A review of genomic data warehousing systems

Integration and Querying of Heterogeneous Omics Semantic Annotations for Biomedical and Biomolecular Knowledge Discovery

Background:

Exploring various functional aspects of a biological cell system has been a focused research trend for last many decades. Biologists, scientists and researchers are continuously striving for unveiling the mysteries of these functional aspects to improve the health standards of life. For getting such understanding, astronomically growing, heterogeneous and geographically dispersed omics data needs to be critically analyzed. Currently, omics data is available in different types and formats through various data access interfaces. Applications which require offline and integrated data encounter a lot of data heterogeneity and global dispersion issues.

Objective:

For facilitating especially such applications, heterogeneous data must be collected, integrated and warehoused in such a loosely coupled way so that each molecular entity can computationally be understood independently or in association with other entities within or across the various cellular aspects.

Methods:

In this paper, we propose an omics data integration schema and its corresponding data warehouse system for integrating, warehousing and presenting heterogeneous and geographically dispersed omics entities according to the cellular functional aspects.

Results & Conclusion:

Such aspect-oriented data integration, warehousing and data access interfacing through graphical search, web services and application programing interfaces make our proposed integrated data schema and warehouse system better and useful than other contemporary ones.

The TargetMine Data Warehouse: Enhancement and Updates

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 https://targetmine.mizuguchilab.org. The TargetMine source code is available at https://github.com/chenyian-nibio/targetmine-gradle.

Data Warehousing with TargetMine for Omics Data Analysis

Most biological processes including diseases are multifactorial and determined by a complex interplay of various genetic and environmental factors. This chapter aims to provide a user guide to data querying, analysis, and visualization with TargetMine and the associated auxiliary toolkit. We have also discussed some of the commonly used data queries for the researchers who are interested in gene set analysis within a data warehouse framework. Overall, TargetMine provides a convenient web browser-based interface that enables the discovery of new hypotheses interactively, by performing analysis of omics data using complicated searches without any scripting and programming efforts on the part of the user and also by providing the results in an easy-to-comprehend output format.

How the strengths of Lisp-family languages facilitate building complex and flexible bioinformatics applications

We present a rationale for expanding the presence of the Lisp family of programming languages in bioinformatics and computational biology research. Put simply, Lisp-family languages enable programmers to more quickly write programs that run faster than in other languages. Languages such as Common Lisp, Scheme and Clojure facilitate the creation of powerful and flexible software that is required for complex and rapidly evolving domains like biology. We will point out several important key features that distinguish languages of the Lisp family from other programming languages, and we will explain how these features can aid researchers in becoming more productive and creating better code. We will also show how these features make these languages ideal tools for artificial intelligence and machine learning applications. We will specifically stress the advantages of domain-specific languages (DSLs): languages that are specialized to a particular area, and thus not only facilitate easier research problem formulation, but also aid in the establishment of standards and best programming practices as applied to the specific research field at hand. DSLs are particularly easy to build in Common Lisp, the most comprehensive Lisp dialect, which is commonly referred to as the 'programmable programming language'. We are convinced that Lisp grants programmers unprecedented power to build increasingly sophisticated artificial intelligence systems that may ultimately transform machine learning and artificial intelligence research in bioinformatics and computational biology.

AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture

The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.

Knowledge Discovery in Biological Databases for Revealing Candidate Genes Linked to Complex Phenotypes

Genetics and “omics” studies designed to uncover genotype to phenotype relationships often identify large numbers of potential candidate genes, among which the causal genes are hidden. Scientists generally lack the time and technical expertise to review all relevant information available from the literature, from key model species and from a potentially wide range of related biological databases in a variety of data formats with variable quality and coverage. Computational tools are needed for the integration and evaluation of heterogeneous information in order to prioritise candidate genes and components of interaction networks that, if perturbed through potential interventions, have a positive impact on the biological outcome in the whole organism without producing negative side effects. Here we review several bioinformatics tools and databases that play an important role in biological knowledge discovery and candidate gene prioritization. We conclude with several key challenges that need to be addressed in order to facilitate biological knowledge discovery in the future.

