De novo genome assembly - T.Seemann - IMB winter school 2016 - brisbane, au - 4 july 2016
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This document discusses de novo genome assembly, which is the process of reconstructing long genomic sequences from many short sequencing reads without the aid of a reference genome. It is challenging due to factors like short read lengths, repetitive sequences that complicate the assembly graph, and sequencing errors. The goals of assembly are to produce contiguous sequences with high completeness and correctness by resolving overlaps between reads into consensus sequences. Metrics like N50, core gene content, and read remapping are used to assess assembly quality.
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De novo genome assembly - T.Seemann - IMB winter school 2016 - brisbane, au - 4 july 2016
- 1. De novo Genome Assembly A/Prof Torsten Seemann Winter School in Mathematical & Computational Biology - Brisbane, Australia - 4 July 2016
- 2. Introduction
- 3. The human genome has 47 pieces MT (or XY)
- 4. The shortest piece is 48,000,000 bp Total haploid size 3,200,000,000 bp (3.2 Gbp) Total diploid size 6,400,000,000 bp (6.4 Gbp) ∑ = 6,400,000,000 bp
- 5. Human DNA iSequencer ™ 46 chromosomal and1 mitochondrial sequences In an ideal world ... AGTCTAGGATTCGCTATAG ATTCAGGCTCTGATATATT TCGCGGCATTAGCTAGAGA TCTCGAGATTCGTCCCAGT CTAGGATTCGCTAT AAGTCTAAGATTC...
- 6. The real world ( for now ) Short fragments Reads are stored in “FASTQ” files Genome Sequencing
- 9. Genome assembly (the red pill)
- 10. De novo genome assembly Ideally, one sequence per replicon. Millions of short sequences (reads) A few long sequences (contigs) Reconstruct the original genome from the sequence reads only
- 11. De novo genome assembly “From scratch”
- 12. An example
- 13. A small “genome” Friends, Romans, countrymen, lend me your ears; I’ll return them tomorrow!
- 14. • Reads ds, Romans, count ns, countrymen, le Friends, Rom send me your ears; crymen, lend me Shakespearomics Whoops! I dropped them.
- 15. • Reads ds, Romans, count ns, countrymen, le Friends, Rom send me your ears; crymen, lend me • Overlaps Friends, Rom ds, Romans, count ns, countrymen, le crymen, lend me send me your ears; Shakespearomics I am good with words.
- 16. • Reads ds, Romans, count ns, countrymen, le Friends, Rom send me your ears; crymen, lend me • Overlaps Friends, Rom ds, Romans, count ns, countrymen, le crymen, lend me send me your ears; • Majority consensus Friends, Romans, countrymen, lend me your ears; (1 contig) Shakespearomics We have reached a consensus !
- 17. Overlap - Layout - Consensus Amplified DNA Shear DNA Sequenced reads Overlaps Layout Consensus ↠ “Contigs”
- 18. Assembly graphs
- 19. Overlap graph
- 20. Another example is this
- 21. Overlaps find
- 22. Do the graph traverse Size matters not. Look at me. Judge me by my size, do you? Size matters not. Look at me. Judge me by my soze, do you? 2 supporting reads 1 supporting reads
- 23. So far, so good.
- 25. Why is it so hard?
- 26. What makes a jigsaw puzzle hard? Repetitive regions Lots of pieces Missing pieces Multiple copies No corners (circular genomes) No box Dirty pieces Frayed pieces : .,
- 27. What makes genome assembly hard Size of the human genome = 3.2 x 109 bp (3,200,000,000) Typical short read length = 102 bp (100) A puzzle with millions to billions of pieces Storing in RAM is a challenge ⇢ “succinct data structure” 1. Many pieces (read length is very short compared to the genome)
- 28. What makes genome assembly hard Finding overlaps means examining every pair of reads Comparisons = N×(N-1)/2 ~ N2 Lots of smart tricks to reduce this close to ~N 2. Lots of overlaps
- 29. What makes genome assembly hard ATATATATATATATATATATATATATATATATATATATATATATATATATATATATATATATA TATATATA TATATATA TATATATA TATATATA TATATATA TATATATA TATATATA TATATATA TATATATA TATATATA TATATATA 3. Lots of sky (short repeats) How could we possibly assemble this segment of DNA? All the reads are the same!
- 30. What makes genome assembly hard Read 1: GGAACCTTTGGCCCTGT Read 2: GGCGCTGTCCATTTTAGAAACC 4. Dirty pieces (sequencing errors) 1. The error in Read 2 might prevent us from seeing that it overlaps with Read 1 2. How would we know which is the correct sequence?
