Abstract

   Biological data analysis is the key to new discoveries in disease
   biology and drug discovery. The rapid proliferation of high-throughput
   ‘omics’ data has necessitated a need for tools and platforms that allow
   the researchers to combine and analyse different types of biological
   data and obtain biologically relevant knowledge. We had previously
   developed TargetMine, an integrative data analysis platform for target
   prioritisation and broad-based biological knowledge discovery. Here, we
   describe the newly modelled biological data types and the enhanced
   visual and analytical features of TargetMine. These enhancements have
   included: an enhanced coverage of gene–gene relations, small molecule
   metabolite to pathway mappings, an improved literature survey feature,
   and in silico prediction of gene functional associations such as
   protein–protein interactions and global gene co-expression. We have
   also described two usage examples on trans-omics data analysis and
   extraction of gene-disease associations using MeSH term descriptors.
   These examples have demonstrated how the newer enhancements in
   TargetMine have contributed to a more expansive coverage of the
   biological data space and can help interpret genotype–phenotype
   relations. TargetMine with its auxiliary toolkit is available at
   [33]https://targetmine.mizuguchilab.org. The TargetMine source code is
   available at [34]https://github.com/chenyian-nibio/targetmine-gradle.

   Keywords: data warehouse, integrative data analysis, multi-omics data
   analysis, gene prioritisation, drug discovery, data mining, knowledge
   discovery

Introduction

   The rapid proliferation of high-throughput omics technologies has
   revolutionised biological research by significantly adding new omics
   data. However, as the experimental datasets increase in size and
   complexity, extraction of meaningful biological knowledge becomes
   qualitatively more difficult, expensive and labourious. Therefore,
   there is an ever widening gulf between data generation and the rate at
   which it can be properly analysed ([35]Greene et al., 2014). Proper
   mining and curation of large biological datasets are necessary to
   develop an improved understanding of living systems and of disease
   pathogenesis.

   An integrative multi-omics approach combines different types of
   biological data into a single analytical framework to understand the
   relationships between different cellular components ([36]Zhu et al.,
   2012; [37]Yan et al., 2018). Such analyses are useful to develop
   analytical models that can interpret genotype–phenotype relationships,
   garner the knowledge of pathways involved in cellular events and
   diseases, help pinpoint targets (such as gene and proteins) of
   biological and therapeutic interest and potentially develop
   intervention methods than can counteract undesirable phenotypic
   progression (i.e. diseases) ([38]Sun and Hu, 2016; [39]Hasin et al.,
   2017).

   A major challenge in multi-omics data analysis is the availability of
   clean and usable biological data. We have previously developed
   TargetMine, an integrated data warehouse based on the object-oriented
   InterMine data warehouse framework ([40]Smith et al., 2012;
   [41]Kalderimis et al., 2014; [42]Triplet and Butler, 2014), which
   models biological entities (such as genes and proteins) as ‘objects’
   that are described by a set of attributes and their relationships with
   other objects are modelled as ‘references’. The InterMine system allows