![# Initialize a vector to store Fibonacci numbers
Fibonacci <- numeric(50)
# Set the first two elements
Fibonacci[1] <- Fibonacci[2] <- 1
# Generate the rest of the Fibonacci numbers
for (i in 3:50) {
Fibonacci[i] <- Fibonacci[i - 2] + Fibonacci[i - 1]
}
# Print the first 50 Fibonacci numbers
cat("First 50 Fibonacci numbers:n")
cat(Fibonacci, sep = ", ")
# Create a bar plot
barplot(Fibonacci, names.arg = 1:50, main = "First 50
Fibonacci Numbers", xlab = "Index", ylab = "Fibonacci
Value")](https://image.slidesharecdn.com/2024-bioinformaticscourse-240122154825-b206a370/85/2024-bioinformatics-course-pdf-19-320.jpg)
2024-bioinformatics course.pdf
- 1. Biyoinformatik Biyoinformatik Akademi Akademi Your bioinformatics Path Exploring Bioinformatics: Masteringthe Path to Excellence ERES Biotech Ocak 2024 Elçin Ekşi
- 2. Agenda Overview of Bioinformatics Dataand Biology Historical Milestones Pioneering Achievements in Bioinformatics Evolution sc-RNA Sequencing Example Peeling Back the Layers of Single-Cell RNA Sequencing Hands-On: R Programming with Fibonacci Bridging Theory and Application with R Biyoinformatik Biyoinformatik Akademi Akademi
- 3. About the Biyoinformatik Akademi ERES Biotechnology,a leader in biotech R&D, has a decade of experience in bioinformatics education. We've established the Bioinformatics Academy, offering a specialized online platform for academics, researchers, and professionals seeking expertise in the field. Our experienced instructors and updated content ensure support throughout your bioinformatics journey.
- 4. Bioinformatics Bioinformatics is theinterdisciplinary field that amalgamates biological sciences with computational methodologies to interpret and analyze complex biological data. Scope and Intersection Data-Driven Exploration Medical Applications Genomic Revolution Structural Biology Toolbox of Methods Biyoinformatik Biyoinformatik Akademi Akademi
- 5. How big can it be? Let'sfirst examine together how large a new generation sequencing data can be.
- 6. 1 Let's go tothis link: https://www.ebi.ac.uk/ena/brow ser/home
- 7. 2 In the topright box, type the term "SRR1553610" and search. What do you see in the results?
- 8. 3 Here, there issequenced nucleic acid data for this organism. You can download this data and analyze it yourself. For now, we will just look at the file size.
- 9. 4 In the topsecond right box, type the term "PRJNA257197" and search. What do you see in the results?
- 10. Genomics: 2-40 Exabytesin Next Decade 1 exabyte = 1 000 000 000 gigabytes Stephens, Z. D., Lee, S. Y., Faghri, F., Campbell, R. H., Zhai, C., Efron, M. J., ... & Robinson, G. E. (2015). Big data: astronomical or genomical?. PLoS biology, 13(7), e1002195.
- 11. Genomics: 2-40 Exabytesin Next Decade Moore's Law: Computer power doubles every two years, boosting technology. https://www.genome.gov/about-genomics/fact-sheets/DNA-Sequencing-Costs-Data
- 12.
- 13. Historical Milestones These milestones representkey moments in the integration of biology, computer science, and data analysis, laying the foundation for our current understanding of genomics and computational biology. 1950s-1960s: protein sequences, Margaret Oakley Dayhoff, Atlas of Protein Sequence and Structure 1970s The Needleman–Wunsch algorithm 1990-2003 Human Genome Project 1980s GenBank and DNA Databases
- 14. Historical Milestones 1990s BLAST Algorithm andsequence similarity searching in vast databases 2000s Next-Generation Sequencing (NGS) 2010 - today Omics era, gene editing, new methods ... 2010s Big Data Challenges and Machine Learning
- 15.
- 16. Advancing studies without labexperiments.
- 17. RNA-seq data fromGEO Data analysis significant results
- 18. Let's create a chart of Fibonacci numbers. trinket.io https://trinket.io/R/6ff5a74fe0 Tryit Let's change the numbers and generate different charts Rstudio RStudio’yu indirip inceleyebilirsiniz.
- 19. # Initialize avector to store Fibonacci numbers Fibonacci <- numeric(50) # Set the first two elements Fibonacci[1] <- Fibonacci[2] <- 1 # Generate the rest of the Fibonacci numbers for (i in 3:50) { Fibonacci[i] <- Fibonacci[i - 2] + Fibonacci[i - 1] } # Print the first 50 Fibonacci numbers cat("First 50 Fibonacci numbers:n") cat(Fibonacci, sep = ", ") # Create a bar plot barplot(Fibonacci, names.arg = 1:50, main = "First 50 Fibonacci Numbers", xlab = "Index", ylab = "Fibonacci Value")