Nucleic Acids Research Advance Access originally published online on June 11, 2009
Nucleic Acids Research 2009 37(Web Server issue):W160-W165; doi:10.1093/nar/gkp484
Nucleic Acids Research, 2009, Vol. 37, No. suppl_2 W160-W165
© 2009 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
PLAN2L: a web tool for integrated text mining and literature-based bioentity relation extraction
Martin Krallinger1,*,
Carlos Rodriguez-Penagos2,
Ashish Tendulkar1 and
Alfonso Valencia1
1Structural Biology and Biocomputing programme, Spanish National Cancer Center (CNIO), Melchor Fernandez Almagro 3, Madrid, 28029, Spain and 2Barcelona Media - Centre d'Innovacio, Av. Diagonal 177, 08018 Barcelona, Spain
*To whom correspondence should be addressed. Tel: +34 91 224 6900; Fax: +34 91 224 6980; Email: mkrallinger{at}cnio.es
Received March 16, 2009. Revised May 13, 2009. Accepted May 19, 2009.
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ABSTRACT
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There is an increasing interest in using literature mining techniques
to complement information extracted from annotation databases
or generated by bioinformatics applications. Here we present
PLAN2L, a web-based online search system that integrates text
mining and information extraction techniques to access systematically
information useful for analyzing genetic, cellular and molecular
aspects of the plant model organism
Arabidopsis thaliana. Our
system facilitates a more efficient retrieval of information
relevant to heterogeneous biological topics, from implications
in biological relationships at the level of protein interactions
and gene regulation, to sub-cellular locations of gene products
and associations to cellular and developmental processes, i.e.
cell cycle, flowering, root, leaf and seed development. Beyond
single entities, also predefined pairs of entities can be provided
as queries for which literature-derived relations together with
textual evidences are returned. PLAN2L does not require registration
and is freely accessible at
http://zope.bioinfo.cnio.es/plan2l.
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INTRODUCTION
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Gene regulatory mechanisms and protein interactions are studied
in detail to understand how complex developmental processes
are controlled. Biological annotation databases provide functional
descriptions of gene products, the basic components of such
biological processes, through manual literature inspection,
resulting generally in associations of biological entities to
a set of controlled vocabulary terms contained in structured
database records (
1). Despite the obvious strength of controlled
vocabularies for annotation consistency, information exchange
and data analysis, functional annotations of proteins do not
provide a straightforward way to trace back the biological evidence
supporting each annotation, making it sometimes cumbersome for
human domain experts to directly interpret under which biological
context and experimental conditions a given annotation applies.
Considering the growing amount of published articles, to manually
annotate newly described functional gene product characterizations
as well as maintenance and update of already annotated entities
is a challenging task. This motivated recent attempts to enable
a more systematic access to relevant information hidden in large
literature repositories using text mining and information extraction
(IE) technologies, with the aim of not only supporting the literature
curation process, but especially for providing suitable information
retrieval systems useful for life sciences (
2). Results generated
by text mining systems have the general advantage to be directly
interpretable by the end users, the human domain experts, in
case direct links to the textual evidences are supported. Currently
existing online literature mining systems mostly focus on very
particular biological aspects, such as the extraction of protein–protein
interactions (
3), protein–keyword co-mentions or gene
regulation events without directly integrating all these heterogeneous
relation types into a single application. Relevance, not only
of individual bio-entities but also of their interactions and
regulation events for developmental processes studied in model
organisms such as the plant
Arabidopsis thaliana have not been
addressed previously using text mining approaches.
Arabidopsis thaliana, the first plant to be completely sequenced, is being
used not only for experimental research, but also increasingly
by systems biology and bioinformatics approaches to understand
and model central biological processes (
4). Databases like TAIR
[The Arabidopsis Information Resource, (
5)] or UniProt (
6) are
providing plant biologists with valuable infrastructures of
manually curated information, but only few attempts have been
made to implement literature mining applications for this model
organism. The Dragon Plant Biology Explorer (DPBE) was an online
text mining application for plant biology based on integration
and combination of collections of manually curated vocabularies
compiled for several topics to facilitate a more targeted literature
search (
7). PubSearch constitutes another system for semi-automated
retrieval of literature that can be used to curate articles
to extract manually annotations for gene products with Gene
Ontology terms (
8). It is primarily based on simple term matching
and does not explore machine learning techniques to provide
more sophisticated retrieval capabilities. Another system primarily
used for literature curation by model organism databases is
Textpresso (
9), now integrated at TAIR to facilitate a better
access to information relevant for
Arabidopsis.
