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Integrating Diverse Biological Sources And Computational Methods For The Analysis Of High Throughput Expression Data
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Book Synopsis Integrating Diverse Biological Sources and Computational Methods for the Analysis of High-throughput Expression Data by : Nitesh Kumar Singh
Download or read book Integrating Diverse Biological Sources and Computational Methods for the Analysis of High-throughput Expression Data written by Nitesh Kumar Singh and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela
Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by MDPI. This book was released on 2021-02-05 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Book Synopsis The Analysis and Integration of High-throughput Biological Data for Pathway Discovery by : Ryan Matthew Kelley
Download or read book The Analysis and Integration of High-throughput Biological Data for Pathway Discovery written by Ryan Matthew Kelley and published by . This book was released on 2009 with total page 170 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has seen the creation and maturation of a number of new technologies designed to study life on a genome-wide scale. However, the sheer volume of data generated by these methods surpasses the analytical and critical abilities of a single researcher. For this reason, it is necessary to create new computational methods to assist in the analysis of these new sources of data. Both yeast-two hybrid and co-immunoprecipitation followed by mass spectrometry allow the determination of binding interactions between proteins. Functional (genetic) interactions are determined via SGA (Synthetic Genetic Array) and E-MAP (Epistasis Mini-array Profile). In Chapters 2 and 3, we develop algorithms to integrate these two types of interactions together for the purpose of biological pathway discovery. Moreover, our approaches create maps of genetic interactions that provide a picture of the global organization of pathways and complexes within the cell. Expression arrays are a genome-wide quantitative assay for mRNA levels within the cell. Using fluorescent dyes, two different biological samples can be directly compared on a single array slide. In Chapter 4, we identify a gene-specific dye bias in this type of expression array data. We improve upon a maximum likelihood method in order to remove the effect of this bias. Using novel control experiments, we show that this enhanced analysis yields results that more reproducible. Complementary to expression profiling, deletion fitness profiling quantifies the relative fitness defect of every deletion strain in Saccharomyces cerevisiae under a particular stress. In Chapters 5 and 6, we discuss how to use the type of pathway information uncovered in Chapters 2 and 3 to improve the analysis of both expression and deletion fitness profiling datasets. We apply these methods to the study of two different cellular stresses in Saccharomyces cerevisiae, arsenic exposure and adaptation to oxidative stress.
Book Synopsis Data Analysis and Visualization in Genomics and Proteomics by : Francisco Azuaje
Download or read book Data Analysis and Visualization in Genomics and Proteomics written by Francisco Azuaje and published by John Wiley & Sons. This book was released on 2005-06-24 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Analysis and Visualization in Genomics and Proteomics is the first book addressing integrative data analysis and visualization in this field. It addresses important techniques for the interpretation of data originating from multiple sources, encoded in different formats or protocols, and processed by multiple systems. One of the first systematic overviews of the problem of biological data integration using computational approaches This book provides scientists and students with the basis for the development and application of integrative computational methods to analyse biological data on a systemic scale Places emphasis on the processing of multiple data and knowledge resources, and the combination of different models and systems
Book Synopsis Computational Methods for the Analysis of Genomic Data and Biological Processes by : Francisco A. Gómez Vela
Download or read book Computational Methods for the Analysis of Genomic Data and Biological Processes written by Francisco A. Gómez Vela and published by . This book was released on 2021 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Book Synopsis Integrating Omics Data by : George Tseng
Download or read book Integrating Omics Data written by George Tseng and published by Cambridge University Press. This book was released on 2015-09-23 with total page 497 pages. Available in PDF, EPUB and Kindle. Book excerpt: In most modern biomedical research projects, application of high-throughput genomic, proteomic, and transcriptomic experiments has gradually become an inevitable component. Popular technologies include microarray, next generation sequencing, mass spectrometry and proteomics assays. As the technologies have become mature and the price affordable, omics data are rapidly generated, and the problem of information integration and modeling of multi-lab and/or multi-omics data is becoming a growing one in the bioinformatics field. This book provides comprehensive coverage of these topics and will have a long-lasting impact on this evolving subject. Each chapter, written by a leader in the field, introduces state-of-the-art methods to handle information integration, experimental data, and database problems of omics data.
