Bioinformatics Associate/ Analyst Facilitator Guide PDF

Facilitator Guide Sector Life Sciences Bioinformatics Sub-Sector Associate/ Contract Research Analyst Occupa on Bioinforma cs Reference ID: LFS/Q3904, Version 2.0 NSQF level: 4 Life Sc...

Facilitator Guide Sector Life Sciences Bioinformatics Sub-Sector Associate/ Contract Research Analyst Occupa on Bioinforma cs Reference ID: LFS/Q3904, Version 2.0 NSQF level: 4 Life Sciences Sector Skill Development Council C/o 14, Palam Marg, Pandav Nagar, Sector B1 Vasant Vihar, New Delhi, Delhi 110057 Phone: 011-41042407 - 08 emali: [email protected] Website: www.lsssdc.in First Edition, December 2023 Under Creative Commons License: Attribution-ShareAlike: CC BY-SA This license lets others remix, tweak, and build upon your work even for commercial purposes, as long as they credit you and license their new creations under the identical terms. This license is often compared to “copyleft” free and open-source software licenses. All new works based on yours will carry the same license, so any derivatives will also allow commercial use. This is the license used by Wikipedia and is recommended for materials that would benefit from incorporating content from Wikipedia and similarly licensed projects. Disclaimer The information contained herein has been obtained from various reliable sources. Life Sciences Sector Skill Development Council disclaims all warranties to the accuracy, completeness or adequacy of such information. LSSSDC shall have no liability for errors, omissions, or inadequacies, in the information contained herein, or for interpretations thereof. Every effort has been made to trace the owners of the copyright material included in the book. The publishers would be grateful for any omissions brought to their notice for acknowledgements in future editions of the book. No entity in LSSSDC shall be responsible for any loss whatsoever, sustained by any person who relies on this material. All pictures shown are for illustration purpose only. The coded boxes in the book called Quick Response Code (QR code) will help to access the e-resources linked to the content. These QR codes are generated from links and YouTube video resources available on Internet for knowledge enhancement on the topic and are not created by LSSSDC. Embedding of the link or QR code in the content should not be assumed endorsement of any kind. LSSSDC is not responsible for the views expressed or content or reliability of linked videos. LSSSDC cannot guarantee that these links/QR codes will work all the time as we do not have control over availability of the linked pages. ii  Skilling is building a be er India. If we have to move India towards development then Skill Development should be our mission. Shri Narendra Modi Prime Minister of India iii Facilitator Guide iv Acknowledgements Life Sciences Sector Skills Council would like to express its gratitude to all the individuals and institutions who contributed in different ways towards the preparation of this “Facilitator Guide”. Without their contribution it could not have been completed. Special thanks are extended to those who collaborated in the preparation of its different modules. Sincere appreciation is also extended to all who provided peer review for these modules. The preparation of this handbook would not have been possible without the Life Sciences Industry’s support. Industry feedback has been extremely encouraging from inception to conclusion and it is with their input that we have tried to bridge the skill gaps existing today in the industry. This facilitator guide is dedicated to the aspiring youth who desire to achieve special skills which will be a lifelong asset for their future endeavours. v  Facilitator Guide About this Guide The facilitator guide (FG) for Bioinformatics Associate/ Analyst is primarily designed to facilitate skill development and training of people, who want to become professional Bioinformatics Associate/ Analyst in various stores. The facilitator guide is aligned to the Qualification Pack (QP) and the National Occupational Standards (NOS) as drafted by the Sector Skill Council (TSSC) and ratified by National Skill Development Corporation (NSDC). It includes the following National Occupational Standards (NOSs)- 1. LFS/N3909: Formulate a series of computer-based algorithms for data management of large repository of biological samples 2. LFS/N3910: Use statistical tool and programming scripts for data mining and data transformation 3. LFS/N3911: Perform Data Delivery and Reporting 4. SSC/N9001: Manage your work to meet requirements 5. LFS/N0107: Coordinate with Supervisors and Other Cross-functional team members 6. DGT/VSQ/N0102: Employability Skills (60 Hours) Post this training, the participants will be able to perform tasks as professional Assistant Technician (Wireless). We hope that this Facilitator Guide provides a sound learning support to our young friends to build a lucrative career in the Telecom Skill Sector of our country. vi  Bioinformatics Associate/ Analyst Table of Contents S. No. Modules and Units Page No 1.  Orientation for Bioinformatics Occupation (Bridge Module) 1 Unit 1.1 - Introduction to Bioinformatics 3 2.  Fundamental Concepts of Bioinformatics (Bridge Module) 7 Unit 2.1 - Concepts of Bioinformatics 9 3.  Introduction to Programming Scripts (LFS/N3910, V2.0) 13 Unit 3.1 - Concepts of Bioinformatics 15 4.  Machine Learning and Image Analysis (LFS/N3910, V2.0) 19 Unit 4.1 - Introduction to Machine Learning 21 5.  Statistical Methods and Tools for Data Extraction and Preparation (LFS/N3910, V2.0) 25 Unit 5.1 - Statistics and Data Analysis Essentials 27 6. Data Mining (LFS/N3910, V2.0) 31 Unit 6.1 - Statistics and Data Analysis Essentials 33 7.  Basics of Algorithm Development and implementation (LFS/N3909, V2.0) 37 Unit 7.1 - Foundations of Algorithmic Design and Programming 39 8.  Introduction to Computational Biology (LFS/N3909, V2.0) 43 Unit 8.1 - Introduction to Computational Biology 45 9.  Introduction to Biological Databases (LFS/N3909, V2.0) 49 Unit 9.1 - Cataloging and Categorizing Biological Databases 51 10.  Biological Data Analysis (LFS/N3909, V2.0) 55 Unit 10.1 - Techniques and Standards 57 11.  Data Delivery and Reporting (LFS/N3911, V2.0) 61 Unit 11.1 - Reporting and Organizing the Results of Data Analysis 63 12. Work Management (LFS/N9001, V2.0) 67 Unit 12.1 - Usage of Appropriate Resources to Meet Work Requirements Efficiently 69 vii Facilitator Guide S. No. Modules and Units Page No 13.  Coordinate with Supervisor and Cross-functional Teams (LFS/N0107, V8.0) 73 Unit 13.1 - Coordination with Manager, Team Members, and Cross-functional Teams 75 14. Employability Skills (DGT/VSQ/N0102) (60 Hrs.) 79 Employability Skills is available at the following location : https://www.skillindiadigital.gov.in/content/list Scan the QR code below to access the ebook 15.  Annexures 81 Annexure I: Training Delivery Plan 82 Annexure II: Assessment Criteria 96 Annexure III: List of QR Codes Used in PHB 101 viii 1. Orientation for Bioinformatics Occupation Unit 1.1 - Introduction to Bioinformatics Bridge Module  Facilitator Guide Key Learning Outcomes By the end of this module, the participants will be able to: 1. Outline the life sciences industry and bioinformatics occupation 2. Illustrate the organizational structure and employment benefits in the life sciences Industry 3. Explain the regulatory framework, rules, and regulations applicable for bioinformatics in the life sciences Industry 4. Explain the role of a Bioinformatics Associate/ Analyst and required skills and its career path 2  Bioinformatics Associate/ Analyst Unit 1.1: Introduction to Bioinformatics Unit Objectives By the end of this unit, the participants will be able to: 1. Identify the basics of life science 2. Discuss the uses of Bioinformatics 3. Describe the scope of work in the life science industry 4. Analyze Rules applicable for bioinformatics in the life sciences Industry 5. Recognize the skills and qualifications needed to work as a Bioinformatics Associate/ Analyst Resources to be Used Laptop/projector for presentations, Whiteboard and markers, Printed handouts on the merging of biology and computer science, Access to relevant websites and online tools showcasing bioinformatics applications Do Engage participants with real-world examples of bioinformatics applications. Encourage questions and discussions throughout the session. Relate concepts to participants’ existing knowledge in biology and technology. Start the session with a engagement activity to familiarize the participants will one another. Activity 1. Activity Name: Name Game (Ice Breaker) 2. Objective: This activity is focused on breaking the ice between the participants so that they can come up confidently in putting forward their opinion 3. Type of Activity: Group activity 4. Resources: Participant Handbook, Pen, Notebook, Writing Pad, etc. 5. Duration of the Activity: 60 minutes 6. Instructions: Arrange the class in a semi-circle/circle Say your name aloud and start playing the game with your name. Say, Now, each of you shall continue with the game with your names till the last person in the circle/ semi-circle participates. Listen to and watch the trainees while they play the game. Ask questions and clarify if you cannot understand or hear a trainee. Discourage any queries related to one’s financial status, gender orientation or religious bias during the game 3  Facilitator Guide Try recognising each trainee by their name because it is not recommended for a trainer to ask the name of a trainee during every interaction 7. Outcome: This activity has focused on breaking the ice between the participants so that they can come up confidently, putting forward their opinion. Say Hello everyone! Welcome to our session on Introduction to Bioinformatics. Today, we’re diving into the exciting world where biology, computer science, and data analysis come together to unlock the mysteries of life. By the end of this session, you’ll understand the pivotal role Bioinformatics Associates/Analysts play in deciphering complex biological information. We’ll explore how their skills contribute to biomedical research, drug discovery, and genomics. Bioinformatics is a rapidly evolving field, shaping the future of healthcare, agriculture, and our understanding of life itself. Today’s knowledge is your gateway to becoming a key player in this dynamic intersection of biology and technology. Ask Can you think of any real-life examples where biology and technology intersect? How do you believe data analysis can contribute to understanding disease mechanisms? In your opinion, why is it essential for bioinformatics experts to keep up with new technologies? Elaborate The role of Bioinformatics Associates/Analysts in understanding DNA sequences and protein structures. How their knowledge contributes to disease mechanisms, drug interactions, and personalized therapy. The significance of bridging the gap between biology and technology for advancements in healthcare and agriculture. Demonstrate Demonstrate a simple bioinformatics tool for DNA sequence analysis, illustrating how technology aids in understanding genetic information. 