Transcriptional changes in response to X chromosome dosage in the mouse: implications for X inactivation and the molecular basis of Turner Syndrome.

BACKGROUND: X monosomic mice (39,XO) have a remarkably mild phenotype when compared to women with Turner syndrome (45,XO). The generally accepted hypothesis to explain this discrepancy is that the number of genes on the mouse X chromosome which escape X inactivation, and thus are expressed at higher levels in females, is very small. However this hypothesis has never been tested and only a small number of genes have been assayed for their X-inactivation status in the mouse. We performed a global expression analysis in four somatic tissues (brain, liver, kidney and muscle) of adult 40,XX and 39,XO mice using the Illumina Mouse WG-6 v1_1 Expression BeadChip and an extensive validation by quantitative real time PCR, in order to identify which genes are expressed from both X chromosomes. RESULTS: We identified several genes on the X chromosome which are overexpressed in XX females, including those previously reported as escaping X inactivation, as well as new candidates. However, the results obtained by microarray and qPCR were not fully concordant, illustrating the difficulty in ascertaining modest fold changes, such as those expected for genes escaping X inactivation. Remarkably, considerable variation was observed between tissues, suggesting that inactivation patterns may be tissue-dependent. Our analysis also exposed several autosomal genes involved in mitochondrial metabolism and in protein translation which are differentially expressed between XX and XO mice, revealing secondary transcriptional changes to the alteration in X chromosome dosage. CONCLUSIONS: Our results support the prediction that the mouse inactive X chromosome is largely silent, while providing a list of the genes potentially escaping X inactivation in rodents. Although the lower expression of X-linked genes in XO mice may not be relevant in the particular tissues/systems which are affected in human X chromosome monosomy, genes deregulated in XO mice are good candidates for further study in an involvement in Turner Syndrome phenotype.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

A survey of best practices for RNA-seq data analysis.

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.This is the final published version. It first appeared at http://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0881-8

Cell type-specific expression analysis to identify putative cellular mechanisms for neurogenetic disorders

Recent advances have substantially increased the number of genes that are statistically associated with complex genetic disorders of the CNS such as autism and schizophrenia. It is now clear that there will likely be hundreds of distinct loci contributing to these disorders, underscoring a remarkable genetic heterogeneity. It is unclear whether this genetic heterogeneity indicates an equal heterogeneity of cellular mechanisms for these diseases. The commonality of symptoms across patients suggests there could be a functional convergence downstream of these loci upon a limited number of cell types or circuits that mediate the affected behaviors. One possible mechanism for this convergence would be the selective expression of at least a subset of these genes in the cell types that comprise these circuits. Using profiling data from mice and humans, we have developed and validated an approach, cell type-specific expression analysis, for identifying candidate cell populations likely to be disrupted across sets of patients with distinct genetic lesions. Using human genetics data and postmortem gene expression data, our approach can correctly identify the cell types for disorders of known cellular etiology, including narcolepsy and retinopathies. Applying this approach to autism, a disease where the cellular mechanism is unclear, indicates there may be multiple cellular routes to this disorder. Our approach may be useful for identifying common cellular mechanisms arising from distinct genetic lesions

A comparison of brain gene expression levels in domesticated and wild animals

This is an open-access article distributed under the terms of the Creative Commons Attribution License.-- et al.Domestication has led to similar changes in morphology and behavior in several animal species, raising the question whether similarities between different domestication events also exist at the molecular level. We used mRNA sequencing to analyze genome-wide gene expression patterns in brain frontal cortex in three pairs of domesticated and wild species (dogs and wolves, pigs and wild boars, and domesticated and wild rabbits). We compared the expression differences with those between domesticated guinea pigs and a distant wild relative (Cavia aperea) as well as between two lines of rats selected for tameness or aggression towards humans. There were few gene expression differences between domesticated and wild dogs, pigs, and rabbits (30-75 genes (less than 1%) of expressed genes were differentially expressed), while guinea pigs and C. aperea differed more strongly. Almost no overlap was found between the genes with differential expression in the different domestication events. In addition, joint analyses of all domesticated and wild samples provided only suggestive evidence for the existence of a small group of genes that changed their expression in a similar fashion in different domesticated species. The most extreme of these shared expression changes include up-regulation in domesticates of SOX6 and PROM1, two modulators of brain development. There was almost no overlap between gene expression in domesticated animals and the tame and aggressive rats. However, two of the genes with the strongest expression differences between the rats (DLL3 and DHDH) were located in a genomic region associated with tameness and aggression, suggesting a role in influencing tameness. In summary, the majority of brain gene expression changes in domesticated animals are specific to the given domestication event, suggesting that the causative variants of behavioral domestication traits may likewise be different.This work was funded by the Max Planck Society and a European Research Council grant (233297, TWOPAN) to SP. FWA is supported by a grant from the German Science Foundation (DFG grant AL 1525/1-1). MS was supported by a CAS young scientists fellowship (2009Y2BS12) and a National Science Foundation of China research grant (31010022). JAB-A is supported by fellowship (SFRH/BPD/65464/2009).Peer Reviewe