Book Synopsis Gene Expression Data Analysis by : Pankaj Barah
Download or read book Gene Expression Data Analysis written by Pankaj Barah and published by CRC Press. This book was released on 2021-11-21 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Development of high-throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA sequencing are two such widely used high-throughput technologies for simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward the identification of interesting patterns that are relevant for a given biological question requires high-performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge. Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written with a multidisciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives. Readers will be able to acquire both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data and validating results. This book will benefit students, researchers, and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data. Key Features: An introduction to the Central Dogma of molecular biology and information flow in biological systems A systematic overview of the methods for generating gene expression data Background knowledge on statistical modeling and machine learning techniques Detailed methodology of analyzing gene expression data with an example case study Clustering methods for finding co-expression patterns from microarray, bulkRNA, and scRNA data A large number of practical tools, systems, and repositories that are useful for computational biologists to create, analyze, and validate biologically relevant gene expression patterns Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences
Book Synopsis Data Integration in the Life Sciences by : Patrick Lambrix
Download or read book Data Integration in the Life Sciences written by Patrick Lambrix and published by Springer Science & Business Media. This book was released on 2010-08-11 with total page 224 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development and increasingly widespread deployment of high-throughput experimental methods in the life sciences is giving rise to numerous large, c- plex and valuable data resources. This foundation of experimental data und- pins the systematic study of organismsand diseases, which increasinglydepends on the development of models of biological systems. The development of these models often requires integration of diverse experimental data resources; once constructed, the models themselves become data and present new integration challenges for tasks such as interpretation, validation and comparison. The Data Integration in the Life Sciences (DILS) Conference series brings together data and knowledge management researchers from the computer s- ence research community with bioinformaticians and computational biologists, to improve the understanding of how emerging data integration techniques can address requirements identi?ed in the life sciences. DILS 2010 was the seventh event in the series and was held in Goth- burg, Sweden during August 25–27, 2010. The associated proceedings contain 14 peer-reviewed papers and 2 invited papers. The sessions addressed ontology engineering, and in particular, evolution, matching and debugging of ontologies, akeycomponentforsemanticintegration;Web servicesasanimportanttechn- ogy for data integration in the life sciences; data and text mining techniques for discovering and recognizing biomedical entities and relationships between these entities; and information management, introducing data integration solutions for di?erent types of applications related to cancer, systems biology and - croarray experimental data, and an approach for integrating ranked data in the life sciences.
Book Synopsis Computational Methods for Interactive and Explorative Study Design and Integration of High-throughput Biological Data by : Andreas Friedrich
Download or read book Computational Methods for Interactive and Explorative Study Design and Integration of High-throughput Biological Data written by Andreas Friedrich and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The increase in the use of high-throughput methods to gain insights into biological systems has come with new challenges. Genomics, transcriptomics, proteomics, and metabolomics lead to a massive amount of data and metadata. While this wealth of information has resulted in many scientific discoveries, new strategies are needed to cope with the ever-growing variety and volume of metadata. Despite efforts to standardize the collection of study metadata, many experiments cannot be reproduced or replicated. One reason for this is the difficulty to provide the necessary metadata. The large sample sizes that modern omics experiments enable, also make it increasingly complicated for scientists to keep track of every sample and the needed annotations. The many data transformations that are often needed to normalize and analyze omics data require a further collection of all parameters and tools involved. A second possible cause is missing knowledge about statistical design of studies, both related to study factors as well as the required sample size to