Cross-Kingdom Standards in Genomics, Epigenomics and Metagenomics

Cross-Kingdom Standards in Genomics,
Epigenomics and Metagenomics
(for this world and maybe others)
Christopher E. Mason, Ph.D.
Associate Professor
Department of Physiology and Biophysics &
The Institute for Computational Biomedicine (ICB),
Meyer Cancer Center, Feil Family Brain and Mind Research Institute,
at Weill Cornell Medicine,
Fellow of the Information Society Project, Yale Law School
June 29th , 2017

Bifurcation of sequencing types:
Platform Instrument Template Preparation Chemistry Avearge Length Longest Read
Illumina HiSeq2500 BridgePCR/cluster Rev. Term., SBS 100 150
Illumina HiSeq2000 BridgePCR/cluster Rev. Term., SBS 100 150
Illumina MiSeq BridgePCR/cluster Rev. Term., SBS 250 300
GnuBio GnuBio emPCR Hyb-Assist Sequencing 1000* 64,000*
Life Technologies SOLiD 5500 emPCR Seq. by Lig. 75 100
LaserGen LaserGen emPCR Rev. Term., SBS 25* 100*
Pacific Biosciences RS Polymerase Binding Real-time 1800 15,000
454 Titanium emPCR PyroSequencing 650 1100
454 Junior emPCR PyroSequencing 400 650
Helicos Heliscope adaptor ligation Rev. Term., SBS 35 57
Intelligent BioSystems MAX-Seq Rolony amplification Two-Step SBS (label/unlabell) 2x100 300
Intelligent BioSystems MINI-20 Rolony amplification Two-Step SBS (label/unlabell) 2x100 300
ZS Genetics N/A Atomic Lableing Electron Microscope N/A N/A
Halcyon Molecular N/A N/A Direct Observation of DNA N/A N/A
Platform Instrument Template Preparation Chemistry Avearge Length Longest Read
IBM DNA Transistor N/A none Microchip Nanopore N/A N/A
NABsys N/A none Nanochannel N/A N/A
Bionanogenomics N/A anneal 7mers Nanochannel N/A N/A
Life Technologies PGM emPCR Semi-conductor 150 300
Life Technologies Proton emPCR Semi-conductor 120 240
Life Technologies Proton 2 emPCR Semi-conductor 400* 800*
Genia N/A none Protein nanopore (a-hemalysin) N/A N/A
Oxford Nanopore MinION none Protein Nanopore 10,000 10,000*
Oxford Nanopore GridION 2K none Protein Nanopore 10,000 500,000*
Oxford Nanopore GridION 8K none Protein Nanopore 10,000 500,000*
*Values are estimates from companies that have not yet released actual data
Optical Sequencing
Electical Sequencing
Table 1: Types of High-Throughput Sequencing Technologies
Mason, Porter, Smith, 2014

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359768/pdf/jbt.17-2801-006-jbt.17-2801-006.pdf
Many standards and controls being
built and now available

Acronym Group Type Agency/GroupWeb site(s) for Consortiums, Data Sets, Methods, and/or Materials
GIAB Genome in a Bottle
DNA and
cells
NIST https://sites.stanford.edu/abms/giab
Nex-StoCT
Next-generation Sequencing: Standardization of
Clinical Testing (Nex-StoCT) II
DNA CDC http://www.cdc.gov/ophss/csels/dlpss/Genetic_Testing_Quality_Practices/ngsqp.html
GeT-RM
Genetic Testing Reference Materials
Coordination Program
DNA CDC http://wwwn.cdc.gov/clia/Resources/GetRM/default.aspx
http://www.fda.gov/ScienceResearch/BioinformaticsTools/MicroarrayQualityControlProject/
http://www.nature.com/nbt/collections/seqc/index.html
http://www.fda.gov/ScienceResearch/BioinformaticsTools/MicroarrayQualityControlProject/
http://www.nature.com/nbt/focus/maqc/index.html
http://www.abrf.org/index.cfm/group.show/NextGenerationSequencing%28NGS%29.75.htm
http://www.biotech.cornell.edu/news/abrf-next-generation-sequencing-study-webinar
GEUVADIS Genetic European Variation in Health and DiseaseRNA EU http://www.geuvadis.org
http://www.nist.gov/mml/bbd/ercc.cfm
https://www.lifetechnologies.com/order/catalog/product/4456740
ERCC2 External RNA Control Consortium 2 RNA NIST http://www.nist.gov/mml/bbd/ercc2.cfm
SIRV Spike-In RNA Variant Mixes RNA Lexigen https://www.lexogen.com/sirvsrelease/
MBQC Microbiome Quality Control Consortium meta MBQC www.mbqc.org
IMMSA
International Metagenomics and Microbiome
Standards Consortium
meta NIST http://www.nist.gov/mml/bbd/microbial_metrology/immsa-mission-statement.cfm
IHMS International Human Microbiome Standardsmeta meta www.microbiome-standards.org/
BiOMICs Bio-OMICS mixed kingdom DNA standard
meta
and cells
Zymo http://www.zymobiomics.com/
ATCC
International Metagenomics and Microbiome
Standards Consortium
meta ATCC http://www.atcc.org/products/all/CCL-186.aspx
BEI International Human Microbiome Standardsmeta NIAID https://www.beiresources.org/Catalog/otherProducts/HM-782D.aspx
EMP Earth Microbiome Project meta EMP http://earthmicrobiome.org/
XMP eXtreme Microbiome Project meta XMP http://extrememicrobiome.org/
MGRG Metagenomics Research Group meta ABRF http://blog.abrf.org/
MetaSUB
International Metagenomics and Metadesign of
Subways and Urban Biomes
meta ABRF http://www.metasub.org
MAQC / MAQC2
SEQC / SEQC2
Microarray Quality Control Consortium
Sequencing Quality Control Consortium
ABRF-NGS
Registry of Standard Biological Parts DNA iGEM
genome/epigenome
ABRF
Association of Biomolecular Resource Facilities
(ABRF) Next-generation Sequencing
RNA
FDA
transcriptome/epitranscriptome
RNA
metagenome/metatranscriptome
Molecular Standards for Assessing Library, Sequencing, and Analysis Methods in DNA, RNA, and metagenomics
http://parts.igem.org/Main_Page
ERCC External RNA Control Consortium NIST
DNA
RNA
FDA
RSBP

