Advances in Biological Science Research: A Practical Approach

Advances in Biological Science Research: A Practical Approach provides discussions on diverse research topics and methods in the biological sciences in a single platform. This book provides the latest technologies, advanced methods, and untapped research areas involved in diverse fields of biological science research such as bioinformatics, proteomics, microbiology, medicinal chemistry, and marine science. Each chapter is written by renowned researchers in their respective fields of biosciences and includes future advancements in life science research.

• Discusses various research topics and methods in the biological sciences in a single platform
• Comprises the latest updates in advanced research techniques, protocols, and methods in biological sciences
• Incorporates the fundamentals, advanced instruments, and applications of life science experiments
• Offers troubleshooting for many common problems faced while performing research experiments

• Language: English
• Publisher: Academic Press
• Release date: May 17, 2019
• ISBN: 9780128174982

Book preview

Advances in Biological Science Research

A Practical Approach

Edited by

Surya Nandan Meena

Biological Oceanography Division, National Institute of Oceanography, Dona Paula, Goa, India

Milind Mohan Naik

Department of Microbiology, Goa University, Goa, India

Table of Contents

Cover image

Title page

Copyright

Contributors

Preface

Acknowledgments

Chapter 1. Bioinformatics methods: Application toward analyses and interpretation of experimental data

1.1. Aim of the chapter

1.2. DNA sequencing

1.3. Identification of organisms from nucleotide sequence

1.4. Microbial ecology statistics

1.5. Biostatistics

1.6. Advanced bioinformatics tools in biological sciences

1.7. Conclusion

Chapter 2. Genome sequence analysis for bioprospecting of marine bacterial polysaccharide-degrading enzymes

2.1. Introduction

2.2. Marine polysaccharides and polysaccharide-degrading bacteria: an overview

2.3. Identification of polysaccharide-degrading genes through genome annotation

2.4. Identification of polysaccharide-degrading genes in newly sequenced bacterial genome: a guide for beginners

2.5. Genome sequence analysis unravels organization of polysaccharide-degrading genes as polysaccharide utilization loci

2.6. Genome annotation: a potential tool for the elucidation of glycometabolism pathways

2.7. CAZy database: a promising tool for the classification of polysaccharide-degrading genes/enzymes identified in newly sequenced genomes

2.8. Validation of computationally identified polysaccharide-degrading genes in the genomes of marine bacteria

Chapter 3. Proteomics analysis of Mycobacterium cells: Challenges and progress

3.1. Introduction

3.2. Proteome analysis of axenic mycobacteria

3.3. Proteome analysis of mycobacteria-infected cells

3.4. Proteome analysis of mycobacteria-containing host vacuoles

3.5. Conclusion

Chapter 4. Plant proteomics: A guide to improve the proteome coverage

4.1. Introduction

4.2. Hurdles associated with plant proteins sample preparation for mass spectrometry–based proteomics

4.3. Primary considerations to design suitable workflows for plant proteomics

4.4. Advances and applications in plant proteomics

4.5. Conclusion and future perspective

Chapter 5. Structural analysis of proteins using X-ray diffraction technique

5.1. Introduction

5.2. Historical background

5.3. X-ray crystallography

5.4. Protein X-ray crystallography

5.5. Advances in protein crystallography

5.6. Case study: extended spectrum β-lactamases

5.7. Conclusion

Chapter 6. Technological advancements in industrial enzyme research

6.1. Introduction

6.2. Enzyme discovery

6.3. Enzyme customization

6.4. Improvement of existing enzymes through mutagenic approaches

6.5. High-throughput screening of genetic variants for novel enzyme production

6.6. Immobilization of enzymes

6.7. Enzyme inhibitor studies

6.8. Enzyme promiscuity and multifunctional enzyme studies

6.9. Sequence-dependent approach of the novel gene encoding the target enzyme/protein

6.10. Function-based identification of the novel gene

6.11. Identification of the novel gene by sequencing techniques

6.12. Improvement of enzymatic catalysis by microbial cell surface display

6.13. Conclusion

Chapter 7. Biotechnological implications of hydrolytic enzymes from marine microbes

7.1. Introduction

7.2. Applications of marine hydrolases

7.3. Prospecting the use of hydrolytic enzymes from marine microbes

Chapter 8. Recent advances in bioanalytical techniques using enzymatic assay

8.1. Introduction

8.2. Classification of biosensors

8.3. Enzyme biosensors for environmental monitoring

8.4. Enzyme biosensors for food quality monitoring

8.5. Future prospects and conclusions

Chapter 9. Microbial lectins: Roles and applications

9.1. Introduction

9.2. Roles and mechanism of lectin action

9.3. Applications of microbial lectins

9.4. Conclusion

Chapter 10. Biodegradation of seafood waste by seaweed-associated bacteria and application of seafood waste for ethanol production

10.1. Introduction

10.2. Materials and methods

10.3. Results and discussion

10.4. Application of seafood waste for bioethanol production

Chapter 11. Phosphate solubilization by microorganisms: Overview, mechanisms, applications and advances

11.1. Introduction

11.2. Phosphate-solubilizing microorganisms: an overview

11.3. Phosphate solubilizing microorganisms: mechanisms

11.4. Phosphate-solubilizing microorganisms: applications and advances

11.5. Conclusion

Chapter 12. Metagenomics a modern approach to reveal the secrets of unculturable microbes

12.1. Introduction

12.2. History of metagenomic approach

12.3. Approach, strategies, and tools used in the metagenomic analysis

12.4. Application of the metagenomic approach

12.5. Conclusion remarks

Chapter 13. Halophilic archaea as beacon for exobiology: Recent advances and future challenges

