Applied Bioinformatics

At last, here is a baseline book for anyone who is confused by cryptic computer programs, algorithms and formulae, but wants to learn about applied bioinformatics. Now, anyone who can operate a PC, standard software and the internet can also learn to understand the biological basis of bioinformatics, of the existence as well as the source and availability of bioinformatics software, and how to apply these tools and interpret results with confidence. This process is aided by chapters that introduce important aspects of bioinformatics, detailed bioinformatics exercises (including solutions), and to cap it all, a glossary of definitions and terminology relating to bioinformatics.

An accessible guide that introduces students in all areas of life sciences to bioinformatics Basic Applied Bioinformatics provides a practical guidance in bioinformatics and helps students to optimize parameters for data analysis and then to draw accurate conclusions from the results. In addition to parameter optimization, the text will also familiarize students with relevant terminology. Basic Applied Bioinformatics is written as an accessible guide for graduate students studying bioinformatics, biotechnology, and other related sub-disciplines of the life sciences. This accessible text outlines the basics of bioinformatics, including pertinent information such as downloading molecular sequences (nucleotide and protein) from databases; BLAST analyses; primer designing and its quality checking, multiple sequence alignment (global and local using freely available software); phylogenetic tree construction (using UPGMA, NJ, MP, ME, FM algorithm and MEGA7 suite), prediction of protein structures and genome annotation, RNASeq data analyses and identification of differentially expressed genes and similar advanced bioinformatics analyses. The authors Chandra Sekhar Mukhopadhyay, Ratan Kumar Choudhary, and Mir Asif Iquebal are noted experts in the field and have come together to provide an updated information on bioinformatics. Salient features of this book includes: Accessible and updated information on bioinformatics tools A practical step-by-step approach to molecular-data analyses Information pertinent to study a variety of disciplines including biotechnology, zoology, bioinformatics and other related fields Worked examples, glossary terms, problems and solutions Basic Applied Bioinformatics gives students studying bioinformatics, agricultural biotechnology, animal biotechnology, medical biotechnology, microbial biotechnology, and zoology an updated introduction to the growing field of bioinformatics.

This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own advantages. He argues that this is the case because unsupervised methods are easy to learn since tensor decomposition is a conventional linear methodology. This book starts from very basic linear algebra and reaches the cutting edge methodologies applied to difficult situations when there are many features (variables) while only small number of samples are available. The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topics. Allows readers to analyze data sets with small samples and many features; Provides a fast algorithm, based upon linear algebra, to analyze big data; Includes several applications to multi-view data analyses, with a focus on bioinformatics.

With reference to India; contributed articles.

This book introduces readers to the basic principles of bioinformatics and the practical application and utilization of computational tools, without assuming any prior background in programming or informatics. It provides a coherent overview of the complex field and focuses on the implementation of online tools, genome databases and software that can benefit scientists and students in the life sciences. Training tutorials with practical bioinformatics exercises and solutions facilitate the understanding and application of such tools and interpretation of results. In addition, a glossary explains terminology that is widely used in the field. This straightforward introduction to applied bioinformatics offers an essential resource for students, as well as scientists seeking to understand the basis of sequencing analysis, functional genomics and protein structure predictions.

Multivariate analysis is a mainstay of statistical tools in the analysis of biomedical data. It concerns with associating data matrices of n rows by p columns, with rows representing samples (or patients) and columns attributes of samples, to some response variables, e.g., patients outcome. Classically, the sample size n is much larger than p, the number of variables. The properties of statistical models have been mostly discussed under the assumption of fixed p and infinite n. The advance of biological sciences and technologies has revolutionized the process of investigations of cancer. The biomedical data collection has become more automatic and more extensive. We are in the era of p as a large fraction of n, and even much larger than n. Take proteomics as an example. Although proteomic techniques have been researched and developed for many decades to identify proteins or peptides uniquely associated with a given disease state, until recently this has been mostly a laborious process, carried out one protein at a time. The advent of high throughput proteome-wide technologies such as liquid chromatography-tandem mass spectroscopy make it possible to generate proteomic signatures that facilitate rapid development of new strategies for proteomics-based detection of disease. This poses new challenges and calls for scalable solutions to the analysis of such high dimensional data. In this volume, we will present the systematic and analytical approaches and strategies from both biostatistics and bioinformatics to the analysis of correlated and high-dimensional data.

The fungal kingdom consists of a wide variety of organisms with a diverse range of forms and functions. Fungi have been utilized for thousands of years and their importance in agriculture, medicine, food production and the environmental sciences is well known. New advances in genomic and metabolomic technologies have allowed further developments in the use of fungi in industry and medicine, increasing the need for a compilation of new applications, developments and technologies across the mycological field. Applied Mycology brings together a range of contributions, highlighting the diverse nature of current research. Chapters include discussions of fungal associations in the environment, agriculture and forestry, long established and novel applications of fungi in fermentation, the use of fungi in the pharmaceutical industry, the growing recognition of fungal infections, current interests in the use fungal enzymes in biotechnology and the new and emerging field of myconanotechnology. Demonstrating the broad coverage and importance of mycological research, this book will be of interest to researchers and students in all biological sciences.

Nowadays it is difficult to imagine an area of knowledge that can continue developing without the use of computers and informatics. It is not different with biology, that has seen an unpredictable growth in recent decades, with the rise of a new discipline, bioinformatics, bringing together molecular biology, biotechnology and information technology. More recently, the development of high throughput techniques, such as microarray, mass spectrometry and DNA sequencing, has increased the need of computational support to collect, store, retrieve, analyze, and correlate huge data sets of complex information. On the other hand, the growth of the computational power for processing and storage has also increased the necessity for deeper knowledge in the field. The development of bioinformatics has allowed now the emergence of systems biology, the study of the interactions between the components of a biological system, and how these interactions give rise to the function and behavior of a living being. This book presents some theoretical issues, reviews, and a variety of bioinformatics applications. For better understanding, the chapters were grouped in two parts. In Part I, the chapters are more oriented towards literature review and theoretical issues. Part II consists of application-oriented chapters that report case studies in which a specific biological problem is treated with bioinformatics tools.

Every researcher in genomics and proteomics now has access to public domain databases containing literally billions of data entries. However, without the right analytical tools, and an understanding of the biological significance of the data, cataloging and interpreting the molecular evolutionary processes buried in those databases is difficult, if