Testing and benchmarking for RNA standards
(FDA’s SEQC and ABRF-NGS study)
RNA-seq Standards
Li, Tighe et al., Nature Biotechnology, Sept. 2014
SEQC Consortium, Nature Biotechnology, Sept. 2014
Li, Łabaj, Zumbo, et al., Nature Biotechnology, Sept. 2014http://www.nature.com/nbt/collections/seqc/index.html

Even with >12 billion reads, more genes
appear and are annotation/tool dependent.
http://www.nature.com/nbt/focus/seqc/index.html

Reference DNA,
TruSeq PCR-free 350
FFPE, TruSeq Nano
FFPE, TruSeq PCR-free
maternal
paternal
son
son
(Coriell)
A B C C2
Personal Genome Project
NIST Reference Human Genomes
C2f
Phase 2 DNA Samples: human

Ste Eco Pflu pool
%GC: 28 47 72
Reference bacterial genomes
TruSeq PCR-free 550
Phase 2 DNA Samples: bacterial

Generate standardized human genome sequencing datasets
Measure intra- and inter-lab variation
Lab 1
HiSeq X Ten, 2x150
Lab 2
Lab 3
3 flow cells
15 libraries
Lab 1
HiSeq 4000, 2x150
Lab 2
Lab 3
3 flow cells
15 libraries
Lab 1
NextSeq 500 High Output, 2x150
Lab 2
Lab 3
6 flow cells
9 libraries
Subtotal: 15 flow cells, 54 libraries
Library kits: TruSeq PCR Free, 350 bp inserts
HiSeq 2500 v4 1T, 2x125
3 flow cells
15 libraries
Lab 1
Lab 2
Lab 3

Lab 1
Compare NIST and Coriell stock cell culture genomes
Evaluate Coriell cell culture as an FFPE reference material
HiSeq X Ten, 2x150
1 flow cell
7 libraries
Library kits: TruSeq PCR Free and TruSeq Nano, 350 bp inserts

Lab 1
MiSeq v3, 2x300
Lab 2
Lab 3
3 flow cells
36 libraries
Lab 1
HiSeq 2500 v3 Rapid Run, 2x250
Lab 2
Lab 3
6 flow cells
45 libraries
Generate standardized human genome sequencing datasets
Measure sequencing performance at GC composition extremes
Library kit: TruSeq PCR Free, 550 bp inserts for bacteria, 350 bp for sample C

Reference DNA,
AmpliSeq Exome
Ste Hah Mil pool
Samples
maternal
paternal
son
%GC: 28 47 72
A B C C2
Personal Genome Project
NIST Reference Human Genomes
Reference bacterial genomes
Ion Xpress Plus
Fragment Library
Life Technologies

Measure sequencing performance at GC composition extremes
Lab 1
RS II Sequel
Lab 2
Lab 3
Pacific Biosciences

Samples and Platforms – All tested in triplicate across three distinct sites
Platform Human DNA Bacterial DNA
Illumina HiSeq X Ten A, B, C, C2, C2f
Illumina HiSeq 4000 A, B, C
Illumina HiSeq 2500 v4 1T A, B, C
Illumina HiSeq 2500 v3 Rapid Run C Ste, Eco, Mil, P
Illumina NextSeq 500 High Output C
Illumina MiSeq Ste, Eco, Mil, P
Life Tech Proton A, B, C exomes Ste, Eco, Mil, P
Life Tech S5 A, B, C exomes Ste, Eco, Mil, P
Life Tech PGM Ste, Eco, Mil, P
Pacific Biosciences Ste, Eco, Mil, P
Oxford Nanopore Ste, Eco, Mil, P
maternal
paternal
son
son
(Coriell)
A B C C2
Ste Eco Pflu pool
Human Trio Bacterial Isolates and Mixture

Sequencing summary
• 286/307 libraries have been sequenced
• Completion date for all data collection will be
August 2017
– First data is posted
– Submit manuscripts by October
• Data is being analyzed by a team of 25
bioinformatics specialist
– most are members of ABRF-NGS and GBIRG
– some are outside of ABRF

