RNA-Seq 데이터에서 유전자의 랭킹을 책정하기 위한 네트워크 접근법을 사용한 정보 과학 시스템

학위논문(박사)--서울대학교 대학원 :자연과학대학 협동과정 생물정보학전공,2019. 8. 김선.RNA-seq 기술은 게놈 규모의 전사체를 고해상도로 분석 가능하게 만들었으나, 일반적으로 전사체 데이터에서 나타나는 유전자의 수는 많기 때문에 추가 분석 없이 연구 목표와 관련된 유전자를 식별하기가 어렵다. 따라서 전사체 데이터 분석은 종종 생물 네트워크, 유전자 정보 데이터베이스, 문헌 정보 같이 서로 다른 자원을 활용하여 분석하게 된다. 그러나 자원들 간의 관계는 이질적인 부분이 존재하여 서로 직접적으로 연결하여 해석하기 어려우며 어떠한 유전자가 실험 목표와 관련이 있는지를 구체적으로 이해하기 힘들다. 따라서 특정 연구 목표와 관련 있는 핵심 유전자를 효과적으로 결정하고 설명하기 위해서는 이러한 이질적인 자원을 효과적으로 통합할 강력한 전산 기법이 필요하다. 본 논문에서는 네트워크 기반 접근법을 사용하여 전사체 데이터를 분석하고 실험 목표와 관련 있는 유전자를 찾기 위한 세 가지 생물 정보 시스템을 개발했다. 첫 번째 연구는 RNA-Seq 데이터의 특성을 활용하여 샘플 수가 적은 유전자 녹아웃 (KO) 마우스 실험에서 중요한 유전자를 찾기 위한 정보학 시스템을 개발하였다. 이 시스템은 유전자 조절 네트워크 (GRN)와 패스웨이 정보를 사용하여 유의함이 적은 Differentially Expressed Gene (DEG)를 제거하고 단일 염기 변이 (SNV) 정보를 사용하여 샘플 간 유전적 차이로 인해 다를 수 있는 유전자를 제거한다. 이 연구는 네트워크와 SNV 정보의 통합을 통해서 후보 유전자의 수를 유의미하게 줄일 수 있음을 보여주었다. 두 번째 연구는 사용자의 실험 목표를 반영할 수 있는 유전자 랭킹 시스템인 CLIP-GENE을 개발하였다. CLIP-GENE은 쥐의 전사인자 KO 실험에서 유전자를 랭킹하기 위한 통합 분석 웹 서비스이다. CLIP-GENE은 후보 유전자에 랭킹을 부여하기 위해 GRN, SNV 정보를 이용하여 샘플 개체 간의 차이가 있고 덜 유의미한 후보 유전자를 제거하고 텍스트 마이닝 기술과 단백질-단백질 상호작용 네트워크 정보를 이용하여 사용자의 실험 목표와 관련된 유전자를 랭킹한다. 마지막 연구는 벤 다이어그램을 사용하여 다수의 RNA-Seq 실험을 비교분석 할수 있는 정보 시스템을 개발하였다. RNA-Seq 실험은 일반적으로 비교 및 대조군의 샘플을 비교하여 DEG를 생성하고 벤 다이어그램을 통하여 샘플 간의 차이를 분석한다. 그러나 벤 다이어그램 상에서의 각 영역은 다양한 비율의 DEG를 포함하고 있으며, 특정 영역의 DEG는 서로 다른 비교군(혹은 대조군)에 의한 DEG이기에 단순히 유전자 목록 간의 차이를 비교하는 것은 적절하지 못하다. 이러한 문제를 해결하기 위해 벤 다이어그램과 네트워크 전파(Network Propagation)를 사용한 통합 분석 프레임워크인 Venn-diaNet이 개발했다. Venn-diaNet은 다수의 DEG 목록이 있는 실험의 유전자를 랭킹할 수 있는 정보 시스템이다. 우리는 Venn-diaNet이 서로 다른 조건에서 생물학적 실험을 비교함으로써 원본 논문에 보고된 연구 결과를 재현 할 수 있음을 보여주었다. 정리하면 이 논문은 전사체 데이터로부터 유전자를 랭킹할 수있는 정보 시스템을 개발하기 위해 네트워크 기반 분석법을 다양한 자원들과 결합하였으며, 다른 연구자의 편리한 사용 경험을 위해 친화적인 UI를 가진 웹도구 또는 소프트웨어 패키지로 제작 및 배포하였다.Transcriptomic analysis, the measurement of transcripts on the genome scale, is now routinely performed in high resolution. Since the number of genes obtained in the transcriptome data is usually large, it is difficult for researchers to identify genes that are relevant to their research goals, without additional analysis. Analysis of transcriptome data is often performed utilizing heterogeneous resources such as biological networks, annotated gene information, and published literature. However, the relationship among heterogeneous resources is often too complicated to decipher which genes are relevant to the experimental design. Therefore, powerful computational methods should be coupled with these heterogeneous resources in order to effectively determine and illustrate key genes that are relevant to specific research goals. In my doctoral study, I have developed three bioinformatics systems that use network approaches to analyze transcriptome data and rank genes that are relevant to the experimental design. The first study was conducted to develop a bioinformatics system that could be used to analyze RNA-Seq data of gene knockout (KO) mice, where the sample number is small. In this case, the main objectives were to investigate how the KO gene affects the expression of other genes and identify the key genes that contribute significantly to the phenotypic difference. To address these questions, I developed a gene prioritization system that utilizes the characteristics of RNA-Seq data. The system prioritizes genes by removing the less informative differentially expressed genes (DEGs) using gene regulatory network (GRN) and biological pathways. Next, it filters out genes that might be different due to genetic differences between samples using single nucleotide variant (SNV) information. Consequently, this study demonstrated that the integration of networks and SNV information was able to increase the performance of gene prioritization. The second study was conducted to develop a gene prioritization system that allows the user to specify the context of the experiment. This study was inspired by the fact that the currently available analysis methods for transcriptome data do not fully consider