1. tracking microbes at the strain level
MetaPhlAn v2 and
tracking microbes at the strain level
Edoardo Pasolli
Nicola Segata’s Lab
Laboratory of Computational Metagenomics
Centre for Integrative Biology
University of Trento, Italy
MetaSUB Conference June 20th, 2015

2. The shotgun metagenomic workflow1 2 2

3. Taxonomic profiling: who’s there?
Tons of microbes
Tons of short reads
Shotgun
sequencing
Taxonomic
profiling
Sequenced genomes of (some) microbes
Microbial
taxonomy
Organismal
relative abundances
MetaPhlAn (Segata et al., Nature Methods 2012)
MetaPhlAn v2 (released, under review)

4. Taxonomic profiling: unique marker genes
X is a unique marker gene for clade Y
Gene X
IDEA
Pre-identify markers from reference genomes
Use markers as proxy for taxonomic clades in shotgun metagenomics
18k
15k
12k 9k 6k
2003
2013
2005
2011
2009
2007
2006
2010
2008
2004
2012
Number of microbial organisms in RefSeq 3k 0k
THE INPUT
~ genomes (Bacteria, Archaea, Fungi, Viruses)
~1/10 of genomes are final, ~9/10 draft
~7,100 species (excludes incomplete annotations, spp., etc.)
THE RESULTING DATABASE
~15M total unique marker genes
~1M “most representative” unique marker genes
180±45 markers per species (200 fixed max)
Quasi-markers used to resolve ambiguity in postprocessing
Method: ChocoPhlAn

5. Taxonomic profiling: MetaPhlAn’s overview
Marker database
Clade 1
Clade 2
Metagenome
Marker identification
ChocoPhlAn (offline)
MetaPhlAn
database
Reference genomes + taxonomy
Clade 1
Clade 2
Profiled Metagenome
Mapping

6. Taxonomic profiling: MetaPhlAn’s main features
Species-level resolution
Computational feasibility
Organismal relative abundance rather than DNA concentrations
Consistent detection confidence for all clades
High accuracies for very short reads (as short as ~50nt)
Detection of organisms without sequenced genomes
Main MetaPhlAn2 additions
Profiling not only for Bacteria and Archaea, but also for viruses, Fungi and Protozoa
6-fold increase in the number of considered species: >7000 species
Introduction of the concept of quasi-markers
Improvement of quantitative performances: higher correlation with true abundances, lower false positive and false negative rates
Improvement of computational performances
Addition of strain-specific barcoding for microbial strain tracking
Strain-level identification for organisms with sequenced genomes
Integration with post-processing and visualization tools
Prevotella copri
Profiled thousands of samples in few days

7. http://cibiocm.bitbucket.org - nicola.segata@unitn.it
Thanks!
The Laboratory of Computational Metagenomics
Matthias Scholz
Adrian Tett
Tin Truong
Edoardo Pasolli
Federica Armanini
Francesco Asnicar
Pamela Ferretti
Moreno Zolfo
Thomas Tolio
Serena Manara
Mattia Bolzan
Francesco Beghini
Luca Erculiani -
Owen R White
Dan Littman
Veronica De Sanctis
Roberto Bertorelli
Enrico Blanzieri
Curtis
Huttenhower
Olivier
Jousson
Wendy
Garrett
Doyle
Ward
Jacques
Izard
Marco
Ventura
Flaminia
Catteruccia