4  Bioinformatics Associate/ Analyst Activity (1.1.2 Bioinformatics and its Uses) 1. Activity Name: Bioinformatics Exploration 2. Objective: Understand the practical applications of bioinformatics in analyzing genetic data. 3. Type of Activity: Individual 4. Resources: Laptops, access to online bioinformatics tools, sample DNA sequences 5. Time Duration: 25 minutes 6. Instructions: Provide participants with sample DNA sequences. Guide them to use online bioinformatics tools to analyze and interpret the genetic information. Encourage discussions on findings and potential applications. 7. Outcome: Increased understanding of how bioinformatics tools are applied in analyzing biological data. Notes for Facilitation Encourage active participation and open discussions. Be adaptable to participants’ varying levels of familiarity with biology and technology. Emphasize the importance of continuous learning in the rapidly evolving field of bioinformatics. Encourage participants to explore online resources and stay updated on emerging technologies. Foster a collaborative environment where participants can share insights and experiences related to bioinformatics. 5  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. b. Analyze and interpret biological data 2. c. Weather patterns 3. c. Python 4. c. Strong programming and data analysis skills 5. c. Astrophysics Exercise Descriptive Questions: 1. Refer to Unit 1.1: Introduction to Bioinformatics Topic 1.1.5. Bioinformatics Associate/ Analyst as a Profession 2. Refer to Unit 1.1: Introduction to Bioinformatics Topic 1.1.3. Work Pattern Followed in the Bioinformatics Industry 3. Refer to Unit 1.1: Introduction to Bioinformatics Topic 1.1.2. Bioinformatics and its Uses 4. Refer to Unit 1.1: Introduction to Bioinformatics Topic 1.1.2. Bioinformatics and its Uses 5. Refer to Unit 1.1: Introduction to Bioinformatics Topic 1.1.2. Bioinformatics and its Uses 6 2. Fundamental Concepts of Bioinformatics Unit 2.1 - Concepts of Bioinformatics Bridge Module  Facilitator Guide Key Learning Outcomes By the end of this module, the participants will be able to: 1. Explain the basic concepts of bioinformatics 2. Illustrate the key concepts of techniques used in genomics, transcriptomics, and proteomics 3. Establish the modalities of proteomic studies that are applied in the latest research in life sciences 4. Explain the concepts of dynamic protein biology and create processes of application to translate across biological systems 5. Identify molecular markers and concepts of transcriptomics 6. Recall the basics of whole genome sequencing techniques and annotations 8  Bioinformatics Associate/ Analyst Unit 2.1: Concepts of Bioinformatics Unit Objectives By the end of this unit, the participants will be able to: 1. Demonstrate the basics of Bioinformatics 2. Describe genomics, transcriptomics, and proteomics 3. Discuss modalities of proteomic studies 4. Classify Dynamic protein biology 5. Outline the basics of whole genome sequencing techniques and annotations Resources to be Used Projector and slides for presentations, Whiteboard and markers, Printed handouts on genomics, transcriptomics, proteomics, dynamic protein biology, and whole genome sequencing, Access to relevant websites and online tools showcasing bioinformatics applications Say Hello everyone! Today, we embark on an exciting journey into the world of bioinformatics, genomics, and more. Get ready for a deep dive into the molecular intricacies of life! By the end of this session, you’ll grasp the fundamental concepts of bioinformatics, genomics, transcriptomics, proteomics, dynamic protein biology, and whole genome sequencing. These are the building blocks for understanding life at the molecular level. Understanding bioinformatics and its components is like unlocking the secrets of life’s instruction manual. These concepts are pivotal for anyone interested in unraveling the mysteries of genetics and molecular biology. Ask Can you think of any real-life scenarios where genomics or proteomics could be applied for practical benefits? How do you think dynamic protein biology might be crucial in understanding and treating diseases? Have you ever wondered how whole genome sequencing contributes to our understanding of evolution? Do Introduce each concept sequentially, starting with bioinformatics and progressing through genomics, transcriptomics, proteomics, dynamic protein biology, and whole genome sequencing. Encourage participants to take notes and ask questions throughout the session. Share relevant visual aids and examples to enhance understanding. 9  Facilitator Guide Elaborate Explore Bioinformatics ᴑ The multidisciplinary nature of bioinformatics, combining biology, computer science, and data analysis. ᴑ The role of Bioinformatics Associates/Analysts in organizing, analyzing, and interpreting biological data. Dive into Genomics ᴑ Explore the comprehensive study of an organism’s complete genetic material, including DNA sequence and organization. ᴑ Emphasize the insights genomics provides into genetic makeup and potential functions of an organism. Understand Transcriptomics ᴑ Examine the focus on RNA molecules, particularly mRNA, and its role in revealing gene expression patterns, alternative splicing, and regulatory mechanisms. Explore Proteomics ᴑ Cover various techniques in proteomics, including gel electrophoresis, mass spectrometry, and gel- free methods. ᴑ Stress the importance of studying post-translational modifications, protein-protein interactions, and subcellular localization. Delve into Dynamic Protein Biology: ᴑ Highlight the significance of understanding protein interactions, conformational changes, and the impact of post-translational modifications on protein function. ᴑ Explore key areas like protein folding and their roles in cellular processes. Examine Whole Genome Sequencing: ᴑ Introduce revolutionary sequencing technologies such as Sanger sequencing, next-generation sequencing (NGS), and third-generation sequencing. ᴑ Emphasize the role of bioinformatics in processing and interpreting vast sequencing data and genome annotations. Demonstrate Demonstrate a simple bioinformatics tool for sequence alignment or protein structure prediction, illustrating the practical application of bioinformatics in biological research. Activity (2.1.2 Genomics, Transcriptomics, Proteomics and their Applications) 1. Activity name: Genomic Treasure Hunt 2. Objective: Understand the significance of genomic information and the process of genome annotation. 3. Type of Activity: Group 4. Resources: Printed genomic sequences, markers, whiteboard 5. Time Duration: 30 minutes 10  Bioinformatics Associate/ Analyst 6. Instructions: Divide participants into small groups. Provide each group with a printed genomic sequence. Instruct them to identify and mark genes, regulatory elements, and other functional elements on the sequence. Groups present their annotated sequences, and discuss findings as a class. 7. Outcome: Enhanced understanding of genome annotation and the importance of identifying functional elements in genomics. Notes for Facilitation Encourage active participation and foster a collaborative learning environment. Manage time effectively to cover all topics without rushing through. Emphasize the practical applications of each concept discussed, connecting theory to real-world scenarios. Encourage discussions on the ethical implications of genomic and proteomic research. Remind participants of the interdisciplinary nature of bioinformatics, stressing the importance of collaboration between biologists and computational scientists. 11  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. c. An organism’s complete genetic material 2. b. The study of proteins 3. d. The binding of a signaling molecule to a cell surface receptor 4. d. Third-generation sequencing (e.g., PacBio or Nanopore). 5. b. To identify genes, regulatory regions, and other features in the genome. Descriptive Questions: 1. Refer to Unit 2.1: Concepts of Bioinformatics Topic 2.1.1 Basics of Bioinformatics 2. Refer to Unit 2.1: Concepts of Bioinformatics Topic 2.1.2 Genomics, Transcriptomics, Proteomics and their Applications 3. Refer to Unit 2.1: Concepts of Bioinformatics Topic 2.1.2 Genomics, Transcriptomics, Proteomics and their Applications 4. Refer to Unit 2.1: Concepts of Bioinformatics Topic 2.1.2 Genomics, Transcriptomics, Proteomics and their Applications 5. Refer to Unit 2.1: Concepts of Bioinformatics Topic 2.1.2 Genomics, Transcriptomics, Proteomics and their Applications 12 3. Introduction to Programming Scripts Unit 3.1 - Concepts of Bioinformatics LFS/N3910, V2.0  Facilitator Guide Key Learning Outcomes By the end of this module, the participants will be able to: 1. Explain the basic techniques used to create scripts for automating system administration tasks 2. Demonstrate the use of programming scripts to develop or customize prototype applications 3. Classify Dynamic protein biology 4. Outline and analyze the programming script to test various customized programming applications 5. Demonstrate the use of pattern matching with regular expressions in processing text 14  Bioinformatics Associate/ Analyst Unit 3.1: Concepts of Bioinformatics Unit Objectives By the end of this unit, the participants will be able to: 1. Illustrate the techniques used to create scripts for automating system administration tasks 2. Describe the use of scripting and role of programming scripts 3. Discuss Dynamic protein biology 4. Outline the pattern matching with regular expressions in processing text Resources to be Used Presentation slides on Bioinformatics Associate/Analyst roles, programming scripts, and web frameworks, Code snippets and examples using Flask and Django, Visual aids demonstrating dynamic protein biology and protein behavior, Sample applications for testing new algorithms and tools, Regular expression examples for sequence annotation, Whiteboard and markers. Do Familiarize yourself with programming scripts, Flask, and Django to effectively guide participants. Prepare relevant code snippets and applications for demonstration. Ensure the availability of visual aids and samples for dynamic protein biology studies. Set up a system for participants to practice coding during the session. Test regular expression examples to showcase their application in bioinformatics. Plan interactive activities to reinforce learning. Say Hello everyone! Welcome to today’s session on Concepts of Bioinformatics. I’m excited to explore the fascinating world of bioinformatics with you. Today, we’ll delve into the role of Bioinformatics Associates/Analysts, creating prototype applications using programming scripts and web frameworks. Our objective is to understand the importance of these applications in testing new algorithms, exploring dynamic protein biology, and utilizing regular expressions for sequence annotation. Bioinformatics is at the forefront of modern biology, bridging computer science and life sciences. Understanding these concepts will empower you to contribute to cutting-edge research, disease mechanisms, and drug development. 