the experimental design of gene KO studies. Therefore, I envisaged that users would prefer an analysis method that took into consideration the characteristics of the KO experiments and could be guided by the context of the researcher who has designed and performed the biological experiment. Therefore, I developed CLIP-GENE, a web service of the condition-specific context-laid integrative analysis for prioritizing genes in mouse TF KO experiments. CLIP-GENE prioritizes genes of KO experiments by removing the less informative DEGs using GRN, discards genes that might have sample variance, using SNV information, and ranks genes that are related to the user's context using the text-mining technique, as well as considering the shortest path of protein-protein interaction (PPI) from the KO gene to the target genes. The last study was conducted to develop an informative system that could be used to compare multiple RNA-Seq experiments using Venn diagrams. In general, RNA-Seq experiments are performed to compare samples between control and treated groups, producing a set of DEGs. Each region in a Venn diagram (a subset of DEGs) generally contains a large number of genes that could complicate the determination of the important and relevant genes. Moreover, simply comparing the list of DEGs from different experiments could be misleading because some of the DEG lists may have been measured using different controls. To address these issues, Venn-diaNet was developed, an analysis framework that integrates Venn diagram and network propagation to prioritize genes for experiments that have multiple DEG lists. We demonstrated that Venn-diaNet was able to reproduce research findings reported in the original papers by comparing two, three, and eight biological experiments measured in different conditions. I believe that Venn-diaNet can be very useful for researchers to determine genes for their follow-up studies. In summary, my doctoral study aimed to develop computational tools that can prioritize genes from transcriptome data. To achieve this goal, I combined network approaches with multiple heterogeneous resources in a single computational environment. All three informatics systems are deployed as software packages or web tools to support convenient access to researchers, eliminating the need for installation or learning any additional software packages.Abstract Chapter 1 Introduction 1.1 Challenges of analyzing RNA-seq data 1.1.1 Excessive amount of databases and analysis methods 1.1.2 Knowledge bias that prioritizes less relevant genes 1.1.3 Complicated experiment designs 1.2 My approach to address the challenges for the analysis of RNA-seq data 1.3 Background 1.3.1 Differentially expressed genes 1.3.2 Gene prioritization 1.4 Outline of the thesis Chapter 2 A filtering strategy that combines GRN, SNV information to enhances the gene prioritization in mouse KO studies with small number of samples 2.1 Background 2.2 Methods 2.2.1 First filter: DEG 2.2.2 Second filter: GRN 2.2.3 Third filter: Biological Pathway 2.2.4 Final filter: SNV 2.3 Results and Discussion 2.4 Discussion Chapter 3 An integration of data-fusion and text-mining strategy to prioritize context-laid genes in mouse TF KO experiments 3.1 Background 3.2 Methods 3.2.1 Selection of initial candidate genes 3.2.2 Prioritizing genes with the user context and PPI 3.3 Results and Discussion 3.3.1 Performance with the best context 3.3.2 Performance with the worst context 3.4 Discussion Chapter 4 Integrating Venn diagram to the network-based strategy for comparing multiple biological experiments 4.1 Background 4.2 Methods 4.2.1 Taking input data 4.2.2 Generating Venn diagram of DEG sets 4.2.3 Network propagation and gene ranking 4.3 Results and Discussion 4.3.1 Venn-diaNet for two experiments 4.3.2 Venn-diaNet for three experiments 4.3.3 Venn-diaNet for eight experiments 4.3.4Venn-diaNet performance with different PPI network 4.4 Discussion Chapter 5 Conclusion Bibliography 초록Docto

