A Kahvejian

AC McHardy

C Hyland

D Parks

G Mariscal

J Shon

JW Huss

M Haeussler

OG Pybus

PS Dehal

SM Leach

T Davidsen

T Oinn

V Sintchenko

VM Markowitz

Y Kano

Microbial Informatics and Experimentation

English

PubMed

Vitali Sintchenko

Enrico W Coiera

Springer - Publisher Connector

Translational web robots for pathogen genome analysis

LETTER TO THE EDITOR Open Access
Translational web robots for pathogen genome
analysis
Vitali Sintchenko1,2,3* and Enrico W Coiera3
In this letter, we present the concept of translational
computational web robots that aims to improve the clin-
ical knowledge utilization and microbiology test inter-
pretation. This development promise to accelerate
discovery by searching and will be challenging the posi-
tion of observation and experimentation as the most
productive means of discovery.
With a shift in the ‘wet lab’ bottleneck from data gen-
eration to data analysis, there has been a reinvigoration
of the rusty partnership between biomedical and com-
puter scientists. One of the most appealing targets for
collaboration has been whole genome analysis, especially
the linkage of new sequencing data with the wealth of
unstructured information captured in biomedical litera-
ture. This endeavour holds particular promise for trans-
lational medical microbiology, where a potentially
unlimited number of sequenced microbial genomes
might be coupled with the breadth of infectious diseases
literature. However, generation of meaningful associa-
tions between literature and sequences demands novel
analytical approaches. We argue that web robots, initi-
ally called ‘intelligent web crawlers’, could be invaluable
to microbiologists for such analyses. Many business
intelligence applications are already exploiting web
robots or autonomous programs that methodically tra-
verse the Internet by following hypertext links and then
analyse the results to gain knowledge that enables fulfil-
ment of new tasks. Today, microbiologists often spend
more time looking at nucleic acid sequences than at
colonies on Petri dishes. Therefore, it seems appropriate
to consider robots that could combine the analysis of
publicly available genomes of pathogens with the pub-
lished body of knowledge wide analysis of infectious dis-
eases. Figure 1 illustrates a process flow and methods
that might be employed by web robots designed to
become intelligent assistants to microbiologists.
Searching for online services fragmented by pathogen
and application
While there has indeed been rapid growth in web por-
tals and specialized datasets dedicated to the storage
and molecular analysis of individual pathogens or
microbial genera, we still lack comprehensive web-acces-
sible resources that organise, analyse and predict host-
pathogen interactions. Large databases with genome
annotation and visualisation tools such as the European
Bioinformatics Institute (EBI), the Integrated Microbial
Genomes (IMG) system and Comprehensive Microbial
Resource (CMR) have been developed to allow microbial
genomes to be compared and examined from an evolu-
tionary point of view [1-3]. Other services include online
platforms for comparative genomic analysis of multiple
sequences obtained from metagenomic experiments
using the ‘next generation sequencing platforms’ (e.g.,
CompostBin [4] and PhyloPythia [5]) or search and
reconstruction of metabolic networks for microorgan-
isms like BioCyc, PUMA, Reactome or Meta Shark
http://bioinformatics.leeds.ac.uk/shark/[6]. The func-
tional classification tools available on these resources
have only a weak capability to mine many-to-many
gene-to-term relationships. Recent advances in informa-
tion retrieval allow to link genes and genomes with rele-
vant publications stored in a public domain [7]. Such
‘high-level’ analyses are computationally expensive and
often require parallel computing on distributed comput-
ing systems. Encouraging examples from human genome
analyses include systems like Galaxy http://galaxy.psu.
edu or CLoVR http://clover.igs.umaryland.edu. Locating
online resources which are the most suitable for a speci-
fic analytic task in hand applied to a set of individual
genes or whole genomes has become a challenge. Web
robots with their ability for online service discovery are
likely to markedly increase the effectiveness of analytic
processes.
* Correspondence: vitali.sintchenko@swahs.health.nsw.gov.au
1Centre for Infectious Diseases and Microbiology-Public Health, Institute of
Clinical Pathology and Medical Research, Westmead Hospital, Sydney, New
South Wales, 2145 Australia
Full list of author information is available at the end of the article
Sintchenko and Coiera Microbial Informatics and Experimentation 2011, 1:10
http://www.microbialinformaticsj.com/content/1/1/10
© 2011 Sintchenko and Coiera; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Gaining knowledge by integrating structured and
unstructured information
A true systems biology approach to interpreting gen-
ome-scale data requires the analysis of interactions
among the genes, not just the genes in isolation [8]. The
integration of data acquisition from online databases
with information retrieval from literature text mining is
an important step forward. By discovering and quantify-
ing the associations between genes and functions or
clinical syndromes, computational robots would capture
critical data for translational biomedicine. For example,
these intelligent and tireless robots could be pro-
grammed to measure the similarity between pre-defined
nucleic acid sequences and to mine the literature
content to assess the similarity of the context in which
gene names and clinical entities appear in literature. As
a result of such integration and data analysis, new
knowledge about genome structure and function can be
gained. The formal representation of this knowledge as
a network reveals genome dynamics or patterns of rela-
tionships between genes and infectious disease beha-
viour in multiple dimensions. These knowledge
landscapes can be visualised and potential gaps in our
knowledge identified. Such network visualisation of
associations has been gaining popularity. For example,
several text mining engines have focused on the capture
of gene-protein entities and the construction of the
resulting ‘knowledge networks’ [8]. We recently
Figure 1 Process flow employed by translational robots and subtasks of relevance to the work of a microbiologist.
Sintchenko and Coiera Microbial Informatics and Experimentation 2011, 1:10
http://www.microbialinformaticsj.com/content/1/1/10
Page 2 of 4
presented the first analysis of the infectious disease
knowledge space, created by mining for informative
associations between genes and diseases [9]. A network
representation of multilevel relationships between infec-
tious disease syndromes and pathogens and their gen-
omes was employed to illuminate non-trivial
associations that can point out the biological similarities
in disease pathogenesis and epidemiology. We also pre-
