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Genomics and natural language processing

Key Points

  • Today, the computational exploration and management of large text repositories are usually accomplished with search engines and databases that are based on a suite of text processing, indexing and search tools that are referred to collectively as 'natural language processing' (NLP) technologies.

  • There are three fundamental aspects to NLP: information retrieval, semantics and information extraction.

  • Exploring and managing the biomedical literature with these technologies, however, presents some interesting challenges, primarily because of the relationships between biomedical texts and biological sequences.

  • The associations between biological sequences and texts are a truly unique aspect of the biomedical literature. However, understanding the complex associations that exist between genes, sequences and texts is a daunting task.

  • The flood of sequence information produced by the rapid advances in genomics is creating new ways to explore texts and is blurring the traditional lines that separate bioinformatics and NLP.

  • Biological NLP (bio-NLP) is an emerging field of research that seeks to create tools and methodologies for sequence and textual analysis that combine bioinformatics and NLP technologies in a synergistic fashion.

  • Some bio-NLP researchers are focusing on texts as a means to discover information about protein interactions, and are wrestling with how best to adapt traditional NLP technologies to this task. Others, taking a more sequence-centred approach, are exploring the use of texts as a means to improve sequence-retrieval algorithms and as an aid to sequence annotation.

  • If bio-NLP is to achieve its full potential, it will have to move beyond information management and generate specific predictions pertaining to gene function that can be verified at the bench. The synergistic use of sequence and text to extract latent information from the biomedical literature holds much promise in this regard. Realizing this potential, however, will require more and better ontologies, software that is able to make inferences using sequence and textual information, and access to the full text of articles.


The Human Genome and MEDLINE are both the foci of intense data-mining efforts worldwide. The biomedical literature has much to say about sequence, but it also seems that sequence can tell us much about the biomedical literature. Biological natural language processing is an emerging field of research that seeks to explore systematically the relationships between genes, sequences and the biomedical literature as a basis for a new generation of data-mining tools.

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Figure 1: Correlation between sequence similarity and document similarity.
Figure 2: Semantic classification and definition of terms using a lexicon, thesaurus and a hierarchical ontology.
Figure 3: HMMs are used for part-of-speech tagging, as well as for gene prediction.
Figure 4: Information extraction.


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The authors thank P. Li and S. Lewis for many stimulating discussions on the role of ontologies in biology and natural language processing, G. Marth for many useful comments on the manuscript and R. Mural for professional encouragement.

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Correspondence to Mark D. Yandell.

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A collection of documents that are used for searching or data mining.


This frequently used term also has a formal definition. The accuracy of an algorithm is often defined as 2 × precision × recall/(precision + recall).


A variation of BLAST that uses profiles that are based on sequence multiple-alignments to improve the sensitivity of protein database searches.


A well-annotated database of protein sequences.


These are approaches for identifying terms in a text that belong to a particular semantic class. Gene names in Caenorhabditis elegans, for example, are denoted with three letters followed by a dash and a number — for example, 'dbl-1'. So, this approach to identify C. elegans genes might consist of searching a text for regular expression of three letters, a dash and a number. Such approaches do not work equally well for identifying all genes and generally are not very precise.


The ratio between the observed frequency at which an event occurred and the expected frequency of that event given some statistical model. A term that occurs more frequently in a text, or collection of texts, than would be expected based on its frequency in a corpus will therefore have an odds ratio >1.


(GO). A hierarchical organization of concepts (ontology) with three organizing principles: molecular function, the tasks done by individual gene products, an example of which is 'transcription factor'; biological process, broad biological goals, such as mitosis, that are accomplished by ordered assemblies of molecular functions; cellular component, subcellular structures, locations and macromolecular complexes (examples include the nucleus and the telomere).


A hierarchical organization of concepts, typically used to denote 'more-general-than' and/or 'part-of' relationships.


Homologous genes that originated through speciation (for example, human β-globin and mouse β-globin).


An algorithm that identifies the nouns, verbs and other functional word classes among the words that comprise a sentence.


Homologous genes that originated by gene duplication (for example, human β-globin and human α-globin).


Computer science parlance for an abstract definition that embodies some common and essential syntactic characteristic that belongs to a set of terms. For example, in the popular PERL programming language, the regular expression '\s* \w+\−\d+\s*' will identify any word in a text that consists of one or more letters (or numbers), followed by a dash, and followed by one or more numbers. This regular expression will identify Caenorhabditis elegans gene names.

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Yandell, M., Majoros, W. Genomics and natural language processing. Nat Rev Genet 3, 601–610 (2002).

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