Download to read offline
2. Human and Microorganism Genome Analysis.pptx
- 1. Human and Microorganism GenomeAnalysis naufalsebastian@gmail.com
- 3. What is aGenome? A genome is the complete set of DNA in an organism — kind of like the instruction manual for building and running a living thing. • Human genome: Contains all the genetic instructions that make you you. • Microorganism genome: Includes the genetic code of tiny living things like bacteria, viruses, and fungi.
- 4. What is aGenome?
- 5. What is GenomeAnalysis? Genome analysis is the process of reading and studying an organism's DNA to understand: 1. What genes are present 2. How they work 3. How organisms are related to each other 4. What diseases they might cause (in microorganisms)
- 6. Comparing Human andMicroorganism Genomes Human Genome • Humans have around 20,000–25,000 genes • DNA is stored in 23 pairs of chromosomes • Total length: about 3 billion base pairs • Almost 99.9% of human DNA is the same in every person — the tiny differences make us unique!
- 7. Comparing Human andMicroorganism Genomes Microorganism Genome (Example: Bacteria) • Much smaller genome • Might have only a few thousand genes • DNA is circular, not stored in chromosomes • Can mutate and evolve very fast
- 8. How to Reada DNA Sequence DNA is made of 4 letters: A = Adenine T = Thymine C = Cytosine G = Guanine They pair up: A with T C with G Example: Original DNA: 5’ ATCGGACT 3’ Complement: 3’ TAGCCTGA 5’ This Photo by Unknown Author is licensed under CC BY
- 9. Comparing Human andMicroorganism Genomes Feature Humans E. coli Bacteria Genome Size ~3 billion letters ~4.6 million letters Chromosomes 23 pairs 1 circular chromosome Reproduction Slow Very fast (every 20 mins!)
- 10. What Scientists Lookfor in a Genome In Humans: • Mutations that cause diseases like cystic fibrosis or cancer • Ancestry (Where your family comes from) • Traits like eye color or height • Response to medicine (some drugs work better for certain genes) In Microorganisms: • What species is it? • Is it dangerous (pathogen)? • Is it resistant to antibiotics? • How did it evolve?
- 11. Real-Life Applications Field Useof Genome Analysis Medicine Find genetic diseases, personalize treatment Agriculture Make disease-resistant crops Forensics Solve crimes using DNA evidence Public Health Track virus outbreaks like COVID-19 Evolution See how species changed over time
- 12.
- 13. STEP 1: DefineYour Goal What do you want to find out? Ask yourself: • Am I looking for genetic mutations in humans? • Do I want to identify what microorganisms are in a sample? • Am I comparing two different genomes?
- 14. STEP 2: CollectYour Sample Get DNA from the organism Human Samples: • Spit (saliva) • Blood • Cheek swab Microorganism Samples: • Yogurt (for bacteria) • Soil/water • Feces (for gut microbiome) • Phone or hand swab 📌 Be clean and label your sample!
- 15. STEP 3: Extractthe DNA Break open the cells and pull out the DNA Basic steps: • Use detergent to break membranes • Add salt to help DNA stick together • Add alcohol (ethanol or isopropanol) — DNA becomes visible! You can even do this with a strawberry at home
- 16. STEP 3: Extractthe DNA Scientists take DNA out of human cells or microorganisms.
- 17. STEP 4: Choosethe Right Sequencing Method Choose based on your sample and goal: Goal Method Gen Small gene Sanger 1st Whole genome Illumina 2nd Full bacterial genome Nanopore or PacBio 3rd Identify bacteria types 16S rRNA + Illumina 2nd
- 18. STEP 4: Choosethe Right Sequencing Method Sequencing • This means figuring out the exact order of DNA letters (A, T, C, G). • Example: Sequence gene might look like:
- 19. Computers analyze theDNA to: • Find genes • Compare with other genomes • Identify mutations Step 5: Analyze the Data (Bioinformatics)
- 20. How to: FindGenes, Compare Genomes & Identify Mutations
- 21. Sequencing output Step 1stGen (Sanger) 2nd Gen (NGS) 3rd Gen (Long-read) Data Type Few long reads (500–1000 bp) Millions of short reads (~150–300 bp) Fewer, ultra-long reads (1,000–2,000,000 bp) Output File .ab1 or .fasta .fastq, .bam, .vcf .fastq, .bam, .vcf Speed Slow Fast Fast Cost per Base High Low Lower (but high per run)
- 22. Workflow Species Reference GenomeWhere to Find Human GRCh38 NCBI, Ensembl Mouse GRCm39 Ensembl E. coli NC_000913.3 NCBI Genome
- 23. Real Tools ScientistsUse • BLAST: Compares DNA sequences. • CRISPR: Edits genes. • Genome browsers: Let scientists look at DNA on computers (like Google Maps for genes).
- 24. Types of GenomeAnalysis
- 25. 1st Generation –Sanger Sequencing Type of Analysis Description Gene identification Find and confirm single gene sequences. Mutation validation Confirm mutations/SNPs found from other methods. Cloning verification Check inserted DNA sequences. Phylogenetic analysis Compare genes between species. Barcode/Species ID (COI) For animals/plants using mitochondrial DNA.
- 26. 2nd Generation –NGS (Next Generation Sequencing) Designed for high-throughput and short reads, ideal for large studies. Type of Analysis Description Whole Genome Sequencing (WGS) For human, bacteria, virus genomes. RNA-seq (Transcriptome) Measure gene expression. Variant Calling (SNP/INDEL) Detect mutations and polymorphisms. Microbiome/Metagenomics Identify all microbes in a sample. Epigenomics (ChIP-seq, Bisulfite) Study DNA-protein interaction or methylation. De novo Assembly Assemble genomes from scratch.
- 27. 2nd Generation –NGS (Next Generation Sequencing)
- 28. 3rd Generation –Long-Read Sequencing (PacBio, ONT) Perfect for structural insights, large genomes, and complex regions. Type of Analysis Description Structural Variant Detection Large insertions, deletions, translocations. Full-Length Gene Isoform (Iso-Seq) Identify complete mRNA molecules. De novo Assembly (High Quality) Assemble large genomes with high continuity. Epigenetics (direct methylation detection) ONT and PacBio can detect methylation without chemical treatment. Metagenomics (complex samples) Resolve strain-level differences.
- 29. 3rd Generation –Long-Read Sequencing (PacBio, ONT)
- 31. Feature 1st GenerationSanger Sequencing 2nd Generation Next-Gen (e.g. Illumina) 3rd Generation Real-Time Long Reads (e.g. Nanopore, PacBio) Start Year 1977 ~2005 ~2010 Read Length 500–1000 bp 50–600 bp Up to 2 million bp (often 10,000– 100,000 bp) Throughput Very low (1 at a time) High (millions of reads) Moderate–High (real-time, long reads) Speed Slow (days per sample) Fast (hours–days for whole genome) Real-time (can be fast) Cost per Base Expensive Cheap Cheaper over time (especially for long reads) Accuracy Very high (99.99%) High (98–99%) Lower raw accuracy (~90–95%), but improved with correction Instruments Capillary electrophoresis machines Illumina, Ion Torrent Oxford Nanopore, PacBio SMRT Sample Size Needed Small Small Small (can do single cells!) Best For Small genes, mutation checks, confirmation Whole genomes, large-scale studies Full genome structure, complex regions, fieldwork Example Uses Diagnosing a known gene mutation Cancer, ancestry, microbiome COVID-19 tracking, full microbial genomes
- 32. Key Genome SequencingOutput File Formats Format Extension What's Inside When It's Used FASTQ .fastq or .fq Raw reads + quality scores After sequencing (main raw data) FASTA .fasta or .fa DNA sequences only (no quality) Reference genome or assembled contigs SAM/BAM .sam (text) / .bam (binary) Reads aligned to a reference genome After mapping/alignment VCF .vcf List of genetic variants (SNPs, indels) After variant calling (mutation finding) GFF/GTF .gff, .gtf Gene feature info (start, stop, exon) Genome annotation FAST5 .fast5 Nanopore raw signal data Oxford Nanopore (3rd gen) only BED .bed Regions of interest in the genome For viewing intervals/coverage
- 33.
- 34.
- 35.
- 36.
- 37. • Rehman, A.,Tian, C., He, S., Li, H., Lu, S., Du, X., & Peng, Z. (2024). Transcriptome dynamics of Gossypium purpurascens in response to abiotic stresses by Iso-seq and RNA-seq data. Scientific Data, 11(1), 477. • Santucci, K., Cheng, Y., Xu, S. M., & Janitz, M. (2024). Enhancing novel isoform discovery: leveraging nanopore long-read sequencing and machine learning approaches. Briefings in Functional Genomics, 23(6), 683-694. • Zhang, R., Kuo, R., Coulter, M., Calixto, C. P., Entizne, J. C., Guo, W., ... & Brown, J. W. (2022). A high-resolution single-molecule sequencing-based Arabidopsis transcriptome using novel methods of Iso-seq analysis. Genome biology, 23(1), 149.