Supporting cognition in systems biology analysis: findings on users' processes and design implications

Abstract Background Current usability studies of bioinformatics tools suggest that tools for exploratory analysis support some tasks related to finding relationships of interest but not the deep causal insights necessary for formulating plausible and credible hypotheses. To better understand design requirements for gaining these causal insights in systems biology analyses a longitudinal field study of 15 biomedical researchers was conducted. Researchers interacted with the same protein-protein interaction tools to discover possible disease mechanisms for further experimentation. Results Findings reveal patterns in scientists' exploratory and explanatory analysis and reveal that tools positively supported a number of well-structured query and analysis tasks. But for several of scientists' more complex, higher order ways of knowing and reasoning the tools did not offer adequate support. Results show that for a better fit with scientists' cognition for exploratory analysis systems biology tools need to better match scientists' processes for validating, for making a transition from classification to model-based reasoning, and for engaging in causal mental modelling. Conclusion As the next great frontier in bioinformatics usability, tool designs for exploratory systems biology analysis need to move beyond the successes already achieved in supporting formulaic query and analysis tasks and now reduce current mismatches with several of scientists' higher order analytical practices. The implications of results for tool designs are discussed.http://deepblue.lib.umich.edu/bitstream/2027.42/134554/1/13009_2008_Article_29.pd

The genome of Romanomermis culicivorax:revealing fundamental changes in the core developmental genetic toolkit in Nematoda

Background: The genetics of development in the nematode Caenorhabditis elegans has been described in exquisite detail. The phylum Nematoda has two classes: Chromadorea (which includes C. elegans) and the Enoplea. While the development of many chromadorean species resembles closely that of C. elegans, enoplean nematodes show markedly different patterns of early cell division and cell fate assignment. Embryogenesis of the enoplean Romanomermis culicivorax has been studied in detail, but the genetic circuitry underpinning development in this species has not been explored. Results: We generated a draft genome for R. culicivorax and compared its gene content with that of C. elegans, a second enoplean, the vertebrate parasite Trichinella spiralis, and a representative arthropod, Tribolium castaneum. This comparison revealed that R. culicivorax has retained components of the conserved ecdysozoan developmental gene toolkit lost in C. elegans. T. spiralis has independently lost even more of this toolkit than has C. elegans. However, the C. elegans toolkit is not simply depauperate, as many novel genes essential for embryogenesis in C. elegans are not found in, or have only extremely divergent homologues in R. culicivorax and T. spiralis. Our data imply fundamental differences in the genetic programmes not only for early cell specification but also others such as vulva formation and sex determination. Conclusions: Despite the apparent morphological conservatism, major differences in the molecular logic of development have evolved within the phylum Nematoda. R. culicivorax serves as a tractable system to contrast C. elegans and understand how divergent genomic and thus regulatory backgrounds nevertheless generate a conserved phenotype. The R. culicivorax draft genome will promote use of this species as a research model

Scientists’ sense making when hypothesizing about disease mechanisms from expression data and their needs for visualization support

Abstract A common class of biomedical analysis is to explore expression data from high throughput experiments for the purpose of uncovering functional relationships that can lead to a hypothesis about mechanisms of a disease. We call this analysis expression driven, -omics hypothesizing. In it, scientists use interactive data visualizations and read deeply in the research literature. Little is known, however, about the actual flow of reasoning and behaviors (sense making) that scientists enact in this analysis, end-to-end. Understanding this flow is important because if bioinformatics tools are to be truly useful they must support it. Sense making models of visual analytics in other domains have been developed and used to inform the design of useful and usable tools. We believe they would be helpful in bioinformatics. To characterize the sense making involved in expression-driven, -omics hypothesizing, we conducted an in-depth observational study of one scientist as she engaged in this analysis over six months. From findings, we abstracted a preliminary sense making model. Here we describe its stages and suggest guidelines for developing visualization tools that we derived from this case. A single case cannot be generalized. But we offer our findings, sense making model and case-based tool guidelines as a first step toward increasing interest and further research in the bioinformatics field on scientists’ analytical workflows and their implications for tool design.http://deepblue.lib.umich.edu/bitstream/2027.42/109495/1/12859_2012_Article_6377.pd

A cognitive task analysis of a visual analytic workflow: Exploring molecular interaction networks in systems biology

