We developed an efficient and effective GWAS method to detect epistasis for discovering sophisticated pathogenesis, which is especially important for complex diseases such as Alzheimer's disease (AD). GenEpi is a computational package to uncover epistasis associated with phenotypes by the proposed machine learning approach. GenEpi identifies both within-gene and cross-gene epistasis through a two-stage modeling workflow. In both stages, GenEpi adopts two-element combinatorial encoding when producing features and constructs the prediction models by L1-regularized regression with stability selection. The simulated data showed that GenEpi outperforms other widely-used methods on detecting the ground-truth epistasis. As real data is concerned, this study uses AD as an example to reveal the capability of GenEpi in finding disease-related variants and variant interactions that show both biological meanings and predictive power.

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隨著基因定序技術的成熟及便利, 基因組資料已經變成深度學習下一個最受矚目及最具潛力的焦點, 各大公司也紛紛佈局在AI醫療及基因研究上. 這次 Dosudo 的專訪榮幸的邀請到臺大生物產業機電工程學系的陳倩瑜教授來為我們談談如何將深度學習技術應用在基因資料的分析, 以及為何生物資訊需要結合人工智慧及這領域未來的展望.

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Recent advances in sequencing technology have opened a new era in RNA studies. Novel types of RNAs such as long non-coding RNAs (lncRNAs) have been discovered by transcriptomic sequencing and some lncRNAs have been found to play essential roles in biological processes. However, only limited information is available for lncRNAs in Drosophila melanogaster, an important model organism. Therefore, the characterization of lncRNAs and identification of new lncRNAs in D. melanogaster is an important area of research. Moreover, there is an increasing interest in the use of ChIP-seq data (H3K4me3, H3K36me3 and Pol II) to detect signatures of active transcription for reported lncRNAs. In this study, we have developed a computational approach to identify new lncRNAs from two tissue-specific RNA-seq datasets using the poly(A)-enriched and the ribo-zero method, respectively. In our results, we identified 462 novel lncRNA transcripts, which we combined with 4137 previously published lncRNA transcripts into a curated dataset. We then utilized 61 RNA-seq and 32 ChIP-seq datasets to improve the annotation of the curated lncRNAs with regards to transcriptional direction, exon regions, classification, expression in the brain, possession of a poly(A) tail, and presence of conventional chromatin signatures. Furthermore, we used 30 time-course RNA-seq datasets and 32 ChIP-seq datasets to investigate whether the lncRNAs reported by RNA-seq have active transcription signatures. The results showed that more than half of the reported lncRNAs did not have chromatin signatures related to active transcription. To clarify this issue, we conducted RT-qPCR experiments and found that ~95.24 % of the selected lncRNAs were truly transcribed, regardless of whether they were associated with active chromatin signatures or not.

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It's amazing for salamanders are unique among vertebrates in their ability to completely regenerate amputated limbs through the mediation of blastema cells located at the stump ends. This study aims to know the secret in transcription mechanism of salamander's regeneration.

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機器能跟人類一樣有智慧嗎？近年來，隨著數位化資料的大量累積，人工智慧（AI）的子領域機器學習（machine learning）發展迅速，機器已經能自己從大量資料中學習，「看起來」比以前更有智慧。怎麼讓機器自己學習呢？專長生物資訊的陳教授將為我們介紹機器學習的定義與演進，並帶我們探索機器學習的多元應用，瞭解除了搜尋引擎與智慧型手機之外，機器學習如何輔助基礎醫學研究：探索基因與各種疾病之關聯性，將在未來個人化醫療時代中扮演關鍵角色。

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Previous studies have used target genes shared by two TFs as a clue to infer TF-TF interactions. However, the target genes with low binding affinity are frequently omitted by experimental data, especially when a single strict threshold is employed. This study aims at improving the accuracy of inferring TF-TF interactions.

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Automatic extraction of motifs from biological sequences is an important research problem in study of molecular biology. For proteins, it is desired to discover sequence motifs containing a large number of wildcard symbols, as the residues associated with functional sites are usually largely separated in sequences. This paper proposes an algorithm named WildSpan (sequential pattern mining across large wildcard regions) that incorporates several pruning strategies to largely reduce the mining cost.

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More and more disordered regions have been discovered in protein sequences, and many of them are found to be functionally significant. Previous studies reveal that disordered regions of a protein can be predicted by its primary structure, the amino acid sequence. Recent studies further show that employing evolutionary information such as position specific scoring matrices (PSSMs) improves the prediction accuracy of protein disorder. As more and more machine learning techniques have been introduced to protein disorder detection, extracting more useful features with biological insights attracts more attention.

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Protein sequence clustering has been widely exploited to facilitate in-depth analysis of protein functions and families. For some applications of protein sequence clustering, it is highly desirable that a hierarchical structure, also referred to as dendrogram, which shows how proteins are clustered at various levels, is generated. In this paper, the design of a novel incremental clustering algorithm aimed at generating summarized dendrograms for analysis of protein databases is described. The proposed incremental clustering algorithm employs a statistics-based model to summarize the distributions of the similarity scores among the proteins in the database and to control formation of clusters.

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