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| Author | : Kuan-Bei Chen |
| Publisher | : |
| Total Pages | : 96 |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
| Author | : Anob Mauli Chakrabarti |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
Interactions between proteins and nucleic acid molecules are central to the cellular regulation and homeostasis. To study them, I employ a wide range of computational analysis methods to integrate genomic data from many types of experiment. This thesis has three parts. In the first part, I explore the patterns of indels created by CRISPR-Cas9 genome editing. By thorough characterisation of the precision of editing at thousands of genomic target sites, we identify simple sequence rules that can help predict these outcomes. Furthermore, we examine the role of the structural chromatin context in fine-tuning Cas9-DNA interactions. In the second part, I explore methods to study protein-RNA interactions. I use comparative computational analyses to assess both the data quality of, and data analysis methods for, different crosslinking and immunoprecipitation (CLIP) technologies. I then develop new methods to analyse data generated by hybrid individual-nucleotide resolution CLIP (hiCLIP). By tailoring computational solutions to an understanding of experimental conditions, I improve the overall sensitivity of hiCLIP, and ultimately feedback to drive ongoing experimental development. In the third part, I focus on the Staufen family of double-stranded RNA binding proteins and using hiCLIP data to define transcriptome-wide atlases of RNA duplexes bound by these proteins both in a cell line and in rat brain tissue. Through integration with other data sets, both publicly available and newly generated, I derive insights into their function in RNA metabolism, and in how these interactions change during the course of mammalian brain development with putative roles in ribonucleoprotein complex formation. In summary, I present a range of tailored computational methods and analyses developed to understand interactions between proteins and nucleic acids; aiming to link these interactions to functional outcomes.
| Author | : Annette Höglund |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2006 |
| Genre | : |
| ISBN | : |
| Author | : Yuchun Guo |
| Publisher | : |
| Total Pages | : 135 |
| Release | : 2012 |
| Genre | : |
| ISBN | : |
I present three novel computational methods to address the challenge of identifying protein-DNA interactions at high spatial resolution from noisy ChIP-Seq data. I first present the genome positioning system (GPS) algorithm which predicts protein-DNA interaction events from ChIP-Seq data using a single-base resolution generative probabilistic model. Using synthetic and actual ChIP-Seq data, I show that GPS improves the effective spatial resolution and accuracy in resolving proximal binding events when comparing with existing methods. Second, I present the k-mer set motif (KSM) representation and the k-mer motif alignment and clustering (KMAC) method which discovers DNA-binding motifs from ChIP-Seq derived sequences. I demonstrate that the KSM model is more predictive than the widely used position weight matrix model, and that KMAC outperforms other existing motif discovery programs in recovering known motifs from a large collection of human ChIP-Seq experiments. Finally, I present an integrative method, genome wide event finding and motif discovery (GEM), which models ChIP data with explanatory motifs and binding events at high spatial resolution. The GEM model links binding event discovery and motif discovery with positional priors in the context of a generative probabilistic model of ChIP data and genome sequence. I show that GEM further improve upon previous methods for processing ChIP-Seq and ChIP-exo data to yield unsurpassed spatial resolution and discovery of proximal binding events. GEM enables a systematic analysis of in vivo transcription factor binding to discover hundreds of spatial binding constraints between factors in human and mouse cells, including known factor pairs and novel pairs such as c-Fos:c-Jun/USF1, CTCF/Egr1, and HNF4a/FOXA1. I also discovered a complex spatial binding relationship involved 6 key regulatory factors in mouse embryonic stem (ES) cell that is likely to be functional in ES cell gene regulation. Such computational discoveries propose testable models for regulatory factor interactions that will help elucidate genome function and the implementation of combinatorial control.
| Author | : Guo-Cheng Yuan |
| Publisher | : Humana Press |
| Total Pages | : 271 |
| Release | : 2019-02-14 |
| Genre | : Science |
| ISBN | : 9781493990566 |
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.