RDDpred: a condition-specific RNA-editing prediction model from RNA-seq data

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background RNA-editing is an important post-transcriptional RNA sequence modification performed by two catalytic enzymes, "ADAR"(A-to-I) and "APOBEC"(C-to-U). By utilizing high-throughput sequencing technologies, the biological function of RNA-editing has been actively investigated. Currently, RNA-editing is considered to be a key regulator that controls various cellular functions, such as protein activity, alternative splicing pattern of mRNA, and substitution of miRNA targeting site. DARNED, a public RDD database, reported that there are more than 300-thousands RNA-editing sites detected in human genome(hg19). Moreover, multiple studies suggested that RNA-editing events occur in highly specific conditions. According to DARNED, 97.62 % of registered editing sites were detected in a single tissue or in a specific condition, which also supports that the RNA-editing events occur condition-specifically. Since RNA-seq can capture the whole landscape of transcriptome, RNA-seq is widely used for RDD prediction. However, significant amounts of false positives or artefacts can be generated when detecting RNA-editing from RNA-seq. Since it is difficult to perform experimental validation at the whole-transcriptome scale, there should be a powerful computational tool to distinguish true RNA-editing events from artefacts. Result We developed RDDpred, a Random Forest RDD classifier. RDDpred reports potentially true RNA-editing events from RNA-seq data. RDDpred was tested with two publicly available RNA-editing datasets and successfully reproduced RDDs reported in the two studies (90 %, 95 %) while rejecting false-discoveries (NPV: 75 %, 84 %). Conclusion RDDpred automatically compiles condition-specific training examples without experimental validations and then construct a RDD classifier. As far as we know, RDDpred is the very first machine-learning based automated pipeline for RDD prediction. We believe that RDDpred will be very useful and can contribute significantly to the study of condition-specific RNA-editing. RDDpred is available at http://biohealth.snu.ac.kr/software/RDDpred

Dark Matter in Classically Scale-Invariant Two Singlets Standard Model

We consider a model where two new scalars are introduced in the standard model, assuming classical scale invariance. In this model the scale invariance is broken by quantum corrections and one of the new scalars acquires non-zero vacuum expectation value (VEV), which induces the electroweak symmetry breaking in the standard model, and the other scalar becomes dark matter. It is shown that TeV scale dark matter is realized, independent of the value of the other scalar's VEV. The impact of the new scalars on the Higgs potential is also discussed. The Higgs potential is stabilized when the Higgs mass is over ~120 GeV.Comment: 6 pages, 1 figure, published versio

