• Title, Summary, Keyword: miRNA

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Exploring Cancer-Specific microRNA-mRNA Interactions by Evolutionary Layered Hypernetwork Models (진화연산 기반 계층적 하이퍼네트워크 모델에 의한 암 특이적 microRNA-mRNA 상호작용 탐색)

  • Kim, Soo-Jin;Ha, Jung-Woo;Zhang, Byoung-Tak
    • Journal of KIISE:Computing Practices and Letters
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    • v.16 no.10
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    • pp.980-984
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    • 2010
  • Exploring microRNA (miRNA) and mRNA regulatory interactions may give new insights into diverse biological phenomena. Recently, miRNAs have been discovered as important regulators that play a major role in various cellular processes. Therefore, it is essential to identify functional interactions between miRNAs and mRNAs for understanding the context- dependent activities of miRNAs in complex biological systems. While elucidating complex miRNA-mRNA interactions has been studied with experimental and computational approaches, it is still difficult to infer miRNA-mRNA regulatory modules. Here we present a novel method, termed layered hypernetworks (LHNs), for identifying functional miRNA-mRNA interactions from heterogeneous expression data. In experiments, we apply the LHN model to miRNA and mRNA expression profiles on multiple cancers. The proposed method identifies cancer-specific miRNA-mRNA interactions. We show the biological significance of the discovered miRNA- mRNA interactions.

miRNA Pattern Discovery from Sequence Alignment

  • Sun, Xiaohan;Zhang, Junying
    • Journal of Information Processing Systems
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    • v.13 no.6
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    • pp.1527-1543
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    • 2017
  • MiRNA is a biological short sequence, which plays a crucial role in almost all important biological process. MiRNA patterns are common sequence segments of multiple mature miRNA sequences, and they are of significance in identifying miRNAs due to the functional implication in miRNA patterns. In the proposed approach, the primary miRNA patterns are produced from sequence alignment, and they are then cut into short segment miRNA patterns. From the segment miRNA patterns, the candidate miRNA patterns are selected based on estimated probability, and from which, the potential miRNA patterns are further selected according to the classification performance between authentic and artificial miRNA sequences. Three parameters are suggested that bi-nucleotides are employed to compute the estimated probability of segment miRNA patterns, and top 1% segment miRNA patterns of length four in the order of estimated probabilities are selected as potential miRNA patterns.

Post-transcriptional and translational regulation of mRNA-like long non-coding RNAs by microRNAs in early developmental stages of zebrafish embryos

  • Lee, Kyung-Tae;Nam, Jin-Wu
    • BMB Reports
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    • v.50 no.4
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    • pp.226-231
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    • 2017
  • At the post-transcriptional and translational levels, microRNA (miRNA) represses protein-coding genes via seed pairing to the 3' untranslated regions (UTRs) of mRNA. Although working models of miRNA-mediated gene silencing are successfully established using miRNA transfections and knockouts, the regulatory interaction between miRNA and long non-coding RNA (lncRNA) remain unknown. In particular, how the mRNA-resembling lncRNAs with 5' cap, 3' poly(A)-tail, or coding features, are regulated by miRNA is yet to be examined. We therefore investigated the functional interaction between miRNAs and lncRNAs with/without those features, in miRNA-transfected early zebrafish embryos. We observed that the greatest determinants of the miRNA-mediated silencing of lncRNAs were the 5' cap and 3' poly(A)-tails in lncRNAs, at both the post-transcriptional and translational levels. The lncRNAs confirmed to contain 5' cap, 3' poly(A)-tail, and the canonical miRNA target sites, were observed to be repressed in the level of both RNA and ribosome-protected fragment, while those with the miRNA target sites and without 5' cap and 3' poly(A)-tail, were not robustly repressed by miRNA introduction, thus suggesting a role as a miRNA-decoy.