13.1. Introduction

13.2. Missions with exobiological significance

13.3. Extremophiles–a general overview

13.4. Halophiles in the universe

13.5. Modes of energy generation in halophilic archaea

13.6. Radiation resistance in halophilic archaea

13.7. Halophilic archaea from ancient halite crystals

13.8. Adaptation of halophilic archaea to extreme temperatures and pH

13.9. Growth of halophilic archaea in the presence of perchlorates

13.10. Saline environments in space

13.11. Methods for detecting halophilic archaea in saline econiches

13.12. Conclusion

Chapter 14. Bacterial probiotics over antibiotics: A boon to aquaculture

14.1. Introduction

14.2. The probiotic approach

14.3. Antimicrobial mechanism of probiotics

14.4. Screening and development of probiotics

14.5. Recent probiotics used in aquaculture

14.6. Conclusion and future perspectives

Chapter 15. Recent advances in quorum quenching of plant pathogenic bacteria

15.1. Introduction

15.2. Overview of the different quorum sensing molecules of plant pathogenic bacteria

15.3. Mechanisms of quorum quenching

15.4. Quorum quenching against plant pathogens

15.5. Transgenic plants expressing quorum quenching molecules

15.6. Summary and future research needs

Chapter 16. Trends in production and fuel properties of biodiesel from heterotrophic microbes

16.1. Introduction

16.2. Growth of different sources of biodiesel on various substrates

16.3. Harvesting of cellular biomass from fermentation broth

16.4. Cell lysis

16.5. Lipid extraction

16.6. Transesterification/FAME preparation—conventional two-step, one-step, use of lipases

16.7. Determination of fuel properties of heterotrophic microbes

16.8. Conclusions and future perspectives

Chapter 17. Advances and microbial techniques for phosphorus recovery in sustainable wastewater management

17.1. Introduction

17.2. Technologies for phosphorus recovery

17.3. Struvite crystallization technologies

17.4. Use of struvite as fertilizer and its potential market

17.5. Economic feasibility of struvite recovery process

17.6. Conclusion

Chapter 18. Genotoxicity assays: The micronucleus test and the single-cell gel electrophoresis assay

18.1. Introduction

18.2. Conclusion

Chapter 19. Advances in methods and practices of ectomycorrhizal research

19.1. Introduction

19.2. Benefits of ECM association

19.3. Cultivation and physiology of ECM fungi

19.4. Identification methods of ECM fungi

19.5. Assessment and quantification of ECM

19.6. Stress response and pigments/phenolics in ECM fungi

19.7. Application in forestry: ECM fungi as bioinoculants

19.8. Conclusion

19.9. Future prospects

Chapter 20. Photocatalytic and microbial degradation of Amaranth dye

20.1. Introduction

20.2. Advanced photocatalytic amaranth degradation using titanium dioxide

20.3. Bioremediation of amaranth dye

20.4. Coupling of photocatalysis with bioremediation methods

Chapter 21. Role of nanoparticles in advanced biomedical research

21.1. Introduction

21.2. Cancer therapy

21.3. Metal nanoparticles as drug delivery and anticancer agents

21.4. Metal oxide nanoparticles as drug delivery and anticancer agent

21.5. Carbon-based nanoparticles as drug delivery and anticancer agents

21.6. Conclusions

Chapter 22. Iron-oxygen intermediates and their applications in biomimetic studies

22.1. Introduction

22.2. Mononuclear nonheme iron(III)-superoxo complexes

22.3. Mononuclear nonheme iron(III)-peroxo complex

22.4. Mononuclear nonheme iron(III)-hydroperoxo complex

22.5. Mononuclear high-valent iron(IV)-oxo complex

22.6. Mononuclear nonheme iron(V)-oxo complex

22.7. Application of iron-oxygen intermediates in biomimetics

22.8. Summary

Chapter 23. Frontiers in developmental neurogenesis

23.1. Introduction to neurogenesis

23.2. Signaling pathway cross talk of developmental neurogenesis

23.3. Tools to study developmental neurogenesis

23.4. Conclusion

Chapter 24. Analytical methods for natural products isolation: Principles and applications

24.1. Introduction

24.2. Extraction techniques

24.3. Isolation and purification techniques

24.4. High-performance liquid chromatography

24.5. Spectroscopic methods for characterization

24.6. Chemical profiling of marine sponges: case studies

24.7. Conclusion

Chapter 25. Advanced bioceramics

25.1. Introduction

25.2. Classification of biomaterials

25.3. Applications and properties of bioceramics

25.4. Conclusion and future perspectives

Chapter 26. Production of polyhydroxyalkanoates by extremophilic microorganisms through valorization of waste materials

26.1. Introduction

26.2. Synthesis of polyhydroxyalkanoates

26.3. Classification of PHAs

26.4. Screening, extraction, and characterization of polyhydroxyalkanoates

26.5. Advances in the applications of PHAs

26.6. Extremophilic microorganisms

26.7. Extremophilic microorganisms producing PHAs

26.8. PHAs from renewable resources and agroindustrial wastes

26.9. Conclusions

Chapter 27. Techniques for the mass production of Arbuscular Mycorrhizal fungal species

27.1. Introduction

27.2. Pot/substrate-based mass production system

27.3. The AM host plants

27.4. Root trap cultures

27.5. Plant trap cultures

27.6. Soil as inoculum

27.7. Microenvironment

27.8. Conclusion

Chapter 28. Metagenomics: A gateway to drug discovery

28.1. Introduction

28.2. Approaches to accelerate antibiotic discovery

28.3. Metagenomic or environmental or community genomic sequencing

28.4. How metagenomics facilitates drug discovery

28.5. Conclusion

Chapter 29. Application of 3D cell culture techniques in cosmeceutical research

29.1. Introduction

29.2. Two-dimensional cell system in cosmeceutical research

29.3. Role of three-dimensional cell culture system in cosmeceutical research

29.4. Key features of 3D cell culture

29.5. Diverse application of 3D cell culture

29.6. Preparation of 3D reconstructed human skin model

29.7. Application of 3D skin models in cosmeceutical research

29.8. Conclusion

Chapter 30. Advances in isolation and preservation strategies of ecologically important marine protists, the thraustochytrids

30.1. Introduction

30.2. Occurrence and ecological significance

30.3. Isolation

30.4. Preservation of cultures

30.5. Summary and future prospects

Chapter 31. Advances in sampling strategies and analysis of phytoplankton

31.1. Introduction

31.2. Sampling strategies

31.3. Analysis of phytoplankton

31.4. Primary productivity

31.5. Future perspectives

Index

Copyright

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Contributors

Gauri A. Achari,     Department of Biological Sciences, Birla Institute of Technology and Science Pilani, KK Birla Goa Campus, Zuarinagar, India

Laurence V. Bindschedler,     School of Biological Sciences, Royal Holloway University of London, Egham, Surrey, United Kingdom

Sunita Borkar,     Department of Microbiology, P.E.S's R.S.N. College of Arts and Science, Goa, India

Judith M. Bragança,     Department of Biological Sciences, Birla Institute of Technology and Science (BITS) Pilani, K K Birla, Goa Campus, Zuarinagar, Goa, India

Sandesh T. Bugde,     Department of Chemistry, Goa University, Taleigao Plateau, Goa, India

Lakshangy S. Charya,     Department of Microbiology, Goa University, Taleigao Plateau, Goa, India

Avelyno D'Costa,     Department of Zoology, Goa University, Taleigao Plateau, Goa, India

Priya M. D'Costa,     Department of Microbiology, Goa University, Taleigao Plateau, Goa, India

Varada S. Damare,     Department of Microbiology, Goa University, Taleigao Plateau, Goa, India

Kanchanmala Deshpande,     Department of Chemistry, Goa University, Taleigao Plateau, Goa, India

Shanti N. Dessai,     Department of Zoology, Goa University, Taleigao Plateau, Goa, India

Vazhakatt Lilly Anne Devasia

Department of Biotechnology, Goa University, Goa, India

Present address: Department of Biotechnology, Hindustan College of Arts and Science, Padur, Kelambakkam, Chennai, India

Sunder N. Dhuri,     Department of Chemistry, Goa University, Taleigao Plateau, Goa, India

James Dsouza,     St. Xavier College, Mapusa, Goa, India

Samantha Fernandes,     Department of Biotechnology, Goa University, Taleigao Plateau, Goa, India

Sandeep Garg,     Department of Microbiology, Goa University, Taleigao Plateau, Goa, India

Umesh B. Gawas,     Department of Chemistry, Dnyanprassarak Mandal's College and Research Centre, Assagao, Goa, India

Sanjeev C. Ghadi,     Department of Biotechnology, Goa University, Taleigao Plateau, Goa, India

Shyamalina Haldar,     Department of Microbiology, Goa University, Taleigao Plateau, Goa, India

Sarvesh S. Harmalkar,     Department of Chemistry, Goa University, Taleigao Plateau, Goa, India

Md Imran,     Department of Biotechnology, Goa University, Taleigao Plateau, Goa, India

Srijay Kamat,     Department of Biotechnology, Goa University, Taleigao Plateau, Goa, India

R. Kanchana,     Department of Biotechnology, Parvatibai Chowgule College of Arts and Science -Autonomous, Margao, Goa, India

Savita Kerkar,     Department of Biotechnology, Goa University, Taleigao Plateau, Goa, India

Hetika Kotecha,     Department of Biotechnology, Goa University, Taleigao Plateau, Goa, India

M.K. Praveen Kumar,     Department of Zoology, Goa University, Taleigao Plateau, Goa, India

R.K. Kunkalekar,     Department of Chemistry, Goa University, Taleigao Plateau, Goa, India

Mahesh S. Majik,     Department of Chemistry, Goa University, Taleigao Plateau, Goa, India

Vinod K. Mandrekar,     Department of Chemistry, St. Xavier's College, Mapusa, Goa, India

Kabilan Mani,     Department of Biotechnology, PSG College of Technology, Coimbatore, India

Surya Nandan Meena,     Biological Oceanography Division, National Institute of Oceanography, Dona Paula, Goa, India

Abhishek Mishra,     Dixa Education and Research, Alto Porvorim, Goa, India

Geetesh K. Mishra,     Multiscale Fluid Mechanics Lab, School of Mechanical Engineering, Sungkyunkwan University, Suwon, South Korea

Chellandi Mohandass,     Biological Oceanography Division, National Institute of Oceanography, Dona Paula, Goa, India

Pranay P. Morajkar,     Department of Chemistry, Goa University, Taleigao Plateau, Goa, India

Sajiya Yusuf Mujawar,     Laboratory of Bacterial Genetics and Environmental Biotechnology, Department of Microbiology, Goa University, Goa, India

Usha D. Muraleedharan,     Department of Biotechnology, Goa University, Goa, India

Milind Mutnale,     National Centre for Polar and Ocean Research (NCPOR), Vasco-da-Gama, Goa, India

Srikanth Mutnuri,     Applied and Environmental Biotechnology Laboratory, Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, K K Birla Goa Campus, Zuarinagar, Goa, India

Amarja P. Naik,     Department of Chemistry, Goa University, Taleigao Plateau, Goa, India

Kiran Suresh Naik,     Department of Chemistry, P.E.S.'s R.S.N. College of Arts & Science, Farmagudi, Ponda, Goa, India

Milind Mohan Naik,     Department of Microbiology, Goa University, Goa, India

Ravidas K. Naik,     ESSO-National Centre for Polar and Ocean Research, Vasco, Goa, India

Bhanudas R. Naik,     Department of Chemistry, Goa University, Taleigao Plateau, Goa, India

Prachi Parab,     Department of Microbiology, Goa University, Goa, India

Chhaya Patole,     Proteomics Division, National Centre for Biological Sciences, Bengaluru, India

Flory Pereira,     PES's Ravi Sitaram Naik College of Arts and Science, Department of Microbiology, Ponda, Goa, India

Preethi B. Poduval,     Department of Biotechnology, Goa University, Taleigao Plateau, Goa, India

Meghanath Shambhu Prabhu

Porter School of the Environment and Earth Sciences, Tel Aviv University, Tel Aviv, Israel

Applied and Environmental Biotechnology Laboratory, Department of Biological Sciences, Birla Institute of Technology and Science, Pilani, K K Birla Goa Campus, Zuarinagar, Goa, India

Neha Prabhu,     Department of Microbiology, Goa University, Taleigao Plateau, Goa, India

R. Ramesh,     Crop Improvement and Protection Section, ICAR-Central Coastal Agricultural Research Institute, Old Goa, India

Gouri Raut,     Bioenergy division, Agharkar Research Institute, Pune, India

Ameeta RaviKumar,     Institute of Bioinformatics and Biotechnology (IBB), Savitribai Phule Pune University, Pune, India

Bhakti B. Salgaonkar,     Department of Biological Sciences, Birla Institute of Technology and Science (BITS) Pilani, K K Birla, Goa Campus, Zuarinagar, Goa, India