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SYSTEM DESCRIPTION
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PLAN2L is an online text mining application dedicated to improve
retrieval of knowledge by integrating and scoring information
extracted from textual sources for various biological topics
related with the description interactome associations in
Arabidopsis.
Figure 1 shows a general flow chart of the PLAN2L system 1 (a
more complete description of the technical aspects can be found
in the documentation of the web system).
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TECHNICAL DESCRIPTION OF THE TEXT MINING PIPELINE
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A document retrieval pipeline that takes into account several
sources of evidence for the determining whether a given article
is associated to
A. thaliana was implemented exploiting: (i)
external references derived from multiple databases providing
annotations for
Arabidopsis proteins. (ii) Organism and taxonomic
name tagging using dictionary lookup based on a species lexicon
derived from the NCBI Taxonomy that was automatically extended
using a rule-based approach to account for typographical variants
and abbreviations of species names. (iii) Keyword-based retrieval
from PubMed and PubMed Central. Additionally, a full text collection
of
Arabidopsis-related articles was constructed from a local
repository of open access full-text articles as well as using
customized article collection tools. Plain text conversion was
carried out through a combination of systems including pdftotext
and PDFlib. The detection of links between the literature and
protein or genes of PLAN2L is based on the construction and
lookup of a gene lexicon. This gene dictionary integrated
A. thaliana gene names and symbols derived from multiple databases,
including TAIR, SwissProt and from a collection of gene and
protein names identified by a machine learning-named entity
recognition program (ABNER) as well as based a rule-based approach
considering morphological cues and name length to identify potential
Arabidopsis gene symbols. Lexicon expansion using manually crafted
rules was carried out. To detect gene regulatory relations,
we adapted an IE architecture relying on a pipeline of semantic/syntactic
rules. We applied part-of-speech tagging of each word using
a GENIA-trained version of Treetager (
10). Some of the POS tags
were automatically substituted with more semantically oriented
labels (e.g. organisms, protein/gene names and activation verbs).
The text with mixed syntactic and semantic tags was fed into
a SCOL parser (
11), which generated a tree-like structure by
applying a modified CASS grammar. These rules constitute cascades
of finite-state automata, and use patterns that combine both
grammatical- and biological-meaning features in the linguistic
structure. We implemented extensions of the rules to handle
frequent phrase coordination and prepositional anaphora. The
extraction of protein interaction evidence associations was
addressed using a machine learning sentence classifier approach
relying on manually selected interaction evidence sentences
(
12). The used sentence classifier relies on a Support Vector
Machines algorithm trained on set of manually classified interaction
evidence passages derived from a collection used at the second
BioCreative challenge (
12), and obtained a performance of 89.75
for precision and 92.62 for recall using a radial basis kernel
function on a balanced test set. For retrieving protein localization
descriptions, we explored both the use of semantic–syntactic
frames for extracting a fine-grained association between proteins
and subcellular location mentions together with a machine learning
sentence classifier for retrieving protein localization description
sentences in general. The initial step followed, consisted in
the construction of a sub-cellular location dictionary that
integrates location keywords and synonyms derived from SwissProt
together with cellular component terms from Gene Ontology. Location
term mentioning sentences were manually revised to derive hand-crafted
location frames. Additionally, a location sentence classifier
was constructed using a collection of 2264 protein location
description sentences. A central component of PLAN2L is the
scoring of each evidence sentence according to its relevance
for complex temporal biological events (topics), at the cellular
level (cell cycle) as well as at the level of developmental
processes. We therefore implemented a classifier for scoring
cell-cycle relevant abstracts and document passages. The SVM
text classifier was trained on a collection of cell-cycle relevant
abstracts and nonrelevant abstracts and then applied to a literature
collection of abstracts and full-text articles mentioning
A. thaliana genes. Additionally, four specific sentence classifiers
for the most relevant developmental processes in higher plants,
namely (i) flowering, (ii) leaf development, (iii) root development
and (iv) seed development/germination have been developed. The
tool provides a comprehensive approach to assist in the selection
and ranking of genes, proteins, documents and terms relevant
to a specific biological process for this model organism. Additional
details on the different modules, their characteristics and
assessment are provided at the PLAN2L web.
