Computational Methods For Processing And Analyzing Large Scale Genomics Datasets

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Computational Methods for Processing and Analyzing Large Scale Genomics Datasets

Author : Olivera Grujic
Publisher :
ISBN 13 :
Total Pages : 144 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Computational Methods for Processing and Analyzing Large Scale Genomics Datasets by : Olivera Grujic

Download or read book Computational Methods for Processing and Analyzing Large Scale Genomics Datasets written by Olivera Grujic and published by . This book was released on 2016 with total page 144 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation develops computational methods for analyzing large-scale genomic and epigenomic datasets. We developed a supervised machine learning approach to predict non-exonic evolutionarily conserved regions in the human genome based on vast amount of functional genomics data. The resulting probabilistic predictions provide a resource for prioritizing functionally important regulatory regions in the human genome. We also developed a method for identifying from large-scale gene expression datasets genes that are differentially expressed in both blood and brain from 12 vervet monkeys, which we used to identify 29 transcripts whose expression is variable between individuals and heritable. Additionally, we developed a method using a global search optimization algorithm to successfully improve a model of human thyroid hormone regulation dynamics leading to a better fit of data for thyrotoxicosis. Together, these three approaches have the potential to impact the understanding and eventual treatment of disease.

Computational Genomics with R

Author : Altuna Akalin
Publisher : CRC Press
ISBN 13 : 1498781861
Total Pages : 462 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Computational Genomics with R by : Altuna Akalin

Download or read book Computational Genomics with R written by Altuna Akalin and published by CRC Press. This book was released on 2020-12-16 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Genomics with R provides a starting point for beginners in genomic data analysis and also guides more advanced practitioners to sophisticated data analysis techniques in genomics. The book covers topics from R programming, to machine learning and statistics, to the latest genomic data analysis techniques. The text provides accessible information and explanations, always with the genomics context in the background. This also contains practical and well-documented examples in R so readers can analyze their data by simply reusing the code presented. As the field of computational genomics is interdisciplinary, it requires different starting points for people with different backgrounds. For example, a biologist might skip sections on basic genome biology and start with R programming, whereas a computer scientist might want to start with genome biology. After reading: You will have the basics of R and be able to dive right into specialized uses of R for computational genomics such as using Bioconductor packages. You will be familiar with statistics, supervised and unsupervised learning techniques that are important in data modeling, and exploratory analysis of high-dimensional data. You will understand genomic intervals and operations on them that are used for tasks such as aligned read counting and genomic feature annotation. You will know the basics of processing and quality checking high-throughput sequencing data. You will be able to do sequence analysis, such as calculating GC content for parts of a genome or finding transcription factor binding sites. You will know about visualization techniques used in genomics, such as heatmaps, meta-gene plots, and genomic track visualization. You will be familiar with analysis of different high-throughput sequencing data sets, such as RNA-seq, ChIP-seq, and BS-seq. You will know basic techniques for integrating and interpreting multi-omics datasets. Altuna Akalin is a group leader and head of the Bioinformatics and Omics Data Science Platform at the Berlin Institute of Medical Systems Biology, Max Delbrück Center, Berlin. He has been developing computational methods for analyzing and integrating large-scale genomics data sets since 2002. He has published an extensive body of work in this area. The framework for this book grew out of the yearly computational genomics courses he has been organizing and teaching since 2015.

Computational Methods for the Analysis of Genomic Data and Biological Processes

Author : Francisco A. Gómez Vela
Publisher : MDPI
ISBN 13 : 3039437712
Total Pages : 222 pages
Book Rating : 4.0/5 (394 download)

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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.

Computational Methods for Next Generation Sequencing Data Analysis

Author : Ion Mandoiu
Publisher : John Wiley & Sons
ISBN 13 : 1118169484
Total Pages : 460 pages
Book Rating : 4.1/5 (181 download)

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Book Synopsis Computational Methods for Next Generation Sequencing Data Analysis by : Ion Mandoiu