PROJECT 1
Illumina HiSeq X Ten Lab 1 (NYGC)
Lab 2 (Broad)
Lab 3 (Hudson Alpha)
----------------------------------------------------------------------------------------------------------------------------------------------
HiSeq 4000 Lab 1 (Cornell)
Lab 2 (U Mich.)
Lab 3 (Mayo)
----------------------------------------------------------------------------------------------------------------------------------------------
HiSeq 2500v4 Lab 1 (Cornell)
Lab 2 (U Roch.)
Lab 3 (Baylor)
----------------------------------------------------------------------------------------------------------------------------------------------
NextSeq 500 Lab 1 (MBF)
Lab 2 (DFCI)
Lab 3 (USC)
----------------------------------------------------------------------------------------------------------------------------------------------
Life Tech Ion Proton Lab 1 (NCI)
Lab 2 (UofU)
Lab 3 (MSK)
----------------------------------------------------------------------------------------------------------------------------------------------
Ion S5 Lab 1 (NCI)
    
    
    
    
    
   
  
  
A B C1 C2 C3
  
    
    
    
 
    
  
  
Downloading from Signiant
Waiting to hear from Don Baldwin

Data format on NCBI
ftp://ftp-
trace.ncbi.nlm.nih.gov/gia
b/ftp/use_cases/ABRF_NG
S/
project1_NIST-
PGP
illumina_hiseq2500v4
illumina_hiseq4000
illumina_hiseqxten
illumina_nextseq500
lifetech_ionproton
lifetech_ions5
project3_MGRG_
bacterial_genomes
illumina_miseqv3
lifetech_ions5
lifetech_ionpgm
HISX10-101-A_R1.fastq.gz
HISX10-101-B_R1.fastq.gz
HISX10-101-C1_R1.fastq.gz
HISX10-100-A.bam
HISX10-100-B.bam
HISX10-100-C1.bam
HISX10-100-C2.bam
HISX10-100-C3.bam
README
md5 checksums

QC pipeline
• samtools stats
– number of reads; average read length; total output;
depth of coverage; average Q score
• bedtools coverage
– assess depth of coverage for exomic runs (AmpliSeqExome.20131001.designed.bed)
• samtools flagstat / bedtools genomecov
– mapping statistics / breadth of coverage against reference genome
• picard MarkDuplicates
– duplicate marked fastqs
• bbtools clumpify.sh
– group overlapping reads into clumps to optimize compression

Sequencer/Lab
Sequenced
Sequence
QC
Mapped
Mapping
QC
Duplicate
marked
Clumpified
HiSeq X Ten NYGC
Broad
HudsonAlpha
HiSeq 4000 WCM
UMichigan
Mayo
HiSeq 2500 WCM
URochester
Baylor
NextSeq 500 MBF
DFCI
Ion Proton NCI
UofU
Ion S5 NCI
MiSeq MCF
UC Davis
UMiss
Ion S5 NCI
WCM
DFCI
PGM MSK
UofU
NCI
Project 1
Project 3
QC status

Li et al., 2014. Multi-platform
assessment of transcriptome
profiling using RNA-seq in the
ABRF next-generation
sequencing study. Nature
Biotechnology 10.138/nbt.2972
Platform Lab Mean read length (bp) Sample Number of reads Output (Mb) Depth Average Q Score % reads mapped
Life Tech Ion Proton NCI 187 A (1) 48,550,237 9,079 264.55 24.1 99.35
187 A (2) 32,789,386 6,132 186.51 23.9 99.45
185 A (3) 28,771,679 5,323 165.28 24.4 99.6
189 B (1) 43,004,287 8,128 238.67 24.2 99.37
183 B (2) 40,448,021 7,402 220.11 23.8 99.38
184 B (3) 23,865,020 4,391 136.86 24.3 99.48
183 C (1) 51,663,758 9,454 278.99 23.7 99.38
186 C (2) 37,234,686 6,926 212.28 23.8 99.44
185 C (3) 62,971,920 11,650 367.42 23.9 98.76
U of Utah 178 A (1) 30,664,548 5,458 142.33 24.1 99.33
148 A (2) 5,647,744 836 183.09 22.6 99.4
161 A (3) 94,375,852 15,195 468.13 23.7 98.78
181 B (1) 53,281,165 9,644 295.40 23.8 98.58
179 B (2) 25,927,117 4,641 142.33 24.2 99.5
179 B (3) 46,412,175 8,308 256.29 23.6 99.49
179 C (1) 34,850,185 6,238 188.51 23.7 99.55
146 C (2) 41,374,282 6,041 206.27 22.6 99.53

Sequence Quality Histograms
Illumina HiSeq 2500 Illumina HiSeq 4000 Illumina NextSeq 500
Illumina X Ten IonTorrent PGM IonTorrent Proton

Per Sequence GC Content
Illumina HiSeq 2500 Illumina HiSeq 4000 Illumina NextSeq 500
Illumina X Ten IonTorrent PGM IonTorrent Proton

On the larger side
Mike Schatz

Some insertions harder to see
Mike Schatz

But!
There is more
than one genome:

http://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1005413

Genomic Classification gives more
granularity of species present

Dynamic, the Gut Is.
Measure it carefully, we must.
Aaron Del Duca

Ongoing efforts to reduce variance
(or embrace it when helpful)
Aaron Del Duca

16S rRNA is only a part of the
erudition
Lan Y, Rosen G, Hershberg R. “Marker genes that are less conserved in their sequences are useful for predicting genome-
wide similarity levels between closely related prokaryotic strains.” Microbiome. 2016.
“16s rRNA predicts genome-wide levels of similarity very well for distantly related prokaryotes,
but not for closely related ones.”