15  Facilitator Guide Ask Can you think of any examples where programming scripts and web frameworks are used in daily life or industry? How do you think the study of protein behavior can impact our understanding of diseases and drug development? Have you ever encountered regular expressions in any online forms or applications? How were they used? Elaborate Bioinformatics Associates/Analysts create prototype applications using programming scripts and web frameworks like Flask or Django to test new algorithms and tools. These applications showcase innovative data analysis methods and visualization techniques, especially in dynamic protein biology studies crucial for disease mechanisms and drug development. Customized applications require thorough testing to ensure accuracy and reliability. Analyzing scripts is essential for verifying algorithm implementation, data handling, and producing accurate results. Regular expressions are powerful tools for text processing in bioinformatics, particularly for sequence annotation. Demonstrate Demonstrate the creation of a simple prototype application using Flask or Django, highlighting the testing process for new algorithms. Activity (3.1.2 Use of Scripting and Role of Programming Scripts) 1. Activity name: Bioinformatics Coding Challenge 2. Objective: Participants will practice creating a prototype bioinformatics application using Flask or Django, integrating dynamic protein biology concepts and regular expressions. 3. Type of Activity: Group 4. Resources: Laptops with Python installed, code snippets, sample datasets, whiteboard for brainstorming. 5. Time Duration: 30 minutes 6. Instructions: Divide participants into small groups. Provide a coding challenge related to dynamic protein biology and sequence annotation. Encourage collaboration and problem-solving. Each group presents their prototype application and explains the coding decisions. Facilitate a brief discussion on different approaches and solutions. 7. Outcome: Participants gain hands-on experience in creating bioinformatics applications, reinforcing their understanding of the topics covered. 16  Bioinformatics Associate/ Analyst Notes for Facilitation Encourage active participation and questions. Foster a collaborative learning environment. Emphasize the importance of thorough testing for bioinformatics applications. Guide participants in practical analysis of scripts for algorithm verification. Highlight the significance of regular expressions in text processing for sequence annotation. 17  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. a. JavaScript 2. d. Shell scripting 3. c. To quickly create and test application concepts 4. c. Study of proteins’ dynamic behavior and functions Descriptive Questions: 1. Refer to Unit 3.1: Concepts of Bioinformatics Topic 3.1.1 Techniques used to create scripts for automating system administration tasks 2. Refer to Unit 3.1: Concepts of Bioinformatics Topic 3.1.2 Use of Scripting and Role of Programming Scripts 3. Refer to Unit 3.1: Concepts of Bioinformatics Topic 3.1.2 Use of Scripting and Role of Programming Scripts 4. Refer to Unit 3.1: Concepts of Bioinformatics Topic 3.1.3 Dynamic Protein Biology 5. Refer to Unit 3.1: Concepts of Bioinformatics Topic 3.1.2 Use of Scripting and Role of Programming Scripts 18 4. Machine Learning and Image Analysis Unit 4.1 - Introduction to Machine Learning LFS/N3910, V2.0  Facilitator Guide Key Learning Outcomes By the end of this module, the participants will be able to: 1. Articulate the basics of machine learning, including linear models, nearest neighbors, probabilistic ML, and computer vision. 2. Illustrate various Python models like support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, and K-means clustering. 3. Discuss the validation process of machine learning models 4. Describe theoretical concepts, practice boosting and bagging techniques, and decode accuracy metrics 20  Bioinformatics Associate/ Analyst Unit 4.1: Introduction to Machine Learning Unit Objectives By the end of this unit, the participants will be able to: 1. Explain machine learning and its models 2. Discuss about Python models 3. Describe linear models, nearest neighbors, probabilistic ML, and computer vision 4. Outline the testing process of models 5. Explain bagging techniques, decode accuracy metrics, and theatrical concepts Resources to be Used Presentation slides on Machine Learning fundamentals, Python for ML, TensorFlow, and scikit-learn, Code examples illustrating linear regression, classification, and probability distributions, Visual aids for interpreting visual data, such as images and videos, Evaluation metrics handouts covering accuracy, precision, recall, F1- score, and ROC-AUC, Whiteboard and markers, Laptops with Python and relevant libraries installed Do Familiarize yourself with Python, TensorFlow, and scikit-learn. Prepare code snippets for linear regression, classification, and probability distributions. Set up a system for participants to practice coding during the session. Ensure visual aids and examples for interpreting visual data are ready. Review evaluation metrics and cross-validation techniques. Plan interactive activities to reinforce learning. Say Hello everyone! Welcome to today’s session on Introduction to Machine Learning. I’m thrilled to explore the exciting world of machine learning with you. Today, we’ll dive into the fundamentals of machine learning, from the basics of training models to evaluating their performance. Our goal is to understand how machine learning enables computers to learn from data and make predictions without explicit programming. Machine learning is revolutionizing various industries, from healthcare to finance. Understanding these concepts will empower you to harness the power of data to make informed predictions and decisions. 21  Facilitator Guide Ask Can you think of any daily life examples where predictions are made based on data, and the system learns from its mistakes? Have you ever used or heard of Python for any applications? How do you think it could be useful in machine learning? In what situations do you encounter visual data, and how could machine learning help in interpreting or analyzing it? Elaborate Machine Learning enables computers to learn from data, allowing models to make predictions or decisions without explicit programming. Python is a widely-used language for building ML models, with libraries like TensorFlow and scikit-learn providing essential tools. Find linear relationships in data for regression and classification. Make predictions based on similar data points by incorporating uncertainty and probability distributions into predictions. Focus on interpreting visual data, such as images and videos, using machine learning techniques. Evaluation involves metrics like accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation ensures model generalization. Bagging methods (e.g., Random Forest) combine multiple models to reduce variance. Accuracy metrics help assess model performance. Theoretical concepts like bias-variance tradeoff, Occam’s Razor, and the No Free Lunch Theorem guide model development and selection. Demonstrate Demonstrate the creation of a simple linear regression model using Python and visualize the results using a scatter plot. Activity (4.1.2 Python Models) 1. Activity name: Model Evaluation Challenge 2. Objective: Participants will practice evaluating machine learning models using different metrics and understanding the bias-variance tradeoff. 3. Type of Activity: Group 4. Resources: Laptops with Python and relevant libraries, dataset for classification, whiteboard for discussion. 5. Time Duration: 30 minutes 6. Instructions: Divide participants into small groups. Provide a dataset for a classification problem. 22  Bioinformatics Associate/ Analyst Ask each group to build and evaluate a classification model using different metrics. Discuss the results, emphasizing the importance of choosing appropriate evaluation metrics. 7. Outcome: Participants gain practical experience in evaluating machine learning models and understanding the impact of metrics on model selection. Notes for Facilitation Encourage collaboration and discussion among participants. Provide additional resources for further exploration. Emphasize the significance of evaluation metrics in assessing model performance. Discuss the practical implications of the bias-variance tradeoff in model development. Encourage participants to explore real-world applications of machine learning in their fields of interest. 23  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. a. A branch of AI focused on data analysis 2. c. Python 3. d. Logistic Regression 4. a. A method for combining multiple models to reduce variance 5. b. To assess model generalization and reduce over-fitting Descriptive Questions: 1. Refer to Unit 4.1: Introduction to Machine Learning Topic 4.1.1 Machine Learning and Its Models 2. Refer to Unit 4.1: Introduction to Machine Learning Topic 4.1.2 Python Models 3. Refer to Unit 4.1: Introduction to Machine Learning Topic 4.1.3 Linear Models, Nearest Neighbors, Probabilistic ML, and Computer Vision 4. Refer to Unit 4.1: Introduction to Machine Learning Topic 4.1.5 Bagging and Boosting 5. Refer to Unit 4.1: Introduction to Machine Learning Topic 4.1.6 Bagging Techniques, Decode Accuracy Metrics, and Theatrical Concepts 24 5. Statistical Methods and Tools for Data Extraction and Preparation Unit 5.1 - Statistics and Data Analysis Essentials LFS/N3910, V2.0  Facilitator Guide Key Learning Outcomes By the end of this module, the participants will be able to: 1. Outline data characteristics and its distribution 2. Explain the basic concepts of descriptive statistics, correlation, and regression 3. Identify Bayes theorem, sampling, distribution and hypothesis theorem 4. Describe the methods of data analysis and statistical tools 5. Apply the basics of inferential statistics for data 6. Interpret statistical outputs to inform work-oriented decisions 7. Demonstrate different methods of data analysis for the problem under investigation 8. Apply the descriptive statistics methods for quantitative reasoning and data visualization 9. Practice various statistical tools to manage data, run analyses and produce data visualization 26  Bioinformatics Associate/ Analyst Unit 5.1: Statistics and