Gene Prediction Applying Ontology And Machine Learning Volume Ii

Gene prediction refers to the process of identifying the regions of genomic DNA that encodes genes using computational methods. It is an important part of bioinformatics. Gene prediction is the first step for annotating large and contiguous sequences. It aids in identifying the essential elements of the genome including functional genes, intron, splicing sites, exon, and regulatory sites. It is also used in describing the individual genes based on their functions. Protein function prediction is an important part of genome annotation. Lately, high-throughput sequencing technologies have led to development of prediction methods. Gene ontology (GO) is one of the databases that are available for identifying the functional properties of proteins. Research in this domain is now focused on efficiently predicting the GO terms. Researches are ongoing on the use of machine learning algorithms for functional prediction as these algorithms use rule-based approaches to integrate large amounts of heterogeneous data and detect patterns. mSplicer, mGene, and CONTRAST are methods that use machine learning techniques for gene prediction. Gene prediction methods are widely used in fields like structural genomics, functional genomics, and genome studies. This book traces the progress of gene prediction and the application of ontology and machine learning. It is appropriate for students seeking detailed information in this area of study as well as for experts.

Gene prediction refers to the process of identifying the regions of genomic DNA that encodes genes using computational methods. It is an important part of bioinformatics. Gene prediction is the first step for annotating large and contiguous sequences. It aids in identifying the essential elements of the genome including functional genes, intron, splicing sites, exon, and regulatory sites. It is also used in describing the individual genes based on their functions. Protein function prediction is an important part of genome annotation. Lately, high-throughput sequencing technologies have led to development of prediction methods. Gene ontology (GO) is one of the databases that are available for identifying the functional properties of proteins. Research in this domain is now focused on efficiently predicting the GO terms. Researches are ongoing on the use of machine learning algorithms for functional prediction as these algorithms use rule-based approaches to integrate large amounts of heterogeneous data and detect patterns. mSplicer, mGene, and CONTRAST are methods that use machine learning techniques for gene prediction. Gene prediction methods are widely used in fields like structural genomics, functional genomics, and genome studies. This book traces the progress of gene prediction and the application of ontology and machine learning. It is appropriate for students seeking detailed information in this area of study as well as for experts.

Gene prediction refers to the process of identifying the regions of genomic DNA that encodes genes using computational methods. It is an important part of bioinformatics. Gene prediction is the first step for annotating large and contiguous sequences. It aids in identifying the essential elements of the genome including functional genes, intron, splicing sites, exon, and regulatory sites. It is also used in describing the individual genes based on their functions. Protein function prediction is an important part of genome annotation. Lately, high-throughput sequencing technologies have led to development of prediction methods. Gene ontology (GO) is one of the databases that are available for identifying the functional properties of proteins. Research in this domain is now focused on efficiently predicting the GO terms. Researches are ongoing on the use of machine learning algorithms for functional prediction as these algorithms use rule-based approaches to integrate large amounts of heterogeneous data and detect patterns. mSplicer, mGene, and CONTRAST are methods that use machine learning techniques for gene prediction. Gene prediction methods are widely used in fields like structural genomics, functional genomics, and genome studies. This book traces the progress of gene prediction and the application of ontology and machine learning. It is appropriate for students seeking detailed information in this area of study as well as for experts.

The objective of biological research is to understand the structural and the functional aspects of life. Though living organisms are diverse in almost every aspect, they are made of cells, and share the same machinery for their basic functions. The structural and functional aspect of life is traceable to genes, given that the information from the genes determine the protein composition and thereby the function of the cell. Hence, predicting the functions of individual genes is the gate way for understanding the blueprint of life. The rationale behind the ongoing genome sequencing projects is to utilize the sequence information to understand the genes and their functions. The exponential increase in the amount of sequence information enunciated the need for an automated approach to acquire knowledge about their biological function. This book introduces the general strategies used in the automated annotation of genes and protein sequences. Specifically, it describes a method utilizing the machine learning approach to predict gene function. This book is addressed to researchers involved in predicting gene function and applying machine learning algorithms to other biological problems.

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.

