With the rapid development of sequencing technologies, it becomes more and more easily for researchers to analyse the expression level of molecules or variations in genome, transcriptome, and proteome through wet lab. These recent technological advances have helped the life science community by revealing complex diseases risk factors such as gene variations or expressions, clinical phenotypes, and etc. In comparison with the biological findings from wet lab, more complex diseases characterisations need to be mined from this new huge amount of sequencing data.
To this end, it is urgent to develop methods and tools for integrating omics data from diverse labs. One way would be to evaluate associations between diseases and identify novel disease types sub-types by analyzing individual omics data from multiple labs. e.g. LncRNAs biomarkers, associated with clinically molecular sub-type and prognosis of diffuse large B cell lymphoma, was discovered and validated by re-annotating the probes and analyzing data of multiple microarray platforms. Another way would be to reveal potential characteristic of diseases by integrating multi-level omics data. e.g. Complex disease gene targets could be predicted by integrating summary data from GWAS and eQTL studies. The integration of omics data is hard to most of the biologist, since it needs dozens of computational tools. Thus, it is very important to establish automated pipelines that combine these tools. In summary, the current challenge for understanding complex disease is to mine novel and precise characterization through fusing multi-level omics data using system biology approaches.
Therefore, we propose to conduct a Research Topic on ‘System Biology Methods and Tools for Integrating Omics Data’. The subtopics include, but are not limited to:
• Statistical methods for integrating multi-level omics data.
• Statistical methods for integrating summary data.
• Tools and databases for analyzing omics data.
• Pipeline for analyzing sequencing data.
• Machine learning methods for integrating multi-level omics data.
With the rapid development of sequencing technologies, it becomes more and more easily for researchers to analyse the expression level of molecules or variations in genome, transcriptome, and proteome through wet lab. These recent technological advances have helped the life science community by revealing complex diseases risk factors such as gene variations or expressions, clinical phenotypes, and etc. In comparison with the biological findings from wet lab, more complex diseases characterisations need to be mined from this new huge amount of sequencing data.
To this end, it is urgent to develop methods and tools for integrating omics data from diverse labs. One way would be to evaluate associations between diseases and identify novel disease types sub-types by analyzing individual omics data from multiple labs. e.g. LncRNAs biomarkers, associated with clinically molecular sub-type and prognosis of diffuse large B cell lymphoma, was discovered and validated by re-annotating the probes and analyzing data of multiple microarray platforms. Another way would be to reveal potential characteristic of diseases by integrating multi-level omics data. e.g. Complex disease gene targets could be predicted by integrating summary data from GWAS and eQTL studies. The integration of omics data is hard to most of the biologist, since it needs dozens of computational tools. Thus, it is very important to establish automated pipelines that combine these tools. In summary, the current challenge for understanding complex disease is to mine novel and precise characterization through fusing multi-level omics data using system biology approaches.
Therefore, we propose to conduct a Research Topic on ‘System Biology Methods and Tools for Integrating Omics Data’. The subtopics include, but are not limited to:
• Statistical methods for integrating multi-level omics data.
• Statistical methods for integrating summary data.
• Tools and databases for analyzing omics data.
• Pipeline for analyzing sequencing data.
• Machine learning methods for integrating multi-level omics data.
