This repository aims to provides codes for sequencing informatics, focusing on the analysis of genomic datasets using R, Python, and Fiji/ImageJ. It is designed to equip learners with the necessary skills to become professional programmers, data analysts, and data scientists with a strong emphasis on statistics. The topics covered in this repository are specifically tailored to the field of plant science, encompassing genetics, plant breeding, genomics, bioinformatics, and statistical analysis. Click here for more {https://github.com/danielecook/Awesome-Bioinformatics#annotation}
Key Features:
Practical Hands-On Approach: This repository adopts a practical and hands-on approach to learning sequencing informatics. The content is structured to guide learners through real-world genomic analysis scenarios, enabling them to gain valuable experience in applying R, Python, and Fiji/ImageJ for the analysis of plant science datasets.
Prior Knowledge Required: Due to the nature of the repository's content, learners are expected to have prior knowledge and experience in R, Python, and Fiji/ImageJ. Familiarity with statistical concepts, bioinformatics, and the basics of genomics will also be beneficial for a comprehensive understanding of the training materials.
Focused on Plant Science: All examples, case studies, and datasets used within this repository are carefully selected to align with the field of plant science. By incorporating plant science-related topics, learners will gain practical insights into how to leverage sequencing informatics for genetic analysis, plant breeding, genomics research, and statistical exploration in the context of plant sciences.
Statistical Analysis Emphasis: Recognizing the vital role of statistics in genomic analysis, this repository places significant emphasis on statistical concepts, methodologies, and tools. Learners will acquire a solid foundation in statistical analysis and explore various statistical approaches used in plant science research, providing them with the necessary skills to extract meaningful insights from genomic datasets.
Repository Structure:
Tutorials and Guides: This section provides a series of step-by-step tutorials and guides that walk learners through different aspects of sequencing informatics. Topics covered include data preprocessing, quality control, alignment, variant calling, differential expression analysis, pathway analysis, and visualization. Each tutorial is accompanied by code examples, explanations, and practical exercises to reinforce learning.
Projects and Case Studies: Here, learners will find a collection of projects and case studies that integrate R, Python, and Fiji/ImageJ with plant science datasets. These projects simulate real-world genomic analysis scenarios, enabling learners to apply their skills and gain hands-on experience in tackling diverse research questions within the plant sciences.
Datasets and Examples: This section offers a curated selection of plant science datasets accompanied by code examples in R and Python. These resources serve as a valuable practice ground for learners to explore data manipulation, visualization, statistical analysis, and interpretation within the context of genomics research.
External Resources: To support continuous learning, this repository provides a compilation of external resources such as textbooks, online courses, research articles, and documentation. These resources serve as references for learners to delve deeper into the intricacies of sequencing informatics, statistics, and bioinformatics.
Contributions:
Contributions from the community are welcomed to enhance the repository's content and learning materials. If you have suggestions, improvements, or additional resources related to sequencing informatics, statistical analysis, or plant science, please feel free to submit a pull request or open an issue.
Embark on your journey to become proficient in sequencing informatics by accessing the "Sequencing Informatics" repository. We are excited to empower you with the skills and knowledge required to excel in genomics research, data analysis, and statistical exploration within the plant sciences.
Happy coding and genomic exploration!