Using pre-existing datasets to combine published information with new metrics would help researchers construct a broader picture of chromatin in disease
Using pre-existing datasets to combine published information with new metrics would help researchers construct a broader picture of chromatin in disease. A computational biology goal is the near-real-time integration of epigenomic data sets, irrespective of the laboratory they were generated in—similar to a blood pressure, ECG or troponin test. In addition, epigenome modeling must become dynamic, considering cell-to-cell variability and changes over time due to normal physiological or pathological stressors. Probabilistic modeling and machine learning can help such model creation, while finding (and quantifying) previously identified developing chromatin properties that match heart health changes. A 3D genome representation, for example, may reveal a structural or accessibility attribute connected to health or disease that no single epigenomic test alone can discover. Such strategies can expand basic knowledge of biology and illness.
Incorporating wet and dry lab training components to teach schemes to foster the formation of more diverse technical repertoires. Data mining and fresh data collection will revolutionize how we handle chromatin challenges in coming years. Knowing how computers solve problems (as opposed to how people do) and how to computationally phrase questions would create a shared vocabulary that completes tasks. Team members don't need all the big data skills, but a collaborative attitude is important for effective large-scale epigenomic research. UCLA's QCBio Collaboratory is a great platform for teaching non-programmers and facilitating cooperation to resolve biological issues.
It also encourages the use of open source technology by making genomics datasets available to non-experts.There are already many bioinformatics tools—and others will be developed to introduce new understanding—but basic knowledge of how computers work and how to answer big-data questions will continue to empower scientists to test the most meaningful hypotheses with appropriate tools to reveal new insights about cardiac biology.