Download or read book Computational Frameworks for Indel aware Evolutionary Analysis Using Large scale Genomic Sequence Data written by Wei Wang and published by . This book was released on 2021 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the development of sequencing techniques, genetic sequencing data has been extensively used in evolutionary studies. The phylogenetic reconstruction problem, which is the reconstruction of evolutionary history from biomolecular sequences, is a fundamental problem. The evolutionary relationship between organisms is often represented by phylogeny, which is a tree or network representation. The most widely-used approach for reconstructing phylogenies from sequencing data involves two phases: multiple sequence alignment and phylogenetic reconstruction from the aligned sequences. As the amount of biomolecular sequence data increases, it has become a major challenge to develop efficient and accurate computational methods for phylogenetic analyses of large-scale sequencing data. Due to the complexity of the phylogenetic reconstruction problem in modern phylogenetic studies, the traditional sequence-based phylogenetic analysis methods involve many over-simplified assumptions. In this thesis, we describe our contribution in relaxing some of these over-simplified assumptions in the phylogenetic analysis.Insertion and deletion events, referred to as indels, carry much phylogenetic information but are often ignored in the reconstruction process of phylogenies. We take into account the indel uncertainties in multiple phylogenetic analyses by applying resampling and re-estimation. Another over-simplified assumption that we contributed to is adopted by many commonly used non-parametric algorithms for the resampling of biomolecular sequences, all sites in an MSA are evolved independently and identically distributed (i.i.d). Many evolution events, such as recombination and hybridization, may produce intra-sequence and functional dependence in biomolecular sequences that violate this assumption. We introduce SERES, a resampling algorithm for biomolecular sequences that can produce resampled replicates that preserve the intra-sequence dependence. We describe the application of the SERES resampling and re-estimation approach to two classical problems: the multiple sequence alignment support estimation and recombination-aware local genealogical inference. We show that these two statistical inference problems greatly benefit from the indel-aware resampling and re-estimation approach and the reservation of intra-sequence dependence.A major drawback of SERES is that it requires parameters to ensure the synchronization of random walks on unaligned sequences. We introduce RAWR, a non-parametric resampling method designed for phylogenetic tree support estimation that does not require extra parameters. We show that the RAWR-based resampling and re-estimation method produces comparable or typically better performance than the traditional bootstrap approach on the phylogenetic tree support estimation problem.We further relax the commonly used assumption of phylogeny. Evolutionary history is usually considered as a tree structure. Evolutionary events that cause reticulated gene flow are ignored. Previous studies show that alignment uncertainty greatly impacts downstream tree inference and learning. However, there is little discussion about the impact of MSA uncertainties on the phylogenetic network reconstruction. We show evidence that the errors introduced in MSA estimation decrease the accuracy of the inferred phylogenetic network, and an indel-aware reconstruction method is needed for phylogenetic network analysis.In this dissertation, we introduce our contribution to phylogenetic estimation using biomolecular sequence data involving complex evolutionary histories, such as sequence insertion and deletion processes and non-tree-like evolution.

Download or read book Distance aware Algorithms for Scalable Evolutionary and Ecological Analyses written by Metin Balaban and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Thanks to the advances in sequencing technologies in the last two decades, the set of available whole-genome sequences has been expanding rapidly. One of the challenges in phylogenetics is accurate large-scale phylogenetic inference based on whole-genome sequences. A related challenge is using incomplete genome-wide data in an assembly-free manner for accurate sample identification with reference to phylogeny. This dissertation proposes new scalable and accurate algorithms to address these two challenges. First, I present a family of scalable methods called TreeCluster for breaking a large set of sequences into evolutionary homogeneous clusters. Second, I present two algorithms for accurate phylogenetic placement of genomic sequences on ultra-large single-gene and whole-genome based trees. The first version, APPLES, scales linearly with the reference size while APPLES-2 scales sub-linearly thanks to a divide-and-conquer strategy based on the TreeCluster method. Third, I develop a solution for assembly-free sample phylogenetic placement for a particularly challenging case when the specimen is a mixture of two cohabiting species or a hybrid of two species. Fourth, I address one limitation of assembly-free methods--their reliance on simple models of sequence evolution--by developing a technique to compute evolutionary distances under a complex 4-parameter model called TK4. Finally, I introduce a divide-and-conquer workflow for incrementally growing and updating ultra-large phylogenies using many of the ingredients developed in other chapters. This workflow (uDance) is accurate in simulations and can build a 200,000-genome microbial tree-of-life based on 388 marker genes.