Download or read book Computational Methods for Next Generation Sequencing Data Analysis written by Ion Mandoiu and published by John Wiley & Sons. This book was released on 2016-10-03 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces readers to core algorithmic techniques for next-generation sequencing (NGS) data analysis and discusses a wide range of computational techniques and applications This book provides an in-depth survey of some of the recent developments in NGS and discusses mathematical and computational challenges in various application areas of NGS technologies. The 18 chapters featured in this book have been authored by bioinformatics experts and represent the latest work in leading labs actively contributing to the fast-growing field of NGS. The book is divided into four parts: Part I focuses on computing and experimental infrastructure for NGS analysis, including chapters on cloud computing, modular pipelines for metabolic pathway reconstruction, pooling strategies for massive viral sequencing, and high-fidelity sequencing protocols. Part II concentrates on analysis of DNA sequencing data, covering the classic scaffolding problem, detection of genomic variants, including insertions and deletions, and analysis of DNA methylation sequencing data. Part III is devoted to analysis of RNA-seq data. This part discusses algorithms and compares software tools for transcriptome assembly along with methods for detection of alternative splicing and tools for transcriptome quantification and differential expression analysis. Part IV explores computational tools for NGS applications in microbiomics, including a discussion on error correction of NGS reads from viral populations, methods for viral quasispecies reconstruction, and a survey of state-of-the-art methods and future trends in microbiome analysis. Computational Methods for Next Generation Sequencing Data Analysis: Reviews computational techniques such as new combinatorial optimization methods, data structures, high performance computing, machine learning, and inference algorithms Discusses the mathematical and computational challenges in NGS technologies Covers NGS error correction, de novo genome transcriptome assembly, variant detection from NGS reads, and more This text is a reference for biomedical professionals interested in expanding their knowledge of computational techniques for NGS data analysis. The book is also useful for graduate and post-graduate students in bioinformatics.

Computational Methods for Analysis of Large-Scale Epigenomics Data

Author : Petko Plamenov Fiziev
Publisher :
ISBN 13 :
Total Pages : 248 pages
Book Rating : 4.:/5 (14 download)

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Book Synopsis Computational Methods for Analysis of Large-Scale Epigenomics Data by : Petko Plamenov Fiziev

Download or read book Computational Methods for Analysis of Large-Scale Epigenomics Data written by Petko Plamenov Fiziev and published by . This book was released on 2018 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Reverse-engineering and understanding the regulatory dynamics of genes is key to gaining insights into many biological processes on molecular level. Advances in genomics technologies and decreasing costs of DNA sequencing enabled interrogating relevant properties of the genome, collectively referred to as epigenetics, on very large scale. This work presents results from two collaborative projects with experimental biologists and two new general computational methods for analysis of high-throughput epigenomic data. The first collaborative project is joint work with Dr. Kathrin Plath and members of her lab at UCLA on studying the epigenetics of somatic cell reprogramming in mouse. By generating and analyzing a large compendium of genomics datasets at four distinct stages during reprogramming, we discovered key properties of the regulatory dynamics during this process and proposed new ways to improve its efficiency. The first computational method in this work, ChromTime, presents a novel framework for modeling spatio-temporal dynamics of chromatin marks. ChromTime detects expanding, contracting and steady domains of chromatin marks from time course epigenomics data. Applications of the method to a diverse set of biological systems show that predicted dynamic domains likely mark important regulatory regions as they associate with changes in gene expression and transcription factor binding. Furthermore, ChromTime enables analyses of the directionality of spatio-temporal dynamics of epigenetic domains, which is a previously understudied aspect of chromatin dynamics. Our results uncover associations between the direction of expanding and contracting domains of several chromatin marks and the direction of transcription of nearby genes. The second collaborative project is joint work with cancer researchers, Dr. Lynda Chin and Dr. Kunal Rai and members of their labs at MD Anderson Cancer Center in Houston, TX. Within this project we studied the epigenetics of melanoma cancer progression. Our collaborators generated genome-wide maps for a large number of histone modifications, DNA methylation and gene expression in tumorigenic and non-tumorigenic human melanocytes. By comparing these maps we discovered that loss of acetylation marks at regulatory regions is characteristic of tumorigenic melanocytes and that modulating acetylation levels can impact tumorigenic potential of cells. In addition, we developed a novel nanostring assay for interrogating the chromatin state at a small subset of genomic locations, which can potentially be used for diagnostic or prognostic purposes in future. The second computational method presented in this work, CSDELTA, is designed to detect differential chromatin sites from genome-wide chromatin state maps in groups with multiple samples. Biological relevance of detected differential sites is supported by associations with changes in gene expression and transcription factor binding. Furthermore, CSDELTA models the functional similarity between chromatin states and improves upon the resolution of detection compared to existing methods, which enables more accurate downstream analyses to gain insights into the regulatory dynamics of biological systems.

