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The advancements of genomics and data integration in sarcoma research

Ricardo J. Flores ()
Aaron J. Kelly ()
Manjula Nakka ()
Xiang Chen ()
Jiayi Sun ()
Tsz-Kwong Man ()


In the age of big data, genomics and clinical research have reached a crossroads. A wealth of data is being generated, but it is becoming increasingly complicated to analyze these data to extract meaningful results. The ability to understand biological systems holistically has unprecedented potential to transform how cancers are treated. Recent major advances leading biomedical research towards “systems medicine” have been fueled by high-throughput platforms, such as microarrays and next-generation sequencing, which can capture vast amounts of data in different genomic spaces. Unfortunately, because of high dimensionality and complex relationships among these data, inferring comprehensive and useful biological models has proven computationally and statistically challenging. However, novel bioinformatic methods for data integration of cancer genomic datasets have been developed. In this review, we will describe the applications of various genomic approaches in sarcoma research and introduce bioinformatic methods for data integration. With the continuing evolution of technological and bioinformatic methodologies, the application of big data within clinics and hospitals will ultimately result in significant improvements on how cancers are detected and treated.


omics; genomics; sarcomas; sequencing; microarray; data integration; bioinformatics; big data

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