Download or read book Computational Tools for the Analysis of High throughput Genome scale Sequence Data written by David Adrian Lopez and published by . This book was released on 2016 with total page 83 pages. Available in PDF, EPUB and Kindle. Book excerpt: As high-throughput sequence data becomes increasingly used in a variety of fields, there is a growing need for computational tools that facilitate analyzing and interpreting the sequence data to extract biological meaning. To date, several computational tools have been developed to analyze raw and processed sequence data in a number of contexts. However, many of these tools primarily focus on well-studied, reference organisms, and in some cases, such as the visualization of molecular signatures in expression data, there is a scarcity or complete absence of tools. Furthermore, the compendium of genome-scale data in publicly accessible databases can be leveraged to inform new studies. The focus of this dissertation is the development of computational tools and methods to analyze high-throughput genome-scale sequence data, as well as applications in mammalian, algal, and bacterial systems. Chapter 1 introduces the challenges of analyzing high-throughput sequence data. Chapter 2 presents the Signature Visualization Tool (SaVanT), a framework to visualize molecular signatures in user-generated expression data on a sample-by-sample basis. This chapter demonstrates that SaVanT can use immune activation signatures to distinguish patients with different types of acute infections (influenza A and bacterial pneumonia), and determine the primary cell types underlying different leukemias (acute myeloid and acute lymphoblastic) and skin disorders. Chapter 3 describes the Algal Functional Annotation Tool, which biologically interprets large gene lists, such as those derived from differential expression experiments. This tool integrates data from several pathway, ontology, and protein domain databases and performs enrichment testing on gene lists for several algal genomes. Chapter 4 describes a survey of the Chlamydomonas reinhardtii transcriptome and methylome across various stages of its sexual life cycle. This chapter discusses the identification and function of 361 gamete-specific and 627 zygote-specific genes, the first base-resolution methylation map of C. reinhardtii, and the changes in chloroplast methylation throughout key stages of its life cycle. Chapter 5 presents a comparative genomics approach to identifying previously uncharacterized bacterial microcompartment (BMC) proteins. Based on genomic proximity of genes in 131 fully-sequenced bacterial genomes, this chapter describes new putative microcompartments and their function.

Download or read book Probabilistic Model Based Approach to Evolutionary Analysis of Non Coding Sequences written by Jaebum Kim and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Non-coding sequences, constituting a large fraction of genomic DNA, are of great importance because (i) they harbor functional elements that are involved in the regulation of gene expression and (ii) they are essential for the study of genome structure and evolution. The availability of genome sequences of closely related species has provided opportunities to analyze non-coding sequences by comparing multiple genomes from different species. The success of comparative genomic studies relies on bioinformatics tools that aid the comparison and analysis of genome sequences. Here, we propose and develop computational tools to evolutionarily analyze non-coding sequences, which are based on probabilistic models of sequence evolution. We present a probabilistic framework for finding the locations of insertions and deletions (indels) in a multiple alignment. Its performance is found to be better than that obtained by a parsimony-based method. We study the evolution of sequences involved in the regulation of body patterning in the Drosophila embryo, reporting statistical evidence in favor of key evolutionary hypotheses related to regulatory elements and constraints on indels. We also propose a new simulation scheme for generating biologically realistic benchmarks for the alignments of non-coding sequences. This scheme is used to construct benchmarks for Drosophila non-coding sequences, and evaluation results are shown for several multiple alignment and indel annotation tools on those benchmarks. Finally, we develop a probabilistic framework for multiple sequence alignment that finds an optimal alignment by incrementally building up alignment columns, based on a model for the evolution of three sequences and the joint probability of an alignment column as a substitute for the traditionally used sum-of-pairs score. We find that the new framework produces alignments of much greater specificity than state-of-the-art methods, without compromising too much in terms of sensitivity. The computational tools developed here will play a significant role in solving many biological problems and further contribute to broaden our understanding of organismal diversity and evolution.