Screening of whole genome sequences identified high-impact variants for stallion fertility

Background

Stallion fertility is an economically important trait due to the increase of artificial insemination in horses. The availability of whole genome sequence data facilitates identification of rare high-impact variants contributing to stallion fertility. The aim of our study was to genotype rare high-impact variants retrieved from next-generation sequencing (NGS)-data of 11 horses in order to unravel harmful genetic variants in large samples of stallions.

Methods

Gene ontology (GO) terms and search results from public databases were used to obtain a comprehensive list of human und mice genes predicted to participate in the regulation of male reproduction. The corresponding equine orthologous genes were searched in whole genome sequence data of seven stallions and four mares and filtered for high-impact genetic variants using SnpEFF, SIFT and Polyphen 2 software. All genetic variants with the missing homozygous mutant genotype were genotyped on 337 fertile stallions of 19 breeds using KASP genotyping assays or PCR-RFLP. Mixed linear model analysis was employed for an association analysis with de-regressed estimated breeding values of the paternal component of the pregnancy rate per estrus (EBV-PAT).

Results

We screened next generation sequenced data of whole genomes from 11 horses for equine genetic variants in 1194 human and mice genes involved in male fertility and linked through common gene ontology (GO) with male reproductive processes. Variants were filtered for high-impact on protein structure and validated through SIFT and Polyphen 2. Only those genetic variants were followed up when the homozygote mutant genotype was missing in the detection sample comprising 11 horses. After this filtering process, 17 single nucleotide polymorphism (SNPs) were left. These SNPs were genotyped in 337 fertile stallions of 19 breeds using KASP genotyping assays or PCR-RFLP. An association analysis in 216 Hanoverian stallions revealed a significant association of the splice-site disruption variant g.37455302G>A in NOTCH1 with the de-regressed estimated breeding values of the paternal component of the pregnancy rate per estrus (EBV-PAT). For 9 high-impact variants within the genes CFTR, OVGP1, FBXO43, TSSK6, PKD1, FOXP1, TCP11, SPATA31E1 and NOTCH1 (g.37453246G>C) absence of the homozygous mutant genotype in the validation sample of all 337 fertile stallions was obvious. Therefore, these variants were considered as potentially deleterious factors for stallion fertility.

Conclusions

In conclusion, this study revealed 17 genetic variants with a predicted high damaging effect on protein structure and missing homozygous mutant genotype. The g.37455302G>A NOTCH1 variant was identified as a significant stallion fertility locus in Hanoverian stallions and further 9 candidate fertility loci with missing homozygous mutant genotypes were validated in a panel including 19 horse breeds. To our knowledge this is the first study in horses using next generation sequencing data to uncover strong candidate factors for stallion fertility.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2608-3) contains supplementary material, which is available to authorized users.

An integrative data analysis platform for gene set analysis and knowledge discovery in a data warehouse framework

Data analysis is one of the most critical and challenging steps in drug discovery and disease biology. A user-friendly resource to visualize and analyse high-throughput data provides a powerful medium for both experimental and computational biologists to understand vastly different biological data types and obtain a concise, simplified and meaningful output for better knowledge discovery. We have previously developed TargetMine, an integrated data warehouse optimized for target prioritization. Here we describe how upgraded and newly modelled data types in TargetMine can now survey the wider biological and chemical data space, relevant to drug discovery and development. To enhance the scope of TargetMine from target prioritization to broad-based knowledge discovery, we have also developed a new auxiliary toolkit to assist with data analysis and visualization in TargetMine. This toolkit features interactive data analysis tools to query and analyse the biological data compiled within the TargetMine data warehouse. The enhanced system enables users to discover new hypotheses interactively by performing complicated searches with no programming and obtaining the results in an easy to comprehend output format.

