Download as PDF, PPTX
Uploaded byKristen DeAngelis
04_Assembly_2022.pdf
This document discusses genome assembly from metagenomic sequencing data. It defines key terms like metagenome assembled genomes (MAGs) and describes how genome assembly works, including using de Bruijn graphs to assemble short sequencing reads into longer contigs and scaffolds. The document also outlines several measures used to assess genome assembly quality, such as coverage, contig length metrics like N50 and N75, and completeness and contamination measurements.
More Related Content
04_Assembly_2022.pdf
- 1. Genome Assembly MICROBIO 590BBioinformatics Lab: Bacterial Genomics Professor Kristen DeAngelis UMass Amherst Fall 2022 1
- 2. Lecture Learning Goals •Define metagenome assembled genomes (MAGs), and explain how sequence assembly can help to reveal novel microbial diversity. • Define the hallmarks of good sequence assembly, including coverage, read length, quality and others. • Distinguish between sequence reads, contigs, and scaffolds. • Describe the conceptual idea behind how de Bruijn graphs are employed in sequence assembly. 2
- 3. Molecular approaches aka‘Omics • Amplicon-based sequencing • limited capacity for discovery • Better for phylogenetics because traits can be aligned • Shotgun sequencing • Lots of room for discovery • Half of sequences have no known homology
- 4. Detecting unculturable bacteria •Genomics – sequencing whole genomes, DNA • Transcriptomics – sequencing RNA from genomes • Proteomics – sequencing proteins • Metabolomics – identification of all metabolites, usually by analytical chemistry • Meta- • Add the prefix “meta-” to any of the above, and it refers to sequencing mixed communities instead of single species • For example, Metagenomics – sequencing mixed communities • For example, Metatranscriptomics – sequencing RNA from mixed communities
- 5.
- 6.
- 8. Bacterial phyla with fewto no cultivated representatives
- 9. Bacterial phyla withfew to no cultivated representatives • The 13 “main” phyla were described by Carl Woese in his classic 1987 paper • Since then, sequencing revealed that microbial diversity is much greater than this! • Most cultivated, characterized bacteria fall into one of five phyla • Proteoabcteria, Firmicutes, Actinobacteria, Bacteroidetes and Cyanobacteria • Animal diversity is similar: most animal species belong to a few animal phyla e.g. nematodes (round worms) and arthropods (insects)
- 10. Assembling whole genomesfrom metagenomic data using binning • Short sequences are “binned” based on shared characteristics • GC content • taxonomy • Coverage (sequence depth) • K-mer frequency http://dx.doi.org/10.3389/fmicb.2015.01451 10
- 11. Genome Assembly • Millions orBillions of pieces • Many malformed pieces à errors • Missing pieces à coverage • Pieces mixed from another puzzle à contamination • Lots of identical blue sky à repetitive regions 11
- 12.
- 13. Assembling a bookfrom word fragments ... 13
- 14. Genomes are assembledfrom DNA fragments 14
- 15. Sequence reads àContigs à Scaffolds 15
- 16. Sequence reads àContigs à Scaffolds 16
- 17. Methods of GenomeAssembly: Graph-based methods 17
- 18. Methods of GenomeAssembly: de Bruijn graphs 18
- 19. Methods of GenomeAssembly: de Bruijn graphs • Make one node for every distinct word or phrase • For every pair of adjacent words in the phrases, add a corresponding directed edge • Graph can have more than one edge pointing from one node to another (multigraph) 19 tomorrow and tomorrow and tomorrow tomorrow and
- 20. Methods of GenomeAssembly: de Bruijn graphs 20
- 21. Methods of GenomeAssembly: de Bruijn graphs 21
- 22. Methods of GenomeAssembly: de Bruijn graphs 22
- 23. Methods of GenomeAssembly: de Bruijn graphs 23
- 24. Methods of GenomeAssembly: de Bruijn graphs 24
- 25. Methods of GenomeAssembly: de Bruijn graphs 25
- 26. Methods of GenomeAssembly: de Bruijn graphs 26
- 27. Methods of GenomeAssembly: de Bruijn graphs 27
- 28. Methods of GenomeAssembly: de Bruijn graphs 28
- 29. Methods of GenomeAssembly: de Bruijn graphs 29
- 30. Methods of GenomeAssembly: de Bruijn graphs 30
- 31. Methods of GenomeAssembly: de Bruijn graphs 31
- 32. Methods of GenomeAssembly: de Bruijn graphs 32
- 33. Methods of GenomeAssembly: de Bruijn graphs 33
- 34. Methods of GenomeAssembly: de Bruijn graphs 34
- 35.
- 36.
- 37.
- 38. Dealing with Double-Strandednessin DNA Sequences 38
- 39. Dealing with Double-Strandednessin DNA Sequences 39
- 40. What about palindromicsequences? 40
- 41. Generating overlapping readsin Illumina Sequencing 41
- 42. Generating overlapping readsin Illumina Sequencing 42
- 43. Long-read, third generationsequencing technology 43
- 44. What makes agood assembly? 44
- 45. What makes agood assembly? 45
- 46. What makes agood assembly? 46
- 47. Coverage • Low coverage •Beginning of rain fall • Random distribution across landscape • Some areas remain dry • High coverage • Middle/end of storm • Rain covers landscape • Some puddles get deep 47
- 48. Coverage • Coverage (ordepth) in DNA sequencing is the number of unique reads that include a given nucleotide in the reconstructed sequence • Average coverage per genome is the number of reads (N) multiplied by the read length (L) divided by the genome size (G) 48
- 49. 1X coverage genomesequence 49 John, Gibbons, via
- 50. 2X coverage genomesequence 50 John, Gibbons, via
- 51. 4X coverage genomesequence 51 John, Gibbons, via
- 52. 8X coverage genomesequence 52 John, Gibbons, via
- 53. Poisson distribution ofgenome coverage 53 John, Gibbons, via At 20X coverage, only 45% of the genome is covered >20X At 40X coverage, 95% of the genome is covered >30X Coverage
- 54.
- 55.
- 56. Measures of GenomeAssembly Quality • Expectations • Bacteria tend to have all genes on one circular chromosome • Bacterial genomes tend to be between 1 and 10 Mbp • Presence of certain single-copy ‘housekeeping’ marker genes • Measures • Contigs: few and long • Contamination: the occurrence of more than one in a given genome or bin, since marker genes are typically single-copy • Completeness: value obtained by the proportion of the missing marker genes to the total number of markers used 56
- 57. Measures of GenomeAssembly Quality • N50, N75 • N50 is the length for which the collection of all contigs of that length or longer covers at least half (50%) the total base content of the Assembly. • It serves as a median value for assessing whether the Assembly is balanced towards longer contigs (higher N50) or shorter contigs (lower N50). • N75 is used for the same purpose but is the length is set at 75% of total base content instead of 50%. 57 https://hoytpr.github.io/bioinformatics-semester/materials/genomics-assembly-reporting/
- 58. Measures of GenomeAssembly Quality • N50, N75 • N50 is the length for which the collection of all contigs of that length or longer covers at least half (50%) the total base content of the Assembly. • It serves as a median value for assessing whether the Assembly is balanced towards longer contigs (higher N50) or shorter contigs (lower N50). • N75 is used for the same purpose but is the length is set at 75% of total base content instead of 50%. 58
- 59. Measures of GenomeAssembly Quality • L50, L75: • L50 is the number of contigs equal to or longer than the N50 length. In other words, L50, is the minimal number of contigs that contain half the total base content of the Assembly. • L75 is used for the same purpose in reference to the N75 length. 59
- 60. Measures of GenomeAssembly Quality • Completeness – % essential single copy genes present • Contamination - % duplication of essential single copy genes 60 Parks et al., Genome Research. 2015
- 61. 61 Bowers et al.Nat. Biotech, 2017
- 62. Lecture Learning Goals •Define metagenome assembled genomes (MAGs), and explain how sequence assembly can help to reveal novel microbial diversity. • Define the hallmarks of good sequence assembly, including coverage, read length, quality and others. • Distinguish between sequence reads, contigs, and scaffolds. • Describe the conceptual idea behind how de Bruijn graphs are employed in sequence assembly. 62
- 63.