Download or read book Digraphs written by Jorgen Bang-Jensen and published by Springer Science & Business Media. This book was released on 2013-06-29 with total page 769 pages. Available in PDF, EPUB and Kindle. Book excerpt: The study of directed graphs (digraphs) has developed enormously over recent decades, yet the results are rather scattered across the journal literature. This is the first book to present a unified and comprehensive survey of the subject. In addition to covering the theoretical aspects, the authors discuss a large number of applications and their generalizations to topics such as the traveling salesman problem, project scheduling, genetics, network connectivity, and sparse matrices. Numerous exercises are included. For all graduate students, researchers and professionals interested in graph theory and its applications, this book will be essential reading.

Download or read book Large Scale Computations in Genomic and Epigenomic Analysis written by Chen (Chandler) Zuo and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic and epigenomic studies aim to elucidate genomic regulatory mechanisms under various biological conditions. The next-generation sequencing technology has been widely applied in this area to generate vast data from different organisms, cell types and experiments. The availability of these data has motivated me to develop several computational algorithms with data scalability and time efficiency. Chapter 2 introduces an empirical Bayesian framework, ChIP-Seq Statistical Power (CSSP), for calculating the required sequencing depth for ChIP-seq experiments. ChIP-seq is the state-of-the-art technology to study transcription factor binding and protein interactions. The sequencing depth of such an experiment determines the power of detecting interacting genome regions with the protein. By predicting statistical power with multiple testing adjustment, CSSP facilitates the experimental design using low-sequenced pilot experiments. Chapter 3 introduces a software package, atSNP (affinity testing for Single Nucleotide Polymorphism), a highly scalable computational tool to identify putative regulatory SNPs using transcription factor binding motifs. atSNP implements innovative algorithms using the importance sampling technique. It easily scales up to analyses involving millions of SNP-motif pairs, which can not be achieved using the existing tools. Chapter 4 and 5 studies the integrative modeling for general genomic and epigenomic data. Chapter 4 introduces the MBASIC framework (Matrix Based Analysis for State-space Inference and Clustering), a unified approach to analyze data from different types of experiments, including but not restricted to transcription factor binding, gene expression and allele-specific binding. I have also developed an Expectation and Maximization algorithm to jointly estimate all parameters in the hierarchical model. In Chapter 5, I cast the MBASIC framework in a Bayesian setting to develop a MAD-Bayes algorithm. This algorithm is derived under the small-variance asymptotic view of the K-means algorithm. It shows an order-of-magnitude decrease in time costs compared to the Expectation and Maximization algorithm.

Download or read book Large Scale Computations in Genomic and Epigenomic Analysis written by and published by . This book was released on 2015 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: Genomic and epigenomic studies aim to elucidate genomic regulatory mechanisms under various biological conditions. The next-generation sequencing technology has been widely applied in this area to generate vast data from different organisms, cell types and experiments. The availability of these data has motivated me to develop several computational algorithms with data scalability and time efficiency. Chapter 2 introduces an empirical Bayesian framework, ChIP-Seq Statistical Power (CSSP), for calculating the required sequencing depth for ChIP-seq experiments. ChIP-seq is the state-of-the-art technology to study transcription factor binding and protein interactions. The sequencing depth of such an experiment determines the power of detecting interacting genome regions with the protein. By predicting statistical power with multiple testing adjustment, CSSP facilitates the experimental design using low-sequenced pilot experiments. Chapter 3 introduces a software package, atSNP (affinity testing for Single Nucleotide Polymorphism), a highly scalable computational tool to identify putative regulatory SNPs using transcription factor binding motifs. atSNP implements innovative algorithms using the importance sampling technique. It easily scales up to analyses involving millions of SNP-motif pairs, which can not be achieved using the existing tools. Chapter 4 and 5 studies the integrative modeling for general genomic and epigenomic data. Chapter 4 introduces the MBASIC framework (Matrix Based Analysis for State-space Inference and Clustering), a unified approach to analyze data from different types of experiments, including but not restricted to transcription factor binding, gene expression and allele-specific binding. I have also developed an Expectation and Maximization algorithm to jointly estimate all parameters in the hierarchical model. In Chapter 5, I cast the MBASIC framework in a Bayesian setting to develop a MAD-Bayes algorithm. This algorithm is derived under the small-variance asymptotic view of the K-means algorithm. It shows an order-of-magnitude decrease in time costs compared to the Expectation and Maximization algorithm.