Average Amino Acid identity (AAi) 16s rRNA
Escherichia/Shigella lineage is poorly defined by 16S

Metagenomics can expand the microbiome
to query across kingdoms
Data Type 16S 18S ITS Shotgun
Taxonomic Classification Yes Yes Yes Yes
Prokaryotes Yes No No Yes
Archaea Yes No No Yes
Eukaryotes No Yes Yes Yes
Parasites No Yes No Yes
Plasmids No No No Yes
Phages No No No Yes
Human Ancestry No No No Yes
Biosynthetic Gene Clusters No No No Yes
Antimicrobial Resistance (AMR) Markers No No No Yes
Kingdom Specificity Yes Yes Yes No
Approximate Raw Cost / Sample $100 $100 $125 $300
From https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5359768/

For complex metagenomic samples,
we see similar challenges as SEQC
Millions of Reads
NumberofSpecies
Elizabeth Hénaff
Abundance (MetaPhlAn)

But!
Are we any good at measuring?

Microbial Reference Standards
• Human Microbiome project control- BEI-ATCC
• DNA reference standards HM276D and hm782D- will not be made any longer
• Zymo Research Microbial Community Standard (BioOMICs)
• 8 bacteria and 2 yeast
• ATCC bacterial standards (4 genomic, 2 whole cell)
• ABRF MGRG Class I Reference community (available from ATCC)
• Genomic DNA -11 strains G+, G-, GC high/low, BCL1
• Whole cell fixed and counted
• ABRF Synthetic Metagenomics Reference
– 16s and Genomic in IDT gblock
– Zika, fungi, HPV, Plasmids, Malaria, parasite, KC775387
– Same 11 bacteria
• NIST microbial reference
• 4 Bacteria including food pathogens
• “Robogut” culture- artificial poop
• Emma Allen-Vercoe-Canada
• MetaQuins (Garvan)

International Standards Being Tested
https://www.nist.gov/mml/bbd/immsa-mission-statement

ATCC Mixed and Titrated controls

Preservation of ratios is essential, yet
rare.
Dev Mittar, ATCC

Other sources of variation
Dev Mittar, ATCC

• Sequins are synthetic DNA standards that ‘mirror’ and match the sequencing,
assembly and alignment of microbe genomes.
• Their synthetic sequence allows them to be added directly to a user’s DNA sample
prior to library preparation and sequencing, and thereby act as internal reference
controls.
W W W. S E Q U I N S . X Y Z

Diagnostic performance – assess the sensitivity and specificity for
detecting pathogens in a sample.
Quantitative accuracy – measure quantitative performance of a NGS
library, and the impact of sequence coverage on analysis (see over).
Sequins can be analyzed as internal controls throughout the NGS workflow:
Normalization – sequins can act as scaling factors to normalize between
multiple samples for more accurate comparisons.
Quality control and troubleshooting – calibrate and optimize library
preparation, sequencing and analysis steps.

• Metaquins are titrated across a 105 –fold concentration range to form a quantitative
ladder.
• This ladder can be used to assess quantitative accuracy, sensitivity limits and the
impact of sequencing coverage on de novo assembly.
• Alternative mixtures can be used to assess fold-change differences between samples

Zymo Research BioPool: ZymoBIOMICS Microbial Community Standard

Can show very reproducible,
cross-kingdom recapitulation of species
Cycles of PCR
1 3 6 9 12

Additional Zymo BioOMICs testing

Genetics and Epigenetics of
Anti-microbial Resistance (AMR)
http://gcgh.grandchallenges.org/grant/global-distribution-and-epigenetic-stratification-antimicrobial-resistance

Open, Transparent, Global Collaboration
3 Goals:
1. Geospatial Metagenomic and Forensic Maps
2. Anti-microbial resistance (AMR) marker tracking
3. New Biosynthetic Gene Clusters (BGCs); new drugs
www.metasub.org

http://metasub.org/2017-sample-map/

(3)
Computational
Testing of
Standards

http://www.biorxiv.org/content/early/2017/06/28/156919.1

Algorithm:
BLAST-
MEGAN
CLARK /
CLARK-S
Diamond-
MEGAN
GOTTCHA Kraken LMAT MetaFlow MetaPhlAn2 NBC PhyloSift
Year of release 2015 2015 2014 2015 2014 2015 2016 2014 2010 2014
Version number
MEGAN:
v5.10.6
v1.2.2-beta
v0.7.9.58,
MEGAN:
v5.10.6
v1.0b, db
v20150825
v0.10.5-beta,
"standard db"
v1.2.6 v0.9.2 v2.0.0 Webserver v1.0.1
Classification heuristic (aligner) Alignment Kmer Alignment
Marker (BWA
alignment)
Kmer Kmer
Alignment/
coverage
(BLAST)
Marker
(bowtie2 or
sam file)
Kmer
Marker (LAST +
hmmalign)
species 269899 1335 269899 1335 1381 5754 1313 3848 650 2685
% in db 99.87% 98.58% 99.87% 97.94% 97.30% 97.68% 94.08% 99.10% 59.97% 99.61%
taxa 280062 2488 280062 2498 2513 20265 1321 12926 960 9776
species 6707 123 6707 140 143 333 143 228 62 134
% in db 100% 92.31% 100% 100% 100% 100% 96.92% 100% 56.92% 100%
taxa 6878 144 6878 168 272 401 143 300 72 187
species 10750 4289 10750 4323* 4243 4348 777 3449 * 15
taxa 106851 4381 106851 4420* 4420 14525 5 3522 2080* 18
species 87132 0 87132 0 0 337 0 73 49242* 220
% in db 100% 0% 100% 0% 0% 100% 0% 100% 0% 100%
taxa 88375 0 88375 0 0 513 0 74 49242* 2042
species 357291 1* 357291 0 1* 1643 0 38 0 1921
taxa 464911 1* 464911 0 1* 1677 0 38 0 13212
Includes human Yes
No (human
database
available)
Yes No
No (human
database
available)
Yes No No No Yes
Facilitates custom databases Yes Yes Yes Yes Yes Yes Yes Yes
Webserver -
No/
Standalone -
Yes
Yes
Strain-Level Yes No Yes Yes Yes Yes No Yes Yes No
AMR & virulence markers No No No No No Yes No No No No
Input fasta, fastq
fastq, fasta,
txt
fasta, fastq fastq fasta,fastq fastq,fastq fasta
fastq, fasta,
sam
fasta, fastq fastq, fasta
Output daa, sam, csv csv daa, sam, csv sam,tsv txt tsv csv txt, biom txt txt
Paired-end No Yes No No Yes Yes Yes* Yes No Yes
Read-level classification Yes Yes Yes No Yes Yes No No Yes No
Multi-thread No Yes Yes Yes Yes Yes
BLAST - yes,
MEGAN - no
Yes
Webserver -
Yes /
Standalone -
No
Yes
Visualization Yes
Krona plots,
histograms
Yes Krona plots
Krona plots via
MetAMOS
No No
Heatmaps,
Krona plots,
GraPhlAn
No
Phylogenetic
trees, Krona
plots
Web interface No No No No Yes, optionally No No
Yes,
optionally
Yes,
optionally
No
License
Free for
academic use
GPL
Free for
academic use
GPL GPL GPL GPL MIT GPL GPL
Tutorial
ab.inf.uni-
tuebingen.de/
software/meg
an
clark.cs.ucr.edu
github.com/b
buchfink/dia
mond
lanl-
bioinformatic
s.github.io/G
OTTCHA
ccb.jhu.edu/softw
are/kraken/MANU
AL.html
sourceforge.n
et/p/lmat/wik
i/Example%20
LMAT%20Run
/
github.com/al
exandrutome
scu/metaflow
bitbucket.org
/nsegata/met
aphlan/wiki/
MetaPhlAn_Pi
pelines_Tutor
nbc.ece.drexe
l.edu/tutorial.
php
phylosift.wordp
ress.com/tutori
als/running-
phylosift/illumin
a-tutorial/
Archaea (65 in evaluation)
DatabaseSize
Table 1: Algorithm Types and Parameters of Usage and Reporting
Bacteria (777 in evaluation)
Viruses (1 in evaluation)
UsageParameters
Fungi (3 in evaluation)
Other eukaryotes
Background