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FUNCTIONALITY AND USAGE
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The PLAN2L interface handles user-provided plain text keywords,
protein/gene names or symbols. Some of the components allow
additionally searching with gene or protein identifiers. The
currently supported identifiers include TAIR gene identifiers
and UniProt primary accession numbers. Based on a user survey
that we carried out to get feedback from biologists on the PLAN2L
system, aspects that were positively rated included its easy
to use and intuitive query interface and the direct retrieval
of evidence sentences for multiple topics. Aspects that were
improved according to the user comments covered additional documentation
on the system and the sentence scoring mechanism. PLAN2L supports
six types of search strategies, each with its own query page,
to avoid introducing unnecessary complexity through advanced
search interfaces with complicated menu options. We will briefly
describe each of these six search types and provide a case study
to illustrate the type of results generated by PLAN2L.
- The basic search type allows retrieval of multiple biological topic associations for a given user query, consisting in ranked lists of evidence sentences. The default ranking is based on the association strength of the article for the organism source (i.e. A. thaliana), sorted in descending order. The structured result table page provides the document identifier (PMID) from which the evidence sentence has been derived, the extracted sentence text showing the color highlighted user query term as well as the relevance of the sentence for multiple biological topics: cell cycle, gene regulation, protein interaction, cellular location, flowering, leaf development, root development and seed development. Additional external links to other resources are also included, namely the BioCreative metaserver, iHOP, WikiGene and TAIR. In the results page, color-coded scores are given for each category represented visually by a unique informative glyph. Scores above classifier cutoff are displayed in green, otherwise in red. The evidence sentences can be dynamically reranked for each of the categories, and to facilitate rapid visual cueing of the association between the scores and their class, a background-coloring schema was used.
- PLAN2L also facilitates retrieval of physical interaction associations for a given query protein or protein identifier. Each of the co-mentioned proteins is returned together with its corresponding identifier, interaction evidence sentence and experimental interaction detection method keywords. Ranking of the interaction association evidence sentences is based on the corresponding interaction sentence classifier score.
- In case of regulatory associations, the typical user-specified query consists in a protein/gene name or in which the system returns a tabular regulation evidence summary. To facilitate the interpretation of the directionality of a given regulatory event, a color-coding strategy was used to highlight the regulator and regulated gene, together with a key sentence display. To qualify the type of regulation, PLAN2L labels each regulation evidence as activation, repression or undefined (for unclear cases).
- The sub-cellular location evidence sentences for a given protein can also be retrieved by PLAN2L. Complementing the location sentence scoring of the basic search option, more accurate location sentence retrieval, integrating also location term co-occurrence and location association words is available through PLAN2L.
- As A. thaliana is one of the most important model organisms for studying the plant cell-cycle, PLAN2L integrates a cell-cycle relevance ranking of PubMed abstracts using a document classification tool. The Arabidopsis literature can be searched using gene names or keywords and the resulting abstracts are ranked according to their cell-cycle relevance (cell-cycle score from the document classifier). Also cell-cycle relevant terms are tagged. The species ambiguity score of abstracts can be used to determine how specific a document is for Arabidopsis, something useful when several species are mentioned in the same article.
- In order to enable more effective searches of association evidences existing between two biological entities of interest, PLAN2L provide the association retrieval interface, where for two entities entered by the user, the application returns a set of sentence where both entities are co-mentioned. Each evidence sentence is scored according to the biological topics supported for the basic search query. The default ranking is based on the interaction sentence classifier scores. The typical time taken for text processing is 2–3 min, but in case of heavy server load and the actual query type, some jobs might take between 4 and 5 min, upon completion of the search task PLAN2L displays its results together with hyperlinks to the resources providing links to the original article.
