This research presents a genetic variant discovery SoC for analyzing Next-generation sequencing data which is embedded with the processorconnected with some peripherals. With hardware-optimized algorithm sBWT, we greatly speed up the process of short reads mapping by k-ordered FM-indexsuffix grouping. After short reads mapping, we utilized de bruijn graph for sequence assembly to rebuild haplotypes,built haplotypes are compared to the reference genome to detect genome variations. The detected variations are written to files for further clinical diagnosisresearch analysis.

The interpretation of next generation sequencing is a big challenge. In order to improve the molecular diagnosis of the patient, we developed the AI_Variant prioritizer, a machine learning based variant prioterization system that can help to prioritize candidate variants for human disease. This module highlighted the possible disease causing variants to help clinician to increase the disease diagnostic yield and decrease the load of manpower.

Epidermolysis bullosa (EB) is an inherited blistering skin diseaseis also one of the most "urgent, severe, difficultrare" diseases. Since there are at least 21 disease-causing genes involved in EB with markedly variable manifestations, it is particularly difficult for accurate diagnosis. In recent years, next generation sequencing (NGS) has been widely applied in the genetic diagnosis of rare hereditary disorders. The EB team in NCKU established the protocol of EB diagnosis by combining NGSvarious tools of pathologymolecular studies. Till now, we have completed the genetic diagnosis for more than 50 Taiwanese EB patients.

R3CE (Rapid, Reproducible, Rare Cell 3D Expansion) is a clinically validated platform capable of generating single cell-derived organoids within one to four weeks. It outperforms currently 3D culture technologies in terms of yields, time to form organoids,easiness of operation. It also demonstrates similar yieldscost structure to 2D culture protocols. R3CE is ready for drug testing for all clinical samplesprovides sufficient quantity of genomic samples derived from cancer patients. It would help conduct clinical cancer treatment in the translational study.