Download or read book Computational Algorithms for Comparative Genomics written by Khalid Mahmood and published by . This book was released on 2012 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in high throughput genome sequencing has presented an opportunity to study how species are related, especially, in terms of their evolution and molecular functions. However, the capability to generate genome sequence data outweighs the ability to decipher and translate this data to biological information. Therefore, computational methods play a key role in deciphering large and complex genome data that is essential for bridging the growing gap between genes of known and unknown functions. To this end, computational comparative genomics is an essential task for studying the organization, topology and conservation of genes and strings of genes that lends to a better biological understanding of gene function and annotation. At the core of comparative genomic is the task of identifying gene relationships or matches across genomes. However, large dimensionality of genome data and complex evolutionary artefacts means that gene matching is a non-trivial task and new computational approaches are constantly required to address these issues. This thesis presents new algorithms for gene matching to identify gene relationships across genomes (or complete proteomes). Novel computational methods are presented here that (1) perform comparisons between small related species such as microbial strains, (2) calculate gene matching on large-scale genome data to identify gene orthologs, conserved gene strings and evolutionary rearrangements, (3) calculate complex orthologous relationships such as co-orthologs and (4) calculate rapid large-scale sequence comparisons. The methods described here are applied to a variety of genome comparisons ranging from small microbial strains to large eukarytoes such as human, mouse and rat genomes. The results from these comparisons revealed orthologous and co-orthologous genes, syntenic regions, conserved gene strings and genome rearrangements with high accuracy. Further experiments have also shown the methods described here to be computationally efficient and robust.