Sanika Samant,     Department of Biotechnology, Goa University, Goa, India

Suvidha Samant,     Dixa Education and Research, Alto Porvorim, Goa, India

Kashif Shamim,     Laboratory of Bacterial Genetics and Environmental Biotechnology, Department of Microbiology, Goa University, Goa, India

Priyanka V. Shirodkar,     Department of Biotechnology, Goa University, Goa, India

S.K. Shyama,     Department of Zoology, Goa University, Taleigao Plateau, Goa, India

Akshaya Sridhar,     Department of Biotechnology, PSG College of Technology, Coimbatore, India

Abhilash Sundarasami,     Department of Biotechnology, PSG College of Technology, Coimbatore, India

Diviya Chandrakant Vaingankar,     Department of Microbiology, Goa University, Goa, India

Poonam Vashist,     Department of Biotechnology, Goa University, Goa, India

Preface

Biological sciences are the study of life and living organisms, their life cycles, adaptations and environment. "Advances in Biological Sciences – A Practical Approach"describes recent progress in various rapidly growing biological sciences, such as bioinformatics, genomics, metagenomics, proteomics, enzymology, agriculture and marine microbiology, bioremediation, medicinal chemistry, and nanotechnology. This book consists of a total of thirty one chapters, each of which has been contributed by highly qualified professionals (professors, assistant professors, scientists, postdoctoral fellows, and senior research scholars) in the respective fields of research.

The chapter on bioinformatics describes the analysis and interpretation of biological experimental data using online bioinformatics tools or software. The genomics chapter describes the step-by-step strategy for identifying the gene of interest in the newly sequenced bacterial genome. Recent advances in metagenomics-based approaches have revolutionized microbial ecology science and have led to the discovery of some of the new biocatalytic molecules. In addition, metagenomics is undoubtedly the key to the discovery of secondary metabolites that can meet the urgent demand for new medicines from natural sources.

Proteomics chapters provide an overview of appropriate experimental workflows for plant proteomics, suggestions to improve the extraction and preparation of plant protein samples. Further current proteomic analysis is underway to better understand intracellular pathogen survival and disease persistence. An overview of X-ray diffraction technique the working principle, the instrumentation, and its usefulness in the structural characterization of the protein has been detailed. This section describe the various types of lectin proteins from different microbial source and also describe their role and action mechanisms. The section on enzymology explains bioanalytical techniques; principles of various enzyme inhibition assay's parameters required to optimize enzyme testing; and analytical merit characteristics for enzyme testing and its applicability to real sample analysis. Further emphasis is placed on information on routine protocols, aimed at highlighting and better understanding some of the challenges faced during the enzyme characterization/purification studies.

The section on microbiology presents an overview of microorganisms that solubilize phosphates, recent progress and future challenges with halophilic archaea as a beacon for exobiology, the use of probiotics and their mechanisms to control aquatic pathogens, and recent new research findings on the control of plant pathogenic diseases through various quorum quenching strategies. The reader will also be briefed about the advances in microbial techniques for the recovery of phosphorus in sustainable wastewater management, advances in ectomycorrhizal research methods, and techniques for the mass production of fungal arbuscular mycorrhiza.

Some chapters describe the photocatalytic and microbial degradation of amaranth dye, biodegradation of seafood waste by bacteria associated with seaweed and the use of seafood waste for the production of ethanol, the production of Polyhydroxyalkanoates by microorganisms in extreme econiches, with a particular focus on the use of cheaply available waste materials such as carbon substrates for Polyhydroxyalkanoates synthesis.

Chapters on medicinal chemistry present the various analytical techniques for the extraction, purification, and characterization of metabolites from natural sources. Special attention is focused on a discussion on case studies involving isolation of marine, microbial, and terrestrial natural products with the help of suitable examples. Another chapter discusses the advances in bioceramics, their biocompatibility, classification, application, and further research on bioceramics. Latest advancement in application of various 3D reconstructed human skin models for the screening of natural chemical compounds of cosmeceutical potential have been included.

The aquatic science section discusses the advances in sampling strategies and analysis of phytoplankton and current strategies for the isolation and preservation of ecologically important marine protists. Further, some chapters describe the role of nanoparticles in advanced biomedical research with reference to drug delivery and anticancer therapy. For better understanding of the mechanism of DNA damage as well as regions of the genome that are prone to alterations, micronucleus test and the single-cell gel electrophoresis assay techniques are described with the latest modifications.

This book describes the updates on the methodologies and protocols being used by researchers in their routine experiments of biological sciences. Greater emphasis has been given to the basic fundamentals and the latest techniques or methods in routine experiments. We have given equal importance to text and illustrations, therefore, sincere efforts have been made to support the textual clarifications and explanations with the help of flow charts, tables, and figures. It is written in a clear and concise language to enhance the self-motivation of the researchers.

The book will help graduate and postgraduate students to explore their research careers. In addition, recently updated information on various research fields and techniques in biological sciences will definitely benefit university professors, university lecturers, and scientists from different life sciences institutions worldwide.

Surya Nandan Meena

Milind Mohan Naik

Acknowledgments

A book of this nature is possible only when several diligent and hardworking minds come together with a single purpose to make a book of high standards. We editors will need a flower garden to present a flower to all those who have provided invaluable support in compiling this book from concept formulation to the present form.

We are grateful to all the authors who contributed to this book for their excellent knowledge of the multiple aspects of the subject. The contributors made the book truly exceptional and novel. We are sincerely grateful to all reviewers for sharing their valuable time and critical reviews that have really brought the book's quality to the fore. We are extremely grateful to Prof. Sanjeev Ghadi, Prof. Santosh Dubey, Prof. M. K. Janarthanam, Prof. Santosh Tilve, Prof. Prabhat Sharma, Prof. Sandeep Garg and Dr. Ram Swaroop Meena for their advice during the tenure of this work. We thank them for participating in discussions, reviewing the work, and giving us the freedom to approach them.

We would like to thank the all the staff members of the Elsevier book publication team for their direct or indirect support, in particular, Linda Versteeg-Buschman (Acquisitions Editor), Sandra Harron (Editorial Project Manager), Sandhya Narayanan (Copyright Coordinator), and Poulouse Joseph (Production Manager) for their step-by-step technical support. We appreciate the Elsevier facility in the form of an EMSS (electronic submission system for manuscripts). It is a user-friendly online tool that helps to organize the book's large content and is easy to communicate with writers and publishers in two directions.

