Bacteria and Archaea display a variety of phenotypic traits and can adapt to diverse ecological niches. However, systematic annotation of prokaryotic phenotypes is lacking. We have therefore developed ProTraits, a resource containing ∼545 000 novel phenotype inferences, spanning 424 traits assigned to 3046 bacterial and archaeal species. These annotations were assigned by a computational pipeline that associates microbes with phenotypes by text-mining the scientific literature and the broader World Wide Web, while also being able to define novel concepts from unstructured text. Moreover, the ProTraits pipeline assigns phenotypes by drawing extensively on comparative genomics, capturing patterns in gene repertoires, codon usage biases, proteome composition and co-occurrence in metagenomes. Notably, we find that gene synteny is highly predictive of many phenotypes, and highlight examples of gene neighborhoods associated with spore-forming ability. A global analysis of trait interrelatedness outlined clusters in the microbial phenotype network, suggesting common genetic underpinnings. Our extended set of phenotype annotations allows detection of 57 088 high confidence gene-trait links, which recover many known associations involving sporulation, flagella, catalase activity, aerobicity, photosynthesis and other traits. Over 99% of the commonly occurring gene families are involved in genetic interactions conditional on at least one phenotype, suggesting that epistasis has a major role in shaping microbial gene content.

1. The landscape of microbial
phenotypic traits and
associated genes
Maria Brbić1, Matija Piškorec 1, Vedrana Vidulin 1, Anita Kriško 2,
Tomislav Šmuc 1, Fran Supek 1,3*
1 Rudjer Boskovic Institute, Zagreb, Croatia
2 Mediterranean Institute of Life Sciences, Split, Croatia
1,3 Centre for Genomic Regulation, Barcelona, Spain
* current address: Institute for Research in Biomedicine
(IRB Barcelona)
published as: Brbić M et al. (2016) Nucleic Acids Research

2. Microbial phenomics
• Prokaryotes display a variety of phenotypic traits
ARCHAEABACTERIA

3. The amount of prokaryotic
genomes is rapidly increasing
The scientific literature abounds with
trait descriptions stored as
unstructured text
High-quality phenotype
annotation of microbes
are not keeping pace
Relying on manual curation does not
scale with the increasing volume of
scientific publications (and also
sequenced genomes)

4. Adressing the scarcity of systematic phenotype
annotations
We have developed the ProTraits
pipeline that:
relies on text mining of biological
literature to annotate microbes with
phenotypic traits
is able to define novel phenotypic
concepts from free text
draws extensively on comparative
genomics to validate text-mining
inferences
http://protraits.irb.hr/
published as: Brbić M et al. (2016) Nucleic Acids Research

5. Inital data sets
Description of sites where a microbe
was isolated from  no negative
examples
The most complete sets
currently available – many
microbes are still missing
Collected from 100s of
papers by manual curation
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6. In addition to existing traits…
• We applied non-negative matrix factorization (NMF) to model phenotypic
concepts across texts
 Run NMF on each individual text
corpora
 Cluster topics and retain only those
consistent at least 3 text corpora
 Repeat for different number of factors
(50 &100) and seeds
 Manual curation
NMF-based methodology described in: Brbić M et al. (2016) Nucleic Acids Research

7. Phenotype predictions from text mining
• SVMs with linear kernel trained for 424 phenotypes, 6 text corpora (Wikipedia,
PubMed etc.)
infect lung disease … Plant pathogen Gram positive Mesophilic …
Escherichia coli 0 0 1
Bacillus subtilis 0 1 1
Instances:
species/
documents
Attributes: words Class labels
BAG OF
WORDS
• Cross-validation precision-recall
curves used to convert the SVM
confidence scores to precision
(1-FDRs)

8. How accurate are SVM models to predict
phenotypes?
What are the most
discriminative words?
e.g. the ‘halophilic’ phenotype
Brbić M et al. (2016) Nucleic Acids Research

9. In addition to the text mining…
• We predicted phenotypes using 5 (independant) types of genomic data:
1. Proteome composition
2. Gene repertoires
3. Metagenomic co-occurrence
4. Gene neighbourhood
5. Translation efficiency
Thus far have been used to
predict 10s of phenotypes
Novel use to systematically
predict many phenotypes
all predictions browsable/available at:
http://protraits.irb.hr/

10. How many novel phenotypes do we infer?
~180, 000
at FDR<10%
~308, 000
at FDR<10%
~545, 000
at FDR<10%

11. Are these predictions correct?
?
two human experts
validated ~2500
predictions by
literature curation 

12. The ProTraits pipeline for phenotype prediction
next up:
Finding
gene-trait
associatons
using ProTraits
predictions
a network of co-occurence of ~400 phenotypic traits across organisms
all annotations available at:
http://protraits.irb.hr/

13. Gene-phenotype associations
Can we increase the power
to detect gene-phenotype
associations with ProTraits
predictions?
• We tested for significant associations of ~80 000
COG/NOG gene families to 332 phenotypes
using logistic regression, while controlling for
confounding effects of phylogenetic relatedness
of organisms ~20 000 associations at
FDR<10% with known labels
~117 000 associations at
FDR<10% with ProTraits labels
pathogenic to plants?
previously: 61 assoc. genes
with ProTraits: 648 genes
pathogenic to animals?
Previously: 57 assoc. genes
w/ProTraits: 1187 genes
Brbić M et al. (2016) Nucleic Acids Research

14. Are these associations correct?
• We validated gene-phenotype associations for sporulation and
flagellar phenotypes in Bacillus subtilis

15. Validation: Gene Ontology enrichment
• We linked the individual phenotypes
to genes, which were highly enriched
in various biological functions on average,
15 GO terms per
phenotype at FDR<10%
with known labels
increases to
23 GO terms per
phenotype at FDR<10%
with added ProTraits
labels
GO termTrait
iron-reducer iron ion
binding
halophilic sodium ion
transport
pathogenic
in mammals
pathogenesis
lactic/cheese
/food/ferme
nt/milk
galactose
metabolic
process
example associations that are significant only
when using ProTraits phenotype annotations:

16. Epistatic interactions between gene families
Synergistic epistatic
interaction
Antagonistic
epistatic interaction
Interaction may not be
evident without
knowing the
phenotypic labels
... i.e. not obvious simply from looking at phylogenetic profiles! These are 3-way interactions: gene-gene-phenotype.

17. Epistatic interactions between COGs
We tested for significant epistatic interactions of ~2700 COG/NOG gene families to phenotypes,
while controlling for confounding effects of phylogenetic relatedness of organisms
5.7*105 interactions at FDR<1%
with known labels
3.9*106 interactions at FDR<1%
with ProTraits labels
known
sporulation
genes new sporulation
genes
validated by
independant
data:
Meeske et al.
(2016) PLOS Biol
genetic interactions conditional
on the ‘endospore+’ phenotype
Genomic signatures of
epistasis are useful for
inferring gene function
Brbić M et al. (2016) Nucleic Acids Research

18. Travel to the meeting was made possible, in part, by a travel
award from the NSF. (to M.B.)
Predictions available at: protraits.irb.hr
Contact: maria.brbic@irb.hr / fran.supek@irb.hr
This work was supported by the FP7 FET project MAESTRA, and by the Croatian Science Foundation project.
Brbić M et al. (2016) Nucleic Acids Research: