Computational DNA Motif Discovery in Plant Promoters PDF Download
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Author: François Fauteux
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Languages : en
Pages :
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Author: François Fauteux
Publisher:
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Languages : en
Pages :
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Author: Nadia Chaidir
Publisher:
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Category :
Languages : en
Pages :
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"Large-scale genome-wide comparative analyses are now made possible by the increasing number of publicly available high-quality genome sequence data for numerous plant species. To understand the mechanisms of transcriptional regulation, computational analysis tools were used to find overrepresented and conserved DNA sequences, i.e. cis-regulatory elements. Datasets used as positive input for computational identification of regulatory regions commonly include promoters of co-regulated genes or promoters of orthologous genes (Wang and Stormo, 2003).We discovered de novo motif using two approaches, seperately; 1) discovery based on orthology relationship of the genes in 18 plant species and 2) discovery based on co-regulated genes in specific tissues from soybean gene expression RNA-Seq data. In the first approach, a combination of several bioinformatics tools were used to predict motifs in promoter region based on clusters of orthologous genes in whole-genome datasets of Arabidopsis lyrata, Arabidopsis thaliana, Brachypodium distachyon, Carica papaya, Chlamydomonas reinhardtii, Glycine max, Linus usitatissimum, Malus domestica, Manihot esculenta, Medicago truncatula, Oryza sativa, Physcomitrella patens, Populus trichocarpa, Selaginella moellendorfii, Sorghum bicolor, Vitis vinivera, Volvox carteri and Zea mays. The results have shown that many promoters of orthologous plant genes contain similar cis-regulatory motifs. In addition, inclusion of more evolutionary distant organism led to detection of very conserved motifs, i.e. motifs that have similar function in wider variety of organisms. In the second approach, bioinformatics tools were used to find motifs in promoter region of co-regulated genes in shoot apical meristem and shoot epidermis of three soybean cultivars. The results have shows that promoters of co-regulated genes in specific tissues contain similar cis-regulatory motifs.Since generating genome-scale datasets requires extensive computational resources that are not always readily available, we created a relational database that houses pre-computed and post-processed whole-genome comparative analysis of promoter regions. The database contains motif sequences, annotations, clusters of orthologous genes and other useful information associated with them, for 18 plant genomes." --
Author: Nikolay Kolchanov
Publisher: Springer Science & Business Media
ISBN: 1441971521
Category : Science
Languages : en
Pages : 373
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Book Description
- The data gathered can be used to solve a wide range of problems - for basic science and applied science
Author: DENISON. KURUVILLA
Publisher:
ISBN:
Category :
Languages : en
Pages : 54
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Book Description
A critical step in understanding the mechanisms of regulation of gene expression is the ability to successfully identify and study regulatory elements. These elements, which serve as binding sites for transcription factors, are often difficult to identify due to the limited knowledge available on transcription factors and their mechanism of control. Computational motif discovery approaches offer a solution to this problem by searching for short sequences within regulatory regions, without making much prior assumptions about the transcription factor or its binding mechanism. Many of these tools have been used to identify potential transcription factor binding sites on promoter sequences. In the fly genome, only ~35% of the promoters contain any of the known promoter motifs. This suggests that there are many core promoter motifs that remain unknown and are important in regulation of gene expression.^One reason for the difficulty in identifying new promoter motifs could be that most of the motif discovery methods have essentially focused on promoters as a single set. By studying the promoter as subsets, we may be able to achieve a better signal to noise ratio for computational motif discovery. We grouped promoters into three separate subsets based on their location with respect to the transcription start site, the unique promoters (UPs), the first alternative promoters (FAPs) and the downstream alternative promoters (DAPs). Multiple motif discovery tools were used to identify potential promoter motifs in these sets. These motifs were then clustered based on similarity and the most similar motifs were merged together. A total of 104 potential promoter motifs were identified in this study. Among the 104 motifs identified, 59 motifs (56.7%) were found in DAPs, 24 motifs (23%) in the FAPs and 21 motifs (20.2%) in the UPs sets.^This indicated that by including the DAPs and the FAPs in this study, we were able to identify many new promoter motifs in these subsets. The motif characteristics (position bias, strand bias, promoter bias, information content and promoter class bias) of each of the motifs were studied individually and the motifs were ranked based on the presence of these characteristics.
Author: Lutz Nover
Publisher: Springer Science & Business Media
ISBN: 3540480374
Category : Science
Languages : en
Pages : 279
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Book Description
The control of plant gene expression at the transcriptional level is the main subject of this volume. Genetics, molecular biology and gene technology have dramatically improved our knowledge of this event. The functional analysis of promoters and transcription factors provides more and more insights into the molecular anatomy of initiation complexes assembled from RNA polymerase and the multiplicity of helper and control proteins. Formation of specific DNA-protein complexes - activating or repressing transcription - is the crux of developmental or environmental control of gene expression. The book presents an up-to-date, critical overview of this rapidly advancing field.
Author: Altuna Akalin
Publisher: CRC Press
ISBN: 1498781861
Category : Mathematics
Languages : en
Pages : 462
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Book Description
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.
Author: Ying Xu
Publisher: World Scientific
ISBN: 1860949827
Category : Medical
Languages : en
Pages : 494
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Book Description
Over 500 prokaryotic genomes have been sequenced to date, and thousands more have been planned for the next few years. While these genomic sequence data provide unprecedented opportunities for biologists to study the world of prokaryotes, they also raise extremely challenging issues such as how to decode the rich information encoded in these genomes. This comprehensive volume includes a collection of cohesively written chapters on prokaryotic genomes, their organization and evolution, the information they encode, and the computational approaches needed to derive such information. A comparative view of bacterial and archaeal genomes, and how information is encoded differently in them, is also presented. Combining theoretical discussions and computational techniques, the book serves as a valuable introductory textbook for graduate-level microbial genomics and informatics courses.
Author: Jin Xiong
Publisher: Cambridge University Press
ISBN: 113945062X
Category : Science
Languages : en
Pages : 360
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Book Description
Essential Bioinformatics is a concise yet comprehensive textbook of bioinformatics, which provides a broad introduction to the entire field. Written specifically for a life science audience, the basics of bioinformatics are explained, followed by discussions of the state-of-the-art computational tools available to solve biological research problems. All key areas of bioinformatics are covered including biological databases, sequence alignment, genes and promoter prediction, molecular phylogenetics, structural bioinformatics, genomics and proteomics. The book emphasizes how computational methods work and compares the strengths and weaknesses of different methods. This balanced yet easily accessible text will be invaluable to students who do not have sophisticated computational backgrounds. Technical details of computational algorithms are explained with a minimum use of mathematical formulae; graphical illustrations are used in their place to aid understanding. The effective synthesis of existing literature as well as in-depth and up-to-date coverage of all key topics in bioinformatics make this an ideal textbook for all bioinformatics courses taken by life science students and for researchers wishing to develop their knowledge of bioinformatics to facilitate their own research.
Author: Nello Cristianini
Publisher: Cambridge University Press
ISBN: 9780521856034
Category : Computers
Languages : en
Pages : 200
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Book Description
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.
Author: Eugene V. Koonin
Publisher: Springer Science & Business Media
ISBN: 1475737831
Category : Science
Languages : en
Pages : 482
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Book Description
Sequence - Evolution - Function is an introduction to the computational approaches that play a critical role in the emerging new branch of biology known as functional genomics. The book provides the reader with an understanding of the principles and approaches of functional genomics and of the potential and limitations of computational and experimental approaches to genome analysis. Sequence - Evolution - Function should help bridge the "digital divide" between biologists and computer scientists, allowing biologists to better grasp the peculiarities of the emerging field of Genome Biology and to learn how to benefit from the enormous amount of sequence data available in the public databases. The book is non-technical with respect to the computer methods for genome analysis and discusses these methods from the user's viewpoint, without addressing mathematical and algorithmic details. Prior practical familiarity with the basic methods for sequence analysis is a major advantage, but a reader without such experience will be able to use the book as an introduction to these methods. This book is perfect for introductory level courses in computational methods for comparative and functional genomics.