Computational Methods for the Analysis of Genomic Data and Biological Processes

Author : Francisco A. Gómez Vela
Publisher :
ISBN 13 : 9783039437726
Total Pages : 222 pages
Book Rating : 4.4/5 (377 download)

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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.

Computational Methods for Efficient Processing and Analysis of Short-read Next-Generation DNA Sequencing Data

Author : Praveen Nadukkalam Ravindran
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (134 download)

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Book Synopsis Computational Methods for Efficient Processing and Analysis of Short-read Next-Generation DNA Sequencing Data by : Praveen Nadukkalam Ravindran

Download or read book Computational Methods for Efficient Processing and Analysis of Short-read Next-Generation DNA Sequencing Data written by Praveen Nadukkalam Ravindran and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: DNA sequencing has transformed the discipline of population genetics, which seeks to assess the level of genetic diversity within species or populations, and infer the geographic and temporal distributions between members of a population. Restriction-site associated DNA sequencing (RADSeq) is a NGS technique, which produce data that consists of relatively short (typically 50 to 300 nucleotide) fragments or "reads" of sequenced DNA and enables large-scale analysis of individuals and populations. In this thesis, we describe computational methods, which use graph-based structures to represent these short reads obtained and to capture the relationships among them. A key challenge in RADSeq analysis is to identify optimal parameter settings for assignment of reads to loci (singular: Locus), which correspond to specific regions in the genome. The parameter sweep is computationally intensive, as the entire analysis needs to be run for each parameter set. We propose a graph-based structure (RADProc), which provides persistence and eliminates redundancy to enable parameter sweeps. For 20 green crab samples and 32 different parameter sets, RADProc took only 2.5 hours while the widely used Stacks software took 78 hours. Another challenge is to identify paralogs, sequences that are highly similar due to recent duplication events, but occur in different regions of the genome and should not to be merged into the same locus. We introduce PMERGE, which identifies paralogs by clustering the catalog locus consensus sequences based on similarity. PMERGE is built on the fact that paralogs may be wrongly merged into a single locus in some but not all samples. PMERGE identified 62%-87% of paralogs in the Atlantic salmon and green crab datasets. Gene flow is the movement of alleles, specific sequence variants at a given locus, between populations and is an important indicator of population mixing that changes genetic diversity within the populations. We use the RADProc graph to infer gene flow among populations using allele frequency differences in exclusively shared alleles in each pair of populations. The method successfully inferred gene flow patterns in simulated datasets and provided insights into reasons for observed hybridization at two locations in a green crab dataset.

Computational Methods for Single-Cell Data Analysis

Author : Guo-Cheng Yuan
Publisher : Humana Press
ISBN 13 : 9781493990566
Total Pages : 271 pages
Book Rating : 4.9/5 (95 download)

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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.

Big Data Analytics in Genomics

Author : Ka-Chun Wong
Publisher : Springer
ISBN 13 : 3319412795
Total Pages : 426 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Big Data Analytics in Genomics by : Ka-Chun Wong

Download or read book Big Data Analytics in Genomics written by Ka-Chun Wong and published by Springer. This book was released on 2016-10-24 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: This contributed volume explores the emerging intersection between big data analytics and genomics. Recent sequencing technologies have enabled high-throughput sequencing data generation for genomics resulting in several international projects which have led to massive genomic data accumulation at an unprecedented pace. To reveal novel genomic insights from this data within a reasonable time frame, traditional data analysis methods may not be sufficient or scalable, forcing the need for big data analytics to be developed for genomics. The computational methods addressed in the book are intended to tackle crucial biological questions using big data, and are appropriate for either newcomers or veterans in the field.This volume offers thirteen peer-reviewed contributions, written by international leading experts from different regions, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA. In particular, the book surveys three main areas: statistical analytics, computational analytics, and cancer genome analytics. Sample topics covered include: statistical methods for integrative analysis of genomic data, computation methods for protein function prediction, and perspectives on machine learning techniques in big data mining of cancer. Self-contained and suitable for graduate students, this book is also designed for bioinformaticians, computational biologists, and researchers in communities ranging from genomics, big data, molecular genetics, data mining, biostatistics, biomedical science, cancer research, medical research, and biology to machine learning and computer science. Readers will find this volume to be an essential read for appreciating the role of big data in genomics, making this an invaluable resource for stimulating further research on the topic.