의학 연구에서의 과학적 증거의 활용을 위한 시각적 분석 시스템 디자인

학위논문(박사) -- 서울대학교대학원 : 공과대학 컴퓨터공학부, 2022. 8. 서진욱.Evidence-based medicine, "the conscientious, explicit, and judicious use of current best evidence in healthcare and medical research" [98], is one of the most widely accepted medical paradigms of modern times. Searching, reviewing, and synthesizing reliable and high-quality scientific evidence is the key step for the paradigm. However, despite the widespread use of the EBM paradigm, challenges remain in applying Evidence-based medicine protocols to medical research. One of the barriers to applying the best scientific evidence to medical research is the severe literature and clinical data overload that causes the evidence-based tasks to be tremendous time-consuming tasks that require vast human effort. In this dissertation, we aim to employ visual analytics approaches to address the challenges of searching and reviewing massive scientific evidence in medical research. To overcome the burden and facilitate handling scientific evidence in medical research, we conducted three design studies and implemented novel visual analytics systems for laborious evidence-based tasks. First, we designed PLOEM, a novel visual analytics system to aid evidence synthesis, an essential step in Evidence-Based medicine, and generate an Evidence Map in a standardized method. We conducted a case study with an oncologist with years of evidence-based medicine experience. In the second study, we conducted a preliminary survey with 76 medical doctors to derive the design requirements for a biomedical literature search. Based on the results, We designed EEEVis, an interactive visual analytic system for biomedical literature search tasks. The system enhances the PubMed search result with several bibliographic visualizations and PubTator annotations. We performed a user study to evaluate the designs with 24 medical doctors and presented the design guidelines and challenges for a biomedical literature search system design. The third study presents GeneVis, a visual analytics system to identify and analyze gene expression signatures across major cancer types. A task that cancer researchers utilize to discover biomarkers in precision medicine. We conducted four case studies with domain experts in oncology and genomics. The study results show that the system can facilitate the task and provide new insights from the data. Based on the three studies of this dissertation, we conclude that carefully designed visual analytics approaches can provide an enhanced understanding and support medical researchers for laborious evidence-based tasks in medical research.근거중심의학(Evidence-Based Medicine)이란 "임상 치료 및 의학 연구에서 현재 존재하는 최고의 증거를 양심적이고, 명백하며, 분별 있게 이용하는 방법론"이며 [98], 현대 의학에서 가장 널리 받아들여지는 의학 패러다임이다. 신뢰할 수 있는 고수준의 과학적 근거를 검색, 검토, 합성하는 것이야 말로 근거중심의학의 핵심이다. 하지만, 근거중심의학이 이미 광범위하게 사용되고 있음에도 불구하고, 의학 연구에 근거중심의학의 프로토콜을 실천하는 데에는 여전히 많은 어려움이 따른다. 의료 문헌 정보, 임상 정보 및 유전체학 정보까지 연구자가 검토해야 할 근거의 양은 방대하며 광범위하다. 또한 의학과 기술의 발전으로 인해 점차 더 빠른 속도로 늘어나고 있기에, 이를 모두 엄밀히 검토하기 위해서는 막대한 양의 시간과 인력이 있어야 한다. 본 논문은 시각적 분석 방법론을 접목하여 의학 연구에서 방대한 과학적 증거를 검색하고 검토할 시 발생하는 막대한 인적 자원의 과부하 문제를 완화하고자 한다. 