Machine Learning Strategies For Alternative Splicing
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Machine Learning Strategies For Alternative Splicing
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Author : Zhicheng Pan
language : en
Publisher:
Release Date : 2021
Machine Learning Strategies For Alternative Splicing written by Zhicheng Pan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Alternative splicing (AS) is a fundamental biological process that diversifies the transcriptomes and proteomes. Aberrant splicing is the main cause of rare diseases and cancers. Our understanding of AS is far from complete, resulting in a limited comprehension of phenotypic effects of splicing dysregulation. Recent advances in next-generation sequencing (NGS) technologies have revolutionized the discoveries of AS. There are considerable efforts put into generating a large compendium of RNA-seq datasets. These datasets offer an opportunity to study the regulation of AS in tissues, cell stages, and perturbation of biological conditions at unprecedented resolutions and scales. However, utilizing the large number of datasets to make biological discoveries remains a challenge. In this dissertation, we developed machine-learning-based strategies to integrate various types of RNA-seq datasets and transform them into biological knowledge, thereby enabling discoveries towards regulatory mechanisms and functional consequences of AS. In the first part of the dissertation, we report a deep-learning-based computational framework, Deep-learning Augmented RNA-seq analysis of Transcript Splicing (DARTS), that utilizes the Bayesian integration of deep-learning-based predictions with empirical RNA datasets to make inference of differential alternative splicing between biological samples. RNA sequencing (RNA-seq) analysis of alternative splicing is largely limited by depending on high sequencing coverage. DARTS transforms large amounts of publicly available RNA-seq datasets into biological knowledge of how splicing is regulated through deep learning, thus enabling researchers to better characterize alternative splicing inaccessible from RNA-seq datasets with modest coverage. In the second part of the dissertation, we present a computational tool, Systematic Investigation of Retained Introns (SIRI), to quantify unspliced introns and describe a deep-learning-based computational framework to investigate the sequence preferences of different intron groups across subcellular locations. Steps of mRNA maturation occur in distinct cellular locations, while subcellular distribution of processed and unprocessed transcripts often miss in transcriptomic analyses. We employed SIRI to measure intron levels in subcellular locations across cell development and identified four intron groups that have disparate patterns of RNA enrichment across subcellular locations. Through the deep-learning based framework, we identified a set of triplet motifs and sequence conservation patterns that are predictive of intron behavior among biological conditions. In the third part of the dissertation, we exhibit a deep-learning-based tissue-specific framework, individualized Deep-learning Analysis of RNA Transcript Splicing (iDARTS), for predicting splicing levels. The rapid accumulation of RNA-seq datasets matched with whole exome or genome sequencing yields enormous variants underlying diseases, traits, and cancer. Interpreting the functional consequences of these variants remains a challenge in disease diagnostics and precision medicine. iDARTS leverages the publicly available RNA-seq datasets to model the cis RNA sequence features and trans RNA binding protein levels determinants of AS, allowing precise predictions of genetic splice-altering variants. We demonstrated that predicted splice-altering variants are functionally relevant and related to cancer development when analysing ~10 million intronic and exonic variants with iDARTS. Applying iDARTS to interpret functional consequences of variants of uncertain significance in clinical studies, we found that predicted splice-altering variants are ten times enriched in pathogenic categories over benign categories. Our results indicate that iDARTS will benefit large-scale screening disease-implicated variants, thus improving disease diagnosis and enabling discoveries for precision medicine. In the fourth part of the dissertation, we study the underlying mechanisms of N6-methyladenosine (m6A) RNA modification by investigating the biological consequences of arginine methylation of METTL14 through transcriptome-wide profiling of m6A. Arginine methylation of METTL14 controls m6A deposition in mammalian cells. Mouse embryonic stem cells (mESCs) expressing arginine methylation-deficient METTL14 exhibit significantly reduced global m6A levels. These arginine methylation-dependent m6A sites identified from transcriptome-wide analysis are associated with enhanced translation of genes essential for the repair of DNA interstrand crosslinks. Collectively, these findings reveal important aspects of m6A regulation and new functions of arginine methylation in RNA metabolism.
Deep Learning For Alternative Splicing
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Author : Richard Brown
language : en
Publisher:
Release Date : 2019
Deep Learning For Alternative Splicing written by Richard Brown and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Machine Learning In Computational Biology
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Author : Ofer Shai
language : en
Publisher:
Release Date : 2009
Machine Learning In Computational Biology written by Ofer Shai and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with categories.