sented a new scalable computational approach that may
improve our understanding of host-pathogen relation-
ships and facilitate the discovery and assessment of hid-
den associations in biomedicine [9].
While there are several well-studied genes that have
hundreds of indexed citations in the literature, that
degree of functional annotation falls off steeply: almost
80% of Entrez Gene entries had five or fewer linked
references in PubMed and almost 50% had zero linked
references [10]. The process of annotating gene function
typically entails large-scale efforts by the model organ-
ism community and genome annotation centres. Infer-
ences regarding the likely function of a molecule are
largely based on factors such as homology and interac-
tion partners. However, curated interactions represent
less than 1% of the true protein-protein interactions
recorded in the Database of Interacting Proteins [8].
While protein interactions are reasonably well charac-
terised experimentally in yeast, protein interactions in
prokaryotic organisms remain less understood. The tra-
ditional model of centralised curation is not scaling well
with the rate of data generation, and complementary
approaches based on community intelligence have been
advocated [10].
Unstructured knowledge indexed by the National
Library of Medicine and made available through
PubMed has an annual growth rate of more than 4.8%.
Structured knowledge in the form of molecular biology
databases has also increased significantly. An annual
compendium of molecular biology databases in 2008
contained 1, 078 databases. Platforms like caBIG (Can-
cer Biomedical Informatics Grid), i2B2 (integrating biol-
ogy and bedside) and GenGIS have taken on the
challenge of the integration of different data types from
genomes and associated phenomes in order to allow the
study of whole genome associations and the identifica-
tion of ‘hot-spots’ for new drugs [11].
Applying new knowledge to interpret genomes and to
address ‘big’ evolutionary questions
The capacity of web robots to generate new associa-
tional knowledge makes them far more powerful than
currently used workflow engines, which intend to auto-
mate data analysis tasks [12-14]. Translational robots
are to be charged with the task of applying genome-phe-
nome-syndrome associations to optimize the profiling of
pathogens with epidemic potential. A pathogen profile
can be defined as a set of attributes or markers that
allow comparison with existing or future databases and
linkage of clinical observations and patient outcomes to
genome, transcriptome, proteome and metabolome data
[15]. Pathogen profiling would allow prognostic categor-
ization of patients and validation of markers that can be
used for earlier diagnosis and to predict and monitor
response to treatment. These biomarkers, in turn,
should also improve efficiency in outbreak investigation
and disease monitoring.
The continuing strong growth (with a current rate of
about 650 genomes per year) in genomic data is
enabling new high-resolution surveillance of organisms
as well as assisting the study of ‘big’ unanswered evolu-
tionary questions. Coupled with the repeated sampling
of pathogen populations, these approaches enable the
assessment of genome phylodynamics, evolutionary
scales and the richness of microbial communities
[11,16]. Genome sequences form an essential starting
point for genome-wide functional studies aiming to
decipher the mechanisms involved in the colonisation of
the host and subsequent infection transmission and dis-
ease. Comparative analyses of these sequences, such as
genome alignments and preliminary annotation of cod-
ing sequences, can point to traits associated with
macro- and micro-diversity. They refine our understand-
ing of the population biology of microbial species and
advance the power of homology inference methods and
function predictions. Importantly, they also enable
queries in opposite directions from phenome to genome
to reveal functions of hypothetical proteins and
unknown coding sequences.
The complexity of analysis of pathogen genomes has
increased markedly, but there has been an even greater
increase in opportunities for an immensely deeper
understanding of the causes of disease and of better tar-
geting interventions [17]. However, despite the ever-
increasing quantity of papers published on the role of
genes in diseases and the growing number of prokaryo-
tic and eukaryotic genomes and individual genes being
deposited into public databases, efficient tools to sup-
port knowledge discovery remain few and far between.
Translational robots are likely to close this gap and to
help in the testing of the key assumption that the synth-
esis of microbial genomic data with clinical and popula-
tion health data and publicly available evidence actually
improves the quality of clinical decision making and
public health outcomes.
Science has been lagging behind other industries
which have been heavily investing in business intelli-
gence in the last decade, with an 11.2% growth of
investment in these technologies since 2006 [18]. Recent
initiatives like the RoboEarth project http://www.
Sintchenko and Coiera Microbial Informatics and Experimentation 2011, 1:10
http://www.microbialinformaticsj.com/content/1/1/10
Page 3 of 4
roboearth.org, aiming to build a parallel World Wide
Web for robots where they can share the information
and learn from each other, are paving a new road for
data analysis. The concept of web robots allows us to
address the growing gap between the exponential
growth of publicly available data and our capacity to
leverage it. Robotic assistants can improve the knowl-
edge utilization, accelerate discovery by searching and
challenge the position of observation and experimenta-
tion as the most productive means of discovery. Compu-
tational data integration and analysis still has a long
journey ahead of it, but the light is beginning to shine
brightly on its path. We are another step closer to Nor-
bert Wiener’s prediction that machines might eventually
devalue the brain as emphatically as the first Industrial
Revolution did the hand.
Author details
1Centre for Infectious Diseases and Microbiology-Public Health, Institute of
Clinical Pathology and Medical Research, Westmead Hospital, Sydney, New
South Wales, 2145 Australia. 2Sydney Emerging Infections and Biosecurity
Institute and Sydney Medical School-Westmead, University of Sydney,
Sydney, New South Wales, 2006 Australia. 3Centre for Health Informatics,
Australian Institute of Health Innovation, University of New South Wales,
Sydney, New South Wales, 2052 Australia.
Received: 20 September 2011 Accepted: 31 October 2011
Published: 31 October 2011
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doi:10.1186/2042-5783-1-10
Cite this article as: Sintchenko and Coiera: Translational web robots for
pathogen genome analysis. Microbial Informatics and Experimentation
2011 1:10.
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Page 4 of 4