이를 위하여 근거중심의학의 절차 중 특히 인력 소모가 막심한 절차들을 선정하고, 이러한 난관을 극복하고 보다 효율적이고 효과적으로 데이터에서 유의미한 정보를 도출할 수 있게끔 보조하는 세 가지 시각적 분석 시스템들을 구현하였으며, 각각의 시스템에 관한 디자인 연구를 수행하였다. 우선 첫 디자인 연구에서는 근거중심의학 연구에 있어 필수적 단계인 근거 합성 방법론의 하나인 근거 매핑(Evidence Mapping) 과정을 지원하기 위한 시각적 분석 시스템 PLOEM을 설계했다. 그리고 이를 검증하기 위해 다년간의 근거 기반 의료 경험이 있는 종양학자와 함께 사례 연구를 수행했다. 두 번째 디자인 연구에서는 의학 문헌 검색 시스템의 요구사항 분석을 위해 총 76명의 의사를 상대로 설문조사를 진행하였고, 이러한 분석을 바탕으로 대화형 시각적 분석 시스템인 EEEVis를 설계했다. 이 시스템은 여러 종의 서지 정보 시각화 인터페이스와 PubTator의 주석 정보를 활용하여 PubMed 검색 엔진의 검색 결과를 증강하는 시스템이며, 이를 평가하기 위해 총 24명의 의사와 함께 사용자 연구를 수행하였다. 이 연구 결과를 바탕으로 의학 문헌 검색 시스템에 대한 설계 지침과 과제를 제시한다. 마지막으로 세 번째 디자인 연구에서는 임의의 유전자군의 유전자 발현 패턴을 주요 암 유형에 따라 시각화하고 분석할 수 있는 시스템인 GeneVis를 설계하였다. 암 유형에 따른 유전자 발현 패턴의 분석과 비교는 암 연구자들이 정밀 의학에서 생체 지표(Biomarker)를 발견하기 위해 빈번히 수행하는 작업이다. 우리는 종양학 전문가 및 유전체학 전문가 총 4인을 대상으로 사례 연구를 진행하였고, 그 결과 GeneVis가 해당 작업을 더 수월하게 수행하는 것과 기존의 데이터에서 새로운 정보를 도출하는 것에 도움이 되었음을 확인하였다. 위의 세 디자인 연구의 결과를 바탕으로, 본 논문은 사용자 분석과 작업 분석을 동반한 시각적 분석 방법론이 의학 연구의 근거 관련 작업의 어려움을 해소하고, 분석 데이터에 대한 보다 나은 이해를 제공하는 것이 가능하다고 결론 내린다.CHAPTER1 Introduction 1 1.1 Background and Motivation 1 1.2 Dissertation Outline 5 CHAPTER2 Related Work 7 2.1 Evidence Mapping: Graphical Representation for a Scientific Evidence Landscape 7 2.2 Scientific Literature Visualizations and Bibliography Visualizations 9 2.3 Visual Anlytics Systems for Genomics Data sets and Research Tasks 10 CHAPTER3 PLOEM: An Interactive Visualization Tool for Effective Evidence Mapping with Biomedical literature 12 3.1 Introduction 12 3.2 Visual Representations and Interactions of PLOEM 14 3.2.1 Overview of the PICO Criteria 14 3.2.2 Trend Visualization with the Timeline view 17 3.2.3 Representing the PICO Co-occurrence with the Relation view 20 3.2.4 Study detail view 22 3.3 Usage Scenarios: Visualizing Various Study Sizes with PLOEM 23 3.4 Conclusion 24 CHAPTER4 EEEvis: Efficacy improvement in searching MEDLINE database using a novel PubMed visual analytic system 26 4.1 Introduction 26 4.1.1 Motivation 26 4.1.2 Preliminary Survey: A Questionnaire on conventional literature search methods 28 4.1.3 Design Requirements for Biomedical Literature Search Systems 36 4.2 System and Interface Implementation of EEEVis 37 4.2.1 System Overview 37 4.2.2 Bibliography Filters 40 4.2.3 Timeline View 41 4.2.4 Co-authorship Network View 43 4.2.5 Article List and Detail View 44 4.3 User Study 46 4.3.1 Participants 46 4.3.2 Procedures 48 4.3.3 Results and Observations 50 4.4 Discussion 54 4.4.1 Design Implications 56 4.4.2 Limitations and Future Work 57 4.5 Conclusions 59 CHAPTER5 GeneVis: A Visual Analytics Systemfor Gene Signature Analysis in Cancers 68 5.1 Motivation 68 5.2 System and Interface Implementation 69 5.2.1 System Overview 69 5.2.2 Gene Expression Detail View 71 5.2.3 Gene Vector Projection View 72 5.2.4 Gene x Cancer Type Heatmap view 74 5.2.5 User Interaction in Multiple Coordinated Views 76 5.3 Case Studies 76 5.3.1 Participants 76 5.3.2 Task and Procedures 76 5.3.3 Case1: Identifying SimilarGeneSignatures with TGFB1in Hallmark Gene Sets 80 5.3.4 Case2: Identifying Cluster Patterns in the HRD data set 81 5.3.5 Results 82 5.4 Summary 85 CHAPTER6 Conclusion and future work 86 6.1 Conclusion 86 6.2 Future Work 87 Abstract (Korean) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102박

