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![• Simplest sequence file format
• Unique identifiers!
• “Fasta wide” format has the whole sequence on one line
• Even easier to parse in a computer script
Fasta
>identifier1 [optional information]
CCGATCATATGACTAGCATGCATCGATCGATCGACTAGCATTT
AGAGCTACGATCAGCACTACACGCTTTGTATGATTGGCGGCGG
CTATTATATTGGGA
>identifier2 [optional information]
GAGAGCTACGATCAGAGCTACGATCAGCACTACACGCTTTGTA
TGATTGGCCCCCTATATTGGGACACGATCAGCACTACACGCTT
TGTATGATTGGCGGCGGCTATCCGATCAT](https://image.slidesharecdn.com/20150629-cabbioassembly-160408091803/85/Metagenome-Sequence-Assembly-CABBIO-20150629-Buenos-Aires-7-320.jpg)
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Metagenome Sequence Assembly (CABBIO 20150629 Buenos Aires)
- 1. Bas E. Dutilh Bacteriófagos:Aspectos básicos y moleculares. Aplicaciones Biotecnológicas Buenos Aires, June 29th 2015 Shotgun sequence assembly
- 2. Method Read lengthAccuracy Million reads Time Cost per M 454 100-700 99% 1 1 day $10 Illumina 50-300 98% 3,000 1-2 days $0.10 IonTorrent 100-400 98% 40-80 2 hours $1 PacBio 1,000-30,000 87% 0.05 2 hours $1 Sanger 400-1,200 99.9% n/a 2 hours $2,400 SOLiD 50 99.9% 1,200 1-2 weeks $0.13 Sequencing specs* * these numbers change all the time!
- 4. Lengths of readsand genomes NGS technologies provide reads of 50 to max. 30,000 bp, but most genomes are much longer Gago, Science 2009
- 5. Nucleotide codes Description Bases AAdenine A 1 C Cytosine C G Guanine G T Thymine T U Uracil U W Weak A T 2 S Strong C G M aMino A C K Keto G T R puRine A G Y pYrimidine C T B not A (B after A) C G T 3 D not C (D after C) A G T H not G (H after G) A C T V not T (V after T/U) A C G N aNy base (not a gap) A C G T 4 - Gap (no nucleotide) 0
- 6. Sequence File Formats •Different file formats for different uses • Competing formats developed in parallel • Some easy to read, some easy to parse
- 7. • Simplest sequencefile format • Unique identifiers! • “Fasta wide” format has the whole sequence on one line • Even easier to parse in a computer script Fasta >identifier1 [optional information] CCGATCATATGACTAGCATGCATCGATCGATCGACTAGCATTT AGAGCTACGATCAGCACTACACGCTTTGTATGATTGGCGGCGG CTATTATATTGGGA >identifier2 [optional information] GAGAGCTACGATCAGAGCTACGATCAGCACTACACGCTTTGTA TGATTGGCCCCCTATATTGGGACACGATCAGCACTACACGCTT TGTATGATTGGCGGCGGCTATCCGATCAT
- 8. • Based onFasta format • Contains information about quality of each nucleotide • Quality estimated by sequencing machine @SRR014849.1 EIXKN4201CFU84 length=93 GGAAATGGAGTACGGATCGATTTTGTTTGGAACCGAAAGGGTC + hhhhhhhhhhhhhhhh7F@71,'";C?,B;?6B;:EA1EA1E% • Four lines per sequence: 1. Identifier line starting with @ 2. DNA sequence on one line 3. Second identifier line starting with + (identifier optional) 4. String of quality scores on one line Fastq
- 9. Quality scores Phred10: 10-1 chance that the base is wrong 90% accuracy; 10% error rate Phred 20: 10-2 chance that the base is wrong 99% accuracy ; 1% error rate Phred 30: 10-3 chance that the base is wrong 99.9% accuracy ; 0.1% error rate Etcetera
- 10. ASCII character codes Fastq quality score: Phred score + 33, converted to ASCII text Note: old Illumina format was different! @SRR014849.1 EIXKN4201CFU84 length=93 GGAAATGGAGTACGGATCGATTTTGTTTGGAACCGAAAGGGTC + hhhhhhhhhhhhhhhh7F@71,'";C?,B;?6B;:EA1EA1E%
- 11. Quality profile ofreads March 2011
- 12. Quality profile ofreads October 2011
- 13. Random genome, randomcoverage • Average depth: – Genome size G – Base depth B=40x – Read length L=100 bp – K-mer size K=25 bp C = B * (L - K + 1) / L • Uncovered bases: u = G * eC CTAGTATTATTGCTGCTCATAAAGTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCTTATTGGTTGGCTTAAACCTAAAAGAGTTGAAGTTAA
- 14. What is easierto assemble? Random sequences, or real genomes?
- 15. Sequence assembly Reads Scaffold Orderand orientation of contigs Sizes of the gaps between contigs (filled with NNN) Contigs Consensus sequence of assembled reads Includes alignment of all reads
- 16.