69 titrated data sets
Dataset File Data Type Description No. of Genomes Total No. of Reads Read Length Strain Level Publication/Source Mean AUPR across all tools
bmi_reads.fasta.gz Simulated mock human salivary microbiome 10 5541101 100 Y Hasan et al., 2014 0.62
ds.7.fq.gz Simulated Unambiguously mapping reads, "simBA-525" 525 5727654 100 Y Ounit and Lonardi, 2016 0.79
ds.buccal.fq.gz Simulated Unambiguously mapping reads, "Buc12" 12 600000 100 Y Ounit and Lonardi, 2016 0.69
ds.cityparks.fq.gz Simulated Unambiguously mapping reads, "CParMed48" 48 1200000 100 Y Ounit and Lonardi, 2016 0.87
ds.gut.fq.gz Simulated Unambiguously mapping reads, "Gut20" 20 500000 100 Y Ounit and Lonardi, 2016 0.82
ds.hous1.fq.gz Simulated Unambiguously mapping reads, "Hou31" 31 750000 100 Y Ounit and Lonardi, 2016 0.86
ds.hous2.fq.gz Simulated Unambiguously mapping reads, "Hou21" 21 500000 100 Y Ounit and Lonardi, 2016 0.82
ds.nycsm.fq.gz Simulated Unambiguously mapping reads, "NYCSM20" 20 500000 100 Y Rachid Ounit 0.83
ds.soil.fq.gz Simulated Unambiguously mapping reads, "Soi50" 50 2500000 100 Y Ounit and Lonardi, 2016 0.88
eval_carma.fasta.gz Simulated mock community 25 25000 265.36 Gerlach & Stoye, 2011 0.84
eval_RAIphy.fasta.gz Simulated mock community 53 477000 238 Nalbantoglu et al, 2011 0.7
HC1.fasta.gz Simulated high-complexity, evenly distributed mock community 100 999998 88.6431 Segata et al., 2013 0.93
HC2.fasta.gz Simulated high-complexity, evenly distributed mock community 100 999991 88.6289 Segata et al., 2013 0.92
LC1.fasta.gz Simulated low-complexity, log-normally distributed mock community 25 249995 88.6083 Segata et al., 2013 0.62
simHC.fasta.gz Bio/Sim assembled contigs from sequenced isolates designed to simulate a high-complexity community lacking a dominant population113 116771 949.511 Mavromatis et al., 2007 0.88
simLC.fasta.gz Bio/Sim assembled contigs from sequenced isolates designed to simulate a low-complexity community dominated by one population, with other species at low abundances113 97495 951.959 Mavromatis et al., 2007 0.82
simMC.fasta.gz Bio/Sim assembled contigs from sequenced isolates designed to simulate a medium-complexity community with multiple dominant populations113 114457 969.085 Mavromatis et al., 2007 0.76
even_454_SRR072233.fastq.gz Biological HMP Mock community - 454 sequencing 23 1386198 534.218 HMP, http://hmpdacc.org/HMMC/ 0.78
even_illum_SRR172902.fastq.gz Biological HMP Mock community - Illumina sequencing 23 6562065 75 HMP, http://hmpdacc.org/HMMC/ 0.83
MGRG_10ng_Repli_g_08142015_ATGTCA_L001_R1/2_001.fastq.gz Biological Lab constructed metagenome - Illumina HiSeq4000 5 3216104 301 Scott Tighe, ABRF MGRG 0.66
MGRG_1ng_Repli_g_08142015_GTCCGC_L001_R1/2_001.fastq.gz Biological Lab constructed metagenome - Illumina HiSeq4000 5 2811864 301 Scott Tighe, ABRF MGRG 0.65
MGRG_5ng_Repli_g_08142015_CCGTCC_L001_R1/2_001.fastq.gz Biological Lab constructed metagenome - Illumina HiSeq4000 5 2478166 301 Scott Tighe, ABRF MGRG 0.67
MGRG_Half_ng_Repli_g_08142015_GTGAAA_L001_R1/2_001.fastq.gzBiological Lab constructed metagenome - Illumina HiSeq4000 5 2814482 301 Scott Tighe, ABRF MGRG 0.63
MGRG_Normal_08142015_CGTACG_L001_R1/2_001.fastq.gz Biological Lab constructed metagenome - Illumina HiSeq4000 5 2946836 301 Scott Tighe, ABRF MGRG 0.59
QiagenFX_Assay_BioPool_1_Cycle_02042016_CTGAAGCT-TATAGCCT_L001_R1/2_001.fastq.gzBiological Mock microbial community, "BioOmics" 10 626368 602 Zymo 0.72
SRR033547.fastq.gz Biological Mixed DNA library 10 112 118.196 Y JGI, SRP001692 0.41
ds.frankengenome.fq.gz Simulated Unambiguously mapping reads, cross-domain species 66 612471 150 Y Rachid Ounit
ds.frankengenome.mix.fq.gz Simulated Negative control 0 1000000 600 Rachid Ounit
LM.fastq.gz Simulated Negative control with reads composed of >= 5 nullomers 0 1000000 100 Ounit and Lonardi, 2016
MH1.fastq.gz Simulated Negative control with reads composed of >= 4 nullomers 0 1000000 100 Ounit and Lonardi, 2016
MH2.fastq.gz Simulated Negative control with reads composed of >= 3 nullomers 0 1000000 100 Ounit and Lonardi, 2016
SL126486_0.fastq.gz Biological Negative control, human DNA spiked into MoBio PowerSoil kit 1 25428856 100 HudsonAlpha
P00134-R1/2.fastq.gz Biological NYC subway sample Unknown 3386006 125 Afshinnekoo et al., 2015
P00497_Deep.100.Mreads.fastq.gz Biological Highly sequenced NYC subway sample Unknown 100000000 100 Afshinnekoo et al., 2015
P00497_Deep.10.Mreads.fastq.gz Biological Downsampled NYC subway sample P00497 Unknown 10000000 100 Afshinnekoo et al., 2015
b1.fail.2d.fastq.gz Biological Lab constructed metagenome - MinION SQK-MAP005 2D fail data 5 423 1202.62 Scott Tighe + Mason Lab, ABRF
b1.pass.2d.fastq.gz Biological Lab constructed metagenome - MinION SQK-MAP005 2D pass data 5 97 1106.63 Scott Tighe + Mason Lab, ABRF

All freely available at NIST IMMSA site
https://ftp-private.ncbi.nlm.nih.gov/nist-immsa/IMMSA/

A brewing disaster?
Same .fastq file; 15 tools; log10 differences

Different tools are driven by different
data aspects

Abundance is much harder
𝑖=0
𝑛
𝑦𝑖 − 𝑥𝑖 ,

(3)
What have we
been missing?

Unknown
Organisms
48.313%
Bacteria
46.927%
Eukaryota
0.771%
Ambiguous
4.184%
Viruses
0.032%
Archaea
0.003%
Plasmids
0.001%
Afshinnekoo E, Meydan C, et al., Cell Systems, 2015.
What is there, and what are they making?