Data Analysis Essentials Unit Objectives By the end of this unit, the participants will be able to: 1. Explain the data characteristics 2. Basic concepts of descriptive statistics, correlation, and regression 3. Demonstrate Bayes theorem, sampling, distribution and hypothesis theorem 4. Describe the methods of data analysis and statistical tools 5. Discuss inferential statistics 6. Demonstrate different methods of data analysis for the problem under investigation 7. Apply the descriptive statistics methods and practice various statistical tools Resources to be Used Presentation slides covering key topics in statistics and data analysis, Descriptive statistics examples and data sets for hands-on practice, Visual aids for probability distributions, histograms, and data visualization techniques, Statistical software such as R, Python (with NumPy and Pandas), SAS, and Excel, Whiteboard and markers, Laptops with statistical software installed. Do Familiarize yourself with statistical software and data analysis techniques. Prepare examples and datasets for hands-on practice. Set up a system for participants to practice statistical analyses during the session. Ensure visual aids and software are ready for demonstration. Review key statistical concepts and terminology. Plan interactive activities to reinforce learning. Say Hello everyone! Welcome to our exploration of Statistics and Data Analysis Essentials. I’m thrilled to guide you through the fundamental concepts that form the backbone of understanding and interpreting data. Today, we’ll delve into the core principles of statistics and data analysis. Our objective is to equip you with the knowledge and skills needed to explore, summarize, and draw meaningful insights from data. Whether you’re in business, science, or any field dealing with data, a solid grasp of statistics is essential. It’s the key to making informed decisions and drawing reliable conclusions from data patterns. 27  Facilitator Guide Ask Can you think of situations in your daily life where understanding data characteristics, like central tendency or variability, could be beneficial? Have you ever encountered situations where data analysis played a crucial role in decision-making? How do you think descriptive statistics and data visualization can aid in presenting information effectively? Elaborate Data characteristics involves exploring key attributes like central tendency, variability, and distribution shape, often visualized through histograms or probability density functions. Descriptive statistics are fundamental for summarizing and presenting data. They encompass measures like mean, median, mode, and variability measures such as standard deviation. Bayesian Theorem is used to update probabilities, and sampling involves selecting a subset of data points from a larger population. Probability distributions, like the normal distribution, underpin many statistical analyses. Various methods of data analysis include exploratory data analysis (EDA), inferential statistics, machine learning, and more. Statistical tools encompass software like R, Python, SAS, and Excel. Inferential statistics involve drawing conclusions about populations from sample data. Confidence intervals provide estimates of population parameters, while hypothesis tests determine statistical significance. Interpreting statistical outputs is crucial for informed decision-making. P-values, effect sizes, and confidence intervals play key roles in understanding results. Different problems require tailored data analysis methods, such as time series analysis for temporal data and cluster analysis for identifying groupings within data. Descriptive statistics, including measures of central tendency and variability, are used to summarize data. Statistical tools are applied to manage and analyze data. Data cleaning, preprocessing, and visualization tools help in effective data analysis. Data analysis and statistical methods are indispensable for making sense of data, drawing conclusions, and informing decision-making across diverse fields. Demonstrate Demonstrate the process of creating a histogram using statistical software to visualize the distribution of data. 28  Bioinformatics Associate/ Analyst Activity (5.1.1 Data Characteristics in Bioinformatics) 1. Activity name: Data Analysis Challenge 2. Objective: Participants will practice descriptive statistics and data visualization on a provided dataset. 3. Type of Activity: Group 4. Resources: Laptops with statistical software, dataset, whiteboard for discussion. 5. Time Duration: 30 minutes 6. Instructions: Divide participants into small groups. Provide a dataset with instructions for calculating descriptive statistics and creating visualizations. Each group presents their findings, discussing patterns and insights. 7. Outcome: Participants gain hands-on experience in applying descriptive statistics and data visualization techniques to real-world data. Notes for Facilitation Encourage collaboration and active participation. Provide additional resources for further self-study. Emphasize the practical application of statistical methods in various domains. Encourage participants to explore and apply statistical tools relevant to their fields. Highlight the importance of clear communication of statistical results for effective decision-making. 29  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. d. Variance 2. b. Strength and direction of a relationship between variables 3. b. Updating probabilities based on new evidence 4. a. The complexity of the tool Descriptive Questions: 1. Refer to Unit 5.1: Statistics and Data Analysis Essentials Topic 5.1.1: Data Characteristics in Bioinformatics 2. Refer to Unit 5.1: Statistics and Data Analysis Essentials Topic 5.1.2 Descriptive Statistics, Correlation, and Regression 3. Refer to Unit 5.1: Statistics and Data Analysis Essentials Topic 5.1.3: Bayes Theorem, Sampling, Distribution and Hypothesis Theorem 4. Refer to Unit 5.1: Statistics and Data Analysis Essentials Topic 5.1.6 Methods of Data Analysis 5. Refer to Unit 5.1: Statistics and Data Analysis Essentials Topic 5.1.5 Inferential Statistics for Data 30 6. Data Mining Unit 6.1 - Statistics and Data Analysis Essentials LFS/N3910, V2.0  Facilitator Guide Key Learning Outcomes By the end of this module, the participants will be able to: 1. Acquire data warehouse basics, its lifecycle and implementation 2. Demonstrate the skills to classify and cluster data using outlier analysis 3. Describe different forecasting techniques 4. Describe the concept of Hadoop and Rlanguage 5. Outline the data analytics project lifecycle 6. Plan for the data import from different databases 7. Perform data mining from a large source of data 32  Bioinformatics Associate/ Analyst Unit 6.1: Statistics and Data Analysis Essentials Unit Objectives By the end of this unit, the participants will be able to: 1. Discuss data warehouse basics 2. Demonstrate outlier analysis 3. Outline forecasting techniques 4. Identify Hadoop and Rlanguage 5. Perform data import and data mining Resources to be Used Presentation slides covering key topics in statistics and data analysis, Examples and datasets for hands- on practice, Visual aids for data warehousing, data classification, clustering, forecasting, Hadoop, R, data analytics lifecycle, database querying, and data mining, Whiteboard and markers, Laptops with relevant software installed. Do Familiarize yourself with data warehousing concepts, clustering techniques, forecasting methods, Hadoop, R, and data mining algorithms. Prepare examples and datasets for hands-on practice. Set up a system for participants to practice analytics techniques during the session. Ensure visual aids and software are ready for demonstration. Review key concepts related to data analytics. Plan interactive activities to reinforce learning. Say Hello everyone! Welcome to our journey through Statistics and Data Analysis Essentials. I’m excited to explore the crucial aspects of data analytics with you. Today, we’ll dive into the fundamentals of data warehousing, clustering, forecasting, and data analytics tools. Our goal is to equip you with the knowledge and skills needed to analyze and extract valuable insights from data. In the dynamic field of data analytics, understanding these essentials is like having a toolkit for uncovering meaningful patterns, making predictions, and ultimately driving informed decision-making. 33  Facilitator Guide Ask Can you think of situations in your daily life where data warehousing principles could be beneficial, perhaps in organizing and managing information? How do you imagine clustering techniques might be useful in categorizing or identifying patterns in large datasets? Have you ever encountered situations where forecasting techniques could have helped in planning for the future, based on historical data? Elaborate Understanding the fundamentals of data warehousing involves knowledge of data collection, integration, storage, and retrieval. The data warehousing lifecycle encompasses ETL, data modeling, and reporting. Data classification and clustering techniques are essential skills in data analytics, helping categorize data into meaningful groups or identifying outliers for valuable insights. Familiarity with forecasting techniques, including time series analysis, regression, and machine learning models, is crucial for making informed predictions based on historical data. Hadoop, a distributed computing framework, and R, a programming language for statistical computing, are essential technologies for handling large datasets and performing advanced analytics tasks. Data analytics projects follow a structured lifecycle, including defining objectives, data collection, exploratory data analysis (EDA), model building, validation, and deployment. Data analysts often need to work with data from various sources, including databases. Importing data requires knowledge of database querying languages like SQL to ensure accuracy and consistency. Data mining involves discovering patterns, relationships, and insights within large datasets using various algorithms such as association rule mining, clustering, and classification. Demonstrate Demonstrate the process of data classification and clustering using a sample dataset, highlighting the identification of meaningful groups and outliers. Activity (6.1.2 Outlier Analysis in Bioinformatics) 1. Activity name: Data Analytics Lifecycle Simulation 2. Objective: Participants will simulate the lifecycle of a data analytics project, from defining objectives to model deployment. 3. Type of Activity: Group 4. Resources: Whiteboard, markers, laptops with analytics software. 5. Time Duration: 30 minutes 6. Instructions: Divide participants into groups. Assign each group a stage of the data analytics lifecycle. 