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.
Machine Learning Techniques On Gene Function Prediction
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Author : Quan Zou
language : en
Publisher: Frontiers Media SA
Release Date : 2019-12-04
Machine Learning Techniques On Gene Function Prediction written by Quan Zou and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-04 with categories.
Alternative Splicing
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Author : Peter Scheiffele
language : en
Publisher: Springer Nature
Release Date : 2022-07-27
Alternative Splicing written by Peter Scheiffele and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-27 with Science categories.
This detailed volume collects commonly used and cutting-edge methods to analyze alternative splicing, a key step in gene regulation. After an introduction of the alternative splicing mechanism and its targeting for therapeutic strategies, the book continues with techniques for analyzing alternative splicing profiles in complex biological systems, visualizing and localizing alternative spliced transcripts with cellular and sub-cellular resolution, probing regulators of alternative splicing, as well as assessing the functional consequences of alternative splicing. Written for the highly successful Methods in Molecular Biology series, chapters include introduction to their respective topics, lists of the necessary materials and reagents, step-by-step, reproducible protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and practical, Alternative Splicing: Methods and Protocols serves as an ideal guide for both RNA aficionados that want to implement novel approaches in their labs and novices undertaking alternative splicing projects.
Machine Learning Techniques On Gene Function Prediction Volume Ii
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Author : Quan Zou
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-11
Machine Learning Techniques On Gene Function Prediction Volume Ii written by Quan Zou and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-11 with Science categories.
Bioinformatics Analyses Of Alternative Splicing Est Based And Machine Learning Based Prediction
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Author : Jing Xia
language : en
Publisher:
Release Date : 2008
Bioinformatics Analyses Of Alternative Splicing Est Based And Machine Learning Based Prediction written by Jing Xia and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
Alternative splicing is a mechanism for generating different gene transcripts (called iso- forms) from the same genomic sequence. Finding alternative splicing events experimentally is both expensive and time consuming. Computational methods in general, and EST analy- sis and machine learning algorithms in particular, can be used to complement experimental methods in the process of identifying alternative splicing events. In this thesis, I first iden- tify alternative splicing exons by analyzing EST-genome alignment. Next, I explore the predictive power of a rich set of features that have been experimentally shown to affect al- ternative splicing. I use these features to build support vector machine (SVM) classifiers for distinguishing between alternatively spliced exons and constitutive exons. My results show that simple, linear SVM classifiers built from a rich set of features give results comparable to those of more sophisticated SVM classifiers that use more basic sequence features. Finally, I use feature selection methods to identify computationally the most informative features for the prediction problem considered.
Handbook Of Machine Learning Applications For Genomics
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Author : Sanjiban Sekhar Roy
language : en
Publisher: Springer Nature
Release Date : 2022-06-23
Handbook Of Machine Learning Applications For Genomics written by Sanjiban Sekhar Roy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-23 with Technology & Engineering categories.
Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.
Crop Improvement By Omics And Bioinformatics
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Author : Yan Zhao
language : en
Publisher: Frontiers Media SA
Release Date : 2024-04-11
Crop Improvement By Omics And Bioinformatics written by Yan Zhao and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-11 with Science categories.
Crop improvement has been continuously driven by the demand for food security and sustainability. The caloric and nutritional needs of a growing world population require that global food production increase by one billion tons over the next few decades, but the current growth rate falls far short. Moreover, rapid changes in the environment are accelerating land degradation, aggravating pests and diseases, introducing extreme stresses, and reducing crop productivity. Genetic technologies and molecular breeding tools offer novel opportunities for modern crop breeding. In the past few decades, remarkable progress has been achieved in the discovery of genes for crop yield, quality, and resistance and in the dissection of plant molecular mechanisms. With the continuous advancement in sequencing technology, molecular markers, and gene editing, a large number of excellent crop varieties have been cultivated.
Applying Machine Learning Techniques To Bioinformatics Few Shot And Zero Shot Methods
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Author : Lilhore, Umesh Kumar
language : en
Publisher: IGI Global
Release Date : 2024-03-22
Applying Machine Learning Techniques To Bioinformatics Few Shot And Zero Shot Methods written by Lilhore, Umesh Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-22 with Computers categories.
Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.