Background: Bioinformatics visualization tools are often not robust enough to support biomedical specialists’ complex exploratory analyses. Tools need to accommodate the workflows that scientists actually perform for specific translational research questions. To understand and model one of these workflows, we conducted a case-based, cognitive task analysis of a biomedical specialist’s exploratory workflow for the question: What functional interactions among gene products of high throughput expression data suggest previously unknown mechanisms of a disease? Results: From our cognitive task analysis four complementary representations of the targeted workflow were developed. They include: usage scenarios, flow diagrams, a cognitive task taxonomy, and a mapping between cognitive tasks and user-centered visualization requirements. The representations capture the flows of cognitive tasks that led a biomedical specialist to inferences critical to hypothesizing. We created representations at levels of detail that could strategically guide visualization development, and we confirmed this by making a trial prototype based on user requirements for a small portion of the workflow. Conclusions: Our results imply that visualizations should make available to scientific users “bundles of features” consonant with the compositional cognitive tasks purposefully enacted at specific points in the workflow. We also highlight certain aspects of visualizations that: (a) need more built-in flexibility; (b) are critical for negotiating meaning; and (c) are necessary for essential metacognitive support

생물학적 네트워크를 이용하여 유전자로부터 패스웨이, 표현형까지의 전사체 공간을 탐색하는 정보학 기법

학위논문(박사)--서울대학교 대학원 :자연과학대학 협동과정 생물정보학전공,2019. 8. 김선.Transcriptome data, genome-wide measurement of transcripts, has been used to increase our understandings of biological processes at transcription level significantly. Analysis of transcriptome data involves a series of steps from identification of differentially expressed genes (DEGs) to pathway enrichment analysis to association with phenotypes. There exist several hurdles at each step that need to be addressed with state of the art bioinformatics techniques. For example, the complex nature of living organisms can be represented as a network where the nodes are the interacting entities such as genes or pathways and the edges are the interactions between the nodes. Network analysis is crucial in that it can reveal the hidden associations between transcriptome data and phenotypes. In addition, network propagation has emerged as a technique to measure the influential power of nodes in a network. Network propagation has demonstrated its utility on biological context by many studies and has been contributing to invaluable discoveries in biological and medical science fields. In my doctoral study, I explored and analyzed trasncriptome at various levels using machine learning, network information and network propagation techniques. My thesis consists of three studies. The first study was to develop an accurate and stable method for determining differentially expressed genes using machine learning techniques. The second study was to develop a novel method to investigate interactions among biological pathways using explicit gene expression information from RNA-seq. The last study was to perform analysis of xenotransplant transcriptome data using various methods including the network propagation technique. In the first study, MLDEG, a machine learning approach to identify DEGs using network property and network propagation, was developed. Currently available DEG detection methods have widely been used and contributed to new biological discoveries. Most of the methods use their own models to define DEGs. However, because the traits of transcriptome data vary significantly depending on the experimental designs and sequencing technologies, a single model can hardly fit all transcriptome data of different traits. In addition, setting cutoff values of p-values and fold change is arbitrary. Thus, the results yielded by the methods are often inconsistent and heterogeneous. MLDEG addresses these issues by building a model that uses network information and network propagation results as features. The goal of MLDEG is to train a model by using network-based features extracted from more likely true and false DEGs and use the model to classify DEGs from the genes that cannot be clearly defined as DEGs by existing methods. Tested on 10 high-throughput RNA-seq data, MLDEG showed better performances than the competing methods. In the second study, I developed a Pathway INTeraction network construction method (PINTnet) that can construct a condition-specific pathway interaction network by computing shortest paths on protein-protein interaction (PPI) networks. Because pathways usually function in a coordinated and cooperative fashion, understanding interactions, or crosstalks, between pathways becomes as important as identifying perturbed single pathway. However, existing methods do not take into account the topological features, treating the pathways just as a set of genes. To solve the problem, PINTnet computes shortest paths on PPI networks mapped to each pair of pathways and creates subnetworks using the shortest paths. It then measures the activation status of pathway interaction using the product of closeness centrality and explicit gene expression quantity. The performance of PINTnet was evaluated using three high-throughput RNAseq data and successfully reproduced the findings in the original papers of the data. In the last study, I participated in a xenotransplantation study to elucidate the cause of chronic phase islet graft loss. Clinical islet transplantation is one of the promising options for type 1 diabetes but long-term outcome of graft function is not yet satisfactory. To reveal the mechanism of the graft loss in chronic phase, I carried out pathway interaction network analysis using PINTnet on a time-series porcine islet-transplanted