We found that each participant had an average of 6,870.7 globally novel variants and 75.3% (831/1103) of the participants harbored at least one PharmGKB-selected high evidence level human leukocyte antigen (HLA) risk allele. 54 PharmGKB-reported high-level instances of evidence of Cytochrome P450 variant-drug pairs, with a population frequency of over 13.2%. We also identified 23 variants in the ACMG secondary finding V3 gene list from 25 participants, suggesting that 1.67% (25/1496) of the population is harboring at least one medical actionable variant. Our carrier status analyses suggest that one in 25 couples (3.94%) would risk having offspring with at least one pathogenic variant, which is in line with rates found in Japan and Singapore. For pathogenic CNV, we detected 6.88% and 2.02% carrier rates for alpha thalassemia and spinal muscular atrophy, respectively.

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To facilitate the process of tailor-making a deep neural network for exploring the dynamics of genomic DNA, we have developed a hands-on package called ezGeno. ezGeno automates the search process of various parameters and network structures and can be applied to any kind of 1D genomic data. Combinations of multiple abovementioned 1D features are also applicable. The ezGeno package can be freely accessed at https://github.com/ailabstw/ezGeno.

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We built DockCoV2 to provide the binding affinity of FDA-approved and Taiwan National Health Insurance (NHI) drugs with seven proteins. This database contains a total of 3,109 drugs. DockCoV2 is available at https://covirus.cc/drugs/

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For the organisms without the reference transcriptome, de novo transcriptome assembly must be carried out prior to quantification. This study investigates how assembly quality affects the performance of quantification based on de novo transcriptome assembly. We examined the over-extended and incomplete contigs, and demonstrated that assembly completeness has a strong impact on the estimation of contig abundance. Then we investigated the behavior of the quantifiers with respect to sequence ambiguity which might be originally presented in the transcriptome or accidentally produced by assemblers. The results suggested that the quantifiers often over-estimate the expression of family-collapse contigs and under-estimate the expression of duplicated contigs. For organisms without reference transcriptome, it remains challenging to detect the inaccurate estimation on family-collapse contigs. On the contrary, we observed that the situation of under-estimation on duplicated contigs can be warned through analyzing the read proportion of estimated abundance (RPEA) of contigs in the connected component inferenced by the quantifiers. In addition, we suggest that the estimated quantification results on the connected component level have better accuracy over sequence level quantification.

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攝影：謝郁震

The Mikado pheasant 帝雉 (Syrmaticus mikado) is a nearly endangered species indigenous to high-altitude regions of Taiwan. We completed the draft genome of the Mikado pheasant, which consists of 1.04 Gb of DNA and 15,972 annotated protein-coding genes. The Mikado pheasant displays expansion and positive selection of genes related to features that contribute to its adaptive evolution, such as energy metabolism, oxygen transport, hemoglobin binding, radiation response, immune response, and DNA repair. To investigate the molecular evolution of the major histocompatibility complex (MHC) across several avian species, 39 putative genes spanning 227 kb on a contiguous region were annotated and manually curated. The MHC loci of the pheasant revealed a high level of synteny, several rapidly evolving genes, and inverse regions compared to the same loci in the chicken. The complete mitochondrial genome was also sequenced, assembled, and compared against four long-tailed pheasants. The results from molecular clock analysis suggest that ancestors of the Mikado pheasant migrated from the north to Taiwan about 3.47 million years ago.

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Bioinformatics has played an important role in annotating the human genome since its draft was first announced in 2001. As the sequencing cost decreased dramatically owing to the advance of next-generation sequencing technology, the need of precisely annotating a personal genome is right around the corner. This talk will start with the success of using structural bioinformatics in predicting the influence of a single nucleotide variation on changing the protein-DNA binding affinity. Next, the concept of deep learning and how it has been used to annotate epigenomes and to explore the roles of cis-regulatory sequence variations will be introduced. As the scale and complexity of personal genomic data analysis increase rapidly, deep learning will definitely become one of the effective ways to associate personal genomic variations with diseases or drug responses. The current status and challenges of using deep learning in annotating personal genomes will be kindly addressed in the talk and it deserves more attentions when designing Bioinformatics education in the near future.

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Oriental fruit fly is a very destructive pest of fruit in areas where it occurs and some of them have insecticide resistance.
The underlying basis of such phenomena can involve complex interactions of multiple genes. This study aims to analyze the specific genes are involved in resistance mechanisms.

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Predicting binding sites of a transcription factor in the genome is an important, but challenging, issue in studying gene regulation. PiDNA, aims at first constructing reliable PWMs by applying an atomic-level knowledge-based scoring function on numerous in silico mutated complex structures, and then using the PWM constructed by the structure models with small energy changes to predict the interaction between proteins and DNA sequences. This study first shows that the constructed PWMs are similar to the annotated PWMs collected from databases or literature. Second, the prediction accuracy of PiDNA in detecting relatively high-specificity sites is evaluated by comparing the ranked lists against in vitro experiments from protein-binding microarrays. Finally, PiDNA is shown to be able to select the experimentally validated binding sites from 10 000 random sites with high accuracy.
PiDNA is available at:

here.

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Insecticide resistance has recently become a critical concern for control of many insect pest species. Genome sequencing and global quantization of gene expression through analysis of the transcriptome can provide useful information relevant to this challenging problem. This study aims at using whole transcriptome analysis developed through de novo assembly to know resistance mechanisms from molecular aspect.

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DNA-binding proteins such as transcription factors use DNA-binding domains (DBDs) to bind to specific sequences in the genome to initiate many important biological functions. This study aims at investigating the possibility of predicting the DNA sequences bound by DNA-binding proteins from the proteins' unbound structures (structures of the unbound state).

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We propose a method for discovering TFBSs, especially gapped motifs. We use ChIP-chip data to judge the binding strength of a TF to a putative target promoter and use orthologous sequences from related species to judge the degree of evolutionary conservation of a predicted TFBS.

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c4Lab was founded in the department of Biomechartronics Engineering, National Taiwan University by Dr. Chien-Yu Chen and her students.