Today, a single laboratory can generate a vast amount of biological data. There is a wealth of data already available in public databases, which makes the modern life sciences almost dependent on bioinformatics. This book brings together an international team of experts to discuss the state-of-the-art from several fields of bioinformatics, from the automatic identification and classification of viruses to the analysis of the transcriptome of single cells and plants, including artificial intelligence algorithms to discover biomarkers and text mining approaches to help in the interpretation of the findings. Machine learning, pattern discovery and analysis, error correction, Bayesian inference and novel computational techniques to discover chromosomal rearrangements continue to play crucial roles in biological discovery, and all of them are explored in chapters of this book. In sum, this book contains high-quality chapters that provide excellent views into key topics of current bioinformatics research, topics that should remain important for the next several years.

Contents

• Foreword
• Preface
• List of Contributors
• 1. Text Mining Gene Selection to Understand Pathological Phenotype Using Biological Big Data
  Christophe Desterke, Hans Kristian Lorenzo, and Jean-Jacques Candelier.
  • INTRODUCTION
  • BIOINFORMATICS TOOLS FOR THE TEXT MINING OF GENE RANKING
  • TEXT MINING GENE SELECTION FOR HYDATIDIFORM MOLES: A CASE STUDY
  • UNDERSTANDING FOCAL SEGMENTAL GLOMERULOSCLEROSIS USING SINGLE CELL TRANSCRIPTOME OF A HEALTHY ADULT DONOR KIDNEY: PROOF OF CONCEPT
  • ONLINE RESULT VISUALIZATION AND THE DEVELOPMENT OF AN INTERACTIVE WEB INTERFACE
  • CONCLUSION
  • REFERENCES
• 2. Single-Cell RNA Sequencing Procedures and Data Analysis
  Markus Wolfien, Robert David, and Anne-Marie Galow.
  • INTRODUCTION
  • THE CAPTURE TECHNIQUE SIGNIFICANTLY DETERMINES THE QUANTITY OF MEASURABLE CELLS
  • THE SEQUENCING TECHNIQUE DETERMINES THE OPTIONS FOR DATA ANALYSES
  • HOW TO CHOOSE THE RIGHT APPROACH?
  • COMPUTATIONAL ANALYSIS
  • DOWNSTREAM ANALYSES
  • CONCLUSION
  • REFERENCES
• 3. Computational Methods for Detecting Large-Scale Structural Rearrangements in Chromosomes
  Muneeba Jilani and Nurit Haspel.
  • INTRODUCTION
  • NGS-BASED METHODS
  • RNA-SEQ BASED METHODS
  • HI-C BASED TECHNIQUES
  • CONCLUSION
  • REFERENCES
• 4. Machine Learning Approaches for Biomarker Discovery Using Gene Expression Data
  Xiaokang Zhang, Inge Jonassen, and Anders Goksøyr.
  • INTRODUCTION
  • EVALUATION OF A BIOMARKER DISCOVERY METHOD
  • COMPARISON OF BIOMARKER DISCOVERY METHODS
  • ENSEMBLE OF MULTIPLE METHODS
  • CONCLUSION
  • REFERENCES
• 5. Bayesian Inference of Gene Expression
  Víctor Jiménez-Jiménez, Carlos Martí-Gómez, Miguel Ángel Del Pozo, Enrique Lara-Pezzi, and Fátima Sánchez-Cabo.
  • INTRODUCTION
  • BUILDING BLOCKS OF BAYESIAN INFERENCE
  • PROBABILISTIC MODELS FOR SINGLE GENE EXPRESSION
  • BAYESIAN INFERENCE OF WHOLE TRANSCRIPTOME EXPRESSION
  • MODELING GENE EXPRESSION ACROSS SAMPLES
  • BAYESIAN DIFFERENTIAL GENE EXPRESSION: A STAN CASE STUDY
  • CONCLUSION
  • REFERENCES
• 6. Comprehensive Evaluation of Error-Correction Methodologies for Genome Sequencing Data
  Yun Heo, Gowthami Manikandan, Anand Ramachandran, and Deming Chen.
  • INTRODUCTION
  • EVALUATION STEPS
  • EXPERIMENTS
  • CONCLUSION
  • REFERENCES
• 7. Plant Transcriptome Assembly: Review and Benchmarking
  Sairam Behera, Adam Voshall, and Etsuko N. Moriyama.
  • INTRODUCTION
  • TRANSCRIPTOME ASSEMBLY STRATEGIES
  • HOW TO EVALUATE TRANSCRIPTOME ASSEMBLY PERFORMANCE
  • HOW TO GENERATE SIMULATED BENCHMARK TRANSCRIPTOME DATASETS
  • PERFORMANCE COMPARISON AMONG TRANSCRIPTOME ASSEMBLERS
  • CONCLUSION
  • REFERENCES
• 8. WeMine Aligned Pattern Clustering System for Biosequence Pattern Analysis
  En-Shiun Annie Lee, Peiyuan Zhou, and Andrew K. C. Wong.
  • INTRODUCTION
  • THE WEMINE SYSTEM
  • APPLICATIONS OF PATTERNS WITH VARYING ASSOCIATION RELATIONSHIPS
  • CONCLUSION
  • REFERENCES
• 9. Rational Design of Profile Hidden Markov Models for Viral Classification and Discovery
  Liliane Santana Oliveira and Arthur Gruber.
  • INTRODUCTION
  • DATABASES OF VIRAL PROFILE HMMS
  • A ROADMAP FOR THE RATIONAL DESIGN OF PROFILE HMMS
  • RATIONAL DESIGN OF PROFILE HMMS
  • SCREENING SEQUENCING DATASETS WITH PROFILE HMMS
  • USING PROFILE HMMS FOR TARGETED SEQUENCE RECONSTRUCTION
  • FINDING MULTIGENE ELEMENTS IN CELLULAR ORGANISMS WITH PROFILE HMMS
  • AN INTEGRATED APPROACH FOR VIRAL RESEARCH USING PROFILE HMMS
  • MINIONDB - A DATABASE OF VIRAL PROFILE HMMS
  • CONCLUSION
  • REFERENCES
• 10. Pattern Discovery and Disentanglement for Aligned Pattern Cluster Analysis and Protein Binding Complexes Detection
  Peiyuan Zhou, En-Shiun Annie Lee, and Andrew K. C. Wong.
  • INTRODUCTION
  • PATTERN DISCOVERY AND DISENTANGLEMENT
  • CASE STUDIES
  • CONCLUSION
  • REFERENCES

Bioinformatics

ISBN: 978-0-6450017-1-6

DOI: https://doi.org/10.36255/exonpublications.bioinformatics.2021

Edited by

Helder I. Nakaya, PhD, Department of Clinical and Toxicological Analyses, School of Pharmaceutical Sciences, University of Sao Paulo, Sao Paulo, Brazil.

Published by

Exon Publications, Brisbane, Australia

Copyright© 2021 Exon Publications

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First Published in March 2021

Printed in Australia