rhesus monkey RNA-seq data and identified the activation of T cell receptor signaling pathway. The analysis results were supported by the biopsy result of liver sample that CD3+ T cell heavily infiltrated the porcine islet. Additionally, I carried out gene prioritization using network propagation to verify five graft loss-relevant scenarios. The result suggested that T cell-mediated long-term graft loss was the most probable scenario. In summary, my doctoral study used network information, network property, and network propagation to identify DEGs and predict pathway interactions. In addition, I participated in a xenotransplantation research and carried out pathway interaction network analysis and network propagation to reveal the possible cause of chronic phase islet graft loss. Utilizing network information and network propagation was very effective to discover the relationships among biological entities and analyze the complex phenotypes.전사 과정에서의 생물학적 프로세스에 대한 이해를 높이는 데 사용되는 전사체 데이터의 분석은 차별 발현 유전자를 찾아내는 것에서부터 표현형에 연관된 패스 웨이 증폭 분석까지의 일련의 단계를 포함한다. 각 단계마다, 넘어야 할 장애물들이 존재하며 이를 극복하기 위한 새로운 생물정보학 기술의 개발은 필수적이다. 예를 들어, 생명체의 복잡한 특성은 유전자 또는 패스웨이가 노드, 그 개체 사이의 상호 작용이 엣지인 네트워크로 나타낼 수 있다. 이 때, 네트워크 분석 기법은 전사체 데이터와 표현형 간의 숨겨진 연관성을 찾는 데 중요한 역할을 할 수 있다. 한 편, 네트워크 전파는 네트워크에서 노드의 영향력을 측정하는 기술로 주목받고 있으며 새로운 생물학적 발견에 기여하는 등, 생물학 및 의학 분야의 많은 연구에서 그 유용성을 입증하였다. 본 논문에서는 이러한 기계 학습, 네트워크 정보 및 네트워크 전파를 이용한 전사체 데이터 분석에 관한 연구에 대해 다룬다. 첫 번째 연구에서는, 네트워크 정보와 네트워크 전파를 이용하여 차별 발현 유전자를 식별하는 기계 학습 접근법(MLDEG)에 관한 연구를 다룬다. 차별 발현 유전자 분석은 생물학 연구에서 새로운 생물학적 지식의 발견에 중요한 역할을 하고 있으나 이를 위한 기존의 분석 도구들이 도출하는 결과는 각기 다르다. 본 연구에서는 네트워크 정보 및 네트워크 전파 결과를 활용하는 모델을 구축하여 이러한 문제를 해결하였다. 본 연구의 목표는 차별 발현 유전자 및 비차별 발현 유전자로서 가장 가능성이 있는 유전자를 선정하여 네트워크 기반 특징을 추출하고 이 특징을 바탕으로 모델을 학습하여 차별 발현 유전자를 분류하는 것이다. 열개의 RNA-seq 데이터를 이용하여 검증한 결과, 기존의 분석 도구들보다 우수한 성능을 보임을 확인하였다. 두 번째 연구에서는 단백질 상호 작용 네트워크상의 최단 경로를 계산하여 특정 실험 조건하에서 패스웨이 상호 작용 네트워크를 구축할 수 있는 패스웨이 상호 작용 네트워크 구축 방법(PINTnet)에 대한 내용을 다룬다. 기존의 방법들은 유전자 사이의 관계를 고려하지 않고 패스웨이를 단순히 유전자의 집합으로만 다루는 문제를 가지고 있다. 본 연구에서는 유전자 사이의 관계를 고려하여 각 패스웨이 쌍에 매핑된 단백질 상호작용 네트워크에서 최단 경로를 계산하고, 이를 통해 만들어진 서브네트워크에서 근접중심성과 유전자 발현량의 곱을 바탕으로 패스웨이 상호작용의 활성화 상태를 측정함으로 문제를 해결하였다. 세 개의 RNA-seq 데이터를 이용하여 PINTnet의 성능을 평가한 결과, 각 데이터의 원 논문에서 주장한 결과를 성공적으로 재현함을 확인하였다. 마지막 연구는 만성 췌도 이식편 소실의 원인을 밝히기 위한 이종장기이식 데이터 분석에 관한 내용을 다룬다. 만성 단계에서의 이식편 소실의 기작을 밝히기 위해, PINTnet을 사용하여 돼지 췌도가 이식된 원숭이의 RNA-seq 데이터를 분석하였고 T 세포 수용체 신호 전달 패스웨이(T cell receptor signalling pathway)가 활성화 되었음을 확인하였다. 해당 원숭이의 간 샘플을 생검하여 CD3+ T 세포가 이식된 췌도에 침투하였음을 확인함으로써 분석 결과가 실제 결과와 일치함을 확인하였다. 한편, 네트워크 전파를 이용하여 다섯 가지 거부 반응 시나리오를 검증하였고 T 세포로 인한 거부반응이 가장 가능성이 높음을 확인하였다. 결론적으로, 본 논문에서는 다양한 전사체 데이터 분석을 수행함에 있어서 네트워크 정보, 네트워크 특성 및 네트워크 전파를 이용한 네트워크 분석 및 기계학습 기법이 유용함을 보였다.Abstract Chapter 1. Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.1.1 An introduction to network theory and its application to the fields of biology . . . . . . . . . . . . . . . . . . . . . 2 1.1.2 An introduction to machine learning . . . . . . . . . . . . 5 1.2 Three problems in my doctoral study . . . . . . . . . . . . . . . . 6 1.2.1 Problem 1: DEG detection . . . . . . . . . . . . . . . . . 6 1.2.2 Problem 2: Pathway interaction analysis . . . . . . . . . . 8 1.2.3 Problem 3: Analysis of transcriptome from pig-to-nonhuman primate islet xenotransplantation . . . . . . . . . . . . . . 10 1.3 My network-based approaches to three research problems . . . . 11 1.4 Outline of thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Chapter 2. A machine learning approach to identify differentially expressed genes using network property and network propagation 14 2.1 Background of differential expression analysis methods . . . . . . 15 2.1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.2 My machine learning approach . . . . . . . . . . . . . . . 17 2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.1 Training and Test Data . . . . . . . . . . . . . . . . . . . 19 2.2.2 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.3 Network Property . . . . . . . . . . . . . . . . . . . . . . 22 2.2.4 Network Propagation . . . . . . . . . . . . . . . . . . . . 23 2.2.5 Machine Learning Algorithm . . . . . . . . . . . . . . . . 24 2.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.1 Experimental Data Description . . . . . . . . . . . . . . . 26 2.3.2 Performance of Network Information Features . . . . . . . 30 2.3.3 Performance Evaluation and Discussion . . . . . . . . . . 34 2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Chapter 3. Construction of condition-specific pathway interaction network by computing shortest paths on weighted PPI 38 3.1 Background of pathway interaction network construction . . . . . 39 3.1.1 The importance of finding perturbed interaction between pathways . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.1.2 Challenges in pathway interaction network construction . 40 3.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.1 Preparation of PPI and pathway information . . . . . . . 41 3.2.2 Defining edges in the pathway network . . . . . . . . . . . 42 3.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.3.1 Data description . . . . . . . . . . . . . . . . . . . . . . . 47 3.3.2 Evaluation criteria . . . . . . . . . . . . . . . . . . . . . . 49 3.3.3 Performance comparison to other methods . . . . . . . . . 51 3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Chapter 4. Bioinformatics analyses with peripheral blood RNA-sequencing unveiled the cause of the graft loss after pig-to-nonhuman primate islet xenotransplantation model 63 4.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.2.1 Peripheral blood RNA sequencing . . . . . . . . . . . . . 65 4.2.2 Graft loss period-related activated pathways (GLPAPs) defined by TRAP (Time-series RNA-seq analysis package) 66 4.2.3 Pathway interaction network analysis . . . . . . . . . . . 72 4.2.4 Hypothesis evaluation using network propagation . . . . . 75 4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Chapter 5 Conclusion 83 초록 103Docto