make significant discoveries. In this thesis, we develop a multi-tier model for experimental design and a portlet for interactive web-based study design. Through the input of experimental factors and the number of replicates, users can easily create large, factorial experimental designs. Changes or additional metadata can be quickly uploaded via user-defined spreadsheets including sample identifiers. In order to comply with existing standards and provide users with a quick way to import existing studies, we provide full interoperability with the ISA-Tab format. We show that both data model and portlet are easily extensible to create additional tiers of samples annotated with technology-specific metadata. We tackle the problem of unwieldy experimental designs by creating an aggregation graph. Based on our multi-tier experimental design model, similar samples, their sources, and analytes are summarized, creating an interactive summary graph that focuses on study factors and replicates. Thus, we give researchers a quick overview of sample sizes and the aim of different studies. This graph can be included in our portlets or used as a stand alone application and is compatible with the ISA-Tab format. We show that this approach can be used to explore the quality of publicly available experimental designs and metadata annotation. The third part of this thesis contributes to a more statistically sound experiment planning for differential gene expression experiments. We integrate two tools for the prediction of statistical power and sample size estimation into our portal. This integration enables the use of existing data, in order to arrive at more accurate calculation for sample variability. Additionally, the statistical power of existing experimental designs of certain sample sizes can be analyzed. All results and parameters are stored and can be used for later comparison. Even perfectly planned and annotated experiments cannot eliminate human error. Based on our model we develop an automated workflow for microarray quality control, enabling users to inspect the quality of normalization and cluster samples by study factor levels. We import a publicly available microarray dataset to assess our contributions to reproducibility and explore alternative analysis methods based on statistical power analysis.
Book Synopsis Biological Knowledge Discovery Through Mining Multiple Sources of High-throughput Data by :
Download or read book Biological Knowledge Discovery Through Mining Multiple Sources of High-throughput Data written by and published by . This book was released on 2004 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: As we are moving into the post-genomic era, various high-throughput experimental techniques have been developed to characterize biological systems at the genome scale. The high-throughput data are becoming fundamentally important resources to shed new insights on system-level understanding of the 'organization' and 'dynamics' of molecules (e.g. genes and proteins), relationships between them, interaction cascades, pathways, modules and various networks (i.e. regulation, co-expression and metabolism). This dissertation focuses on developing computational tools to facilitate the process of translating the ever-growing volumes of high-throughput data into significant biological knowledge on protein functions, pathways and modules. Although high-throughput data provide a global picture of biological systems about the underlying mechanisms, the details are often noisy. Integration of heterogeneous data that characterize cellular systems from different aspects (i.e. gene expression and protein-protein interactions) can lead to the comprehensive and coherent discoveries of biological insights. We developed a Bayesian probability framework to predict function for unannotated proteins in yeast through integrating protein binary interaction data, protein complex data and microarray gene expression data. We also extended the computational framework to infer biological pathway in an automated and systematical fashion. Besides bottom-up approaches moving from protein functions to pathways, we also applied top-down approaches to model cellular networks, that is, we started from the architecture of a cellular network to identify functional modules. We applied the k-core algorithm to decompose protein interaction and microarray gene co-expression networks, which provides strong support for modularity principles of networks' structure and function. Dynamic functional modules and protein complexes have been identified by clustering the network constructed from multiple sources of high-throughput data, shedding insights into understanding the organization and dynamics of a living cell. We also proposed a consensus approach to model biological pathway by combining different computational tools and integrating multiple sources of high-throughput data. In the future, with the explosion in the quantity and diversity of high-throughput data, it is vital to develop methodologies and innovative tools in bioinformatics to model biological systems and explore biological knowledge in an iterative fashion.
