reffile_01.fasta: Found 99.07% match at the species level (>98%) with: Proteus mirabilis HI4320 strain HI S ribosomal RNA, complete sequence Found 99.07% match at the species level (>98%) with: Proteus mirabilis strain NCTC S ribosomal RNA, partial sequence 01.PG1-88_V3C_barcode_01_reffile_01.fasta: Found 99.48% match at the species level (>98%) with: Proteus mirabilis HI4320 strain HI S ribosomal RNA, complete sequence Found 99.48% match at the species level (>98%) with: Proteus mirabilis strain NCTC S ribosomal RNA, partial sequence 01.PG1-88_V6C_barcode_01_reffile_01.fasta: Found % match at the species level (>98%) with: Proteus mirabilis HI4320 strain HI S ribosomal RNA, complete sequence Found % match at the species level (>98%) with: Proteus penneri strain NCTC S ribosomal RNA, partial sequence Found % match at the species level (>98%) with: Proteus mirabilis strain NCTC S ribosomal RNA, partial sequence Staphylococcus aureus strain MSSA PG1-88_V1C_barcode_02_reffile_02.fasta: Found 98.92% match at the species level (>98%) with: Staphylococcus aureus subsp. anaerobius strain MVF-7 16S ribosomal RNA, partial sequence 02.PG1-88_V3C_barcode_02_reffile_02.fasta: Found % match at the species level (>98%) with: Staphylococcus aureus subsp. aureus N315 strain N315 16S ribosomal RNA, complete sequence Found % match at the species level (>98%) with: Staphylococcus aureus subsp. aureus JH1 strain JH1 16S ribosomal RNA, complete sequence Found % match at the species level (>98%) with: Staphylococcus aureus subsp. aureus strain S33 R 16S ribosomal RNA, complete sequence Found % match at the species level (>98%) with: Staphylococcus simiae CCM 7213 strain CCM S ribosomal RNA, partial sequence Found % match at the species level (>98%) with: Staphylococcus aureus subsp. anaerobius strain MVF-7 16S ribosomal RNA, partial sequence 02.PG1-88_V6C_barcode_02_reffile_02.fasta: Found 98.02% match at the species level (>98%) with: Staphylococcus aureus subsp. aureus N315 strain N315 16S ribosomal RNA, complete sequence

62 Why did so few strains resolve to a single species? Short reads limit information density. But we re limited by the PGM s read length. The information density of the 16s gene in its entirety is not sufficient in many cases. Counterintuitively, full length 16s rrna sequence actually performs worse than short reads in some cases. 100bp, 3bp difference = 97% = different species 200bp, 3bp difference = 98.5% = same species

63 Meanwhile, in the lab using Louis Pasteur methods Spectrum of most often detected bacteria in patients with chronic leg ulcers throughout Germany. Dissemond et al 2013.

64 Adding V1-V2 and V5-V6 to the mix V1-V2 And V5-V6 (Note the new length scale hint of things to come???)

Which hypervariable region to use for clinically important bacteria? Table 1. List of potential 16s rrna amplicons for use on the Ion Torrent Personal Genome Machine (PGM).

65 Which hypervariable region to use for clinically important bacteria? Table 1. List of potential 16s rrna amplicons for use on the Ion Torrent Personal Genome Machine (PGM). The V1-V3 region is too long for the current read length. Table Clinically important strain isolates were sequenced on the Ion Torrent PGM using 4 amplicons. After calling the bacterial identification using a custom analysis pipeline, results were summarized based on the precision of the calls for each amplicon. The V1-V2 amplicon performed better than all other regions with 36 of 41 strains identified to the species level. Taxonomic 16S rrna Hypervariable region Level V1 V3 V6 V1-V2 Kingdom Phylum Class Order Family Genus Species No Call

