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    Collection and storage of previous research work is very important for any field of life. It not only helps in making life and work easy but also fast. Repetition of already done homework only leads to wastage of human resource and time. Therefore, it becomes imperative to properly collect and store the already done work in a central database which can be accessed globally without any hindrances. Especially in scientific world, such databases assume high importance. Even negative results or failed experiments need to be recorded so that science advances globally in a positive direction.

    The collection of biological text corpus is called bibliome. This program was launched by European Bioinformatics group (EBI) in year 2000. The main purpose behind its establishment was to highlight the importance of biological information. Scientists particularly bioinformatists use bibliome very often to discover new genes or to share valuable wet lab information. It has also been used to discover new gene targets and accurately perform molecular modeling of new drugs.

    Bibliome group designs new methods for easy retrieval and annotation/modification of already known literature expressed in a common language (English) in specific domains like biology. These methods are basically related to artificial intelligence i.e. electronical/ machine based retrieval, extraction, processing of biological scientific and technical information.

    Two basic parts of bibliome are:

    1. Information extraction: This includes usage of already established or well studied facts to extract more information. This usually involves annotation of data to establish new genic interactions, protein-protein interactions and DNA-protein interactions. This information can also be used to extract important biochemical information of entities based on set knowledge stored in the form of text corpus in such databases. For example calculating the GC content in a oligonucleotide, annealing temperature for a oligo, possible 2D and 3D structure predictions.
    2. Information retrieval: This includes retrieval of information from patents or bibliographic documents. It is also called as reference search

    These parts are based upon 3 criteria:

    1. Annotations of relations: This includes scientific literature search on hypothesis for which research is being carried out. After retrieval of all possible literature present on the hypothesis “subject”, this system also searches for logical connections that can be established on this basis. Finally it gives all possible relations between the hypothesis and the subject. Examples are biograph relations, Swissprot comments, etc.
    2. Ontology learning: This includes studying the being or existence of a hypothesis with relation to a subject. It also includes acknowledging the existing information and its possible relationship with a hypothesis.
    3. Entity recognition: This includes recognizing the existence of a subject. It is usually retracted from databases like NCBI, SWISS PROT, UNI PROT, ENSEMBLE, etc.

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