34  Bioinformatics Associate/ Analyst Groups collaborate to discuss challenges and make decisions related to their assigned stage. Each group presents their findings and decisions to the class. 7. Outcome: Participants gain a practical understanding of the data analytics lifecycle and the importance of each stage in project success. Notes for Facilitation Encourage collaboration and active participation. Foster an open environment for questions and discussions. Emphasize the practical application of data analytics techniques in real-world scenarios. Highlight the role of each technology and method in solving specific analytical challenges. Encourage participants to explore real-world applications of data analytics in their fields of interest. 35  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. b. Storing and managing large volumes of biological data 2. a. Data extraction 3. a. Identify data errors or anomalies 4. c. Principal Component Analysis (PCA) 5. c. Distributed data storage and processing Descriptive Questions: 1. Refer to Unit 6.1: Statistics and Data Analysis Essentials Topic 6.1.3 Forecasting Techniques in Bioinformatics 2. Refer to Unit 6.1: Statistics and Data Analysis Essentials Topic 6.1.1 Basics of Data Warehouse 3. Refer to Unit 6.1: Statistics and Data Analysis Essentials Topic 6.1.2 Outlier Analysis in Bioinformatics 4. Refer to Unit 6.1: Statistics and Data Analysis Essentials Topic 6.1.3 Forecasting Techniques in Bioinformatics 5. Refer to Unit 6.1: Statistics and Data Analysis Essentials Topic 6.1.4 Hadoop and R language in Bioinformatics Topic 5.1.5 Inferential Statistics for Data 36 7. Basics of Algorithm Development and implementation Unit 7.1 - Foundations of Algorithmic Design and Programming LFS/N3909, V2.0  Facilitator Guide Key Learning Outcomes By the end of this module, the participants will be able to: 1. Explain program design methods 2. Describe algorithm development structures 3. Demonstrate structured programming rules 4. Apply divide and conquer techniques 5. Use logical and algorithmic thoughts 6. Implement data processing and analysis 7. Use algorithm in collective data processing 38  Bioinformatics Associate/ Analyst Unit 7.1: Foundations of Algorithmic Design and Programming Unit Objectives By the end of this unit, the participants will be able to: 1. Outlineprogram design methods 2. Defineand apply algorithm development structures 3. Recall structured programming rules 4. Summarize divide and conquer techniques 5. Illustrate data processing and analysis Resources to be Used Presentation slides covering key topics in algorithmic design and programming, Examples and datasets for hands-on practice, Visual aids for program design, algorithm development, structured programming, divide and conquer techniques, logical and algorithmic thinking, and data processing in bioinformatics, Whiteboard and markers, Laptops with programming environments installed. Do Familiarize yourself with program design methods, algorithm development structures, structured programming rules, divide and conquer techniques, logical and algorithmic thinking, and data processing in bioinformatics. Prepare examples and datasets for hands-on coding exercises. Set up a system for participants to practice coding during the session. Ensure visual aids and coding environments are ready for demonstration. Review key concepts related to algorithmic design and programming. Plan interactive coding activities to reinforce learning. Say Hello everyone! Welcome to our exploration of Foundations of Algorithmic Design and Programming. I’m thrilled to guide you through the essential principles that form the backbone of designing efficient algorithms for solving computational problems. Today, we’ll delve into program design methods, algorithm development, structured programming, and the application of these concepts in bioinformatics. Our goal is to equip you with the skills needed to tackle computational challenges in the field. Whether you’re aspiring to be a bioinformatics analyst or simply looking to enhance your programming skills, understanding these foundations is crucial. It’s about crafting elegant solutions to complex problems efficiently. 39  Facilitator Guide Ask Can you think of instances in your daily life where breaking down a complex problem into smaller parts would make it more manageable? How do you think logical and algorithmic thinking could benefit you in problem-solving, not just in programming but in various aspects of your work or studies? Have you ever encountered situations where structured programming principles could have made code more readable and maintainable? Elaborate Program design methods involve systematically planning and creating software solutions, crucial for tasks like sequence analysis, structural biology, and data visualization in bioinformatics. Algorithm development structures refer to the strategies and frameworks used to create efficient algorithms for tasks such as sequence alignment, genome assembly, and protein structure prediction in bioinformatics. Structured programming rules guide code organization to improve readability and maintainability, ensuring that bioinformatics tools are modular and efficiently manage data and control flow. Divide and conquer techniques involve breaking down complex problems into smaller, manageable subproblems, applied in bioinformatics for tasks like sequence alignment and genome assembly. Logical and algorithmic thinking are essential for bioinformatics analysts to process and analyze biological data, involving designing efficient workflows, creating data processing pipelines, and solving complex biological problems. Data processing and analysis in bioinformatics involve applying computational techniques and algorithms to tasks like DNA sequence alignment, protein structure prediction, and high-throughput data analysis. Algorithms play a crucial role in collective data processing in bioinformatics, where large datasets are analyzed to uncover patterns, associations, and biological insights. Demonstrate Demonstrate the process of structured programming by designing a simple bioinformatics tool, emphasizing modularity and readability. Activity (7.1.2 Algorithm Development Structures) 1. Activity name: Bioinformatics Algorithm Challenge 2. Objective: Participants will collaboratively solve bioinformatics-related algorithmic challenges. 3. Type of Activity: Group 4. Resources: Laptops with programming environments, bioinformatics datasets. 5. Time Duration: 30 minutes 40  Bioinformatics Associate/ Analyst 6. Instructions: Divide participants into small groups. Assign each group a bioinformatics algorithmic challenge (e.g., sequence alignment). Groups collaboratively code a solution and present their approach to the class. Encourage discussions on algorithm efficiency and optimization. 7. Outcome: Participants gain hands-on experience in applying algorithmic principles to bioinformatics problems. Notes for Facilitation Encourage collaboration and active participation. Provide constructive feedback during coding activities. Emphasize the importance of efficiency in algorithm design, especially in bioinformatics where large datasets are common. Encourage participants to think critically about problem-solving strategies and consider the specific challenges posed by bioinformatics tasks. Highlight the interdisciplinary nature of bioinformatics, showcasing how programming skills contribute to advancements in biological research. 41  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. b. To create software solutions to address specific problems 2. d. File input/output 3. c. Encapsulate specific functionality within well-defined modules 4. b. Analyzing high-throughput data 5. c. It aids in solving complex computational problems Descriptive Questions: 1. Refer to Unit 7.1: Foundations of Algorithmic Design and Programming Topic 7.1.1 Program Design Methods 2. Refer to Unit 7.1: Foundations of Algorithmic Design and Programming Topic 7.1.2 Algorithm Development Structures 3. Refer to Unit 7.1: Foundations of Algorithmic Design and Programming Topic 7.1.3 Structured Programming Rules 4. Refer to Unit 7.1: Foundations of Algorithmic Design and Programming Topic 7.1.4 Divide and Conquer Techniques 5. Refer to Unit 7.1: Foundations of Algorithmic Design and Programming Topic 7.1.5 Data Processing and Analysis in Bioinformatics 42 8. Introduction to Computational Biology Unit 8.1 - Introduction to Computational Biology LFS/N3909, V2.0  Facilitator Guide Key Learning Outcomes By the end of this module, the participants will be able to: 1. Perform data mining from a large source of data 2. Categorize various data types used in biology and healthcare 3. Detail the application of statistics 4. Explain fundamental statistical principles relevant to computational biology 5. Explore the genesis of molecular computing, including Adelman’s pioneering experiment 6. Outline the fundamental principles underpinning microarray and molecular methodologies 7. Clarify the basics of evolutionary relationships and the identification of biological samples 8. Demonstrate the use of algorithms for sequence analysis and alignment 9. Describe software, techniques and the role of statistical inference in solving biological problems 44  Bioinformatics Associate/ Analyst Unit 8.1: Introduction to Computational Biology Unit Objectives By the end of this unit, the participants will be able to: 1. Explain various data types used in biology and healthcare 2. Explain fundamental statistical principles 3. Illustrate Adelman’s pioneering experiment 4. Outline the fundamental principles underpinning microarray and molecular methodologies 5. Recall evolutionary relationships and biological samples 6. Demonstrate the use of algorithms for sequence analysis and alignment 7. Describe software, techniques and the role of statistical inference in solving biological problems Resources to be Used Presentation slides covering key topics in computational biology, Relevant research papers, case studies, and articles, Statistical software for demonstrations, Visual aids illustrating molecular computing and microarray methodologies, Whiteboard and markers, Laptops for participants to explore computational biology tools. Do Familiarize yourself with the various data types in biology and healthcare, statistics in computational biology, fundamental statistical principles, molecular computing, and microarray methodologies. Prepare examples and datasets for statistical demonstrations. Set up statistical software for interactive sessions. Ensure visual aids and relevant articles are ready for reference. Encourage participants to bring laptops for hands-on exploration. Say Hello everyone! Welcome to our journey into the fascinating world of Computational Biology. Today, we’ll explore the intersection of biology and computing, unraveling how data and statistics are shaping breakthroughs in healthcare and biological research. Our objective is to understand the diverse data types used in biology, the role of statistics in computational biology, and the foundational principles of molecular computing and microarray methodologies. This knowledge is vital for anyone venturing into the exciting field of computational biology. As we step into the realm of computational biology, we’ll discover how data and statistics empower researchers to unlock the mysteries of biological systems. This understanding is pivotal for anyone interested in contributing to advancements in healthcare and biological sciences. 