Bioinformatics, and by extension omic sciences – the collective disciplines that are dependent on the use of extensive datasets of biological information – present a challenge of data management for researchers all over the world. Big data collected as part of research projects and experiments can be complex, with several kinds of variables involved. Coupled with continuously changing bioinformatics and information technology tools, there is a need to bring a multidisciplinary approach into these fields. Advances in Bioinformatics, Biostatistics and Omic Sciences attempts to realize an integrated approach between all omic sciences, exploring innovative bioinformatics and biostatistical methodologies which enable researchers to unveil hidden sides of biological phenomena. This volume presents reviews on the following topics which give a glimpse of recent advances in the field: - New Integrated Mitochondrial DNA Bioinformatics Pipeline to Improve Quality Assessment of Putative Pathogenic Variants from NGS Experiments - Variant Calling on RNA Sequencing Data: State of Art and Future Perspectives - An innovative Gene Prioritization Pipeline for WES analyses - New Integrated Differential Expression Approach for RNA-Seq Data Analysis - Innovations in Data Visualization for Straightforward Interpretation of Nucleic Acid Omics Outcomes This volume serves as a guide for graduate students in bioinformatics as well as researchers planning new projects as a part of their professional and academic activities.

Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.

This book constitutes the thoroughly refereed post-conference proceedings of the 12th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2015, held in Naples, Italy, in September, 2015. The 21 revised full papers presented were carefully reviewed and selected from 24 submissions. They present problems concerning computational techniques in bioinformatics, systems biology and medical informatics discussing cutting edge methodologies and accelerate life science discoveries, as well as novel challenges with an high impact on molecular biology and translational medicine.

This book constitutes revised selected papers from the 16th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2019, which was held in Bergamo, Italy, during September 4-6, 2019. The 28 full papers presented in this volume were carefully reviewed and selected from 55 submissions. The papers are grouped in topical sections as follows: Computational Intelligence Methods for Bioinformatics and Biostatistics; Algebraic and Computational Methods for the Study of RNA Behaviour; Intelligence methods for molecular characterization medicine; Machine Learning in Healthcare Informatics and Medical Biology; Modeling and Simulation Methods for Computational Biology and Systems Medicine.

Data Analysis for Omic Sciences: Methods and Applications, Volume 82, shows how these types of challenging datasets can be analyzed. Examples of applications in real environmental, clinical and food analysis cases help readers disseminate these approaches. Chapters of note include an Introduction to Data Analysis Relevance in the Omics Era, Omics Experimental Design and Data Acquisition, Microarrays Data, Analysis of High-Throughput RNA Sequencing Data, Analysis of High-Throughput DNA Bisulfite Sequencing Data, Data Quality Assessment in Untargeted LC-MS Metabolomic, Data Normalization and Scaling, Metabolomics Data Preprocessing, and more. - Presents the best reference book for omics data analysis - Provides a review of the latest trends in transcriptomics and metabolomics data analysis tools - Includes examples of applications in research fields, such as environmental, biomedical and food analysis

This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.

Gene-environment (GE) interaction analysis is a statistical method for clarifying GE interactions applicable to a phenotype or a disease that is the result of interactions between genes and the environment. This book is the first to deal with the theme of GE interaction analysis. It compiles and details cutting-edge research in bioinformatics