Download or read book Engineering Scalable Digital Models to Study Major Transitions in Evolution written by Matthew Andres Moreno and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Evolutionary transitions occur when previously-independent replicating entities unite to form more complex individuals. Such major transitions in individuality have profoundly shaped complexity, novelty, and adaptation over the course of natural history. Regard for their causes and consequences drives many fundamental questions in biology. Likewise, evolutionary transitions have been highlighted as a hallmark of true open-ended evolution in artificial life. As such, experiments with digital multicellularity promise to help realize computational systems with properties that more closely resemble those of biological systems, ultimately providing insights about the origins of complex life in the natural world and contributing to bio-inspired distributed algorithm design.Major challenges exist, however, in applying high-performance computing hardware to realize the dynamic, large-scale digital artificial life simulations required for such work. This dissertation presents two new tools designed to facilitate digital multicellularity experiments at scale: the Conduit library for best-effort communication and the hstrat ("hereditary stratigraphy") library, which debuts novel decentralized algorithms to estimate phylogenetic distance between evolving agents.Most current parallel and distributed high-performance computing work emphasizes logical determinism: extra effort is expended to guarantee reliable communication and, when necessary, computation halts in order to await expected messages. Determinism does enable hardware-independent algorithmic results and perfect reproducibility, however adopting a best-effort communication model can substantially reduce synchronization overhead and allow dynamic (albeit, potentially lossy) scaling of communication load to fully utilize available resources. We present a set of experiments to empirically characterize the best-effort communication model implemented by the Conduit library on commercially available high-performance computing hardware. We find that best-effort communication through Conduit enables significantly better computational performance under high thread and process counts and can help achieve significantly better solution quality within a fixed time constraint.In a similar vein, existing digital evolution work that incorporates phylogenetic analysis does so through a perfect tracking model where each birth event is recorded in a centralized data structure. This approach, however, does not easily scale to distributed computing environments where agents may migrate between a dynamic set of disjoint processing elements. Additionally, this perfect tracking approach is not robust to data loss or corruption. To provide for phylogenetic analyses in these environments, we propose an approach to infer phylogenies via heritable genetic annotations. We introduce hereditary stratigraphy, an algorithm that enables efficient, fault-tolerant phylogenetic reconstruction with tunable trade-offs between annotation memory footprint and reconstruction accuracy. For example, this approach can estimate the most recent common ancestor (MRCA) generation of two genomes within 10% relative error with 95% confidence up to a depth of a trillion generations with genome annotations smaller than a kilobyte. We simulate inference over known lineages, recovering up to 85% of the information contained in the original tree using only a 64-bit annotation.We harness these tools in DISHTINY, a distributed digital evolution system designed to study digital organisms as they undergo major evolutionary transitions in individuality. This system allows digital cells to form and replicate kin groups by selectively adjoining or expelling daughter cells. The capability to recognize kin-group membership enables preferential communication and cooperation between cells. We report group-level traits characteristic of fraternal transitions in the natural world. These include reproductive division of labor, resource sharing within kin groups, resource investment in offspring groups, asymmetrical behaviors mediated by messaging, morphological patterning, and adaptive apoptosis. In one detailed case study, we track the co-evolution of novelty, complexity, and adaptation over the evolutionary history of an experiment. We characterize ten qualitatively distinct multicellular morphologies, several of which exhibit asymmetrical growth and distinct life stages. Our case study suggests a loose, sometimes divergent, relationship can exist among novelty, complexity, and adaptation.The constructive potential inherent in major evolutionary transitions holds great promise for progress toward replicating the capability and robustness of natural organisms. Coupled with shrewd software engineering and innovative model design informed by evolutionary theory, contemporary hardware systems could plausibly already suffice to realize paradigm-shifting advances in open-ended evolution and, ultimately, scientific understanding of major transitions themselves. This work establishes important new tools and methodologies to support continuing progress in this direction.

Download or read book Machine Learning in Computational Biology written by Ofer Shai and published by . This book was released on 2009 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Alternative splicing, the process by which a single gene may code for similar but different proteins, is an important process in biology, linked to development, cellular differentiation, genetic diseases, and more. Genome-wide analysis of alternative splicing patterns and regulation has been recently made possible due to new high throughput techniques for monitoring gene expression and genomic sequencing. This thesis introduces two algorithms for alternative splicing analysis based on large microarray and genomic sequence data. The algorithms, based on generative probabilistic models that capture structure and patterns in the data, are used to study global properties of alternative splicing. GenASAP, the first method to provide quantitative predictions of alternative splicing patterns on large scale data sets, is shown to generate useful and precise predictions based on independent RT-PCR validation (a slow but more accurate approach to measuring cellular expression patterns). In the second part of the thesis, the results obtained by GenASAP are analysed to reveal jointly regulated genes. The sequences of the genes are examined for potential regulatory factors binding sites using a new motif finding algorithm designed for this purpose. The motif finding algorithm, called GenBITES (generative model for binding sites) uses a fully Bayesian generative model for sequences, and the MCMC approach used for inference in the model includes moves that can efficiently create or delete motifs, and extend or contract the width of existing motifs. GenBITES has been applied to several synthetic and real data sets, and is shown to be highly competitive at a task for which many algorithms already exist. Although developed to analyze alternative splicing data, GenBITES outperforms most reported results on a benchmark data set based on transcription data. In the first part of the thesis, a microarray platform for monitoring alternative splicing is introduced. A spatial noise removal algorithm that removes artifacts and improves data fidelity is presented. The GenASAP algorithm (generative model for alternative splicing array platform) models the non-linear process in which targeted molecules bind to a microarray's probes and is used to predict patterns of alternative splicing. Two versions of GenASAP have been developed. The first uses variational approximation to infer the relative amounts of the targeted molecules, while the second incorporates a more accurate noise and generative model and utilizes Markov chain Monte Carlo (MCMC) sampling.