Data Analysis and Visualization in Genomics and Proteomics

Author : Francisco Azuaje
Publisher : John Wiley & Sons
ISBN 13 : 0470094400
Total Pages : 284 pages
Book Rating : 4.4/5 (7 download)

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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

Computational Learning Approaches to Data Analytics in Biomedical Applications

Author : Khalid Al-Jabery
Publisher : Academic Press
ISBN 13 : 0128144831
Total Pages : 312 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Computational Learning Approaches to Data Analytics in Biomedical Applications by : Khalid Al-Jabery

Download or read book Computational Learning Approaches to Data Analytics in Biomedical Applications written by Khalid Al-Jabery and published by Academic Press. This book was released on 2019-11-20 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. Includes an overview of data analytics in biomedical applications and current challenges Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices Provides complete coverage of computational and statistical analysis tools for biomedical data analysis Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor

Computational Methods for Analyzing and Modeling Gene Regulation and 3D Genome Organization

Author : Anastasiya Belyaeva
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (125 download)

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Book Synopsis Computational Methods for Analyzing and Modeling Gene Regulation and 3D Genome Organization by : Anastasiya Belyaeva

Download or read book Computational Methods for Analyzing and Modeling Gene Regulation and 3D Genome Organization written by Anastasiya Belyaeva and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Biological processes from differentiation to disease progression are governed by gene regulatory mechanisms. Currently large-scale omics and imaging data sets are being collected to characterize gene regulation at every level. Such data sets present new opportunities and challenges for extracting biological insights and elucidating the gene regulatory logic of cells. In this thesis, I present computational methods for the analysis and integration of various data types used for cell profiling. Specifically, I focus on analyzing and linking gene expression with the 3D organization of the genome. First, I describe methodologies for elucidating gene regulatory mechanisms by considering multiple data modalities. I design a computational framework for identifying colocalized and coregulated chromosome regions by integrating gene expression and epigenetic marks with 3D interactions using network analysis. Then, I provide a general framework for data integration using autoencoders and apply it for the integration and translation between gene expression and chromatin images of naive T-cells. Second, I describe methods for analyzing single modalities such as contact frequency data, which measures the spatial organization of the genome, and gene expression data. Given the important role of the 3D genome organization in gene regulation, I present a methodology for reconstructing the 3D diploid conformation of the genome from contact frequency data. Given the ubiquity of gene expression data and the recent advances in single-cell RNA-sequencing technologies as well as the need for causal modeling of gene regulatory mechanisms, I then describe an algorithm as well as a software tool, difference causal inference (DCI), for learning causal gene regulatory networks from gene expression data. DCI addresses the problem of directly learning differences between causal gene regulatory networks given gene expression data from two related conditions. Finally, I shift my focus from basic biology to drug discovery. Given the current COVID19 pandemic, I present a computational drug repurposing platform that enables the identification of FDA approved compounds for drug repurposing and investigation of potential causal drug mechanisms. This framework relies on identifying drugs that reverse the signature of the infection in the space learned by an autoencoder and then uses causal inference to identify putative drug mechanisms.

Computational Text Analysis

Author : Soumya Raychaudhuri
Publisher : OUP Oxford
ISBN 13 : 0191513776
Total Pages : 312 pages
Book Rating : 4.1/5 (915 download)

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Book Synopsis Computational Text Analysis by : Soumya Raychaudhuri

Download or read book Computational Text Analysis written by Soumya Raychaudhuri and published by OUP Oxford. This book was released on 2006-01-26 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together the two disparate worlds of computational text analysis and biology and presents some of the latest methods and applications to proteomics, sequence analysis and gene expression data. Modern genomics generates large and comprehensive data sets but their interpretation requires an understanding of a vast number of genes, their complex functions, and interactions. Keeping up with the literature on a single gene is a challenge itself-for thousands of genes it is simply. impossible. Here, Soumya Raychaudhuri presents the techniques and algorithms needed to access and utilize the vast scientific text, i.e. methods that automatically read the literature on all the genes. Including background chapters on the necessary biology, statistics and genomics, in addition to practical examples of interpreting many different types of modern experiments, this book is ideal for students and researchers in computational biology, bioinformatics, genomics, statistics and computer science

Next Steps for Functional Genomics

Author : National Academies of Sciences, Engineering, and Medicine
Publisher : National Academies Press
ISBN 13 : 0309676738
Total Pages : 201 pages
Book Rating : 4.3/5 (96 download)

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Book Synopsis Next Steps for Functional Genomics by : National Academies of Sciences, Engineering, and Medicine