Book Synopsis Technology and Method Developments for High-throughput Translational Medicine by : Junhee Seok
Download or read book Technology and Method Developments for High-throughput Translational Medicine written by Junhee Seok and published by Stanford University. This book was released on 2011 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Translation of knowledge from basic science to medicine is essential to improving both clinical research and practice. In this translation, high-throughput genomic approaches can greatly accelerate our understanding of molecular mechanisms of diseases. A successful high-throughput genomic study of disease requires, first, comprehensive and efficient platforms to collect genomic data from clinical samples, and second, computational analysis methods that utilize databases of prior biological knowledge together with experimental data to derive clinically meaningful results. In this thesis, we discuss the development of a new microarray platform as well as computational methods for knowledge-based analysis along with their applications in clinical research. First, we and other colleagues have developed a new high-density oligonucleo-tide array of the human transcriptome for high-throughput and cost-efficient analysis of patient samples in clinical studies. This array allows comprehensive examination of gene expression and genome-wide identification of alternative splicing, and also pro-vides assays for coding SNP detection and non-coding transcripts. Compared with high-throughput mRNA sequencing technology, we show that this array is highly re-producible in estimating gene and exon expression, and sensitive in detecting expres-sion changes. In addition, the exon-exon junction feature of this array is shown to im-prove detection efficiency for mRNA alternative splicing when combined with an ap-propriate computational method. We implemented the use of this array in a multi-center clinical program and have obtained comparable levels of high quality and re-producible data. With low costs and high throughputs for sample processing, we antic-ipate that this array platform will have a wide range of applications in high-throughput clinical studies. Second, we investigated knowledge-based methods that utilize prior know-ledge from biology and medicine to improve analysis and interpretation of high-throughput genomic data. We have developed knowledge-based methods to enrich our prior knowledge, illustrate dynamic response to external stimulus, and identify distur-bances in cellular pathways by chemical exposure, as well as discover hidden biological signatures for the prediction of patient outcomes. Finally, we applied a knowledge-based approach in a large scale genomic study of trauma patients. Cooperating with clinical information, prior knowledge improved the interpretation of common and dif-ferential genomic response to injury, and provided efficient risk assessment for patient outcomes. The clinical and genomic data as well as analysis results in this trauma study were systematically organized and provided to research communities as new knowledge of traumatic injury. The microarray platform and knowledge-based methods presented in this thesis provide appropriate research tools for high-throughput translational medicine in a large clinical setting. This thesis is expected to advance understanding and treatment for dis-eases, and finally, improve public health.
Book Synopsis Scalable Computational Methods for the Analysis of High-Throughput Biological Data by :
Download or read book Scalable Computational Methods for the Analysis of High-Throughput Biological Data written by and published by . This book was released on 2012 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt: This primary focus of this research project is elucidating genetic regulatory mechanisms that control an organism's responses to low-dose ionizing radiation. Although low doses (at most ten centigrays) are not lethal to humans, they elicit a highly complex physiological response, with the ultimate outcome in terms of risk to human health unknown. The tools of molecular biology and computational science will be harnessed to study coordinated changes in gene expression that orchestrate the mechanisms a cell uses to manage the radiation stimulus. High performance implementations of novel algorithms that exploit the principles of fixed-parameter tractability will be used to extract gene sets suggestive of co-regulation. Genomic mining will be performed to scrutinize, winnow and highlight the most promising gene sets for more detailed investigation. The overall goal is to increase our understanding of the health risks associated with exposures to low levels of radiation.