Advances in DNA sequencing, as well as the rise of shotgun metagenomics and metabolomics, are rapidly producing complex microbiome datasets for studies of human health and the environment. The large-scale sampling of DNA/RNA from microbes provides a window into the microbiome's interactions with its host and habitat, enables us to predict phenotypic traits of the host/microbiome, aids the discovery of emergent biological function, and supports the medical diagnosis. Researchers try to extract features from DNA/RNA sequencing data and make 1) taxonomic predictions ("Who is there"), 2) function annotations ("What they are doing") and 3) host/microbiome phenotype predictions. This work is to explore different computational methods to address challenges in these three fields. First, taxonomic classification relies on NCBI RefSeq database sequences, which are being added at an exponential rate. Therefore, the incremental learning concept is especially important. Although the incremental naive Bayes classifier (NBC) is a decade old concept, it has not been applied to taxonomic classification in the metagenomics field. In this work, I compare the classification accuracy and runtime of the proposed incremental learning implementation of NBC with the performance of the traditional implementation of NBC and demonstrate a proof of concept of how incremental learning can make taxonomic classification much more efficient in its training process, significantly reducing computation while maintaining accuracy. In addition to predicting taxonomic labels for metagenomic samples, researchers are also interested in identifying different subtypes for one virus since mutations can be introduced during the transmission. "Oligotyping" is an entropy analysis tool developed for subtyping taxonomic units based on 16S rRNA sequences. "Oligotyping" was formulated because the 16S rRNA gene is very conservative and there are only very few mutations in the 16S rRNA gene for some lineages. The SARS-CoV-2 genome, being months old, also has a relatively small amount of mutations. Therefore, the entropy analysis developed for 16S rRNA sequences can be adapted for SARS-CoV-2 viral genome subtyping. However, other researchers were only looking at sequence similarity (and subsequent trees) or important single nucleotide variants individually between the genomes. To my knowledge, I am the first to draw on the "Oligotyping" concept to group mutations as a "barcode" of the viral genome and extend it to define subtypes for SARS-CoV-2 viral genomes. I further add error correction to account for ambiguities in the sequences and, optionally, apply further compression by identifying patterns of base entropy correlation. I demonstrate its application in spatiotemporal analyses of real world SARS-CoV-2 sequences responsible for the COVID-19 pandemic. My method is validated by comparing the subtypes defined to similar subtypes discovered in other literature. Third, microbial survey data is not used efficiently for phenotype prediction. For example, a precise Crohn's disease prediction model can help diagnostics given stool samples collected from subjects. To predict Crohn's disease (or another phenotype) from microbiome composition, researchers usually start by grouping sequences that look similar together into an Operation Taxonomic Unit (OTU) or Amplicon Sequence Variant (ASV) and subsequently learn samples by examining OTU occurrences in different phenotypes. However, only looking at sequence similarity ignores the sequential information contained in DNA sequences. Bioinformatics has been inspired by successes in deep learning applications in Natural Language Processing (NLP). Both convolutional neural network (CNN) and recurrent neural network (RNN) have been utilized to learn DNA sequential information for applications such as transcription factor binding site classification. In my work, I propose to adapt deep learning architectures (such as RNN and attention mechanism) that have been widely used in NLP to develop a "phenotype" classifier. This Read2Pheno classifier can predict "phenotype" based on 16S rRNA reads. I demonstrate how the sequential information learned by the proposed model can provide insights on informative regions in DNA sequences/reads while making accurate predictions. The model is validated by comparing its accuracy with other baseline methods such as a random forest model trained with various features (standard OTU/ASV table and k-mers). Forth, there have been different deep learning based functional annotation models proposed recently. However, these models can only output one class of function annotation predictions, such as Gene Ontology (GO). It is convenient to have a tool that can output function predictions for both function annotation databases. In this work, I first extend the proposed Read2Pheno model to a function prediction model, AttentionGO, and compare the performance with both alignment based and deep learning based models to show that the proposed model can achieve comparable performance with additional interpretability. Second, I explore the possibility of using the proposed AttentionGO classifier in a multi-task learning model to predict three branches of GO terms and KEGG Orthology terms simultaneously. The multi-task learning model is compared with single-task models trained with individual tasks to demonstrate performance improvement.

Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to keep pace with this rapidly-advancing field.

This book provides a practical and self-contained overview of the Gene Ontology (GO), the leading project to organize biological knowledge on genes and their products across genomic resources. Written for biologists and bioinformaticians, it covers the state-of-the-art of how GO annotations are made, how they are evaluated, and what sort of analyses can and cannot be done with the GO. In the spirit of the Methods in Molecular Biology book series, there is an emphasis throughout the chapters on providing practical guidance and troubleshooting advice. Authoritative and accessible, The Gene Ontology Handbook serves non-experts as well as seasoned GO users as a thorough guide to this powerful knowledge system. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.