SNM wants to dedicate efforts to his family members Shree Pana Chand Khokar (father), Smt. Kali Bai (mother), Raghu Nandan (brother), Rukamani and Chandramani (sisters), Rajkumar and Ram Swaroop (brothers in-law) for constant support and inspiration. In addition, SNM recognizes the name of his dearest daughter Bhavya Khokar (Khusi), niece Muskan and dear wife Bhavna for their unseen support in order to achieve this goal. I believe that their presence was energetic to me, and because of them, I could recover myself from the vilest time. MMN proudly acknowledges the name of his loving mother Smt. Manisha Naik and dear wife Pranaya Naik for their constant inspiration.

We would like to acknowledge Prof. Sunil Kumar Singh (Director, NIO, Goa), and Prof. Varun Sahni (Vice-Chancellor, Goa University) and Prof YV Reddy (Registrar Goa University) for the necessary infrastructure and favorable working environment to carry out the task. It would not have been possible for us to undertake the editing of this book involving countless hours, days, and months without the financial support. So here, SNM would like to acknowledge the Department of Science and Technology, Government of India for Financial support through the postdoctoral fellowship scheme (PDF/2016/002012). MMN acknowledges the SERB-DST for financial support (Grant No. YSS/2014/000258).

Surya Nandan Meena

Milind Mohan Naik

Chapter 1

Bioinformatics methods

Application toward analyses and interpretation of experimental data

Shyamalina Haldar     Department of Microbiology, Goa University, Taleigao Plateau, Goa, India

Abstract

The analysis and interpretation of experimental data are the crucial steps in biological research. Multidisciplinary methods through coalition of biological data with statistics are essential for this. Biostatistics and bioinformatics are the platforms that provide support for examination and understanding of the biological information. This chapter focuses on statistical and bioinformatics tools and techniques used to analyze data related to microbial ecology and molecular biology with respect to analysis of nucleotide sequences. Stepwise description of mining, analyses, and interpretation of data is provided in this chapter. This will help to determine the most appropriate method to be applied for a particular analysis to draw the best acceptable inference. In addition, this chapter also enlists the various online available databases and associated tools used to collect and compare biological data and information. Taken altogether, this chapter will enlighten students and researchers with the knowledge on multifarious analytical methods, which will aid better understanding and interpretation of their scientific findings.

Keywords

Analysis; Bioinformatics; Biostatistics; Databases; Ecological; Inference; Information; Interpretation; Mining; Molecular

1.1. Aim of the chapter

This chapter aims to describe the tools and the techniques that are being applied globally for analysis and assessment of biological data. The chapter has been divided into three sections (nucleic acid: 1.2 and 1.3; microbial ecology: 1.4; bio statistics: 1.5).

(1) Section I deals with the bioinformatics methods applied for molecular analyses of nucleic acids. (2) Section II deals with the statistical formulae used to interpret microbial ecological data. (3) Section III describes statistical methods used to compare the biological observations to draw significant conclusions. Care has been taken to present the methods in a stepwise manner with examples for better understanding.

1.2. DNA sequencing

DNA sequencing is the process of determining the order of nucleotides within a DNA molecule. There are two methods of DNA sequencing: Maxam–Gilbert sequencing and Sanger sequencing. The former is a chemical method that chemically modifies the DNA nucleotides and subsequently cleaves the DNA backbone at the sites neighboring to the modified nucleotides [1]. However, due to technical complexity and use of hazardous chemicals, this method is not currently used for standard molecular biology.

Sanger sequencing is the method of DNA sequencing in which dideoxynucleotide phosphates (ddNTPs) are incorporated by DNA polymerase during in vitro DNA replication. Modified ddNTPs terminate DNA strand elongation since they lack a 3′-OH group required for the formation of a phosphodiester bond between two nucleotides, causing DNA polymerase to cease the extension of DNA. Therefore, this is called dye-terminator sequencing. Each of the four ddNTPs (where N   =   A/T/G/C) is labeled with fluorescent dyes that emit light at different wavelengths and therefore can be captured in the form of colored peaks called a chromatogram.

The nucleotide bases of DNA obtained from a chromatogram are converted to text-based FASTA format using the Applied Biosystems to FASTA converter database (www.dnabaser.com/download/Abi-to-Fasta-converter/abi-to-fasta-converter.html). A variety of free software is available for this purpose (chromas, chromaslite, etc.), which can be downloaded and installed.

1.3. Identification of organisms from nucleotide sequence

The DNA sequence obtained in FASTA format uses single-letter codes for each of the nucleotide base without mentioning the source of DNA, i.e., the name of the organism from where the DNA has been isolated. Therefore, the initial analysis of the obtained DNA sequence is to find out the source of DNA, and that is done by Basic Local Alignment Search Tool (BLAST) analysis.

1.3.1. What is BLAST?

BLAST is a program that matches the nucleotides of DNA sequences or the amino acid sequences of proteins. This helps to compare a query sequence (obtained from chromatogram) with a database of sequences (subject sequences,available on the Internet) and identify the sequences from the database that bear a resemblance to the query sequence above a definite threshold. BLAST is classified into different groups based on type of query sequence used (Table 1.1). Of these programs, nucleotide BLAST (BLASTn) and protein blast (BLASTp) are most commonly used since they directly compare the sequences without translations.

1.3.2. Methods for nucleotide BLAST

A stepwise description of nucleotide BLAST analysis is given below.

1. Open NCBI BLAST in Google.

2. Choose BLASTn.

3. Give the FASTA sequence as the query sequence (it must be minimum length of 60 nucleotides) in the blank box provided. Alternatively, you can upload the text file (.txt) or FASTA file (.fq) containing the sequences in FASTA format.

4. Adjust the parameters like database (organism: human, mouse, others [organisms other than mouse/humans]; gene: 16S gene/18S gene, chromosomal genes, etc.) from the dropdown list provided. However, if the organisms are unknown, then you can choose uncultured/environmental sample sequences or the general nucleotide sequences. Click on the [Save Search Strategies] link near the top of the blast results page to save search strategies for future use. You can exclude the organisms that you don't want to be included for comparison by choosing exclude comment.