Book Synopsis Data Integration and Knowledge Discovery Using Biological Networks by : Gaurav Kumar
Download or read book Data Integration and Knowledge Discovery Using Biological Networks written by Gaurav Kumar and published by . This book was released on 2012 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overall objective of this thesis is to analyse and understand the intricate network of protein interactions inside the cell. Proteins are molecular machines, which interact and communicate to perform different cellular functions. Research effort in molecular and cellular biology enables the detection of molecular interactions on a large scale. The experimental results generated by high-throughput studies are archived in various public databases. In this study, statistical and computational approach is used to integrate information from relative inhomogeneous data sources (public databases) derived from high-throughput experiments. Further, the integrated approach is used to explore the relationships within the interacting protein pairs. Graph-based network model is used to determine the protein relationships based on gene ontology (GO) biological processes, molecular functions and cellular components. -- Network approach has enabled researchers to study the pervasive nature of protein interactions in systems biology. Moreover, different computational methods have been developed to analyse networks and their topological properties. Foremost among them are the methods for analysing direct/indirect protein interactions networks by integrating with the other types of -omic data. This thesis demonstrates the statistical significance of protein interaction networks for the study of subcellular localisation, biological processes and molecular functions. It also suggests the significance of network in biological studies. -- The protein-protein interaction (PPIs) network was created by integrating binary protein interactions deposited in various public databases. Similarly, the metabolic network was created by linking proteins via metabolites, i.e. indirect protein interactions. Both PPIs and metabolic networks were analysed to show the difference in network topologies. Further, we compared and contrasted the subcellular localisation of human proteins using PPIs and metabolic networks. The statistical significance of human protein localisation is demonstrated through statistical measures such as Chi-square (χ2) test, protein colocalisation correlation profile and Z-score. These statistical methods are significant to illustrate the cross-talk among various subcellular compartments and highlight the importance of metabolite-linked protein interaction i.e. functional/indirect association in addition to direct physical interaction of proteins. -- Statistical analyses were extended further for human and yeast proteomes to show the influence of protein degree for determining protein relationships for biological process and molecular function. This analysis demonstrates the tendency of proximal proteins in a network to have the same relationships to depend strongly on their degree/connectivity. Comparison of real networks with that of randomized networks i.e. permutation testing, suggests the significance of such relationships at a network distance less than three. Networks are randomized using an edge swapping method and the distance in a network is calculated for the shortest path between each protein pair, using the Floyd-Warshall algorithm. The significance of the network distance less than three holds true up to six levels of depth from the root node (i.e. zero level) in the hierarchy of gene ontology (GO) terms. -- Application of the network study is further demonstrated using ovarian tumour samples. Gene Expression data from the TCGA (The Cancer Genome Atlas) dataset were collected to encode the functional attributes in a Boolean logic framework for the identification of potential genes in the prognosis and therapy risk assessment in the human diseased condition. The differentially expressed genes were then validated in a co-expression network derived from the ovarian samples deposited in the GEO (Gene Expression Omnibus). A set of 17 differentially expressed genes were identified at the high probability score suggesting their importance in the ovarian cancer diseased condition. Three of these have never been reported before as significant for ovarian cancer.
Book Synopsis Statistical and Computational Methods for Comparing High-Throughput Data from Two Conditions by : Xinzhou Ge
Download or read book Statistical and Computational Methods for Comparing High-Throughput Data from Two Conditions written by Xinzhou Ge and published by . This book was released on 2021 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The development of high-throughput biological technologies have enabled researchers to simultaneously perform analysis on thousands of features (e.g., genes, genomic regions, and proteins). The most common goal of analyzing high-throughput data is to contrast two conditions, to identify ``interesting'' features, whose values differ between two conditions. How to contrast the features from two conditions to extract useful information from high-throughput data, and how to ensure the reliability of identified features are two increasingly pressing challenge to statistical and computational science. This dissertation aim to address these two problems regarding analysing high-throughput data from two conditions. My first project focuses on false discovery rate (FDR) control in high-throughput data analysis from two conditions. FDR is defined as the expected proportion of uninteresting features among the identified ones. It is the most widely-used criterion to ensure the reliability of the interesting features identified. Existing bioinformatics tools primarily control the FDR based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions, two requirements that are often unmet in biological studies. In Chapter \ref{chap:clipper}, we propose Clipper, a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper is applicable to identifying both enriched and differential features from high-throughput biological data of diverse types. In comprehensive simulation and real-data benchmarking, Clipper outperforms existing generic FDR control methods and specific bioinformatics tools designed for various tasks, including peak calling from ChIP-seq data, and differentially expressed gene identification from bulk or single-cell RNA-seq data. Our results demonstrate Clipper's flexibility and reliability for FDR control, as well as its broad applications in high-throughput data analysis. My second project focuses on alignment of multi-track epigenomic signals from different samples or conditions. The availability of genome-wide epigenomic datasets enables in-depth studies of epigenetic modifications and their relationships with chromatin structures and gene expression. Various alignment tools have been developed to align nucleotide or protein sequences in order to identify structurally similar regions. However, there are currently no alignment methods specifically designed for comparing multi-track epigenomic signals and detecting common patterns that may explain functional or evolutionary similarities. We propose a new local alignment algorithm, EpiAlign, designed to compare chromatin state sequences learned from multi-track epigenomic signals and to identify locally aligned chromatin regions. EpiAlign is a dynamic programming algorithm that novelly incorporates varying lengths and frequencies of chromatin states. We demonstrate the efficacy of EpiAlign through extensive simulations and studies on the real data from the NIH Roadmap Epigenomics project. EpiAlign can also detect common chromatin state patterns across multiple epigenomes from conditions, and it will serve as a useful tool to group and distinguish epigenomic samples based on genome-wide or local chromatin state patterns.