Database URL:http://targetmine.mizuguchilab.org

Dintor: functional annotation of genomic and proteomic data

Background

During the last decade, a great number of extremely valuable large-scale genomics and proteomics datasets have become available to the research community. In addition, dropping costs for conducting high-throughput sequencing experiments and the option to outsource them considerably contribute to an increasing number of researchers becoming active in this field. Even though various computational approaches have been developed to analyze these data, it is still a laborious task involving prudent integration of many heterogeneous and frequently updated data sources, creating a barrier for interested scientists to accomplish their own analysis.

Results

We have implemented Dintor, a data integration framework that provides a set of over 30 tools to assist researchers in the exploration of genomics and proteomics datasets. Each of the tools solves a particular task and several tools can be combined into data processing pipelines. Dintor covers a wide range of frequently required functionalities, from gene identifier conversions and orthology mappings to functional annotation of proteins and genetic variants up to candidate gene prioritization and Gene Ontology-based gene set enrichment analysis. Since the tools operate on constantly changing datasets, we provide a mechanism to unambiguously link tools with different versions of archived datasets, which guarantees reproducible results for future tool invocations. We demonstrate a selection of Dintor’s capabilities by analyzing datasets from four representative publications. The open source software can be downloaded and installed on a local Unix machine. For reasons of data privacy it can be configured to retrieve local data only. In addition, the Dintor tools are available on our public Galaxy web service at http://dintor.eurac.edu.

Conclusions

Dintor is a computational annotation framework for the analysis of genomic and proteomic datasets, providing a rich set of tools that cover the most frequently encountered tasks. A major advantage is its capability to consistently handle multiple versions of tool-associated datasets, supporting the researcher in delivering reproducible results.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2279-5) contains supplementary material, which is available to authorized users.

Big data and biomedical informatics: a challenging opportunity

Big data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations.

Mining a database of single amplified genomes from Red Sea brine pool extremophiles-improving reliability of gene function prediction using a profile and pattern matching algorithm (PPMA)

Reliable functional annotation of genomic data is the key-step in the discovery of novel enzymes. Intrinsic sequencing data quality problems of single amplified genomes (SAGs) and poor homology of novel extremophile's genomes pose significant challenges for the attribution of functions to the coding sequences identified. The anoxic deep-sea brine pools of the Red Sea are a promising source of novel enzymes with unique evolutionary adaptation. Sequencing data from Red Sea brine pool cultures and SAGs are annotated and stored in the Integrated Data Warehouse of Microbial Genomes (INDIGO) data warehouse. Low sequence homology of annotated genes (no similarity for 35% of these genes) may translate into false positives when searching for specific functions. The Profile and Pattern Matching (PPM) strategy described here was developed to eliminate false positive annotations of enzyme function before progressing to labor-intensive hyper-saline gene expression and characterization. It utilizes InterPro-derived Gene Ontology (GO)-terms (which represent enzyme function profiles) and annotated relevant PROSITE IDs (which are linked to an amino acid consensus pattern). The PPM algorithm was tested on 15 protein families, which were selected based on scientific and commercial potential. An initial list of 2577 enzyme commission (E.C.) numbers was translated into 171 GO-terms and 49 consensus patterns. A subset of INDIGO-sequences consisting of 58 SAGs from six different taxons of bacteria and archaea were selected from six different brine pool environments. Those SAGs code for 74,516 genes, which were independently scanned for the GO-terms (profile filter) and PROSITE IDs (pattern filter). Following stringent reliability filtering, the non-redundant hits (106 profile hits and 147 pattern hits) are classified as reliable, if at least two relevant descriptors (GO-terms and/or consensus patterns) are present. Scripts for annotation, as well as for the PPM algorithm, are available through the INDIGO website.

INDIGO - INtegrated data warehouse of microbial genomes with examples from the red sea extremophiles

BACKGROUND

The next generation sequencing technologies substantially increased the throughput of microbial genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and computational methods are used. Integration of information from different sources is a powerful approach to enhance such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially depends on this annotation but it is hampered by the current lack of suitable information integration and exploration systems for microbial genomes.

RESULTS

We developed a data warehouse system (INDIGO) that enables the integration of annotations for exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments.

CONCLUSIONS

We developed a data warehouse system, INDIGO, which enables comprehensive integration of information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline. The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo.