improving the accuracy of taxonomic classification for ... · organism actually belongs to a novel...
TRANSCRIPT
IDTAXA avoids misclassifying sequences belonging to novel taxonomic groups that are not represented in existing taxonomic databases, which is the predominant type of error made by current classifiers. For example, the popular RDP Classifier incorrectly assigns 26.0% of novel 16S rRNA sequences to an existing taxonomic group when the organism actually belongs to a novel taxonomic group. In contrast, IDTAXA only incorrectly classifies 13.6% of such sequences, while correspondingly improving on the fraction of sequences correctly classified to known taxonomic groups.
2. How well does the IDTAXA algorithm work for taxonomic assignment?
Improving the accuracy of taxonomic classification for identifying taxa in microbiome samples
Adithya Murali1, Aniruddha Bhargava2, & Erik S. Wright3 1Department of Computer Sciences, University of Wisconsin–Madison 2Robotics, Amazon, Inc. 3Department of Biomedical Informatics, University of Pittsburgh
Contact info: [email protected]
a. Comparing performance with different reference taxonomies
It has become increasingly clear that the microbiome is an essential component of human and ecosystem health. Microbiome studies frequently involve sequencing a taxonomic marker, such as the 16S rRNA or ITS, to identify the microorganisms that are present in a sample of interest. We have developed a new method, named IDTAXA, for taxonomic classification of marker gene sequences that exhibits a substantially lower error rate than previous approaches.
Introduction
Results
ConclusionsIDTAXA exhibits substantially lower error rates when classifying novel sequences that are unrepresented in the reference taxonomy. This considerably alters the interpretation of microbiome data because many microbial communities contain a large fraction of previously unknown microorganisms that are not yet represented in taxonomic databases. Collectively, these improvements often lead to substantially different classifications on real microbiome data, which may considerably alter its interpretation.
b. IDTAXA's confidence is more correlated with percent identity
3. IDTAXA is available as a part of the R package DECIPHER or onlinea. How to classify new sequences
with the IDTAXA algorithm:b. Visit http://DECIPHER.codes
1. Classifying marker gene (e.g., 16S) sequences into a taxonomya. Given a reference taxonomy
with sequence representativesb. The goal is to predict the taxon
of a microbiome sequence
AGCGGCAGCACAGAGGAACTTGTTCCTTGG...
Root (97.8%);Bacteria (97.8%);
Proteobacteria (97.8%);Gammaproteobacteria (97.8%);
Enterobacteriales (97.8%);Enterobacteriaceae (97.8%);
Escherichia (95%);E. coli (82%)
Root
Archaea Bacteria
⍺ β ! " ε
Actinobacteria Bacteroidetes Proteobacteria ......
Aeromonadales Enterobacteriales Legionellales ......
Enterobacteriaceae
Citrobacter Erwinia SalmonellaEscherichia ......
E. coli
E. albert
ii
E. fergus
onii
Phylum:
Class:
Order:
Family:
Genus:
Species:
E. marm
otae
E. vuln
eris
...
assign to a taxon
new marker gene sequence (e.g., 16S rRNA):
Taxonomic assignment:
confidences at each rank level
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Fraction of classifiable sequences classified
Erro
r rat
e
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BLASTBLAST−GLOBALRDPSINTAXSPINGOIDTAXA
OC MC
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Fraction of classifiable sequences classified
Erro
r rat
e
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BLASTBLAST−GLOBALRDPSINTAXSPINGOIDTAXA
OC MC
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Fraction of classifiable sequences classified
Erro
r rat
e ●
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BLASTBLAST−GLOBALRDPSINTAXSPINGOIDTAXA
OC MC
0.0 0.2 0.4 0.6 0.8 1.0
0.0
0.2
0.4
0.6
0.8
1.0
Fraction of classifiable sequences classified
Erro
r rat
e
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BLASTBLAST−GLOBALRDPSINTAXSPINGOIDTAXA
OC MC
RDP training set(genus-level 16S)
Contax training set(genus-level 16S)
Warcup training set(species-level ITS)
V4 region of RDP(genus-level 16S)
LearnTaxa
IdTaxa
classifications
iterative refinement of the training set
training set
test sequences
+
Lawsonella
Mycobacterium
Paenarthrobacter
Dysgonomonas
Sediminibacterium
Algoriphagus
Flavobacterium
Vampirovibrionales
Staphylococcus
Erysipelothrix
Nitrospira
Brevundimonas
Methylobacterium
Hyphomicrobium
PhreatobacterAfi
piaFerrovibrioDS
SF69
Novosphing
obiumPorp
hyrobact
erSphingomona
sSphingopyxis
unclassified_Sphi...
Limnobacter
unclassified_Burkhol...
Nitrosomonas
Azonexus
AcinetobacterPseudom
onasStenotrophom
onas
unclassified_Root
domain
phylum
class
order
family
genus
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0 20 40 60 80 100
Confidence
Dis
tanc
e to
cla
ssifi
catio
nDi
stan
ce to
nea
rest
seq
uenc
e in
cla
ssifie
d gr
oup
Confidence of classification
IDTAXA
SPINGO RDP
SINTAX
Example output from classifying a microbiome
sample