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CompostBin : A DNA composition based metagenomic binning
algorithmSourav Chatterji*, Ichitaro Yamazaki, Zhaojun Bai and
Jonathan EisenUC Davis [email protected]
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Overview of TalkMetagenomics and the binning
problem.CompostBin
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The Microbial World
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Exploring the Microbial WorldCulturingMajority 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 DataNature of Metagenomic
DataMosaicIntraspecies polymorphismFragmentaryNew Sequencing
TechnologiesEnormous amount of dataShort Reads
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Metagenomic BinningClassification of sequences by taxa
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Why Bin at all?
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Binning in ActionGlassy 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
GenomeDatabase Search [MEGAN, BLAST]Phylogenetic AnalysisDNA
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 unreliableComplex
Communities Hard to Bin.
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Overview of TalkMetagenomics and the binning
problem.CompostBin
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Genome SignaturesDoes 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 WorldHorizontal Gene TransferRecent Estimates [Ge et
al. 2005]Varies between 0-6% of genes.Typically ~2%.But
Amelioration
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DNA-composition metricsThe K-mer Frequency MetricCompostBin uses
hexamers
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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.DNA-composition
metrics
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PCA separates speciesGluconobacter oxydans[65% GC] and
Rhodospirillum rubrum[61% GC]
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Effect of Skewed Relative AbundanceB. anthracis and L.
monogocytesAbundance 1:1Abundance 20:1
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A Weighting SchemeFor each read, find overlap with other
sequences
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A Weighting SchemeCalculate the redundancy of each
position.Weight is inverse of average redundancy.
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Weighted PCACalculate weighted mean w :
Calculates weighted co-variance matrix Mw
PCs are eigenvectors of Mw.Use first three PCs for further
analysis.NXwN1iiiw==
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Weighted PCACalculate weighted mean w :
Calculates weighted co-variance matrix Mw
PCs are eigenvectors of Mw.Use first three PCs for further
analysis.
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Weighted PCACalculate weighted mean w :
Calculates weighted co-variance matrix Mw
Principal Components are eigenvectors of Mw.Use first three PCs
for further analysis.mw=wiXii=1NNMw=wi(Xi-mw)(Xi-mw)Ti
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Weighted PCA separates speciesB. anthracis and L. monogocytes :
20:1 PCAWeighted PCA
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Un-supervised Classification ?
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Semi-Supervised Classification31 Marker Genes [courtesy Martin
Wu]Omni-presentRelatively Immune to Lateral Gene TransferReads
containing these marker genes can be classified with high
reliability.
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Semi-supervised ClassificationUse a semi-supervised version of
the normalized cut algorithm
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The Semi-supervised Normalized Cut AlgorithmCalculate 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 binsGluconobacter oxydans [0.61],
Granulobacter bethesdensis[0.59] and Nitrobacter hamburgensis
[0.62]
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Generalization to multiple binsGluconobacter oxydans [0.61],
Granulobacter bethesdensis[0.59] and Nitrobacter hamburgensis
[0.62]
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TestingSimulate Metagenomic SequencingSanger ReadsVariables
Number of speciesRelative abundanceGC contentPhylogenetic
DiversityTest 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 DirectionsSatisfactory performanceNo Training
on Existing Genomes Sanger Reads Low number of Species Future
WorkHoly Grail : Complex CommunitiesSemi-supervised
projection?Hybrid Assembly/Binning
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AcknowledgementsUC DavisJonathan Eisen Martin WuDongying
WuIchitaro YamazakiAmber HartmanMarcel HuntemannUC BerkeleyLior
PachterRichard KarpAmbuj TewariNarayanan ManikandanPrinceton
UniversitySimon LevinJosh WeitzJonathan Dushoff
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***We study microbial genomics in our lab. Well, microbes are
small and invisible, they cant speak for themselves. So I will do
some marketing for them.Microbes are also agents of disease. For
example, this is an electron micrograph of Salmonella cells in red
attacking human tissue. For instance, you might remember the
Anthrax scare that was in the news just after the 9/11 attacks.
These are just three among many reasons the understanding the
biology of microbes is really important.-Appearance not a reliable
indicator of what one is looking at.-Cultivate microbes in the
laboratory in artificial media.*The most apparent problem in
metagenomic analysis is binning, the clustering of metagenomic
sequences into taxon-specific bins.*Metagenomics and binning has
applications in the real world too, like the production of wines.
Dont worry, it has nothing to do with storing of wine
bottles.*******Current methods : All dimensions treated
equally.
*Current methods : All dimensions treated equally.
**The species in the example before were equally abundant.
However, **The idea behind the weighting scheme is that sequences
from more abundant species will have more overlap and thus lower
weights.*We use these weights in a variation of the standard PCA
algorithm. In the weighted algorithm, we first calculate the
weighted mean mu_w. Each point is then normalized *We use these
weights in a variation of the standard PCA algorithm. In the
weighted algorithm, we first calculate the weighted mean mu_w. Each
point is then normalized *We use these weights in a variation of
the standard PCA algorithm. In the weighted algorithm, we first
calculate the weighted mean mu_w. Each point is then normalized
***To use this information in clustering, we use a semi-supervised
version of the widely used normalized cut algorithm. *******