Table 1.1

4. Click on BLAST.

5. The output looks like as given in Figs.1.1 and 1.2.

6. Click on each of the item (either each colored line/name of the species) to obtain the description.

Figure 1.1 Distribution of BLAST hits with the subject sequences obtained for a query sequence from NCBI BLAST window. Each line denotes one subject sequence with which the query sequence has shown the similarity. Clicking on each line gives the details of the identity of the species with which the similarity is found. The red (grey in print versions) color indicates the hit score between the subject sequences and the query sequence to be greater than 200.

1.3.3. Interpretation of BLAST results

is a unique identifier assigned to a DNA or protein sequence to track the multiple versions of that sequence record and the related sequence over time in a specific database.

Figure 1.2 Species showing sequence similarity with the query sequence. The accession refers to the unique Genbank identifier for the identified species. Clicking on the accession number will provide the FASTA sequence and the details of the submission about the identified species. For description of the other parameters (score, query coverage, E-value, maximum identity), see the text.

1.3.4. Construction and interpretation of phylogenetic tree

The evolutionary relationships between various species and their phylogeny based upon similarities and dissimilarities in their physical or genetic characteristics is represented by a phylogenetic tree (evolutionary tree). The phyla joined together have a common ancestor phylum (Fig. 1.3). The phylogenetic tree can be constructed directly from the output window of BLAST by clicking the option distance tree results, or it can be calculated with all the obtained FASTA sequences using the Molecular Evolutionary Genetic Analysis (MEGA) software. MEGA is free software (www.megasoftware.net) that uses different methods for phylogenomics analyses [2].

The phylogenetic tree is of two types: rooted and unrooted. The rooted tree contains nodes representing the common ancestor of the descendants, and the edge lengths interpret the time estimates. An unrooted tree illustrates only the relatedness of the leaf nodes and does not require the ancestral root to be known or inferred.

Figure 1.3 Phylogenetic tree (circular form) representing a common ancestor (origin) species (bacteria) with the branching arising from it showing the evolutionary and phylogenetic relationship between the different bacterial species.

The phylogenetic tree with bootstrap values calculates the redundancy of a certain character pattern among taxa. A low bootstrap value indicates claim that a certain taxon is not supported well by certain data [3].

1.3.5. Sequence deposition

Experimentally obtained DNA/protein sequences need to be deposited in the public databases for scientific references. The mandatory requirement for publication of data in a journal is the deposition of the obtained sequences in any public sequence repository. The sequences are deposited directly via online portal of the specific database or are sent via email to the respective authorities of the databases after constructing the file using the program sequin (https://www.ncbi.nlm.nih.gov/genbank/submit/opens).

1.4. Microbial ecology statistics

The principal goal of ecology is to determine the spatial and temporal diversity and abundance of organisms in a particular niche to understand the ecosystem functioning. Though the advent of technologies hold great promise to test ecological theories of quantification of microbial taxa in the environment, robust knowledge on the estimation of diversity is necessary to draw conclusions about the environmental composition. Both the cultivation-dependent (plate count methods or microscopy examinations) and the cultivation-independent (gene-based molecular analyses) require analyses of the data using various statistical parameters. A few of the statistical parameters used for the study are discussed next.

1.4.1. Species composition/species richness

The total number of different species present in a particular ecosystem is referred to as species richness (S), which is dependent on type of sampling. Increasing the area sampled increases observed species richness.

For example, the microorganisms can be grouped under different taxa based on their structure, biochemical properties, and sequence analyses. The species richness in a particular region (seawater; mangrove soil; sand dunes; industrial areas; etc.) will be equal to the total number of observed microbial taxa.

Statistically it is expressed by the richness estimators like Chao1 richness estimator, which is given by the formula as:

Where, Sest   =   number of species estimated, Sobs   =   number of species observed, f1   =   number of singleton taxa (taxa with only one species in that community), and f2   =   number of doubleton taxa (taxa with two species in that community). The higher number of singletons in a sample refers to higher number of undetected taxa and the Chao1 index for such cases will be high.

1.4.2. Species abundance

Abundance refers to relative representation of a species in a particular ecosystem. It takes into account the number of individuals found per taxon/group calculated by dividing the number of species from one group (ni) by the total number of species from all groups (n); usually normalized to logarithmic scale. Frequency histograms (Preston Plots) or rank-abundance diagrams (Whittaker Plots; Fig. 1.4) are used to represent abundance of species in a sample. The rank–abundance curve is a 2D chart with relative abundance on the Y-axis and the abundance rank on the X-axis. The highest abundant species is ranked as 1, similarly followed by 2, 3, and so on in descending order. Species richness and evenness together is shown in a rank–abundance curve. Species richness refers to the number of different species on the chart, i.e., how many species were ranked. The slope of the line in a logarithmic curve represents the species evenness. A steep gradient indicates low evenness or an uneven distribution of species as the high-ranking species have much higher abundances as compared to low-ranking species. The more abundant a particular species is in any system, the more dominant will be that species in that particular environment, thereby reducing the overall species diversity in the system. Hence the rank–abundance curve is also called dominance–diversity curve. Conversely, a shallow gradient rank–abundance curve indicates high evenness as the abundances of different species are similar, i.e., the proportion of species (individuals) in different groups (taxa) are similar. The following two examples illustrate the species abundance and richness from two environmental samples.

Figure 1.4 Rank–abundance curve showing the ranking of the species in a niche according to the abundance. The species are ranked from 1 to 10 (X-axis) according to the descending order of their abundance from 0.30 to 0.01 (Y-axis). The highest proportion of abundance (0.25) is ranked 1 while the lowest proportion of abundance (0.1) recorded is ranked 10.

1.4.2.1. Example 1: illustration for species abundance

The data is given in Table 1.2. Here, the highest abundant taxon is Alphaprotebacteria containing the highest number of observed individuals and hence is given the rank 1, followed by Betaproteobacteria (rank 2), Gammaproteobacteria (rank 3), and so on until Spirochaetes with rank 10 containing the least number of individuals (only 6).