45  Facilitator Guide Ask Can you think of instances where data from genomics, clinical records, or medical images has played a crucial role in healthcare decisions or research? How do you believe statistics can aid in the analysis of complex biological data, and what real-world applications can you envision? Have you heard about any breakthroughs in computational biology that have impacted healthcare or our understanding of biological processes? Elaborate Various data types, including genomic sequences, clinical records, and medical images, are categorized and utilized in biology and healthcare for studying, diagnosing, and treating diseases. Statistics play a crucial role in enabling data analysis, pattern recognition, and hypothesis testing in computational biology, aiding in decision-making, experimental design, and outcome evaluation. Fundamental statistical principles, including probabilistic models, hypothesis testing, and data normalization, are applied in computational biology to make sense of complex biological data. Molecular computing, exemplified by Leonard Adelman’s DNA-based experiments, demonstrates how DNA molecules can be used as a computational substrate, opening new possibilities for solving computational problems using biology. Foundational principles of microarray and molecular methodologies involve studying gene expression, genetic variations, and molecular interactions, providing insights into biological processes and diseases. Demonstrate Demonstrate a statistical analysis using real biological data, emphasizing the importance of statistical principles in extracting meaningful insights. Activity (8.1.4 Fundamental Principles Underpinning Microarray and Molecular Methodologies) 1. Activity name: Exploring Microarray Data 2. Objective: Participants will analyze and interpret microarray data to gain insights into gene expression patterns. 3. Type of Activity: Individual 4. Resources: Laptops with statistical software, microarray datasets. 5. Time Duration: 30 minutes 6. Instructions: Provide participants with microarray datasets. Guide them through the steps of loading, exploring, and analyzing the data using statistical software. Encourage interpretation of gene expression patterns and discussions on potential biological implications. 7. Outcome: Participants gain hands-on experience in working with microarray data, reinforcing the application of statistics in computational biology. 46  Bioinformatics Associate/ Analyst Notes for Facilitation Encourage active participation and discussions. Foster a collaborative learning environment. Emphasize the interdisciplinary nature of computational biology, bridging biology, statistics, and computer science. Highlight the potential impact of computational biology on healthcare and biological research. Encourage participants to explore additional resources and stay updated on advancements in the field. 47  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. c. Computational Biology 2. b. To analyze data and make informed decisions 3. d. Leonard Adelman 4. c. Principles of genetics and molecular biology 5. c. Identifying evolutionary relationships and genetic variations Descriptive Questions: 1. Refer to Unit 8.1: Introduction to Computational Biology Topic 8.1.1 Data Types Used in Biology and Healthcare 2. Refer to Unit 8.1: Introduction to Computational Biology Topic 8.1.2 Fundamental Statistical Principles Relevant to Computational Biology 3. Refer to Unit 8.1: Introduction to Computational Biology Topic 8.1.2 Fundamental Statistical Principles Relevant to Computational Biology 4. Refer to Unit 8.1: Introduction to Computational Biology Topic 8.1.3 Adelman’s Pioneering Experiment 5. Refer to Unit 8.1: Introduction to Computational Biology Topic 8.1.4 Fundamental Principles Underpinning Microarray and Molecular Methodologies 48 9. Introduction to Biological Databases Unit 9.1 - Cataloging and Categorizing Biological Databases LFS/N3909, V2.0  Facilitator Guide Key Learning Outcomes By the end of this module, the trainees will be able to: 1. Recall the biological and medical terminology used in omics projects 2. Explain molecular phylogeny, concepts of phylogenetics and tools for phylogenetic analysis 3. Explain the biological databases and their classification 4. Describe bioinformatics database search engines 5. Identify biological samples, genome browsers, and bioinformatics database search engines 6. Identify visualization tools and other tools used in computational biology 50  Bioinformatics Associate/ Analyst Unit 9.1: Cataloging and Categorizing Biological Databases Unit Objectives By the end of this unit, the participants will be able to: 1. Explain the terminologies used in omics projects 2. Define molecular phylogeny and the concepts 3. Explain the biological databases andbioinformatics database search engines 4. Identify biological samplesand genome browsers 5. Describe tools used in computational biology Resources to be Used Presentation slides covering key topics in cataloging and categorizing biological databases, Access to online biological databases for demonstrations, Visual aids illustrating molecular phylogeny concepts and genome browsers, List of Omics projects and their significance, List of prominent biological databases and their functionalities, Computers for participants to explore genome browsers and databases. Do Familiarize yourself with key concepts such as Omics projects, molecular phylogeny, biological databases, genome browsers, and computational biology tools. Prepare examples and demonstrations using online biological databases. Set up access to genome browsers and databases for participants. Ensure computers are ready for hands-on exploration. Encourage active participation and discussions. Say Hello everyone! Welcome to our exploration of the vast world of biological databases. Today, we’ll delve into Omics projects, molecular phylogeny, and the tools and databases that empower researchers to navigate and analyze biological data efficiently. Our objective is to understand the significance of Omics projects, delve into the fascinating world of molecular phylogeny, explore the wealth of information stored in biological databases, and discover the tools that make computational biology a powerful field. As we navigate the landscape of biological databases, we’ll uncover how these resources are the backbone of modern biology, aiding researchers in understanding evolutionary relationships, storing genetic information, and performing advanced computational analyses. This knowledge is crucial for anyone venturing into the field of bioinformatics. 51  Facilitator Guide Ask Can you think of instances where the study of an organism’s entire set of genes (genomics) or the analysis of RNA molecules (transcriptomics) has played a significant role in biological research or healthcare? How do you envision the importance of molecular phylogeny in understanding evolutionary relationships, and can you provide examples from daily life that relate to these concepts? Have you ever used or explored any biological databases? What kind of information were you looking for, and how did it contribute to your understanding? Elaborate Omics projects encompass diverse biological data fields, including genomics, transcriptomics, proteomics, metabolomics, and epigenomics. Molecular phylogeny involves the study of evolutionary relationships through molecular data analysis, including concepts such as homology, molecular clocks, phylogenetic trees, clades, and outgroups. Biological databases store valuable data, with prominent examples like GenBank, UniProt, and PDB, facilitating data retrieval and analysis. Tools like BLAST aid in efficient data searches. Genome browsers, such as UCSC Genome Browser and Ensembl, allow researchers to visualize and explore genomic data, aiding in the deciphering of genetic information from biological samples. Computational biology relies on various tools, including BLAST, MUSCLE, PhyML, Python, R, and PyMOL, empowering researchers to perform advanced biological analyses efficiently. Demonstrate Demonstrate a live search using BLAST, illustrating how researchers can identify similar sequences in biological databases. Activity (9.1.4 Biological Samples and Genome Browsers) 1. Activity name: Exploring Genome Browsers 2. Objective: Participants will explore genome browsers to visualize and analyze genomic data. 3. Type of Activity: Individual 4. Resources: Computers with access to genome browsers (UCSC Genome Browser or Ensembl). 5. Time Duration: 30 minutes 6. Instructions: Guide participants through accessing a genome browser. Demonstrate features such as zooming, gene annotation, and data retrieval. Encourage participants to explore specific genes or regions of interest. 7. Outcome: Participants gain hands-on experience in navigating genome browsers and visualizing genomic data. 52  Bioinformatics Associate/ Analyst Notes for Facilitation Foster a collaborative learning environment. Encourage participants to share their experiences with biological databases. Emphasize the interdisciplinary nature of bioinformatics, bridging biology, computer science, and data analysis. Highlight the practical applications of biological databases in research and healthcare. Encourage participants to explore additional databases and tools based on their specific interests. 