LIFE SCIENCE ANALYTICS

Complete and comprehensive Life Science research requires specialized capabilities for bioinformatics, cheminformatics, statistical genetics and integrated reference to existing bodies of scientific research. Furthermore, user requirements for these different informatics disciplines vary greatly but, to be successful, these areas need to communicate more. This is best illustrated by failed bioinformatics strategies to identify new targets which do not factor input from chemists in terms of whether the potential target (or related genes in a pathway) might be chemically tractable and not just disease associated.

Bioinformatics

Bioinformatics requires access to, and analysis of, protein and nucleotide sequences, pathways, gene expression, proteomics and genetics data. Such data can be accessed from either remote public data sources such as SwissProt, SRS, PDB, KEGG, HUGO, GO, or internal databases. Industry-standard sequence file formats exist that can be used to integrate bioinformatics applications such as BLAST and CLUSTALW with tools such as the EMBOSS suite.  Other requirements include specialized methods for accessing, cleaning, normalizing and analyzing gene expression/micro-array data sets provided in their native format such as Illumina or Affymetrix.

In addition to generic statistical functions, bioinformatics requires integration with specialized software packages such as Bioconductor and R for important gene expression and statistical genetics capabilities respectively.

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Cheminformatics

In the area of cheminformatics, numerous chemical compound databases, data cartridges and cheminformatics tools exist.  Integration with these tools is a requirement.  Importing and processing data from a wide variety of chemical structure and chemical property formats and of built-in advanced chemistry specific functions are required, including chemistry descriptors and fingerprint calculations, file format transformations, structure searching capabilities, maximum common substructure analysis, R-group deconvolution and enumeration functionality.

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Statistical Genetics

Statistical genetics involves performing SNP chip data analysis for whole genome association studies.  The objective is to rapidly identify phenotype/genotype associations and predictive biomarkers.  It requires analyzing SNP chips from the latest generation of genotyping platforms including Illumina BeadChips and Affymetrix GeneChip Mapping arrays as well as from HapMap, PrettyBase and Pedigree formats. Users must be able to quickly find interesting SNP's and produce reports with easy to understand visualizations.

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Analyzing Scientific Literature

In all areas of Life Science, large bodies of literature exist that need to be incorporated into research and analysis efforts.  Literature analytics, including text cleaning and processing, text-based information extraction, statistical feature extraction and analysis, automatic document clustering and categorization, can be used to combine biological data with textual information, such as that from publications, protocols and other clinical research databases, to help find knowledge in less structured information. For example, by annotating clustered gene expression results using GO and PubMed, it is possible to determine what the relevant papers, concepts or keywords are for a given experiment and to analyze their associations.

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