1.4.2.2. Example 2: comparison of species abundance with richness

The data is given in Table 1.3. Each of the four communities (A–D) in Table 1.3 has total number of individuals (N)   =   30. However, the distribution of individuals under each taxon and also the total number of taxa are different in them. Both community A and B have species richness (S)   =   3 as the total number of taxa is 3 while for C and D communities S   =   5, thereby indicating these latter two communities to have higher species richness. However, with respect to abundance, the distribution of individuals in each taxon is highly even for community C (6 in each taxon), and all the taxa will have same ranking in rank–abundance curve. The curve will be a straight line. For communities B and D, the distribution of individuals in each taxon is less even; thereby the rank–abundance curve will mark the taxa from 1 to 3 and 1 to 5, respectively, in a descending order with respect to number of individuals beginning with Rhodophyta and Glaucophyta, respectively, as first rank. The curve will be a gradient one. However, as compared to D, the curve will be steeper for B as high-ranking Rhodophyta has a very high number of species compared to low-ranking taxa (Chlorophyta and Glaucophyta). As compared to B andand D, A will have more even distribution.

Table 1.2

Table 1.3

1.4.3. Species diversity

Species diversity is the number of different species represented in a given community that takes into account both the species richness and abundance. Communities that are numerically dominated by one or a few species exhibit low evenness (e.g., community B in Example 2), whereas communities where abundance is distributed equally amongst species exhibit high evenness (e.g., community C in Example 2) (Gotelli and Colwell, 2001). The diversity is expressed by one or more indices that quantify the species diversity. Example: Shannon index (or Shannon–Wiener) [4]; Simpson index [5] and Gini–Simpson index. During interpreting ecological terms, each of these indices corresponds to a different thing and their values are therefore not directly comparable. The Shannon index equals log(qD), where, qD   =   inverse of the weighted average of species proportional abundances and in practice quantifies the uncertainty in the species identity of one random individual from the dataset. The Simpson index is represented by 1/qD that refers to the probability of two random individuals in a dataset (with replacement of the first individual before taking the second) to represent the same species. The Gini–Simpson index is given by formula 1   −   1/qD, which refers to the probability of occurrence of different species by two randomly taken individuals [6–8].

The different formulae for calculation of diversity indices are given below:

1. Shannon index

2. Simpson index

3. Gini-Simpson index

4. Berger–Parker index refers to the highest value for pi in a particular dataset, i.e., the proportional abundance of the most abundant type. This refers to the average of the pi values when n approaches infinity, and hence equals the inverse of true diversity of order infinity.

    e.g., p10=proportion of individuals in 10th group; p1=proportion of individuals in first group etc.

In the Example 1 above:

pi for Alphaproteobacteria=110/400=0.275.

pi for Clostridia=35/400=0.0875.

Therefore,

ln(pi) for Alphaproteobacteria=ln 0.275

ln (pi) for Clostridia=ln (0.0875)

Accordingly,

pi ln(pi) for Alphaproteobacteria=0.275×ln0.275

pi ln (pi) for Clostridia=0.0875×ln(0.0875)

Therefore,

(as we are considering two taxa as Alphaproteobacteria and Clostridia). If we would have considered all the 10 taxa from the above example, then i would be 10 and summation will be of the total products of proportion of individuals and their respective ln values from all the 10 groups.

Similarly, Simpson and Gini–Simpson index can also be calculated using the above formula for this dataset of Example 1. Berger–Parker index for this dataset will be 0.275 (i.e., 110/400, as 110 is the largest number of individuals in a group in that dataset).

The diversity of a particular or local space/region/habitat is called alpha diversity (α diversity) [9,10]. When all the species diversities from all the local regions/habitats are considered together, this is called gamma diversity (λ diversity). The ratio of gamma and alpha diversity is called true beta diversity (β diversity), which refers to the ratio between regional and local species diversity.

. This latter type of β diversity is called absolute species turnover.

When there are two subunits, and presence-absence data are used, this can be calculated with the following equation:

Where, S1   =   the total number of species recorded in the first community, S2   =   the total number of species recorded in the second community, and c   =   the number of species common to both communities.

1.4.3.1. Similarity indices

The most important task is to compare the diversity and abundance of species between different samples so as to understand the similarity in species composition between different environments or under different environmental conditions. This is actually the measurement of beta diversity (between sample comparisons). There are numerous ways to visualize and analyze beta diversity, and a thorough review of multivariate techniques that are commonly used by microbial ecologists is presented by Ramette [11].

Following are a few of the statistical indices used to compute the similarities and dissimilarities between different samples with respect to species constituency [12].

1. Jaccard index or Jaccard similarity coefficient compares the similarity and diversity of different sample sets. It is given by the formula:

    Here, a=number of common taxa between samples; b to z=number of taxa exclusive to different samples.

As in

    This is because A and B communities share all the three phyla while B and C have three common phyla. However, compared to B, the community C has two exclusive phyla that are not there in B. Similarly, C and D have all the five shared phyla and no exclusive phyla for them. Jaccard indexcan vary from 0 to 1, where 1 represents the highest similarity when all the phyla are common between all the communities.

2. Sørensen–Dice coefficient (Sørensen index or Dice's coefficient) is another similarity index given by the following formula.

where, a=common/shared taxa while nb to nz refers to the number of individuals in the taxa that are exclusive to the communities. Sørensen similarity index can vary from 0 to 1, where 1 represents the highest similarity when all the phyla are common between all the communities.

1.4.3.2. Dissimilarity indices

The distance/dissimilarity matrix is computed using either of the two methods:

1. Bray–Curtis dissimilarity [13].

2. UniFrac distances [14].

Bray–Curtis dissimilarity index between two communities is calculated by the following formula:

Where, w   =   total number of taxa present in all the communities, a   =   sum of the measures of taxa in one community, and b   =   sum of the measures of taxa in the other community. When proportional abundance is used, a and b equal to 1 and the index collapses to 1   −   w.

UniFrac distances are based on the branches of the phylogenetic tree constructed with the sequences of the species obtained from the different communities that are either shared or unique amongst samples. It depends on the quality of the input tree.

The Bray–Curtis dissimilarity matrix or UniFrac distance matrix is then used as an input for ordination and clustering analyses like principal coordinates, nonmetric multidimensional scaling, and canonical correspondence analysis (CCA) [11]. CCA is used to determine which taxa correspond with specific environmental variables.