No standards yet for THIS!
Ken McGrath

Or THIS!
Gowanus Canal EPA SuperFund site
Brooklyn, NY

Or here - Lake Fryxell, Antarctica
Scott Tighe
Sequencing HW DNA in “the field” with the Oxford Nanopore
Sarah Johnson (PI) expedition G062 team

0
20
40
60
80
100
120
140
160
ng/ul(normalized)
Polyzyme No Enzyme Lysozyme
The MGRG Beta Test Results
• Over 150 sample trials (Polyzyme, PBS only, Polyzyme only)
• 3 trials with Lysozyme alone-Need more data
• 6 labs , 17 matrices
• Any kits

http://www.sigmaaldrich.com/catalog/product/sigma/mac4l?lang=en&region=US
www.extrememicriobiome.org
Multi-enzyme extraction now available

Metagenomes are completed –
lots of potential
David Danko

StuckOnU
MetaSUB metagenomics research comes to the ABRF 2017 in San Diego!
Our research study investigates the microbiome and DNA of your cell phone, as part
of a global study on the genomics of our world’s cities.

36h protocol for 96 samples
+ +

Extract, Prep, Sequence
Daniela Bezdan and Elizabeth Henaff
-sleepless rock stars

Everyone got a metagenomics report

Metagenomic hypothesis
generation machine

Can your phone reveal what you’ve been
eating or doing?

Overall proportion of kingdoms
The two Zymo BioOMICs controls

We have one alien… 94% Unknown!

Scott Tighe
corn
Apple control
Last meal:
Salad
Orange?
Leather purse

We found the MIT man who just ate an
orange!

We found the guy who ate pulled
pork!
101

Comparison of surfaces
Shoes look the least like skin

We can predict who has cats
104

We can predict who has dogs even better
105

Antibiotic Resistance
ABRF_Plate1_C12
ABRF_Plate1_D04
ABRF_Plate1_A07
ABRF_Plate1_F11
ABRF_Plate1_E03
ABRF_Plate1_D12
ABRF_Plate1_D06
ABRF_Plate1_E12
ABRF_Plate1_D07
ABRF_Plate1_E01
ABRF_Plate1_H09
ABRF_Plate1_E07
ABRF_Plate1_F04
ABRF_Plate1_A06
ABRF_Plate1_A02
ABRF_Plate1_H01
ABRF_Plate1_A12
ABRF_Plate1_H04
ABRF_Plate1_H03
ABRF_Plate1_A09
ABRF_Plate1_A03
ABRF_Plate1_E05
ABRF_Plate1_A04
ABRF_Plate1_C09
ABRF_Plate1_F09
ABRF_Plate1_H05
ABRF_Plate1_H12
ABRF_Plate1_G11
ABRF_Plate1_E09
ABRF_Plate1_E06
ABRF_Plate1_C06
ABRF_Plate1_A10
ABRF_Plate1_A05
ABRF_Plate1_E08
ABRF_Plate1_D03
ABRF_Plate1_H10
ABRF_Plate1_B11
ABRF_Plate1_B10
ABRF_Plate1_A08
ABRF_Plate1_D10
ABRF_Plate1_D08
ABRF_Plate1_D05
ABRF_Plate1_H11
ABRF_Plate1_C07
ABRF_Plate1_F08
ABRF_Plate1_C10
ABRF_Plate1_F12
ABRF_Plate1_G12
ABRF_Plate1_H07
ABRF_Plate1_H08
ABRF_Plate1_E10
ABRF_Plate1_D09
ABRF_Plate1_G05
Macrolide mel
Aminoglycoside aadD
MDR−Efflux−pump qacB
MDR−Efflux−pump qacA
Macrolide ermX
Trimethoprim dfrC
Aminoglycoside aadC
Aminoglycoside aph3' Ic
Aminoglycoside aph6 Id
Macrolide msrA
Macrolide ermF
Tetracycline tetK
Macrolide vgaALC
Macrolide vgaA
Macrolide mefA
Tetracycline tetC
Aminoglycoside aph3' Ia
Macrolide erm36
Macrolide msrD
Beta−lactam cfxA3
Penicillin−binding−protein PBP2b
bleomycin resistance protein BRP
Efflux−pump lsaC
Macrolide lmrC
Aminoglycoside aph3'' Ib
Macrolide lnuA
Beta−lactam−resistance blaOXA 85
Macrolide mphC
Macrolide ermC
Macrolide ermA Workplace
Dry Lab
Office
Traveling
Wet Lab
Gender
Female
Male
Antibiotics
No
0
54
72
98
NextSeq_8ul Antibiotic Resistance Percent Total Matches Heatmap
1

Does antibiotic use enrich for
Haemophilus parainfluenzae?

Elizabeth’s outdoorsy bacteria

(5)
What’s next?
What are the
implications?