Mining large collections of gene expression data to elucidate transcriptional regulation of biological processes

A vast amount of gene expression data is available to biological researchers. As of October 2010, the GEO database has 45,777 chips of publicly available gene expression pro ling data from the Affymetrix (HGU133v2) GeneChip platform, representing 2.5 billion numerical measurements. Given this wealth of data, `meta-analysis' methods allowing inferences to be made from combinations of samples from different experiments are critically important. This thesis explores the application of localized pattern-mining approaches, as exemplified by biclustering, for large-scale gene expression analysis. Biclustering methods are particularly attractive for the analysis of large compendia of gene expression data as they allow the extraction of relationships that occur only across subsets of genes and samples. Standard correlation methods, however, assume a single correlation relationship between two genes occurs across all samples in the data. There are a number of existing biclustering methods, but as these did not prove suitable for large scale analysis, a novel method named `IslandCluster' was developed. This method provided a framework for investigating the results of different approaches to biclustering meta-analysis. The biclustering methods used in this work involve preprocessing of gene expression data into a unified scale in order to assess the significance of expression patterns. A novel discretisation approach is shown to identify distinct classes of genes' expression values more appropriately than approaches reported in the literature. A Gene Expression State Transformation (`GESTr') introduced as the first reported modelling of the biological state of expression on a unified scale and is shown to facilitate effective meta-analysis. Localised co-dependency analysis is introduced, a paradigm for identifying transcriptional relationships from gene expression data. Tools implementing this analysis were developed and used to analyse specificity of transcriptional relationships, to distinguish related subsets within a set of transcription factor (TF) targets and to tease apart combinatorial regulation of a set of targets by multiple TFs. The state of pluripotency, from which a mammalian cell has the potential to differentiate into any cell from any of the three adult germ layers, is maintained by forced expression of Nanog and may be induced from a non-pluripotent state by the expression of Oct4, Sox2, Klf4 and cMyc. Analysis of cMyc regulatory targets shed light on a recent proposition that cMyc induces an `embryonic stem cell like' transcriptional signature outside embryonic stem (ES) cells, revealing a cMyc-responsive subset of the signature and identifying ES cell expressed targets with evidence of broad cMyc-induction. Regulatory targets through which cMyc, Oct4, Sox2 and Nanog may maintain or induce pluripotency were identified, offering insight into transcriptional mechanisms involved in the control of pluripotency and demonstrating the utility of the novel analysis approaches presented in this work

Transcriptome and Genome Analyses Applied to Aquaculture Research

Aquaculture is an important economic activity for food production all around the world that has experienced an exponential growth during the last few decades. However, several weaknesses and bottlenecks still need to be addressed in order to improve the aquaculture productive system. The recent fast development of the omics technologies has provided scientists with meaningful tools to elucidate the molecular basis of their research interests. This reprint compiles different works about the use of transcriptomics and genomics technologies in different aspects of the aquaculture research, such as immunity, stress response, development, sexual dimorphism, among others, in a variety of fish and shellfish, and even in turtles. Different transcriptome (mRNAs and non-coding RNAs (ncRNAs)), genome (Single Nucleotide Polymorphisms (SNPs)), and metatranscriptome analyses were conducted to unravel those different aspects of interest

Kinetoplastid Genomics and Beyond

This book includes a collection of eight original research articles and three reviews covering a wide range of topics in the field of kinetoplastids. In addition, readers can find a compendium of molecular biology procedures and bioinformatics tools

Integrative Multi-Omics in Biomedical Research

Genomics technologies revolutionised biomedicine research, but the genome alone is not sufficient to capture biological complexity. Postgenomic methods, typically based on mass spectrometry, comprise the analysis of metabolites, lipids, and proteins and are an essential complement to genomics and transcriptomics. Multidimensional omics is becoming established to provide accurate and comprehensive state descriptions. This book covers the latest methodological developments for, and applications of integrative multi-omics in biomedical research