Download or read book Leveraging Big Data and Machine Learning Technologies for Accurate and Scalable Genomic Analysis written by Lizhen Shi and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data and machine learning technologies have been explored to mine the complex large-scale genomics data. In this dissertation, we first survey some of the existing scalable approaches for genomic analysis and identify the limitations of these solutions. We then investigate the still-unsolved challenges faced by computational biologists in large-scale genomic analysis. Specifically, in terms of using MapReduce-based bioinformatics analysis tools, Hadoop has a large number of parameters to control the behavior of a MapReduce job. The unique characteristics of MapReduce-based bioinformatics tools makes all the existing guidelines inapplicable; In Metagenomics, the intrinsic complexity and massive quantity of metagenomic data create tremendous challenges for microbial genomes recovery; When we applying NLP technologies to genome analysis, the enormous k-mer size and the low-frequency k-mers caused by the sequencing errors post significant challenges for k-mer embedding. To overcome the aforementioned problems, this dissertation introduces three countermeasures. First, we extract the key parameters from the large space of MapReduce parameters and present an exemplary case for tuning MapReduce-based bioinformatics analysis tools based on their unique characteristics. Second, we design and implement SpaRC, a scalable sequence clustering tool built on Apache Spark, to partition reads based on their molecules of origin to enable downstream assembly optimization in Metagenomics. SpaRC achieves high clustering accuracy, with the capability of scaling near linearly with the data size and the number of computing nodes. Lastly, we leverage Locality Sensitive Hashing (LSH) to overcome the two challenges faced by $k$-mer embedding and design LSHvec. With LSHvec, a DNA sequence can be represented as a dense low-dimensional vector. The trained sequence vectors are capable of capturing the rich characteristics of DNA sequences and can be fed to machine learning models for a wide variety of applications in genomics analysis. We compare our approaches with existing solutions. The experiments demonstrate our approaches achieve the state-of-the-art results. We open source our implementation of SpaRC and LSHvec to facilitate comparison of future work and inspire future research in genomic analysis.

Download or read book Introduction to Computational Genomics written by Nello Cristianini and published by Cambridge University Press. This book was released on 2006-12-14 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Where did SARS come from? Have we inherited genes from Neanderthals? How do plants use their internal clock? The genomic revolution in biology enables us to answer such questions. But the revolution would have been impossible without the support of powerful computational and statistical methods that enable us to exploit genomic data. Many universities are introducing courses to train the next generation of bioinformaticians: biologists fluent in mathematics and computer science, and data analysts familiar with biology. This readable and entertaining book, based on successful taught courses, provides a roadmap to navigate entry to this field. It guides the reader through key achievements of bioinformatics, using a hands-on approach. Statistical sequence analysis, sequence alignment, hidden Markov models, gene and motif finding and more, are introduced in a rigorous yet accessible way. A companion website provides the reader with Matlab-related software tools for reproducing the steps demonstrated in the book.

Download or read book Analysis of Phylogenetics and Evolution with R written by Emmanuel Paradis and published by Springer Science & Business Media. This book was released on 2006-11-25 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book integrates a wide variety of data analysis methods into a single and flexible interface: the R language. The book starts with a presentation of different R packages and gives a short introduction to R for phylogeneticists unfamiliar with this language. The basic phylogenetic topics are covered. The chapter on tree drawing uses R's powerful graphical environment. A section deals with the analysis of diversification with phylogenies, one of the author's favorite research topics. The last chapter is devoted to the development of phylogenetic methods with R and interfaces with other languages (C and C++). Some exercises conclude these chapters.