Interaction Trees: Representing Recursive and Impure Programs in Coq

"Interaction trees" (ITrees) are a general-purpose data structure for representing the behaviors of recursive programs that interact with their environments. A coinductive variant of "free monads," ITrees are built out of uninterpreted events and their continuations. They support compositional construction of interpreters from "event handlers", which give meaning to events by defining their semantics as monadic actions. ITrees are expressive enough to represent impure and potentially nonterminating, mutually recursive computations, while admitting a rich equational theory of equivalence up to weak bisimulation. In contrast to other approaches such as relationally specified operational semantics, ITrees are executable via code extraction, making them suitable for debugging, testing, and implementing software artifacts that are amenable to formal verification. We have implemented ITrees and their associated theory as a Coq library, mechanizing classic domain- and category-theoretic results about program semantics, iteration, monadic structures, and equational reasoning. Although the internals of the library rely heavily on coinductive proofs, the interface hides these details so that clients can use and reason about ITrees without explicit use of Coq's coinduction tactics. To showcase the utility of our theory, we prove the termination-sensitive correctness of a compiler from a simple imperative source language to an assembly-like target whose meanings are given in an ITree-based denotational semantics. Unlike previous results using operational techniques, our bisimulation proof follows straightforwardly by structural induction and elementary rewriting via an equational theory of combinators for control-flow graphs.Comment: 28 pages, 4 pages references, published at POPL 202

The Whitham Equation as a Model for Surface Water Waves

The Whitham equation was proposed as an alternate model equation for the simplified description of uni-directional wave motion at the surface of an inviscid fluid. As the Whitham equation incorporates the full linear dispersion relation of the water wave problem, it is thought to provide a more faithful description of shorter waves of small amplitude than traditional long wave models such as the KdV equation. In this work, we identify a scaling regime in which the Whitham equation can be derived from the Hamiltonian theory of surface water waves. The Whitham equation is integrated numerically, and it is shown that the equation gives a close approximation of inviscid free surface dynamics as described by the Euler equations. The performance of the Whitham equation as a model for free surface dynamics is also compared to two standard free surface models: the KdV and the BBM equation. It is found that in a wide parameter range of amplitudes and wavelengths, the Whitham equation performs on par with or better than both the KdV and BBM equations.Comment: 14 pages, 4 figure

Modulational Instability in Equations of KdV Type

It is a matter of experience that nonlinear waves in dispersive media, propagating primarily in one direction, may appear periodic in small space and time scales, but their characteristics --- amplitude, phase, wave number, etc. --- slowly vary in large space and time scales. In the 1970's, Whitham developed an asymptotic (WKB) method to study the effects of small "modulations" on nonlinear periodic wave trains. Since then, there has been a great deal of work aiming at rigorously justifying the predictions from Whitham's formal theory. We discuss recent advances in the mathematical understanding of the dynamics, in particular, the instability of slowly modulated wave trains for nonlinear dispersive equations of KdV type.Comment: 40 pages. To appear in upcoming title in Lecture Notes in Physic

Venn-diaNet : venn diagram based network propagation analysis framework for comparing multiple biological experiments