- 17. Assembly of shotgunsequences • Human genome project –1-2 kb Sanger reads –< 10x coverage –Low error rate • High-throughput (meta-)genomics – Millions/billions of ~100-400 bp reads – Mix of genomes with different coverage – Biases and sequencing errors • Quality drops towards the end of reads • Homo-polymers may be miss-called in 454 or Ion Torrent 2000 NOW
- 18. Assembly strategies • Reference-guidedassembly – Align reads to a (database) of reference genome(s) – Cannot discover: • Larger genomic mutations – Insertions, deletions, rearrangements • Distantly related species • Most viruses • De novo assembly – Requires sufficient coverage x depth – Breaks on repeats and low-coverage regions – Algorithms • Greedy assembly (only to illustrate) • Overlap-layout-consensus • De Bruijn graph
- 19. Reference-guided assembly • Illuminasequencing of community DNA • Same-species genome available (2.8M nt) • Sometimes, only a minority of the reads can be mapped/aligned
- 20. Distant reference • Naturaldiversity of community – “Species” share >94% average nucleotide identity – Consensus = “average” of the species Consensus Genome space Reference Konstantinidis and Tiedje, PNAS 2004
- 21. • The assemblyis a better representation of the community • Can we further approach the consensus genome by re- mapping the reads against this first assembly? Reference Genome space Iterative mapping and assembly First assembly Consensus Dutilh et al. Bioinformatics 2009
- 22. Iteration improves assembly •More mapped reads • Fewer gaps Dutilh et al. Bioinformatics 2009
- 23. De novo assembly Assembly:AACAAGTTA AACAAGT CAAGTTA
- 24. De novo assemblyapproaches • Greedy approach • Overlap-layout-consensus • De Bruijn graphs
- 25. Greedy assembly 1. Sequences(reads) 2. Pairwise all-vs-all similarities 3. Find best matching pair 4. Collapse/assemble • Works well for few, long reads (Sanger) – All-vs-all calculations are expensive – One clear best match • Does not work for high throughput NGS datasets – Many reads -> expensive to calculate – Low coverage requires graph approach (reads/contigs)
- 26. Repetitive sequences • ReadsA-D are from a region with two long repeats • Greedy approach would first join A-D with the largest overlap, and place B-C in a separate contig • Resolving this requires a global view of all the possibilities before joining two reads: a graph repeat repeat DA C B D C BA B D
- 27. What is easierto assemble? Random sequences, or real genomes?
- 28. Assembly as a“graph” problem • Overlap-layout-consensus • De Bruijn Graph • A graph contains nodes and edges node edge
- 29. 1. Identify alloverlaps between reads – Use cutoffs: minimum overlap and percent identity 2. Make graph of overlap connections – Nodes: reads – Edges: overlaps 3. Find Hamiltonian path – Path that contains every node once – No efficient algorithm available 4. Determine consensus at each position TTTGTAATCTTATTGGTTGGCTTAAACCTAAAAGAGTTGAAGTTAA Overlap-layout-consensus CTAGTATTATTGCTGCTCATAAAGTAGCTCCAGCTCATCTTGATACTAAT CTCATAAAGTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCT GTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCTTATTGGTT CTTGATACTAATGCTTTTTGTAATCTTATTGGTTGGCTTAAAC K N LJ M K NLJ M CTAGTATTATTGCTGCTCATAAAGTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCTTATTGGTTGGCTTAAACCTAAAAGAGTTGAAGTTAA
- 30. De Bruijn graph 1.Find every word of length k (k-mer) in every read – K-mer should be long enough to be quite unique, but – … short enough to not break on polymorphisms/errors TTTGTAATCTTATTGGTTGGCTTAAACCTAAAAGAGTTGAAGTTAA CTCATAAAGTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCT GTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCTTATTGGTT CTTGATACTAATGCTTTTTGTAATCTTATTGGTTGGCTTAAAC CTTGATACTAATGCTTTTTGTAATCTTAT TTGATACTAATGCTTTTTGTAATCTTATT TGATACTAATGCTTTTTGTAATCTTATTG GATACTAATGCTTTTTGTAATCTTATTGG ATACTAATGCTTTTTGTAATCTTATTGGT TACTAATGCTTTTTGTAATCTTATTGGTT ACTAATGCTTTTTGTAATCTTATTGGTTG CTAATGCTTTTTGTAATCTTATTGGTTGG TAATGCTTTTTGTAATCTTATTGGTTGGC AATGCTTTTTGTAATCTTATTGGTTGGCT ATGCTTTTTGTAATCTTATTGGTTGGCTT TGCTTTTTGTAATCTTATTGGTTGGCTTA GCTTTTTGTAATCTTATTGGTTGGCTTAA CTTTTTGTAATCTTATTGGTTGGCTTAAA TTTTTGTAATCTTATTGGTTGGCTTAAAC K N L M CTAGTATTATTGCTGCTCATAAAGTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCTTATTGGTTGGCTTAAACCTAAAAGAGTTGAAGTTAA CTAGTATTATTGCTGCTCATAAAGTAGCTCCAGCTCATCTTGATACTAAT J