However, presently a variety of online/downloadable software is available that can calculate all these indices, such as PAST [15] and MOTHUR [16], with the number of individuals in each group/taxa taken as the input only. The software can be freely downloaded (MAC or WINDOWS OS).

1.5. Biostatistics

The experimental observations vary between individuals as well as from time to time for an individual. However, dependable inferences cannot be drawn from mere inspection of the observed values. Hence, experimental data need to be evaluated statistically. Biostatistics helps in systematic arrangement of the data; methodical comparison; interpretation and drawing of inference from the observations. It also helps to predict mathematically the most probable values for biological properties or events. Biostatistics is applied for designing, error calculation, and estimation of reliability and validity of the experimental methods.

1.5.1. Sampling statistics

Xi   =   individual score; n   =   sample size.

Standard deviation (SD)   =   Positive square root of the mean of squared deviations of all the scores from their mean.

SD is important because it helps to measure and express numerically the deviations of the scores of a sample from the mean (central value) and thereby indicates the spread or scatter of the scores around the central value. High SD indicates wide dispersion of scores.

The properties of SD are given below:

1. A change in the single score affects the value of SD

2. Addition or subtraction of a constant number from SD does not affect it. However, multiplication and division by a constant number affects SD identically.

3. If all scores have identical value, SD will be zero.

4. In a small sample size (n<30), extreme scores at the two ends of the frequency distribution might be ruled out, thereby lowering the SD value. Hence, to compensate this, degrees of freedom [df=(n−1)] is introduced instead of n and the new SD is called unbiased SD and it is given by the following formula:

Note: The degrees of freedom (df) of a statistic is defined as the number of scores of a variable that can be altered freely in both magnitude and direction without causing any change to the values of such statistics.

Standard error (SE)   =   A measure of the sampling error that is the deviation of that statistic from the corresponding parameter. It is computed for numerous sample statistics like standard error of mean, proportions, SD, etc.

Where, n   =   sample size and N   =   population size from where the n has been drawn by simple random sampling.

1.5.2. Testing of hypothesis

To assess whether the result of any experimental data is significant, the probability (P) of that result is estimated with the help of the standard score obtained from the observed data and the probability of its random occurrence in the population using normal and t-distributions. For this, the probability of correctness of null hypothesis is assessed. Null hypothesis (H0) proposes to nullify the hypothesis of the investigation if the observed value has evolved by chance due to random sampling or would have been false if the entire population was considered, and therefore it states that the results are not significant. On the other hand, the null hypothesis is contested by alternative hypothesis (Ha). Therefore, to draw any conclusion, the results of the experiments are subjected to the testing of these hypotheses.

To study the significance of difference between means of two or more groups or correlation between variables, H0 assumes that there is no significant difference between the observed means or no significant correlation between the variables. Therefore, probability (P) is calculated for H0, and if this estimated P-value does not exceed a particular chosen level of significance (α), the probability of correctness of H0 is negligible. Therefore, in that case, H0 is rejected and the observed results are considered to be significant.

For biological experiments, α is fixed at any of 0.001, 0.01, 0.02, or 0.05.

One-tailed/two-tailed t-test is performed to evaluate the significance of the difference in results between observations. The former takes into account for both the magnitude and the sign while the latter considers the magnitude only.

1.5.3. Probability distribution

Probability (P) of an incidence is the limit attained by the relative frequency of that incident in a large number of observations or trials. The relative frequency is obtained by dividing the frequency of that phenomenon by the sample size (f/n). Now, for large n the probability of the incident is expressed as a distribution of occurrence of the events between different class intervals of the given variable and is called probability distribution. This is expressed in the form of graphs by plotting scores and the probabilities of the variables along X-axis and Y-axis, respectively. This gives rise to a bell-shaped curve called a normal distribution curve if the variable is a continuous measurement variable and the n is very large (n   >   30) (Fig. 1.5). On the other hand, the probability distribution of scores for a small sample (n   <   30) drawn from normal distribution results in a different type of probability distribution called Student's t-distribution after the pseudonym Student of the discoverer, W. S. Gossett. However, t-scores vary with df; hence observed t-value must be referred to the specific t-distribution for that df.

Figure 1.5 Normal distribution curve. The different regions within the area of the curve are marked within the figure.

1.5.3.1. Example

In an experiment, the mean of weights of 16 boys and 16 girls were found to be 40.3 and 37.5   kg, respectively, whereas SD values amounted to 8.15 and 6.35, respectively, for the two groups. Explain whether there the difference between the means of the weights of girls and the boys is significant or not.

Solution

According to the assumption of H0, there is no significant difference between the means of weights of boys and girls. Whether the probability (P) for this H0 is correct, a two-tail t-test was performed.

T-score (1.084) is compared with critical t-scores at df   =   30 for different levels of significance from the t-table, e.g., t 0.05(30)   =   2.042, t 0.02(30)   =   2.457, t 0.01(30)   =   2.750.

Since calculated t-score (1.084) is lower than even the critical t for 0.05 level of significance, the probability (P) for this H0 is correct. Therefore, null hypothesis cannot be rejected and thereby there is no significant difference between the means of two groups in this study (P   >   .005).

1.6. Advanced bioinformatics tools in biological sciences

Bioinformatics is a continuous emerging field as it is utmost necessary to handle, analyze, and store the volumes of diversified data that are constantly generated worldwide. Though discussion of all the new inventions and tools is beyond the scope of this chapter, in the following section a preliminary idea is given on the basic bioinformatics tools related to nucleotide sequence and phylogenetic analyses along with the presently available databases that are used to store and retrieve information. However, the tool to be used depends on the type of analysis needed.

1.6.1. Sequence analysis

Like BLAST, ClustalW and Clustl Omega are used to match the nucleotide or protein sequences to find their evolutionary history or origin based on homology matrices [17,18]. The similarity in profile patterns for nucleotide or protein sequences is obtained by Expression Profiler and Gene Quiz [19,20]. Besides this, a wide range of bioinformatics tools are presently available that are used for primary sequence analysis like JIGSAW (to find genes and to annotate the splicing sites in the selected DNA sequences), novoSNP (to find the single nucleotide variation in the DNA sequence), WebGeSTer (to search for transcription terminator sequences to predict the termination sites of the genes during transcription), Genscan (to predict the