Can the standards work in space?
http://www.nasa.gov/mission_pages/station/research/experiments/2181.html

Standards beyond Earth
https://en.wikipedia.org/wiki/Cygnus_CRS_OA-7

McIntyre ABR et al., Nature Microgravity, 2016.
https://www.nature.com/articles/npjmgrav201635

SpaceX CRS-9: perfect launch
and booster return
July 18, 2016

Flight data shows very good accuracy (89-92%) for 2D reads
Plus, good read accuracy (76-79%) for 1D reads
for the template/complement measures.
Flight Data Read Accuracy(%ofreads)

The first genome sequenced and
assembled from beyond-Earth reads
http://biorxiv.org/content/early/2016/09/27/077651

Calling current (pA) differences

Direct Detection of Methylation on PacBio
70.5 71.0 71.5 72.0 72.5 73.0 73.5 74.0 74.5
0
100
200
300
400
Fluorescence
intensity(a.u.)
Time (s)
104.5 105.0 105.5 106.0 106.5 107.0 107.5 108.0 108.5
0
100
200
300
400
Fluorescence
intensity(a.u.)
Time (s)
C
T G A TC G T A C
mA
AG TCT A A
G C C A A A
A
Approach: Kinetic detection of methylated bases during SMRT DNA sequencing
Example: N6-methyladenosine (mA)
Flusberg et al, 2010

Bacteria are splattered
with epigenetic marks

mCaller for epigenetics on nanopores
https://github.com/al-mcintyre/mCaller http://biorxiv.org/content/early/2017/04/13/127100

Certain positions of the pore and more
informative then others

Tested a variety of methods
(Recurrent Neural Networks, SVMs, Naïve
Bayes, lasso regression)

As much as 94% Accuracy; 88% single
molecule

Overall hemi-methylation detection
correlation

Covert high school and college drinking WILL get harder

To know what’s real, we will need standards
http://genomeinabottle.org
SEQC2!

Assays/Data for the EpiQC in the SEQC2/GIAB
Sample m6A m4C m5C hm5C RSII MinION
450/850K
Methyl
Array
oxBS WGBS RRBS
Earth
/Space
HG001 + - + + + + +/+ + + + +/-
HG002 + - + + + + +/+ + + + +/-
HG003 + - + + + + +/+ + + + +/-
HG004 + - + + + + +/+ + + + +/-
IMR90 + - + + - - +/+ - + + +/-
DNMT3A/1
KO
+ - + + - - +/+ - + + +/-
Mouse
(BALB/)C
+ - + + + + NA + + + +/+
E. coli (K-12) + + + - + + NA + + - +/+
Lambda - l + + - - + + NA - + - +/+
Chinese 4 + - + + - - +/+ - - - -/-
Base modification Platform / Assay / Location

Conclusions
• Data sets are made and ready for most of the
FDA’s SEQC and ABRF-NGS DNA studies
• Epigenome QC (EpiQC) group is also testing the
same human and metagenone samples for base
modifications
• Metagenomics benchmarking shows striking
differences in default pipelines, even with similar
database sizes and coverage.
• Sequencing experiments can now be planned for
space flight. Maybe Mars.

Deep Gratitude to Many People:
Illumina
Gary Schroth
Marc Van Oene
Univ. Chicago
Yoav Gilad
FDA/SEQC/Fudan Univ.
Leming Shi
NIH/UDP/NCBI
Jean & Danielle Thierry-Mieg
Baylor
Jeff Rogers
MSKCC
Danwei Huangfu
Christina Leslie
Ross Levine
Alex Kentsis
HudsonAlpha
Shawn Levy
Mason Lab
Ebrahim Afshinnekoo
Sofia Ahsanuddin
Noah Alexander
Pradeep Ambrose
Daniela Bezdan
Marjan Bozinoski
Dhruva Chandramohan
Chou Chou
David Danko
Tim Donahoe
Jonathan Foox
Elizabeth Hénaff
Matthew MacKay
Alexa McIntyre
Cem Meyden
Niamh O’Hara
Lenore Pipes
Jake Reed
Heba Shabaan
Delia Tomoiaga
Priyanka Vijay
David Westfall
Cornell/WCM
Scott Blanchard
Selina Chen-Kiang
Olivier Elemento
Samie Jaffrey
Ari Melnick
Margaret Ross
Epigenomics Core
Duke
Stacy Horner
Nandan Gokhale
Icahn/MSSM
Eric Schadt,
Andrew Kasarskis,
Joel Dudley, Ali
Bashir,
Bobby Sebra
ABRF
George Grills
Scott Tighe
Don Baldwin
Miami
Maria E Figueroa
AMNH
George Amato
Mark Sidall
@mason_lab
NYU
Martin Blaser
Jane Carlton
Julia Maritz
Chris Park
MIT Media Lab
Kevin Slavin
Devora Najjar
Regina Flores
Rockefeller
Jeanne Garbarino
Charles Rice
NASA
Aaron Burton
Sarah Castro-Wallace
Kate Rubins
Graham Scott
Craig Kundrot
Jackson Labs
Sheng Li
UVA
Francine Garrett-Bakelman

Cross-Kingdom Standards in Genomics, Epigenomics and Metagenomics

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Was ist angesagt?

Was ist angesagt? (20)

Ähnlich wie Cross-Kingdom Standards in Genomics, Epigenomics and Metagenomics

Ähnlich wie Cross-Kingdom Standards in Genomics, Epigenomics and Metagenomics (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Cross-Kingdom Standards in Genomics, Epigenomics and Metagenomics