- 31. De Bruijn graph 2.Make graph of sequential k-mers in sequence – Nodes: k-mers – Edges: sequential presence of k-mers in reads TTTGTAATCTTATTGGTTGGCTTAAACCTAAAAGAGTTGAAGTTAA CTAGTATTATTGCTGCTCATAAAGTAGCTCCAGCTCATCTTGATACTAAT CTCATAAAGTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCT GTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCTTATTGGTT CTTGATACTAATGCTTTTTGTAATCTTATTGGTTGGCTTAAAC CTTGATACTAATGCTTTTTGTAATCTTAT TTGATACTAATGCTTTTTGTAATCTTATT TGATACTAATGCTTTTTGTAATCTTATTG GATACTAATGCTTTTTGTAATCTTATTGG ATACTAATGCTTTTTGTAATCTTATTGGT TACTAATGCTTTTTGTAATCTTATTGGTT ACTAATGCTTTTTGTAATCTTATTGGTTG CTAATGCTTTTTGTAATCTTATTGGTTGG TAATGCTTTTTGTAATCTTATTGGTTGGC AATGCTTTTTGTAATCTTATTGGTTGGCT ATGCTTTTTGTAATCTTATTGGTTGGCTT TGCTTTTTGTAATCTTATTGGTTGGCTTA GCTTTTTGTAATCTTATTGGTTGGCTTAA CTTTTTGTAATCTTATTGGTTGGCTTAAA TTTTTGTAATCTTATTGGTTGGCTTAAAC K N L J M CTAGTATTATTGCTGCTCATAAAGTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCTTATTGGTTGGCTTAAACCTAAAAGAGTTGAAGTTAA
- 32. De Bruijn graph CTAGTATTATTGCTGCTCATAAAGTAGCTCCAGCTCATCTTGATACTAATGCTTTTTGTAATCTTATTGGTTGGCTTAAACCTAAAAGAGTTGAAGTTAA 3.Find Eulerian path – Path that contains every edge once – Efficient algorithm available • In an optimal sequencing run of a repeat-less genome, there is one path connecting all nodes • In practice (especially in metagenomes) there are many possible structures in the graph • Edge width represents the number of linking reads (depth)
- 33. Possible structures inDe Bruijn graphs • Cycle: path converges on itself – Repeated region on the same contig • Frayed rope: converge then diverge – Repeated region on different contigs • Bubble: paths diverge then converge – Sequencing error in the middle of a read – Polymorphisms • Spur: short dead-ends – Sequencing error at the end of a read – Zero coverage shortly after end of repeat
- 34. What is easierto assemble? Random sequences, or real genomes?
- 35. Examples of DeBruijn graphs 1% 5% 10% 15% Pell PNAS 2012 Sequencing errors in a random circular sequence Peng Bioinformatics 2011 Five E. coli subspecies
- 36. Random versus realsequences Biological sequences are not random Genes, operons, promoters, etcetera Biased nucleotide usage (GC content) Biased oligonucleotide usage (k-mers) Repeated sequences in (meta-)genomes Low-complexity regions Conserved protein domains Duplicated genes, horizontal transfers “Selfish” elements (e.g. transposons, prophages) Polymorphic repeats (haplotypes, strains) …etcetera
- 37. Repeats have multiplesinks/sources
- 38. Repeats have multiplesinks/sources 16s Salmonella has 7 rrn operons Salmonella recombines at rrn operons Helm and Maloy
- 39. Repeated regions • Inoverlap-layout-consensus and De Bruijn graphs reads K-mers Li BFG 2012
- 40. Genome versus metagenome •Depending on diversity – Expect many sequences – Fragmented sequences – Varying read depth • Natural microdiversity • Sequencing errors or natural diversity? • Repeats also include closely related strains, conserved genes, etc. • Depending on coverage – Expect single sequence – Contiguous sequence – Even read depth • Clonal sequence • Identify sequencing errors by low coverage • Repeats consist of duplicated genes and conserved domains
- 41. Chimerization in metagenomeassembly • Both OLC and DBG include “chimera protection” – Break contigs at ambiguities – Works if depth/coverage is high enough contig1 contig2 contig4 contig5 contig3 • Assess final result with different parameters – High versus low stringency assembly • Chimerization is more frequent between closely related strains
- 42. Assembly strategies • Reference-guidedassembly – Align reads to a (database) of reference genome(s) – Cannot discover: • Larger genomic mutations – Insertions, deletions, rearrangements • Distantly related species • Most viruses • De novo assembly – Requires sufficient read lengths, depth, and coverage – Breaks on long repeats and low-coverage regions – Algorithms • Greedy assembly (only to illustrate) • Overlap-layout-consensus • De Bruijn graph
- 43. Scaffolding • Use alignmentsto a related genome sequences to sort and orient de novo contigs Silva et al. Source Code Biol. Med. 2013