Download or read book Next Steps for Functional Genomics written by National Academies of Sciences, Engineering, and Medicine and published by National Academies Press. This book was released on 2020-12-18 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the holy grails in biology is the ability to predict functional characteristics from an organism's genetic sequence. Despite decades of research since the first sequencing of an organism in 1995, scientists still do not understand exactly how the information in genes is converted into an organism's phenotype, its physical characteristics. Functional genomics attempts to make use of the vast wealth of data from "-omics" screens and projects to describe gene and protein functions and interactions. A February 2020 workshop was held to determine research needs to advance the field of functional genomics over the next 10-20 years. Speakers and participants discussed goals, strategies, and technical needs to allow functional genomics to contribute to the advancement of basic knowledge and its applications that would benefit society. This publication summarizes the presentations and discussions from the workshop.

Deep Learning Methods for Mining Genomic Sequence Patterns

Author : Xin Gao
Publisher :
ISBN 13 :
Total Pages : 84 pages
Book Rating : 4.:/5 (126 download)

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Book Synopsis Deep Learning Methods for Mining Genomic Sequence Patterns by : Xin Gao

Download or read book Deep Learning Methods for Mining Genomic Sequence Patterns written by Xin Gao and published by . This book was released on 2018 with total page 84 pages. Available in PDF, EPUB and Kindle. Book excerpt: Nowadays, with the growing availability of large-scale genomic datasets and advanced computational techniques, more and more data-driven computational methods have been developed to analyze genomic data and help to solve incompletely understood biological problems. Among them, deep learning methods, have been proposed to automatically learn and recognize the functional activity of DNA sequences from genomics data. Techniques for efficient mining genomic sequence pattern will help to improve our understanding of gene regulation, and thus accelerate our progress toward using personal genomes in medicine.

Computational Methods in Genome Research

Author : Sándor Suhai
Publisher : Springer Science & Business Media
ISBN 13 : 1461524512
Total Pages : 230 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Computational Methods in Genome Research by : Sándor Suhai

Download or read book Computational Methods in Genome Research written by Sándor Suhai and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: The application of computational methods to solve scientific and pratical problems in genome research created a new interdisciplinary area that transcends boundaries traditionally separating genetics, biology, mathematics, physics, and computer science. Computers have been, of course, intensively used for many year~ in the field of life sciences, even before genome research started, to store and analyze DNA or proteins sequences, to explore and model the three-dimensional structure, the dynamics and the function of biopolymers, to compute genetic linkage or evolutionary processes etc. The rapid development of new molecular and genetic technologies, combined with ambitious goals to explore the structure and function of genomes of higher organisms, has generated, however, not only a huge and burgeoning body of data but also a new class of scientific questions. The nature and complexity of these questions will require, beyond establishing a new kind of alliance between experimental and theoretical disciplines, also the development of new generations both in computer software and hardware technologies, respectively. New theoretical procedures, combined with powerful computational facilities, will substantially extend the horizon of problems that genome research can ·attack with success. Many of us still feel that computational models rationalizing experimental findings in genome research fulfil their promises more slowly than desired. There also is an uncertainity concerning the real position of a 'theoretical genome research' in the network of established disciplines integrating their efforts in this field.

Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine

Author : Ehsan Nazemalhosseini-Mojarad
Publisher : Frontiers Media SA
ISBN 13 : 2832530389
Total Pages : 433 pages
Book Rating : 4.8/5 (325 download)

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Book Synopsis Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine by : Ehsan Nazemalhosseini-Mojarad

Download or read book Computational Methods for Multi-Omics Data Analysis in Cancer Precision Medicine written by Ehsan Nazemalhosseini-Mojarad and published by Frontiers Media SA. This book was released on 2023-08-02 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: Cancer is a complex and heterogeneous disease often caused by different alterations. The development of human cancer is due to the accumulation of genetic and epigenetic modifications that could affect the structure and function of the genome. High-throughput methods (e.g., microarray and next-generation sequencing) can investigate a tumor at multiple levels: i) DNA with genome-wide association studies (GWAS), ii) epigenetic modifications such as DNA methylation, histone changes and microRNAs (miRNAs) iii) mRNA. The availability of public datasets from different multi-omics data has been growing rapidly and could facilitate better knowledge of the biological processes of cancer. Computational approaches are essential for the analysis of big data and the identification of potential biomarkers for early and differential diagnosis, and prognosis.