Sintchenko, Vitali

Coiera, Enrico W

Macquarie University ResearchOnline

Translational web robots for pathogen genome analysis [letter to the editor]

Crossref

AI: The Gene Wiki: community intelligence applied to human gene annotation. Nucleic Acids Res

Ananiadou S: Text mining meets workflow: Linking U-Compare with Taverna. Bioinform

Arkin AP: MicrobesOnline: an integrated portal for comparative and functional genomics. Nucleic Acids Res

Biomedical discovery acceleration, with applications to craniofacial development. PLoS Comput Biol

CM: Annotating genes and genomes with DNA sequences extracted from biomedical articles.

Coiera EW: A PubMed-wide associational study of infectious diseases. PLoS ONE

CompostBin: A DNA compositionbased algorithm for binning environmental shortgun reads.

GenGIS: A geospatial information system for genomic data. Genome Res

GL: Pathogen profiling for disease management and surveillance. Nat Rev Microbiol

Ivanova NN, Kyrpides NC: The integrated microbial genomes system: an expanding comparative anlysis resource. Nucleic Acids Res

JF: What would you do if you could sequence everything? Nat Biotech

Rambaut A: Evolutionary analysis of the dynamics of viral infectious disease. Nat Genetics Rev

Rigoutsos I: Accurate phylogenetic classification of variable-length DNA fragments. Nat Methods

Scientific workflows as productivity tools for drug discovery. Curr Opin Drug Discovery Devel

TAVERNA; a tool for the composition and enactment of bioinformatics workflows. Bioinform

The comprehensive microbial resource. Nucleic Acids Res

Westhead DR: metaSHARK: a WWW platform for interactive exploration of metabolic networks.

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Translational web robots for pathogen genome analysis

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