Download or read book Molecular Evolution and Phylogenetics written by Masatoshi Nei and published by Oxford University Press. This book was released on 2000-07-27 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last ten years, remarkable progress has occurred in the study of molecular evolution. Among the most important factors that are responsible for this progress are the development of new statistical methods and advances in computational technology. In particular, phylogenetic analysis of DNA or protein sequences has become a powerful tool for studying molecular evolution. Along with this developing technology, the application of the new statistical and computational methods has become more complicated and there is no comprehensive volume that treats these methods in depth. Molecular Evolution and Phylogenetics fills this gap and present various statistical methods that are easily accessible to general biologists as well as biochemists, bioinformatists and graduate students. The text covers measurement of sequence divergence, construction of phylogenetic trees, statistical tests for detection of positive Darwinian selection, inference of ancestral amino acid sequences, construction of linearized trees, and analysis of allele frequency data. Emphasis is given to practical methods of data analysis, and methods can be learned by working through numerical examples using the computer program MEGA2 that is provided.

Download or read book Encyclopedia of Bioinformatics and Computational Biology written by and published by Elsevier. This book was released on 2018-08-21 with total page 3421 pages. Available in PDF, EPUB and Kindle. Book excerpt: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics, Three Volume Set combines elements of computer science, information technology, mathematics, statistics and biotechnology, providing the methodology and in silico solutions to mine biological data and processes. The book covers Theory, Topics and Applications, with a special focus on Integrative –omics and Systems Biology. The theoretical, methodological underpinnings of BCB, including phylogeny are covered, as are more current areas of focus, such as translational bioinformatics, cheminformatics, and environmental informatics. Finally, Applications provide guidance for commonly asked questions. This major reference work spans basic and cutting-edge methodologies authored by leaders in the field, providing an invaluable resource for students, scientists, professionals in research institutes, and a broad swath of researchers in biotechnology and the biomedical and pharmaceutical industries. Brings together information from computer science, information technology, mathematics, statistics and biotechnology Written and reviewed by leading experts in the field, providing a unique and authoritative resource Focuses on the main theoretical and methodological concepts before expanding on specific topics and applications Includes interactive images, multimedia tools and crosslinking to further resources and databases

Download or read book Agricultural Bioinformatics written by Kavi Kishor P.B. and published by Springer. This book was released on 2014-07-14 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: A common approach to understanding the functional repertoire of a genome is through functional genomics. With systems biology burgeoning, bioinformatics has grown to a larger extent for plant genomes where several applications in the form of protein-protein interactions (PPI) are used to predict the function of proteins. With plant genes evolutionarily conserved, the science of bioinformatics in agriculture has caught interest with myriad of applications taken from bench side to in silico studies. A multitude of technologies in the form of gene analysis, biochemical pathways and molecular techniques have been exploited to an extent that they consume less time and have been cost-effective to use. As genomes are being sequenced, there is an increased amount of expression data being generated from time to time matching the need to link the expression profiles and phenotypic variation to the underlying genomic variation. This would allow us to identify candidate genes and understand the molecular basis/phenotypic variation of traits. While many bioinformatics methods like expression and whole genome sequence data of organisms in biological databases have been used in plants, we felt a common reference showcasing the reviews for such analysis is wanting. We envisage that this dearth would be facilitated in the form of this Springer book on Agricultural Bioinformatics. We thank all the authors and the publishers Springer, Germany for providing us an opportunity to review the bioinformatics works that the authors have carried in the recent past and hope the readers would find this book attention grabbing.

Download or read book Computational Exome and Genome Analysis written by Peter N. Robinson and published by CRC Press. This book was released on 2017-09-13 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Exome and genome sequencing are revolutionizing medical research and diagnostics, but the computational analysis of the data has become an extremely heterogeneous and often challenging area of bioinformatics. Computational Exome and Genome Analysis provides a practical introduction to all of the major areas in the field, enabling readers to develop a comprehensive understanding of the sequencing process and the entire computational analysis pipeline.