66 Application of V1-V2 16s rrna sequencing to 23 discrepant clinical isolates Vitek2 Vitek (Mass Spec) V1-V2 Sequencing Klebsiella oxytoca Klebsiella oxytoca Klebsiella oxytoca Pantoea spp. Rhanella spp. Yersinia ruckeri Shigella sonnei Escherichia coli Escherichia coli Enterobacter aerogenes Klebsiella pneumoniae Klebsiella pneumoniae Enterobacter aerogenes Escherichia coli Enterobacter aerogenes Shigella sonnei Escherichia coli Shigella flexneri Shigella sonnei Escherichia coli Escherichia coli Serratia fonticola Providencia stuartii Providencia stuartii Gardnerella vaginalis Facklamia hominas Facklamia hominis Citrobacter youngae Rhizopus spp. Serratia entomophila Alcaligenes faecalis Corynebacterium kucheri Alcaligenes faecalis Enterococcus spp. Enterococcus faecalis Ralstonia mannitolilytica Shigella spp. Escherichia coli Escherichia coli Shigella spp. Escherichia coli Escherichia coli Pseudomonas putida Pseudomonas aeruginosa Pseudomonas agarici Gardnerella vaginalis Corynebacterium spp. Mycobacterium cosmeticum Shigella sonnei Escherichia coli Escherichia albertii Shigella spp. Escherichia coli Escherichia coli Corynebacterium striatum Staphylococcus aureus Corynebacterium striatum Raoutella ornithina Enterobacter aerogenes Raoultella planticola Citrobacter sedlakii Actinobacillus minor Citrobacter sedlakii Brevundimonas vesicularis Stenotrophomonas maltophilia Stenotrophomonas maltophilia Proteus vulgaris Serratia marcescens no result Table discrepant clinical strain isolates were sequenced on the Ion Torrent PGM using V1-V2 16s rrna amplicons. After calling the bacterial identification using a custom analysis pipeline, results were compared to those achieved by two commercial bacterial identification platforms (the Vitek and the Vitek 2) that use mass spectrometry and antibody-based technologies to identify bacteria. While the sequence agreed with at least one instrument, they were unable to provide an unequivocal idenitifcation; identity calls in agreement between methods are highlighted.

67 Resolving the identity of 23 discrepant isolates with whole genome sequencing. Table 5. After whole genome sequencing of the 23 discrepant clinical strain isolates and calling the bacterial identification, results were compared to the prior methods. Assuming the whole genome sequencing is correct (bases on E-value of 0 for all whole genome sequencing matches to the NCBI nr database), matching calls from the other methods are highlighted.

68 BugSeq Project SALSA clinic: Southern Arizona Limb Salvage Alliance Collected samples from 12 healing and 26 clinically infected diabetic ulcers. Amplify V1-V2 and sequence. Determine if there is a significant difference between healing and nonhealing ulcers using QIIME.

69 Taxa summary bar chart Courtesy of G. Watts, University of Arizona

70 Alpha rarefaction curves Courtesy of G. Watts, University of Arizona

71 Courtesy of G. Watts, University of Arizona

72 Visualizing the data with PCA Courtesy of G. Watts, University of Arizona

74 Conclusions Longer amplicons provide better discriminatory power for clinical diagnosis The V1-V3 amplicon used in commercial applications (Microseq) is still too long for PGM sequencing, but V1-V2 appears to be very good and can be used with 400bp sequencing. While 16s rrna sequencing on the Ion Torrent PGM is fast and low cost, a different sequencing approach for bacterial identification is warranted for definitive identification of the difficult to discriminate organisms.

75 Neutropenic fever Chemotherapy knocks out the immune system Patients report fever (presumed to be caused by infection) typically ~6 weeks in. 3-20% mortality rate (depending on cancer type) 80% of blood cultures are negative Empiric antibiotic therapy delays in treatment, more days in hospital, loss of chemotherapy sessions, contributes to antibiotic resistance.

76 Whole genome sequencing to diagnose neutropenic fever Extract DNA Blood, swabs collected and processed for pathogen DNA DNA quantitated using qpcr. Preliminary result from patient NF001: 50 picograms bacterial DNA isolated =~10,000 genomes Generate sequencing libraries Amplicon-based library: 16S rrna, fungal ITS, antibiotic resistance genes Whole genome sequence library: Ion Torrent fragment kit Preliminary result: V1-V2 amplicon generated along with whole genome library from 40 picograms DNA Sequencing Libraries mixed 1:20 amplicon to whole genome, and sequenced on an Ion Torrent 316 chip, 400bp chemistry Preliminary result: 3.6 million reads total: 52,000 16S rrna, 3.5 million whole genome. Analysis 16S rrna sequence clustered, clusters identified using local installation of BLAST algorithm and custom perl script. Whole geneme sequence: host reads removed, kmer analysis performed to form matrix of relationships to reference genomes, social network mapping used to visualize results. Preliminary result: culture negative blood patient FN001 was found to contain Pseudomonas fluorescens using whole genome sequence. 25% of whole genome reads were removed as host (human) DNA, leaving 75% for analysis.

Third Generation Sequencing

Third Generation Sequencing By Mohammad Hasan Samiee Aref Medical Genetics Laboratory of Dr. Zeinali History of DNA sequencing 1953 : Discovery of DNA structure by Watson and Crick 1973 : First sequence