Evolutionary Optimization of Models for Mature microRNA Prediction (Mature microRNA 위치 예측 모델의 진화적 최적화)

  • Kim Jin-Han;Nam Jin-Wu;Zhang Byoung-Tak
    • Proceedings of the Korean Information Science Society Conference
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    • pp.67-69
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    • 2006
  • MicroRNA (miRNA)는 생체내에서 gene regulation에 관여하는 핵심 small RNA 중 하나이다. miRNA는 Primary miRNA, Precursor miRNA, mature miRNA의 과정으로 processing 된다. miRNA 최종 형태인 mature miRNA의 정확한 위치 예측은 miRNA 예측의 필수적인 부분이다. 본 논문에서는, 진화적 최적화 예측 모델 중 하나인 유전 알고리즘을 이용하여 mature miRNA의 정확한 위치 예측을 수행한다. 제시된 방법은 이미 알려진 mature miRNA 위치를 positive example로 하고 임의로 생성한 위치를 negative example로 하여 서로의 linear scoring function 적합성 함수의 값 차이가 최대한으로 되도록 예측 모델을 진화시킨다. 유전 알고리즘을 이용한 진화적 최적화 모델로부터 mature miRNA 위치 예측에서 약 1.7nt 오차를 보여 기존의 방법 보다 개선된 성능을 보인다.

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Rules for functional microRNA targeting

  • Kim, Doyeon;Chang, Hee Ryung;Baek, Daehyun
    • BMB Reports
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    • v.50 no.11
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    • pp.554-559
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    • 2017
  • MicroRNAs (miRNAs) are ~22nt-long single-stranded RNA molecules that form a RNA-induced silencing complex with Argonaute (AGO) protein to post-transcriptionally downregulate their target messenger RNAs (mRNAs). To understand the regulatory mechanisms of miRNA, discovering the underlying functional rules for how miRNAs recognize and repress their target mRNAs is of utmost importance. To determine functional miRNA targeting rules, previous studies extensively utilized various methods including high-throughput biochemical assays and bioinformatics analyses. However, targeting rules reported in one study often fail to be reproduced in other studies and therefore the general rules for functional miRNA targeting remain elusive. In this review, we evaluate previously-reported miRNA targeting rules and discuss the biological impact of the functional miRNAs on gene-regulatory networks as well as the future direction of miRNA targeting research.

Global and Local Competition between Exogenously Introduced microRNAs and Endogenously Expressed microRNAs

  • Kim, Doyeon;Kim, Jongkyu;Baek, Daehyun
    • Molecules and Cells
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    • v.37 no.5
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    • pp.412-417
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    • 2014
  • It has been reported that exogenously introduced micro-RNA (exo-miRNA) competes with endogenously expressed miRNAs (endo-miRNAs) in human cells, resulting in a detectable upregulation of mRNAs with endo-miRNA target sites (TSs). However, the detailed mechanisms of the competition between exo- and endo-miRNAs remain uninvestigated. In this study, using 74 microarrays that monitored the whole-transcriptome response after introducing miRNAs or siRNAs into HeLa cells, we systematically examined the derepression of mRNAs with exo- and/or endo-miRNA TSs. We quantitatively assessed the effect of the number of endo-miRNA TSs on the degree of mRNA derepression. As a result, we observed that the number of endo-miRNA TSs was significantly associated with the degree of derepression, supporting that the derepression resulted from the competition between exo- and endo-miRNAs. However, when we examined whether the site proficiency of exo-miRNA TSs could also influence mRNA derepression, to our surprise, we discovered a strong positive correlation. Our analysis indicates that site proficiencies of both exo- and endo-miRNA TSs are important determinants for the degree of mRNA derepression, implying that the derepression of mRNAs in response to exo-miRNA is more complex than that currently perceived. Our observations may lead to a more complete understanding of the detailed mechanisms of the competition between exo- and endo-miRNAs and to a more accurate prediction of miRNA targets. Our analysis also suggests an interesting hypothesis that long 3'-UTRs may function as molecular buffer against gene expression regulation by individual miRNAs.