- 31. What makes genome assembly hard 5. Multiple copies (long repeats) Our old nemesis the REPEAT ! Gene Copy 1 Gene Copy 2
- 32. Repeats
- 33. What is a repeat? A segment of DNA that occurs more than once in the genome
- 34. Major classes of repeats in the human genome Repeat Class Arrangement Coverage (Hg) Length (bp) Satellite (micro, mini) Tandem 3% 2-100 SINE Interspersed 15% 100-300 Transposable elements Interspersed 12% 200-5k LINE Interspersed 21% 500-8k rDNA Tandem 0.01% 2k-43k Segmental Duplications Tandem or Interspersed 0.2% 1k-100k
- 35. Repeats Repeat copy 1 Repeat copy 2 Collapsed repeat consensus 1 locus 4 contigs
- 36. Repeats are hubs in the graph
- 37. Long reads can span repeats Repeat copy 1 Repeat copy 2 long reads 1 contig
- 38. Draft vs Finished genomes (bacteria) 250 bp - Illumina - $250 12,000 bp - Pacbio - $2500
- 39. The two laws of repeats 1. It is impossible to resolve repeats of length L unless you have reads longer than L. 2. It is impossible to resolve repeats of length L unless you have reads longer than L.
- 40. How much data do we need?
- 41. Coverage vs Depth Coverage Fraction of the genome sequenced by at least one read Depth Average number of reads that cover any given region Intuitively more reads should increase coverage and depth
- 42. Example: Read length = ⅛ Genome Length Coverage = ⅜ Depth = ⅜ Genome Reads
- 43. Example: Read length = ⅛ Genome Length Coverage = 4.5/8 Depth = 5/8 Genome Reads Newly Covered Regions = 1.5 Reads
- 44. Example: Read length = ⅛ Genome Length Coverage = 5.2/8 Depth = 7/8 Genome Reads Newly Covered Regions = 0.7 Reads
- 45. Example: Read length = ⅛ Genome Length Coverage = 6.7/8 Depth = 10/8 Genome Reads Newly Covered Regions = 1.5 Reads
- 46. Depth is easy to calculate Depth = N × L / G Depth = Y / G N = Number of reads L = Length of a read G = Genome length Y = Sequence yield (N x L)
- 47. Example: Coverage of the Tarantula genome “The size estimate of the tarantula genome based on k-mer analysis is 6 Gb and we sequenced at 40x coverage from a single female A. geniculata” Sangaard et al 2014. Nature Comm (5) p1-11 Depth = N × L / G 40 = N x 100 / (6 Billion) N = 40 * 6 Billion / 100 N = 2.4 Billion
- 48. Coverage and depth are related Approximate formula for coverage assuming random reads Coverage = 1 - e-Depth
- 49. Much more sequencing needed in reality ● Sequencing is not random ○ GC and AT rich regions are under represented ○ Other chemistry quirks ● More depth needed for: ○ sequencing errors ○ polymorphisms
- 52. Contiguity ● Desire ○ Fewer contigs ○ Longer contigs ● Metrics ○ Number of contigs ○ Average contig length ○ Median contig length ○ Maximum contig length ○ “N50”, “NG50”, “D50”
- 53. Contiguity: the N50 statistic
- 54. Completeness : Total size Proportion of the original genome represented by the assembly Can be between 0 and 1 Proportion of estimated genome size … but estimates are not perfect
- 55. Completeness: core genes Proportion of coding sequences can be estimated based on known core genes thought to be present in a wide variety of organisms. Assumes that the proportion of assembled genes is equal to the proportion of assembled core genes. Number of Core Genes in Assembly Number of Core Genes in Database In the past this was done with a tool called CEGMA There is a new tool for this called BUSCO
- 56. Correctness Proportion of the assembly that is free from errors Errors include 1. Mis-joins 2. Repeat compressions 3. Unnecessary duplications 4. Indels / SNPs caused by assembler
- 57. Correctness: check for self consistency ● Align all the reads back to the contigs ● Look for inconsistencies Original Reads Align Assembly Mapped Read
- 58. Assemble ALL the things
- 59. Why assemble genomes ● Produce a reference for new species ● Genomic variation can be studied by comparing against the reference ● A wide variety of molecular biological tools become available or more effective ● Coding sequences can be studied in the context of their related non-coding (eg regulatory) sequences ● High level genome structure (number, arrangement of genes and repeats) can be studied
- 60. Vast majority of genomes remain unsequenced Estimated number of species ~ 9 Million Whole genome sequences (including incomplete drafts) ~ 3000 Estimated number of species Millions to Billions Genomic sequences ~ 150,000 Eukaryotes Bacteria & Archaea Every individual has a different genome Some diseases such as cancer give rise to massive genome level variation
- 62. Not just genomes ● Transcriptomes ○ One contig for every isoform ○ Do not expect uniform coverage ● Metagenomes ○ Mixture of different organisms ○ Host, bacteria, virus, fungi all at once ○ All different depths ● Metatranscriptomes ○ Combination of above!
- 63. Conclusions
- 64. Take home points ● De novo assembly is the process of reconstructing a long sequence from many short ones ● Represented as a mathematical “overlap graph” ● Assembly is very challenging (“impossible”) because ○ sequencing bias under represents certain regions ○ Reads are short relative to genome size ○ Repeats create tangled hubs in the assembly graph ○ Sequencing errors cause detours and bubbles in the assembly graph
- 66. The End