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orange color, and the Genetic Energy streaming bases design are
trademarks or registered trademarks of Illumina, Inc. All other
brands and names contained herein are the property of their
respective owners. Advancing metagenomics with Illumina sequencing
technology Anthony J. Cox Computational Biology Group Illumina
Cambridge Ltd. 14 th April 2014 Slide 2 2 Challenge: achieving a
seamless end-to- end workflow for metagenomics Case study: Eagle
Creek Reservoir 16S workflow on MiSeq Shotgun metagenomics on
NextSeq Challenge: efficient storage and access for metagenomic
data Contents Slide 3 3 MiSeq HiSeq 2500 15Gb | 25M | 2x300 1000Gb
| 4B | 2x125 NextSeq HiSeq X Ten 120Gb | 400M | 2x150 1800Gb | 6B |
2x150 Decreasing Price Per Gb Increasing System Output Expanded
sequencing portfolio Slide 4 4 Sample Prep Sequencing Analysis
Integration Streamlined end-to-end solution Industrys leading NGS
instruments Storage, Processing, Analysis & Collaboration Suite
of DNA, RNA & Targeted Solutions Slide 5 5 Assessing seasonal
blooms of Cyanobacteria (blue-green algae) in drinking water that
can impact water quality. Collaboration with Center for Earth and
Environmental Science, IUPUI 49 reservoir samples collected in
different months, at discrete depths. Study combines 16S analysis
on MiSeq with shotgun metagenomics on NextSeq Case study: Eagle
Creek reservoir, Indiana By courtesy of: Nicolas Clercin (IUPUI),
Rob Schmeider, Brian Steffy, Clotilde Teiling, Kameran Wong
(Illumina) Slide 6 6 2Q113Q114Q111Q122Q123Q124Q121Q132Q133Q134Q13
MiSeq continuous performance improvements Delivering on promise of
15Gb+, 2x300 bp reads 1 10 20 Output - Gb Faster chemistry Dual
surface imaging Faster chemistry Dual surface imaging Since launch:
10x increase in output 7x decrease in price per data point Since
launch: 10x increase in output 7x decrease in price per data point
New v3 reagent kits 150 & 600-cycle *Prices reflect US List
only Slide 7 7 Workflow overview 16S rRNA Sequencing was done on 27
of the samples Primer pair sequences for V3 and V4 region create a
simple 460 bp long amplicon. Nextera XT indexing kit for 96 samples
in parallel 100,000 reads per sample if using all 96 indexes.
Comparative genomics Phylogenetic classification Genomic DNA
extraction Sample Prep V3V4 region Amplification Library Prep MiSeq
& Primary Analysis Secondary Analysis The Meta-G-Nome DNA
Isolation Kit is used to isolate inhibitor-free, fosmid
cloning-ready DNA from unculturable or difficult-to-culture
microbial species present in environmental water, soil, or compost
samples. Slide 8 8 16S metagenomics on BaseSpace Slide 9 9 Can run
on-instrument using MiSeq Reporter or in cloud with BaseSpace Both
analysis pipelines use the same classification algorithm and
taxonomic database. The classification algorithm is a high
performance implementation of the published RDP Nave Bayesian
Classifier
(http://dx.doi.org/10.1128%2FAEM.00062-07)http://dx.doi.org/10.1128%2FAEM.00062-07
The database is an Illumina-curated version of the GreenGenes
Consortium 16S rRNA database. Redundant sequences and entries with
missing or partial labels are removed. Provides fast, high-accuracy
species-level taxonomic classifications Uses full length of
Illumina paired-end reads Outputs: PDF reports, raw data (CSV),
interactive visualizations Taxonomic classification Slide 10 10
Examples of 16S workflow output PCA plot of normalized relative
abundance of samples Clustering dendrogram Slide 11 11 NextSeq
innovations Consumables Load-and-go flowcell High or medium output
Ships dry All-in-one reagent tray RFID-tagged, ships frozen
All-in-one buffer tray Ships at room temperature Chemistry 2-dye
sequencing chemistry comparable quality to 4-dye Isothermal
amplification No chiller on instrument Optimized reagent