Identification of Caenorhabditis elegans microRNA target using a neural network (신경망을 이용한 예쁜 꼬마 선충 microRNA target 예측)

  • Lee, Wha-Jin;Zhang, Byoung-Tak
    • Proceedings of the Korean Society for Bioinformatics Conference
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    • pp.150-157
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    • 2004
  • microRNA (miRNA)는 21-25 nucleotide (nt)의 single-stranded RNA 분자로서 mRNA의 3' untranslated region (3' UTR)에 상보적으로 결합하여 유전자 발현을 제어하는 새로운 조절물질이다. 지금까지 실험을 통해 수백 개의 miRNA가 알려져 있으나, miRNA에 의해 조절되는 target 유전자는 실험상의 어려움으로 아직까지 거의 알려지지 않았다. miRNA는 서열의 길이가 짧고 target과 느슨한 상보적 결합을 하기 때문에 기존의 서열 비교 방법으로 miRNA의 target을 찾는 것은 쉬운 일이 아니다. 본 논문은 신경망을 이용하여 Caenorhabditis elegans mRNA의 3' UTR에서 miRNA가 결합하는 영역을 예측하였다. 신경망은 복잡한 비선형 데이터를 잘 분리해내고 불완전하고 잡음이 많은 입력에 강하기 때문에 miRNA target 예측에 적합하다. miRNA와 mRNA의 결합 영역을 다양하게 분석하였고 민감도 0.59, 특수도 0.99의 성능을 갖는 신경망을 구현하였다. 신경망 입력 값을 달리하여 각각의 특성이 결과에 미치는 영향을 분석하였고 기존 예측 방법에 의한 결과와 비교하여 성능을 평가하였다.

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Finding Specific Disease Related microRNA Using by Ranking Score with Integrated miRNA Database (miRNA 데이터베이스 통합 및 순위 결정에 의한 특정 질병 관련 microRNA의 추출 방법)

  • Ha, Ji-Hwan;Kim, Hyun-Jin;Park, Sang-Hyun
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.671-674
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    • 2014
  • 최근 MicroRNA(miRNA)가 질병 발생과 밀접한 연관성이 있다고 밝혀진 이래, 이와 관련된 연구가 활발히 진행되고 있다. 하지만 각종 질병 관련 miRNA의 기능과 역할 그리고 질병 발생 메카니즘 등이 명백히 밝혀진 것이 없는 실정이다. 본 논문에서는 여러 종류의 miRNA 데이터베이스(miRecords, miRTarBase, miR2Disease 등)를 통합하고, 본 논문에서 새로이 제안하는 scoring 방법과 특정 질병과 관련된 miRNA의 순위결정과정을 통하여 질병과 연관성이 높은 miRNA을 밝혀내는 방법을 제안한다. 새로이 제안하는 방법을 바탕으로 miRNA와 특정 질병과의 연관성을 효과적으로 밝혀냈다.

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Identification of microRNA target using neural network (신경망을 이용한 microRNA target 예측)

  • 이화진;장병탁
    • Proceedings of the Korean Information Science Society Conference
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    • pp.301-303
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    • 2004
  • microRNA(miRNA)는 -22 nucleotide(nt)의 단일가닥 (single-stranded) RNA 분자로서 mRNA의 3'-untranslated region (3' UTR)에 상보적으로 결합하여 유전자 발현을 제어하는 새로운 조절물질이다. 지금까지 실험을 통해 1184개의 miRNA가 알려져 있으나, miRNA에 의해 조절되는 target유전자는 실험상의 어려움으로 아직까지 거의 알려지지 않았다. miRNA는 서열의 길이가 짧고 target과 느슨한 상보적 결합을 하기 때문에 기존의 서열 비교 방법으로 miRNA의 target을 찾는 것은 쉬운 일이 아니다. 본 논문은 신경망을 이용하여 mRNA의 3' UTR에서 miRNA가 결합하는 영역을 예측하였다. 신경망은 비선형의 데이터를 학습할 수 있어 miRNA target예측에 적합하다. miRNA와 mRhA의 결합 영역을 다양하게 분석하였고 기존 예측방법에 의한 결과와 비교하여 성능을 평가하였다.

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Hypernetwork Classifiers for Microarray-Based miRNA Module Analysis (마이크로어레이 기반 miRNA 모듈 분석을 위한 하이퍼망 분류 기법)

  • Kim, Sun;Kim, Soo-Jin;Zhang, Byoung-Tak
    • Journal of KIISE:Software and Applications
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    • v.35 no.6
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    • pp.347-356
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    • 2008
  • High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.