Book Synopsis Statistical and Computational Methods for Analyzing High-Throughput Genomic Data by : Jingyi Li
Download or read book Statistical and Computational Methods for Analyzing High-Throughput Genomic Data written by Jingyi Li and published by . This book was released on 2013 with total page 226 pages. Available in PDF, EPUB and Kindle. Book excerpt: In the burgeoning field of genomics, high-throughput technologies (e.g. microarrays, next-generation sequencing and label-free mass spectrometry) have enabled biologists to perform global analysis on thousands of genes, mRNAs and proteins simultaneously. Extracting useful information from enormous amounts of high-throughput genomic data is an increasingly pressing challenge to statistical and computational science. In this thesis, I will address three problems in which statistical and computational methods were used to analyze high-throughput genomic data to answer important biological questions. The first part of this thesis focuses on addressing an important question in genomics: how to identify and quantify mRNA products of gene transcription (i.e., isoforms) from next-generation mRNA sequencing (RNA-Seq) data? We developed a statistical method called Sparse Linear modeling of RNA-Seq data for Isoform Discovery and abundance Estimation (SLIDE) that employs probabilistic modeling and L1 sparse estimation to answer this ques- tion. SLIDE takes exon boundaries and RNA-Seq data as input to discern the set of mRNA isoforms that are most likely to present in an RNA-Seq sample. It is based on a linear model with a design matrix that models the sampling probability of RNA-Seq reads from different mRNA isoforms. To tackle the model unidentifiability issue, SLIDE uses a modified Lasso procedure for parameter estimation. Compared with existing deterministic isoform assembly algorithms, SLIDE considers the stochastic aspects of RNA-Seq reads in exons from different isoforms and thus has increased power in detecting more novel isoforms. Another advantage of SLIDE is its flexibility of incorporating other transcriptomic data into its model to further increase isoform discovery accuracy. SLIDE can also work downstream of other RNA-Seq assembly algorithms to integrate newly discovered genes and exons. Besides isoform discovery, SLIDE sequentially uses the same linear model to estimate the abundance of discovered isoforms. Simulation and real data studies show that SLIDE performs as well as or better than major competitors in both isoform discovery and abundance estimation. The second part of this thesis demonstrates the power of simple statistical analysis in correcting biases of system-wide protein abundance estimates and in understanding the rela- tionship between gene transcription and protein abundances. We found that proteome-wide surveys have significantly underestimated protein abundances, which differ greatly from previously published individual measurements. We corrected proteome-wide protein abundance estimates by using individual measurements of 61 housekeeping proteins, and then found that our corrected protein abundance estimates show a higher correlation and a stronger linear relationship with mRNA abundances than do the uncorrected protein data. To estimate the degree to which mRNA expression levels determine protein levels, it is critical to measure the error in protein and mRNA abundance data and to consider all genes, not only those whose protein expression is readily detected. This is a fact that previous proteome-widely surveys ignored. We took two independent approaches to re-estimate the percentage that mRNA levels explain in the variance of protein abundances. While the percentages estimated from the two approaches vary on different sets of genes, all suggest that previous protein-wide surveys have significantly underestimated the importance of transcription. In the third and final part, I will introduce a modENCODE (the Model Organism ENCyclopedia Of DNA Elements) project in which we compared developmental stages, tis- sues and cells (or cell lines) of Drosophila melanogaster and Caenorhabditis elegans, two well-studied model organisms in developmental biology. To understand the similarity of gene expression patterns throughout their development time courses is an interesting and important question in comparative genomics and evolutionary biology. The availability of modENCODE RNA-Seq data for