CompostBin : A DNA composition based metagenomic binning algorithm

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CompostBin A DNA composition based metagenomic
binning algorithm

• Sourav Chatterji, Ichitaro Yamazaki, Zhaojun Bai
  and Jonathan Eisen
• UC Davis
• schatterji_at_ucdavis.edu

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Overview of Talk

• Metagenomics and the binning problem.
• CompostBin

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The Microbial World
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Exploring the Microbial World

• Culturing
• Majority of microbes currently unculturable.
• No ecological context.
• Molecular Surveys (e.g. 16S rRNA)
• who is out there?
• what are they doing?

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Metagenomics
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Interpreting Metagenomic Data

• Nature of Metagenomic Data
• Mosaic
• Intraspecies polymorphism
• Fragmentary
• New Sequencing Technologies
• Enormous amount of data
• Short Reads

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Metagenomic Binning
Classification of sequences by taxa
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Why Bin at all?
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Binning in Action

• Glassy Winged Sharpshooter (Homalodisca
  coagulata).
• Feeds on plant xylem (poor in organic nutrients).
• Microbial Endosymbionts

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Current Binning Methods

• Assembly
• Align with Reference Genome
• Database Search MEGAN, BLAST
• Phylogenetic Analysis
• DNA Composition TETRA,Phylopythia

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Current Binning Methods

• Need closely related reference genomes.
• Poor performance on short fragments.
• Sanger sequence reads 500-1000 bp long.
• Current assembly methods unreliable
• Complex Communities Hard to Bin.

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Overview of Talk

• Metagenomics and the binning problem.
• CompostBin

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Genome Signatures

• Does genomic sequence from an organism have a
  unique signature that distinguishes it from
  genomic sequence of other organisms?
• Yes Karlin et al. 1990s
• What is the minimum length sequence that is
  required to distinguish genomic sequence of one
  organism from the genomic sequence of another
  organism?

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Imperfect World

• Horizontal Gene Transfer
• Recent Estimates Ge et al. 2005
• Varies between 0-6 of genes.
• Typically 2.
• But
• Amelioration

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DNA-composition metrics
The K-mer Frequency Metric
CompostBin uses hexamers
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DNA-composition metrics

• Working with K-mers for Binning.
• Curse of Dimensionality O(4K) independent
  dimensions.
• Statistical noise increases with decreasing
  fragment lengths.
• Project data into a lower dimensional space to
  decrease noise.
• Principal Component Analysis.

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PCA separates species
Gluconobacter oxydans65 GC and Rhodospirillum
rubrum61 GC
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Effect of Skewed Relative Abundance
Abundance 201
Abundance 11
B. anthracis and L. monogocytes
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A Weighting Scheme
For each read, find overlap with other sequences
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A Weighting Scheme
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Calculate the redundancy of each position.
Weight is inverse of average redundancy.
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Weighted PCA

• Calculate weighted mean µw
• Calculates weighted co-variance matrix Mw
• PCs are eigenvectors of Mw.
• Use first three PCs for further analysis.

N
å
X
w
i
i

µ
1
i

w
N
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Weighted PCA

• Calculate weighted mean µw
• Calculates weighted co-variance matrix Mw
• PCs are eigenvectors of Mw.
• Use first three PCs for further analysis.

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Weighted PCA

• Calculate weighted mean µw
• Calculates weighted co-variance matrix Mw
• Principal Components are eigenvectors of Mw.
• Use first three PCs for further analysis.

N
?
w
X
i
i
i
1

m

w
N
?
T
M
w
(
X
)(
X
)

-
m
-
m
w
i
i
w
i
w
i
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Weighted PCA separates species
PCA
Weighted PCA
B. anthracis and L. monogocytes 201
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Un-supervised Classification ?
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Semi-Supervised Classification

• 31 Marker Genes courtesy Martin Wu
• Omni-present
• Relatively Immune to Lateral Gene Transfer
• Reads containing these marker genes can be
  classified with high reliability.

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Semi-supervised Classification
Use a semi-supervised version of the normalized
cut algorithm
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The Semi-supervised Normalized Cut Algorithm

• Calculate the K-nearest neighbor graph from the
  point set.
• Update graph with marker information.
• If two nodes are from the same species, add an
  edge between them.
• If two nodes are from different species, remove
  any edge between them.
• Bisect the graph using the normalized-cut
  algorithm.

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Generalization to multiple bins
Gluconobacter oxydans 0.61, Granulobacter
bethesdensis0.59 and Nitrobacter hamburgensis
0.62
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Generalization to multiple bins
Gluconobacter oxydans 0.61, Granulobacter
bethesdensis0.59 and Nitrobacter hamburgensis
0.62
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Testing

• Simulate Metagenomic Sequencing
• Sanger Reads
• Variables
• Number of species
• Relative abundance
• GC content
• Phylogenetic Diversity
• Test on a real dataset where answer is
  well-established.

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Results
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Results
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Conclusions/Future Directions

• Satisfactory performance
• No Training on Existing Genomes ?
• Sanger Reads ?
• Low number of Species ?
• Future Work
• Holy Grail Complex Communities
• Semi-supervised projection?
• Hybrid Assembly/Binning

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Acknowledgements

• UC Davis

• UC Berkeley

• Jonathan Eisen
• Martin Wu
• Dongying Wu
• Ichitaro Yamazaki
• Amber Hartman
• Marcel Huntemann

• Lior Pachter
• Richard Karp
• Ambuj Tewari
• Narayanan Manikandan

• Princeton University
• Simon Levin
• Josh Weitz
• Jonathan Dushoff

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