Most genes are transcribed into mRNA molecules that are known as the transcriptome. The study of transcriptomic data, or the complete set of RNA transcripts sequenced using high-throughput sequencing is called Transcriptomics. Transcriptomic studies sometimes focus on mRNA molecules that …
NGS in the clinic: from research studies to precision medicine! The interest and the potential of precision diagnostics and personalized therapy are now becoming a reality with large scale data analysis being carried out to understand and adopt Next Generation …
Cancer is one of the deadliest diseases known to mankind. Throughout history, doctors, healers, and researchers have been trying to find a cure for this terrible condition. According to the National Cancer Institute, there are about eight different ways of …
Often times, it is very hard to find the right omics data for your precision oncology research project. Learning about the impact of next-generation sequencing and the explosive growth of publically available data, one might just wonder where the RNA-seq …
The discovery of the role of mRNA as a link between the genome and proteome has led to the development of effective gene expression identification and the quantification technologies, ranging from rtPCR to Western Blots, Microarrays and Next Generation Sequencing. …
Interest in Data Science is growing in many industries as data becomes a key part of business, finance, and today, biomedical research and medicine in general. Biologists, clinicians and lab technicians can set themselves apart, advance research and become more efficient in their day-to-day activities by leveraging data. However, biomedical data of the future is high-throughput, molecular-level data. This data is non-trivial to process, analyze and interpret. High-throughput data like genomics, transcriptomics, metabolomics and even structural chemical data is poised to revolutionize our understanding of health and disease.
Interest in Data Science is growing in many industries as data becomes a key part of business, finance, and today, biomedical research and medicine in general. Biologists, clinicians and lab technicians can set themselves apart, advance research and become more efficient in their day-to-day activities by leveraging data. However, biomedical data of the future is high-throughput, molecular-level data. This data is non-trivial to process, analyze and interpret. High-throughput data like genomics, transcriptomics, metabolomics and even structural chemical data is poised to revolutionize our understanding of health and disease.
Interest in Data Science is growing in many industries as data becomes a key part of business, finance, and today, biomedical research and medicine in general. Biologists, clinicians and lab technicians can set themselves apart, advance research and become more efficient in their day-to-day activities by leveraging data. However, biomedical data of the future is high-throughput, molecular-level data. This data is non-trivial to process, analyze and interpret. High-throughput data like genomics, transcriptomics, metabolomics and even structural chemical data is poised to revolutionize our understanding of health and disease.