Pathway Bridge Based Multiobjective Optimization Approach for Lurking Pathway Prediction

Ovarian carcinoma immunoreactive antigen-like protein 2 (OCIAD2) is a protein with unknown function. Frequently methylated or downregulated, OCIAD2 has been observed in kinds of tumors, and TGFβ signaling has been proved to induce the expression of OCIAD2. However, current pathway analysis tools do not cover the genes without reported interactions like OCIAD2 and also miss some significant genes with relatively lower expression. To investigate potential biological milieu of OCIAD2, especially in cancer microenvironment, a nova approach pbMOO was created to find the potential pathways from TGFβ to OCIAD2 by searching on the pathway bridge, which consisted of cancer enriched looping patterns from the complicated entire protein interactions network. The pbMOO approach was further applied to study the modulator of ligand TGFβ1, receptor TGFβR1, intermediate transfer proteins, transcription factor, and signature OCIAD2. Verified by literature and public database, the pathway TGFβ1- TGFβR1- SMAD2/3- SMAD4/AR-OCIAD2 was detected, which concealed the androgen receptor (AR) which was the possible transcription factor of OCIAD2 in TGFβ signal, and it well explained the mechanism of TGFβ induced OCIAD2 expression in cancer microenvironment, therefore providing an important clue for the future functional analysis of OCIAD2 in tumor pathogenesis

Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease

Rationale: Coronary artery disease (CAD) is a complex phenotype driven by genetic and environmental factors. Ninety-seven genetic risk loci have been identified to date, but the identification of additional susceptibility loci might be important to enhance our understanding of the genetic architecture of CAD. Objective: To expand the number of genome-wide significant loci, catalog functional insights, and enhance our understanding of the genetic architecture of CAD. Methods and Results: We performed a genome-wide association study in 34541 CAD cases and 261984 controls of UK Biobank resource followed by replication in 88192 cases and 162544 controls from CARDIoGRAMplusC4D. We identified 75 loci that replicated and were genome-wide significant (P Conclusions: We identified 64 novel genetic risk loci for CAD and performed fine mapping of all 161 risk loci to obtain a credible set of causal variants. The large expansion of reconstituted gene sets argues in favor of an expanded omnigenic model view on the genetic architecture of CAD