| Author | : Umesh Nandal |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : |
| ISBN | : 9789493330382 |
"Biological processes in a cell are highly dynamic and their regulation involves a multitude of molecular components such as DNA, genes, proteins, and metabolites. It is of critical importance to understand these entities not only as separate elements but also in terms of their interactions with one another. Technological developments have enabled the rapid generation of vast volumes of data for nearly all types of such entities from an individual, referred to as ‘omics’ data. This thesis addresses three main challenges for the integrative analysis of different omics data modalities while taking their inherent heterogeneity into account: (i) ease of accessibility of high-throughput data so that it can be combined with in-house experimental data or used for reanalysis, (ii) integration of information across different resources, and (iii) integration within or across data modalities using networks. Chapter 2 of this thesis describes our approach to build a compendium of functional genomics data retrieved from GEO. With the associated R package compendium and the accompanying MySQL database, pre-processed GEO data from different studies and profiling platforms can be systematically retrieved and stored. Chapter 3 of this thesis describes a problem-driven integrative analysis approach across different data sources to rank candidate proteins for low-abundant spots in 2D-DIGE experiments. Chapter 4 of this thesis describes a network-based integration method to align a pair of gene coexpression networks generated from gene expression data measured across multiple conditions. The method is applied to gene expression data measured in human and mouse immune cell types to study conservation and divergence between the two species. In Chapter 5 of this thesis a special case of this method is used to identify modules conserved between species for a single condition. The method is applied to gene expression data measured in human and mouse livers." --
| Author | : Bairong Shen |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 219 |
| Release | : 2013-11-25 |
| Genre | : Science |
| ISBN | : 9400779755 |
The book introduces the bioinformatics tools, databases and strategies for the translational research, focuses on the biomarker discovery based on integrative data analysis and systems biological network reconstruction. With the coming of personal genomics era, the biomedical data will be accumulated fast and then it will become reality for the personalized and accurate diagnosis, prognosis and treatment of complex diseases. The book covers both state of the art of bioinformatics methodologies and the examples for the identification of simple or network biomarkers. In addition, bioinformatics software tools and scripts are provided to the practical application in the study of complex diseases. The present state, the future challenges and perspectives were discussed. The book is written for biologists, biomedical informatics scientists and clinicians, etc. Dr. Bairong Shen is Professor and Director of Center for Systems Biology, Soochow University; he is also Director of Taicang Center for Translational Bioinformatics.
| Author | : Qinyi Zhou |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : Statistics |
| ISBN | : |
Modern genomic and epigenomic studies have found numerous genomic regions that interact with biochemical factors and regulate gene activities. A lot of studies focus on transcriptional regulation of disease related genes that such genomic regions mediate. Identification of driver elements associated with protein-noncoding regions for a disease is a challenging and unsolved problem. Chapter 2 develops a novel statistical test based on single sequence modeling, named DNAprotein binding changer test. The test predicts insertions and deletions of bases in the genome (InDels) that change protein binding to DNA. It is the first computational and statistical approach that directly evaluates InDel influence on protein binding. The binding changer test statistic we propose is based on binding p-values to the reference and mutation sequences. We employ importance sampling algorithm such that the binding changer pvalue is computed with sequence pairs generated from an importance distribution. We derive the importance distribution along with the optimal tilting parameter that determines the importance distribution to maximize the algorithm efficiency. The binding changer test is a general approach for any protein-binding motifs and InDel mutations found in any disease types. The simulation studies demonstrate that the test is very successful in Type I error control, statistical power increase, and binding changer InDel prediction. From the application to leukemia data, we obtain potential InDels responsible for leukemia through creating or eliminating transcription factor MYC binding. Chapter 3 introduces an integrative analysis to improve the prediction of cancer driver SNVs (Single Nucleotide Variants) that change transcriptional regulation and influence cancer genes by leveraging cancer-specific data collected from experiments. It utilizes an existing noncoding mutation scoring scheme which enables to retain SNVs with high priority. Highly expressed and non-housekeeping TF (transcription factor) genes are selected with mRNA expression data. The SNVs that may cause the TF binding change to the DNA sequence are further predicted with atSNP binding change detection methods. Its application to leukemia SNV data finds potential leukemia associated genes along with driver SNVs and TFs in the cis-regulatory structure. Further, we confirm that the integrative approach improves the power of detecting regulatory mutations.
| Author | : Sriganesh Srihari |
| Publisher | : Morgan & Claypool |
| Total Pages | : 297 |
| Release | : 2017-05-30 |
| Genre | : Science |
| ISBN | : 1970001534 |
Complexes of physically interacting proteins constitute fundamental functional units that drive almost all biological processes within cells. A faithful reconstruction of the entire set of protein complexes (the "complexosome") is therefore important not only to understand the composition of complexes but also the higher level functional organization within cells. Advances over the last several years, particularly through the use of high-throughput proteomics techniques, have made it possible to map substantial fractions of protein interactions (the "interactomes") from model organisms including Arabidopsis thaliana (a flowering plant), Caenorhabditis elegans (a nematode), Drosophila melanogaster (fruit fly), and Saccharomyces cerevisiae (budding yeast). These interaction datasets have enabled systematic inquiry into the identification and study of protein complexes from organisms. Computational methods have played a significant role in this context, by contributing accurate, efficient, and exhaustive ways to analyze the enormous amounts of data. These methods have helped to compensate for some of the limitations in experimental datasets including the presence of biological and technical noise and the relative paucity of credible interactions. In this book, we systematically walk through computational methods devised to date (approximately between 2000 and 2016) for identifying protein complexes from the network of protein interactions (the protein-protein interaction (PPI) network). We present a detailed taxonomy of these methods, and comprehensively evaluate them for protein complex identification across a variety of scenarios including the absence of many true interactions and the presence of false-positive interactions (noise) in PPI networks. Based on this evaluation, we highlight challenges faced by the methods, for instance in identifying sparse, sub-, or small complexes and in discerning overlapping complexes, and reveal how a combination of strategies is necessary to accurately reconstruct the entire complexosome.
| Author | : Yili Chen |
| Publisher | : |
| Total Pages | : 280 |
| Release | : 2007 |
| Genre | : |
| ISBN | : |
