MOTIVATION: Allele typing for Human Leukocyte Antigen (HLA) genes has many important clinical applications. Popular short-read typing can only accurately distinguish alleles at the coding sequence level, which potentially limit our understanding of the effect of variants in non-coding region. Long read data has been proved to be useful in typing HLA alleles in full resolution, but only a few tools are publicly available and with significant limitations in practical application. RESULTS: We developed FuFiHLA, a lightweight open-source software, to type HLA alleles. Currently it supports typing alleles of six HLA genes (HLA-A, HLA-B, HLA-C, HLA-DRB1, HLA-DQA1, and HLA-DQB1) from long reads. Evaluation using 233 PacBio HiFi WGS samples from HPRC shows that FuFiHLA achieves 99.6% accuracy in the full field allele typing and QV as 51.8 for consensus allele sequence construction. Additional testing on four Nanopore R10 reads demonstrates slightly reduced accuracy in the fourth field. AVAILABILITY: FuFiHLA is available at https://github.com/jingqing-hu/FuFiHLA under MIT License.

Symptoms and signs (symptoms in brief) are the essential clinical manifestations for traditional Chinese medicine (TCM) diagnosis and treatments. To gain insights into the molecular mechanism of symptoms, this paper presents a network-based data mining method to integrate multiple phenotype-genotype data sources and predict the prioritizing gene rank list of symptoms. The result of this pilot study suggested some insights on the molecular mechanism of symptoms.

Traditional Chinese Medicine (TCM) is a discipline of clinical medicine, which focuses on individualized diagnosis and treatment based on observation of the clinical manifestations of real-world patients. The complicated interactions between different medical entities play significant role for individualized treatment. In this paper, we aim to find out the meaningful herb-symptom relationship from large number of clinical data with using complex network approach. We construct two different patient networks to verify the positive correlations between herbs and symptoms in TCM clinical treatment.