different developmental stages, tissues and cells of the two organisms enables a transcriptome-wide comparison study to address this question. We undertook a comparison of their developmental time courses and tissues/cells, seeking com- monalities in orthologous gene expression. Our approach centers on using stage/tissue/cell- associated orthologous genes to link the two organisms. For every stage/tissue/cell in each organism, its associated genes are selected as the genes capturing specific transcriptional activities: genes highly expressed in that stage/tissue/cell but lowly expressed in a few other stages/tissues/cells. We aligned a pair of D. melanogaster and C. elegans stages/tissues/cells by a hypergeometric test, where the test statistic is the number of orthologous gene pairs associated with both stages/tissues/cells. The test is against the null hypothesis that the two stages/tissues/cells have independent sets of associated genes. We first carried out the alignment approach on pairs of stages/tissues/cells within D. melanogaster and C. elegans respectively, and the alignment results are consistent with previous findings, supporting the validity of this approach. When comparing fly with worm, we unexpectedly observed two parallel collinear alignment patterns between their developmental timecourses and several interesting alignments between their tissues and cells. Our results are the first findings regarding a comprehensive comparison between D. melanogaster and C. elegans time courses, tissues and cells.
Book Synopsis Plant Bioinformatics by : Khalid Rehman Hakeem
Download or read book Plant Bioinformatics written by Khalid Rehman Hakeem and published by Springer. This book was released on 2017-11-21 with total page 465 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book: (i) introduces fundamental and applied bioinformatics research in the field of plant life sciences; (ii) enlightens the potential users towards the recent advances in the development and application of novel computational methods available for the analysis and integration of plant -omics data; (iii) highlights relevant databases, softwares, tools and web resources developed till date to make ease of access for researchers working to decipher plant responses towards stresses; and (iv) presents a critical cross-talks on the available high-throughput data in plant research. Therefore, in addition to being a reference for the professional researchers, it is also of great interest to students and their professors. Considering immense significance of plants for all lives on Earth, the major focus of research in plant biology has been to: (a) select plants that best fit the purposes of human, (b) develop crop plants superior in quality, quantity and farming practices when compared to natural (wild) plants, and (c) explore strategies to help plants to adapt biotic and abiotic/environmental stress factors. Accordingly the development of novel techniques and their applications have increased significantly in recent years. In particular, large amount of biological data have emerged from multi-omics approaches aimed at addressing numerous aspects of the plant systems under biotic or abiotic stresses. However, even though the field is evolving at a rapid pace, information on the cross-talks and/or critical digestion of research outcomes in the context of plant bioinformatics is scarce. “Plant Bioinformatics: Decoding the Phyta” is aimed to bridge this gap.
Book Synopsis Computational Methods for Single-Cell Data Analysis by : Guo-Cheng Yuan
Download or read book Computational Methods for Single-Cell Data Analysis written by Guo-Cheng Yuan and published by Humana Press. This book was released on 2019-02-14 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This detailed book provides state-of-art computational approaches to further explore the exciting opportunities presented by single-cell technologies. Chapters each detail a computational toolbox aimed to overcome a specific challenge in single-cell analysis, such as data normalization, rare cell-type identification, and spatial transcriptomics analysis, all with a focus on hands-on implementation of computational methods for analyzing experimental data. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Methods for Single-Cell Data Analysis aims to cover a wide range of tasks and serves as a vital handbook for single-cell data analysis.