53  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. c. Investigating small molecules and metabolites 2. c. Evolutionary relationships 3. c. Genetic sequences 4. c. Tissues and DNA 5. c. Conduct sequence similarity searches Descriptive Questions: 1. Refer to Unit 9.1: Cataloging and Categorizing Biological Databases Topic 9.1.1 Terminologies Used in Omics Projects 2. Refer to Unit 9.1: Cataloging and Categorizing Biological Databases Topic 9.1.2 Molecular Phylogeny and Their Concepts 3. Refer to Unit 9.1: Cataloging and Categorizing Biological Databases Topic 9.1.3 Biological Databases and Bioinformatics Database Search Engines 4. Refer to Unit 9.1: Cataloging and Categorizing Biological Databases Topic 9.1.4 Biological Samples and Genome Browsers 5. Refer to Unit 9.1: Cataloging and Categorizing Biological Databases Topic 9.1.4 Biological Samples and Genome Browsers 54 10. Biological Data Analysis Unit 10.1 - Techniques and Standards LFS/N3909, V2.0  Facilitator Guide Key Learning Outcomes By the end of this module, the trainees will be able to: 1. Explain structure predictions and analysis 2. Analyse biological data, produce and interpret the predictions of the software 3. Explain, predict, and perform sequence analysis of nucleic sequence and protein sequence 4. Explain the Institute of Electrical and Electronics Engineers (IEEE) standards applicable for bioinformatics analysis 5. Explain and perform integrative analysis of omics big data 6. Perform structure predictions and analysis 56  Bioinformatics Associate/ Analyst Unit 10.1: Techniques and Standards Unit Objectives By the end of this unit, the participants will be able to: 1. Identify structure predictions and analysis 2. Analyse biological data and interpret the predictions of the software 3. Explain sequence analysis of nucleic sequence and protein sequence 4. Recognize the Institute of Electrical and Electronics Engineers (IEEE) standards applicable for bioinformatics analysis 5. Explain omics big data 6. Perform structure predictions and analysis Resources to be Used Presentation slides covering key topics in techniques and standards in bioinformatics, Access to bioinformatics software tools for demonstrations, Examples of nucleic and protein sequences for sequence analysis, IEEE standards documentation related to bioinformatics, Articles or case studies illustrating the integration of omics data, Computers for participants to explore bioinformatics tools. Do Familiarize yourself with various bioinformatics techniques, software tools, and IEEE standards. Prepare examples and demonstrations using bioinformatics software. Set up access to bioinformatics tools and relevant documentation. Ensure computers are ready for hands-on exploration. Encourage active participation and discussions. Say Hello everyone! Welcome to our journey into the fascinating world of bioinformatics techniques and standards. Today, we’ll explore the methods behind structure prediction, the significance of sequence analysis, and the ethical and technical standards set by the IEEE in bioinformatics. Our objective is to understand the core elements of structure prediction, the role of bioinformatics in analyzing biological datasets, the fundamentals of sequence analysis, and the importance of IEEE standards in guiding ethical and technical aspects of bioinformatics. As we dive into the intricacies of bioinformatics techniques and standards, we’ll uncover how these methodologies shape our understanding of molecular structures, genetic codes, and the responsible conduct of bioinformatics research. This knowledge is essential for anyone entering the field of bioinformatics. 57  Facilitator Guide Ask Can you think of real-life examples where the three-dimensional structure prediction of biological molecules, such as proteins, would be crucial for understanding their functions? How do you envision the integration of omics data influencing advancements in personalized medicine and novel therapies? Have you encountered any ethical considerations in bioinformatics research or data analysis? How do you think standards, such as those set by the IEEE, can address these considerations? Elaborate Structure prediction involves predicting the three-dimensional shapes of biological molecules using methods like homology modeling and molecular dynamics simulations. Analysis deciphers structural data to understand molecular interactions and mechanisms. Bioinformatics plays a pivotal role in analyzing vast biological datasets using advanced software tools. Genomic data identifies disease-related genes, while proteomic data helps understand protein functions, advancing biological knowledge and medical discoveries. Nucleic and protein sequence analysis is fundamental to genetics and bioinformatics. Computational tools are used to explore DNA, RNA, and protein sequences, unveiling hidden patterns, identifying genes, and predicting protein structures. The IEEE provides crucial standards for ethical and technical aspects of bioinformatics, guiding data sharing, privacy protection, and the development of reliable software and algorithms. These standards ensure rigorous quality control and ethical principles in bioinformatics research. Integrating omics data (genomics, transcriptomics, proteomics, etc.) is a cutting-edge bioinformatics approach, enabling researchers to gain a comprehensive view of biological systems. It helps uncover intricate relationships, identify biomarkers, and understand complex diseases, paving the way for personalized medicine. Demonstrate Demonstration a live demonstration of using a bioinformatics tool for sequence analysis, emphasizing how it unveils patterns and identifies genetic information. Activity (10.1.2 Biological Data and Predictions of the Software) 1. Activity name: Exploring Bioinformatics Tools 2. Objective: Participants will explore bioinformatics tools for structure prediction, sequence analysis, and data integration. 3. Type of Activity: Individual 4. Resources: Computers with access to bioinformatics software tools and datasets. 5. Time Duration: 30 minutes 58  Bioinformatics Associate/ Analyst 6. Instructions: Guide participants through accessing and using bioinformatics tools. Demonstrate specific features related to structure prediction, sequence analysis, and data integration. Encourage participants to explore the tools independently. 7. Outcome: Participants gain hands-on experience in using bioinformatics tools for various applications. Notes for Facilitation Foster a collaborative learning environment. Encourage participants to share their experiences with bioinformatics tools and standards. Emphasize the practical applications of bioinformatics techniques in research and medicine. Discuss real-world examples where bioinformatics standards ensure data integrity and ethical research practices. Encourage participants to explore additional bioinformatics tools and standards based on their specific interests. 59  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. c. To predict the function of proteins 2. c. Homology modeling 3. a. Structural analysis 4. d. IEEE (Institute of Electrical and Electronics Engineers) 5. c. Large-scale datasets encompassing multiple biological levels Descriptive Questions: 1. Refer to Unit 10.1: Techniques and Standards Topic 10.1.1 Structure Predictions and Analysis 2. Refer to Unit 10.1: Techniques and Standards Topic 10.1.2 Biological data and predictions of the software 3. Refer to Unit 10.1: Techniques and Standards Topic 10.1.3 Sequence Analysis of Nucleic Sequence and Protein Sequence 4. Refer to Unit 10.1: Techniques and Standards Topic 10.1.4 Institute of Electrical And Electronics Engineers (IEEE) Standards 5. Refer to Unit 10.1: Techniques and Standards Topic 10.1.6 Structure Predictions and Analysis 60 11. Data Delivery and Reporting Unit 11.1 - Reporting and Organizing the Results of Data Analysis LFS/N3911, V2.0  Facilitator Guide Key Learning Outcomes By the end of this module, the trainees will be able to: 1. Analyze data analysis and analysed results 2. Follow technical writing rules 3. Recall data sharing and storage formats 4. Evaluate outcomes, meeting user requirements, validating data 5. Perform data validation and update data on the database 6. Carry out reporting of inaccurate data/information 7. Organize and report the results of the analysis to seniors within given timelines 62  Bioinformatics Associate/ Analyst Unit 11.1: Reporting and Organizing the Results of Data Analysis Unit Objectives By the end of this unit, the participants will be able to: 1. Analyze data analysis and analysed results 2. Recognize technical writing rules 3. Recall data sharing and storage formats 4. Evaluate outcomes, meeting user requirements, validating data 5. Perform data validation and carry out reporting of inaccurate data 6. Organize and report the results of the analysis Resources to be Used Presentation slides covering key topics in reporting and organizing data analysis results, Examples of datasets for demonstration purposes, Access to data visualization tools and technical writing guidelines, Sample reports and documentation for analysis outcomes, Whiteboard or flip chart for collaborative activities, Computers for participants to practice data reporting and analysis. Do Review technical writing rules and data storage formats. Prepare examples and demonstrations showcasing effective data reporting. Familiarize yourself with various data storage formats and databases. Set up access to data visualization tools and databases. Encourage participants to actively participate in discussions and activities. Say Hello everyone! Today, we’re delving into the art of reporting and organizing the results of data analysis. This is a critical skill in the world of data science and analytics, enabling us to effectively communicate insights and drive informed decision-making. Our objective is to understand the process of data analysis, the importance of technical writing rules, various data storage formats, and the crucial steps in reporting and organizing data analysis results. In today’s data-driven world, the ability to communicate analysis outcomes clearly and concisely is a valuable skill. Whether you’re presenting findings to colleagues or stakeholders, understanding how to organize and report results is essential for making an impact. 63  Facilitator Guide Ask Can you think of a situation where clear data reporting could lead to better decision-making in your daily life? How do you currently organize and store data in your work or personal projects? Why do you think adhering to technical writing rules is important in the field of data analysis and reporting? Elaborate Data analysis involves examining data to extract meaningful insights and drawing conclusions. It includes statistical methods and data visualization techniques for effective interpretation. Adhering to technical writing rules is crucial for clear and effective communication of findings. This involves proper documentation, use of technical terminology, and maintaining a structured format. Understanding various data sharing and storage formats is essential, including file types (e.g., CSV, JSON) and database systems (e.g., SQL, NoSQL) to ensure data accessibility and compatibility. Evaluation of outcomes is important to assess whether the analysis meets user requirements and to validate the data used, ensuring accuracy and relevance of the results. Continuously validating data and updating it in the database is essential for maintaining data quality, preventing inaccuracies, and ensuring consistency. Identifying and reporting inaccurate data or information is crucial to maintain data integrity. This involves flagging and addressing data discrepancies. After completing the analysis, organizing and reporting the findings is vital. Timely communication of results to seniors or relevant stakeholders is necessary for informed decision-making. Demonstrate Demonstrate a live demonstration of organizing and reporting data analysis results using a sample dataset and visualization tools. Activity (11.1.6 Organizing and Reporting the Results of the Analysis) 1. Activity name: Effective Data Reporting Workshop 2. Objective: Participants will practice organizing and reporting data analysis results using a provided dataset. 