Case study: AGAMOUS and LEUNIG
As an example, to illustrate the kind of output generated by PLAN2L, we searched the system using the AGAMOUS (TAIR locus AT4G18960). The basic search for this bio-entity returns a range of descriptive sentences, Figure 2a shows the first of them. Each of these evidence sentences has a range of associated topic scores. By looking at the different sentences and their scores, it becomes clear that this gene is important for the flowering process. This is consistent with information obtained through cross-checking with existing annotations reported for this gene, being described in the TAIR database as a floral homeotic gene that specifies floral meristem and carpel and stamen identity. When trying to determine potential interaction partners of AGAMOUS, by the interaction search facility of PLAN2L, retrieved hits include the FLOR1-VSP1 complex (Figure 2b). Regulatory associations of AGAMOUS on the other side show several hits where this gene is regulated by LEUNIG and CLF (Figure 2c). Examining the results more carefully shows that there are cases where the regulation type was correctly labeled as ‘Repression’, but in other cases PLAN2L could not determine correctly the actual regulation type, although the directionality (regulator versus regulated gene) was detected correctly. Based on this regulatory association, LEUNIG, the negative regulator of AGAMOUS should be a transcription factor localized in the nucleus. This could be confirmed when querying the location extraction module with this protein, obtaining as first hit the following evidence sentence: ‘The nuclear localization of LEUNIG-GFP is consistent with a role of LEUNIG as a transcriptional regulator’ (Figure 2d). In case additional evidence sentences for the association of AGAMOUS and LEUNIG in terms of their biological context topic is interesting, for instance, their association might have an implication in the flowering process, this can be easily found out using the association search type. Providing these two entities as queries results in several association evidence sentences (Figure 2e), and when examining their corresponding sentence scores for various topics, it becomes clear that they are in a regulatory association and that they might have an implication in the flowering process.
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Figure 2. Example case. (a) Basic search using AGAMOUS as a query. (b) Results obtained when searching for interaction associations using the same query term. (c) Regulatory associations for AGAMOUS. (d) Localization result for the regulator of AGAMOUS, the LEUNIG transcription factor. (e) Association evidence sentences for AGAMOUS and LEUNIG.
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Implementation, user testing and availability
PLAN2L is mainly written in Python and uses the Zope web application
server (
www.zope.org) to display the online results. Some of
the protein normalization modules are implemented in C, and
a collection of additional preprocessing and NLP components
are written in Perl. The sentence classifier component relied
on the SVMLight package. The relational database server is PostgreSQL,
hosts part of the underlying text corpora and lexical resources.
The Zope server runs on a HP 360 G5 Intel(R) Xeon(TM) CPU 3.40
GHz machine with 3 GB de RAM. The initial version of this system
(PLAN2L-aratreg) has been online since 10 January 2007, and
has been improved based on the demands of the EU funded DIAMONDS
consortium. Since the first of January 2009, the system had
been tested by over 890 visitors. Individual user feedback and
requests are being collected to improve the practical usefulness
of PLAN2L for the plant science community. The PLAN2L system
is available at:
http://zope.bioinfo.cnio.es/plan2l. PLAN2L
has been tested on the most common browsers (Firefox, Safari
and Internet Explorer), accounting for 95.88% of the PLAN2L
users. Online help, including documentation and a tutorial using
prerun example cases as well as additional details on the system
evaluation, and user feedback (FAQ) are provided together with
the online application.
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DISCUSSION AND CONCLUSION
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We described a text mining system, PLAN2L that enables exploration
of literature information at different levels of granularity,
from retrieval of gene description sentences derived from multiple
documents, to qualified biological relations important to understand
the sub-cellular context and both physical as well as regulatory
interaction networks of bio-entities. PLAN2L extracts biological
information from both abstracts as well as full-text articles
and integrates different language processing strategies from
simple co-occurrence to syntactic/semantic rule-based algorithms
and supervised machine learning methods. PLAN2L is intended
to be useful for general retrieval, topic-specific retrieval
as well as for finding association evidences for user-specified
entities and for knowledge and hypothesis confirmation. A similar
strategy as used for PLAN2L can be easily adapted to other model
organisms as well as specific biological topics with minimal
additional manual data preparation.
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FUNDING
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ENFIN (LSHG-CT-2005-518254); the BIOSAPIENS (LSG-CT-2003-503265);
DIAMONDS (LSHG-CT-2004-512143) projects. Funding for open access
charge: ENFIN (LSHG-CT-2005-518254).
Conflict of interest statement. None declared.
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ACKNOWLEDGEMENTS
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Many thanks to Florian Leitner and other users including Ernesto
Ortiz, Cameron MacPherson, Nathan McCorkle, Colleen Doherty
and Anais Baudot for useful feedback.
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REFERENCES
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