Background The main research topic in this paper is how to compare multiple biological experiments using transcriptome data, where each experiment is measured and designed to compare control and treated samples. Comparison of multiple biological experiments is usually performed in terms of the number of DEGs in an arbitrary combination of biological experiments. This process is usually facilitated with Venn diagram but there are several issues when Venn diagram is used to compare and analyze multiple experiments in terms of DEGs. First, current Venn diagram tools do not provide systematic analysis to prioritize genes. Because that current tools generally do not fully focus to prioritize genes, genes that are located in the segments in the Venn diagram (especially, intersection) is usually difficult to rank. Second, elucidating the phenotypic difference only with the lists of DEGs and expression values is challenging when the experimental designs have the combination of treatments. Experiment designs that aim to find the synergistic effect of the combination of treatments are very difficult to find without an informative system. Results We introduce Venn-diaNet, a Venn diagram based analysis framework that uses network propagation upon protein-protein interaction network to prioritizes genes from experiments that have multiple DEG lists. We suggest that the two issues can be effectively handled by ranking or prioritizing genes with segments of a Venn diagram. The user can easily compare multiple DEG lists with gene rankings, which is easy to understand and also can be coupled with additional analysis for their purposes. Our system provides a web-based interface to select seed genes in any of areas in a Venn diagram and then perform network propagation analysis to measure the influence of the selected seed genes in terms of ranked list of DEGs. Conclusions We suggest that our system can logically guide to select seed genes without additional prior knowledge that makes us free from the seed selection of network propagation issues. We showed that Venn-diaNet can reproduce the research findings reported in the original papers that have experiments that compare two, three and eight experiments. Venn-diaNet is freely available at: http://biohealth.snu.ac.kr/software/venndianetThis publication has been funded by (i) Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) the Ministry of Science ICT (MSIT) (No.NRF-2017M3C4A7065887), (ii) The Collaborative Genome Program for Fostering New Post-Genome Industry of the National Research Foundation (NRF), the Ministry of Science and ICT (MSIT) (No.NRF2014M3C9A3063541), and (iii) a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) the Ministry of Health & Welfare, Republic of Korea (Grant number: HI15C3224)

Neuroprotective Effect of Transplanted Human Embryonic Stem Cell-Derived Neural Precursors in an Animal Model of Multiple Sclerosis

BACKGROUND: Multiple sclerosis (MS) is an immune mediated demyelinating disease of the central nervous system (CNS). A potential new therapeutic approach for MS is cell transplantation which may promote remyelination and suppress the inflammatory process. METHODS: We transplanted human embryonic stem cells (hESC)-derived early multipotent neural precursors (NPs) into the brain ventricles of mice induced with experimental autoimmune encephalomyelitis (EAE), the animal model of MS. We studied the effect of the transplanted NPs on the functional and pathological manifestations of the disease. RESULTS: Transplanted hESC-derived NPs significantly reduced the clinical signs of EAE. Histological examination showed migration of the transplanted NPs to the host white matter, however, differentiation to mature oligodendrocytes and remyelination were negligible. Time course analysis of the evolution and progression of CNS inflammation and tissue injury showed an attenuation of the inflammatory process in transplanted animals, which was correlated with the reduction of both axonal damage and demyelination. Co-culture experiments showed that hESC-derived NPs inhibited the activation and proliferation of lymph node-derived T cells in response to nonspecific polyclonal stimuli. CONCLUSIONS: The therapeutic effect of transplantation was not related to graft or host remyelination but was mediated by an immunosuppressive neuroprotective mechanism. The attenuation of EAE by hESC-derived NPs, demonstrated here, may serve as the first step towards further developments of hESC for cell therapy in MS

Recognition and Accommodation at the Androgen Receptor Coactivator Binding Interface

Prostate cancer is a leading killer of men in the industrialized world. Underlying this disease is the aberrant action of the androgen receptor (AR). AR is distinguished from other nuclear receptors in that after hormone binding, it preferentially responds to a specialized set of coactivators bearing aromatic-rich motifs, while responding poorly to coactivators bearing the leucine-rich “NR box” motifs favored by other nuclear receptors. Under normal conditions, interactions with these AR-specific coactivators through aromatic-rich motifs underlie targeted gene transcription. However, during prostate cancer, abnormal association with such coactivators, as well as with coactivators containing canonical leucine-rich motifs, promotes disease progression. To understand the paradox of this unusual selectivity, we have derived a complete set of peptide motifs that interact with AR using phage display. Binding affinities were measured for a selected set of these peptides and their interactions with AR determined by X-ray crystallography. Structures of AR in complex with FxxLF, LxxLL, FxxLW, WxxLF, WxxVW, FxxFF, and FxxYF motifs reveal a changing surface of the AR coactivator binding interface that permits accommodation of both AR-specific aromatic-rich motifs and canonical leucine-rich motifs. Induced fit provides perfect mating of the motifs representing the known family of AR coactivators and suggests a framework for the design of AR coactivator antagonists