3. Type of Activity: Group 4. Resources: Sample dataset, data visualization tools, whiteboard, or flip chart. 5. Time Duration: 30 minutes 6. Instructions: Provide participants with a sample dataset and a reporting template. In groups, participants analyze the data and create a visually appealing report. Each group presents their findings, highlighting key insights and data visualization choices. 7. Outcome: Participants gain hands-on experience in organizing and reporting data analysis results. 64  Bioinformatics Associate/ Analyst Notes for Facilitation Foster a collaborative and interactive learning environment. Encourage participants to share their experiences with data reporting and analysis. Emphasize the importance of clear and concise communication in reporting. Discuss real-world examples where effective data reporting led to positive outcomes. Encourage participants to explore various data storage formats and databases based on their specific needs. 65  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. c. To demonstrate the connection between data and its interpretation. 2. c. To improve clarity, readability, and consistency of the report. 3. b. To save time and ensure consistency in how data is presented. 4. c. Ensure that the analysis aligns with and meets the specified end user requirements. 5. c. Ensure that the chosen data visualization method aligns with the preferences and needs of senior stakeholders. Descriptive Questions: 1. Refer to Unit 11.1: Reporting and organizing the results of data analysis Topic 11.1.1 Data Analysis and Analysed Results 2. Refer to Unit 11.1: Reporting and organizing the results of data analysis Topic 11.1.2 Technical Writing Rules 3. Refer to Unit 11.1: Reporting and organizing the results of data analysis Topic 11.1.6:Organizing and reporting the results of the analysis 66 12. Work Management Unit 12.1 - Usage of Appropriate Resources to Meet Work Requirements Efficiently LFS/N9001, V2.0  Facilitator Guide Key Learning Outcomes By the end of this module, the trainees will be able to: 1. Define the scope of work and working within limits of authority 2. Summarize the details of the work and work environment 3. Explain the importance of maintaining confidentiality 4. Recall organization’s policies and procedures 5. Explain the process of escalation of query, request, complaint and problem resolution 6. Perform organization’s policies and procedures 7. Demonstrate the process of escalation of query, request, complaint and problem resolution Classroom 68  Bioinformatics Associate/ Analyst Unit 12.1: Usage of Appropriate Resources to Meet Work Requirements Efficiently Unit Objectives By the end of this unit, the participants will be able to: 1. Define the scope of work and working within limits of authority 2. Summarize the details of the work and work environment 3. Explain the importance of maintaining confidentiality 4. Recall organization’s policies and procedures 5. Explain the process of escalation of query, request, complaint and problem resolution 6. Perform organization’s policies and procedures 7. Demonstrate the process of escalation of query, request, complaint and problem resolution Resources to be Used Presentation slides covering key topics related to work requirements and efficient task management, Handouts summarizing key points and guidelines for participants, Examples of real-life scenarios related to scope of work, summarizing details, maintaining confidentiality, etc, Case studies on effective escalation processes, Whiteboard or flip chart for collaborative discussions. Do Familiarize yourself with the content and real-life examples for each topic. Prepare interactive exercises and case studies to enhance understanding. Encourage active participation and group discussions. Share relevant workplace examples to illustrate concepts. Provide a safe and open environment for discussions. Say Hello everyone! Today, we’re diving into the essential aspects of efficiently meeting work requirements. This knowledge is fundamental for successful task management and effective teamwork. Let’s explore these key topics together! Our objective is to understand the importance of working within the scope, summarizing work details, maintaining confidentiality, recalling organizational policies, and implementing effective escalation processes. These skills are vital for efficient and compliant work practices. In our professional journey, adhering to the scope of work, summarizing details effectively, and maintaining confidentiality are paramount. These skills ensure that we operate efficiently, protect sensitive information, and contribute positively to our workplace. 69  Facilitator Guide Ask Can you share an experience where understanding the scope of work was crucial in your job or a project? How do you currently summarize complex work details for effective communication within your team? Why do you think maintaining confidentiality is essential in the workplace? Can you think of any real- life examples where this was critical? Elaborate Knowing the scope of work is crucial for employees to manage tasks efficiently and avoid overstepping authority. It includes specific tasks, responsibilities, and boundaries assigned to a role or project. Summarizing work details involves condensing complex information about a project or task into a concise and understandable format. This skill is essential for effective communication within a team. Maintaining confidentiality is vital in the workplace to protect sensitive information, build trust among colleagues, and uphold ethical standards. Breaches of confidentiality can lead to legal and ethical consequences and damage an organization’s reputation. Being knowledgeable about an organization’s policies and procedures is critical for employees to navigate their roles successfully. This knowledge ensures that employees act in compliance with established guidelines, promoting consistency and adherence to company standards. The process of escalation involves a structured approach to handling queries, requests, complaints, and problems. It typically includes steps for reporting issues, seeking resolutions, and involving higher authorities when necessary. Effective escalation processes contribute to efficient issue resolution and customer satisfaction. An organization’s policies and procedures entails putting into action the guidelines and protocols established by the company. This involves following these policies consistently to ensure compliance, accountability, and the achievement of organizational goals. The process of escalation involves applying the established escalation procedures in real-life situations. This can include effectively handling customer complaints, addressing team concerns, and seeking higher-level intervention when required. Demonstrate Demonstrate a live demonstration of the process of escalation using a case study or role-playing scenario. Emphasize the importance of following established protocols for effective issue resolution. Activity (12.1.1 Scope of Work and Working within Limits of Authority) 1. Activity name: Scope of Work Simulation 2. Objective: Participants will engage in a role-playing activity to understand the importance of working within the scope of their roles. 3. Type of Activity: Individual 4. Resources: Role-play scenarios, guidelines for effective role-playing. 70  Bioinformatics Associate/ Analyst 5. Time Duration: 30 minutes 6. Instructions: Assign different roles to participants, each with specific tasks and responsibilities. Participants engage in a role-playing activity, focusing on staying within the assigned scope of work. After the activity, participants discuss their experiences and challenges faced in adhering to the scope. 7. Outcome: Participants gain practical insights into the importance of understanding and adhering to the scope of their roles. Notes for Facilitation Foster a supportive and open learning environment. Encourage participants to share their experiences and insights during discussions. Emphasize the real-world applications of the skills discussed. Share examples of effective escalation processes and their positive impact on problem resolution. Encourage participants to actively apply the knowledge gained in their respective work environments. 71  Facilitator Guide Answers to Exercises for PHB Multiple Choice Questions 1. a. The range of tasks and responsibilities assigned to a specific role or project. 2. c. To protect sensitive information and trust. 3. c. To provide guidelines for consistent and compliant behaviour. Descriptive Questions: 1. Refer to Unit 12.1: Usage of appropriate resources to meet work requirements efficiently: Topic 12.1.1: Scope of Work and Working Within Limits of Authority 2. Refer to Unit 12.1: Usage of appropriate resources to meet work requirements efficiently: Topic 12.1.2: Details of the work and work environment 3. Refer to Unit 12.1: Usage of appropriate resources to meet work requirements efficiently: Topic 12.1.5: Process of escalation of query, request, complaint and problem resolution 4. Refer to Unit 12.1: Usage of appropriate resources to meet work requirements efficiently: Topic 12.1.5: Process of escalation of query, request, complaint and problem resolution 72 13. Coordinate with Supervisor and Cross-functional Teams Unit 13.1 - Coordination with Manager, Team Members, and Cross-functional Teams LFS/N0107, V8.0  Facilitator Guide Key Learning Outcomes By the end of this module, the trainees will be able to: 1. Follow the instructions of the manager to understand the work output requirements 2. Perform the daily tasks assigned by the manager 3. Report any challenges in the project to the manager 4. Coordinate with team members and cross-functional teams for technical support 5. Explain the process of escalation of query, request, complaint and problem resolution 6. Demonstrate how to report any challenges in the project to the manager 7. Demonstrate how to resolve conflict in multiple scenarios 74  Bioinformatics Associate/ Analyst Unit 13.1: Coordination with Manager, Team Members, and Cross-functional Teams Unit Objectives By the end of this unit, the participants will be able to: 1. Follow the instructions of the manager to understand the work output requirements 2. Perform the daily tasks assigned by the manager 3. Report any ch