consumption Optics Solid state optics Leverages advances in
consumer products No alignment needed Fluidics Eliminated fluidic
tubes less dead volume, waste, contamination Automatic post-run
wash protocols Bleach step eliminates carry-over Simultaneous
chemistry & imaging chemistry in one lane while imaging other
pair Slide 12 12 Sample Extraction Library Prep NextSeq Sequencing
Analysis Shotgun metagenomics on NextSeq: workflow overview 11
samples sequenced in 1 NextSeq run 400 million 2150bp read pairs
generated in 29 hours 78.8% of bases exceeded Q30 Analysis done
with MG-RAST Slide 13 13 Seasonal variation in composition at
bottom of lake 25 th July Actinobacteria 33% 23 rd May
Actinobacteria = 76% 23 rd October Actinobacteria=79% Ongoing
challenge: what should be our data analysis pipeline for shotgun
metagenomic data, e.g. on BaseSpace? Several standalone apps for
taxonomic classification Seem to be fewer options for functional
classification Slide 14 14 HiSeq 1 terabase run (R&D data) Per
run you can do up to: 10 genomes 150 exomes 80 WT RNA samples
*Assumes 100Gb, 30x genome; Nextera Rapid Capture Exome; 50M reads
per RNA sample 2 x 125 Cycles Slide 15 15 Challenge: efficient
storage and access for shotgun metagenomic data Resequencing data
(Human genome build ~160 Gbp, ~400 Gbyte FASTQ) FASTQ (gzipped) 150
Gbyte BAM (40 Q-scores) 120 Gbyte BAM (8 Q-scores) 82 Gbyte BAM
(consensus compressed) 60 Gbyte CRAM (consensus compressed) 27
Gbyte Relies heavily on known high-quality reference sequence
Resequencing data (Human genome build 145Gbp, ~160 Gbp, ~400 Gbyte
FASTQ) FASTQ (gzipped, 8 Q-scores) 89 Gbyte BWT compression (now)
37Gbyte BWT compression (likely achievable) 23 Gbyte 89 37Gbyte:
BWT/PPM for reads, simple binning of Q-scores (lossless) Sort reads
for better compression save 4Gbyte (Cox et al., 2012) Discard
uninformative Q-scores (reference free) save 10Gbyte (Janin et al.,
2012) Slide 16 16 Trading compression for searchability
Resequencing data (Human genome build ~165 Gbp) FASTQ (gzipped) 152
Gbyte BWT (searchable) 105 Gbyte NB: 40 Q-scores, both FASTQ and
BWT would be smaller for 8 Q-scores For a query sequence q,
returns: Full FASTQ record (sequence, Q-scores, read names) for all
reads containing q and full FASTQ record of their read pairs Pipe
search output directly to your favourite tool, e.g. Velvet
Applications: In silico pull-down Assembling breakpoints Genotyping
complex variants by tracking k-mers Reads (BWT) :26 Gbyte Q-scores
(razip):64 Gbyte Read names (razip):15 Gbyte Further info:
beetl.github.io/BEETL/, Janin et al. (2014, submitted) Slide 17 17
Thank you! Slide 18 18 Extra slides Slide 19 19 Moleculo Technology
Enables Synthetic Long Reads Up to 10Kb from Illumina short reads
Synthetic long reads 8 10kb Enables fully phased genomes Accurate
de novo assembly of large, complex genomes Synthetic long reads 8
10kb Enables fully phased genomes Accurate de novo assembly of
large, complex genomes Available: Illumina services 2H13 Kit format
early 2014 Available: Illumina services 2H13 Kit format early 2014
Slide 20 20 BaseSpace: Plug and Play Genomic Cloud Solution All you
need is an internet connection Slide 21 21 How Is BaseSpace Being
Used World Wide? Users & Growth Bioinformatics Cloud Computing
Service Illumina Begins Streaming MiSeq Data to the Cloud October
2011 Illumina Begins Data Sharing in the Cloud December 2011
Illumina Begins Streaming HiSeq Data to the Cloud November 2012
BaseSpace Commercial (Supported) Release May 2013 Over 20,000
Instrument Runs Streamed to BaseSpace December 2012 Over 40,000
Instrument Runs Streamed to BaseSpace April 2013 General
Availability of BaseSpace to all HiSeq instruments July 2013 Over
60,000 Instrument Runs Streamed to BaseSpace, and Over 10,000 Apps
Run September 2013
