errors
Much longer sequences
Each genomic region is presented only once
May introduce
errorsHard to perform analysis
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Genome assembly with SPAdes
Содержание
- 1. Genome assembly with SPAdes
- 2. Introduction
- 3. Why to assemble?
- 4. Why to assemble?Sequencing data Billions of short readsSequencing errorsContaminants
- 5. Why to assemble?Sequencing data Billions of short
- 6. Assembly basics
- 7. Assembly in a perfect world
- 8. Assembly in real world
- 9. De novo whole genome assembly
- 10. De novo whole genome assembly
- 11. Genomic repeatsTATTCTTCCACGTAGGGCCTTCCACGCTTCG
- 12. Genomic repeatsTATTCTTC CTTCCACG
- 13. Genomic repeatsTATTCTTC CTTCCACG
- 14. Genomic repeatsTATTCTTCCACGTAGGGGCCTTCCACGCTTCGTATTCTTCCACGCTTCGGGCCTTCCACGTAGG
- 15. Genomic repeatsTATTCTTCCACGTAGG
- 16. Genomic repeatsTATTCTTCCACGTAGG
- 17. SPAdes assembler
- 18. SPAdes first stepsspades.py
- 19. SPAdes first stepsspades.pyspades.py --helpspades.py --test
- 20. SPAdes first stepsspades.pyspades.py --helpspades.py --test-o
- 21. Input data formatsFASTA: .fasta / .faFASTQ: .fastq / .fqGzipped: .gz
- 22. Input data optionsUnpaired readsIllumina unpaired-s single.fastq-s single1.fastq -s single2.fastq ...
- 23. Input data optionsPaired-end readsInterlaced pairs in one file>left_read_idACGTGCAGG…>right_read_idGCTTCGAGG…Separate filesfile1.fastq file2.fastq>left_read_id >right_read_idACGTGCAGG… GCTTCGAGG…
- 24. Input data optionsPaired-end readsInterlaced pairs in one file--pe1-12 file.fastqSeparate files--pe1-1 file1.fastq --pe1-2 file2.fastq
- 25. Input data optionsPaired-end readsInterlaced pairs in one file--pe1-12 file.fastqSeparate files--pe1-1 file1.fastq --pe1-2 file2.fastq --pe1-s unpaired.fastq
- 26. SPAdes performance optionsNumber of threads-t NMaximal available RAM (GB)SPAdes will terminate if exceeded-m M
- 27. Pipeline optionsRun only assembler (input reads are already corrected or quality-trimmed)--only-assembler
- 28. Input data optionsMate-pair reads Cannot be used
- 29. Hybrid assembly optionsPacBio CLR --pacbio pb.fastqOxford Nanopore reads--nanopore nanopore_reads.fastq
- 30. Restarting SPAdesSPAdes / system crashed--continue -o your_output_dir
- 31. Genome assembly evaluation with QUASTCenter for Algorithmic BiotechnologySPbU
- 32. In realitySPAdes ABySS IDBA Ray Velvet ….
- 33. Which assembler to use?ABySSALLPATHS-LGCLCIDBA-UDMaSuRCAMIRARaySOAPdenovoSPAdesVelvetand many more...
- 34. Which assembler to use?Different technologies (Illumina, 454,
- 35. There is no best assembler
- 36. Which assembler to use?Assemblathon 1 & 2Simulated
- 37. Assembly evaluationBasic evaluationNo extra inputVery quickReference-based evaluationA
- 38. Basic statisticsOnly assemblies are needed (no additional input)Very fast to compute
- 39. Contig sizesNumber of contigs
- 40. Contig sizesNumber of contigsNumber of large contigs (i.e. > 1000 bp)
- 41. Contig sizesNumber of contigsNumber of large contigs (i.e. > 1000 bp)Largest contig length
- 42. Contig sizesNumber of contigsNumber of large contigs (i.e. > 1000 bp)Largest contig lengthTotal assembly length
- 43. N50The maximum length X for which the
- 44. N50The maximum length X for which the
- 45. N50The maximum length X for which the
- 46. N50The maximum length X for which the
- 47. N50The maximum length X for which the
- 48. N50The maximum length X for which the
- 49. N50The maximum length X for which the
- 50. N50The maximum length X for which the
- 51. L50The minimum number X such that X
- 52. L50The minimum number X such that X
- 53. N50-variationsN25, N75L25, L75N25 = 100, N75 = 40L25 = 1, L75 = 5
- 54. N50-variationsN25, N75L25, L75N25 = 100, N75 = 40L25 = 1, L75 = 5
- 55. N50-variationsN25, N75L25, L50, L75
- 56. N50-variationsN25, N75L25, L50, L75Nx, Lx
- 57. OtherNumber of N’s per 100 kbp
- 58. OtherNumber of N’s per 100 kbpGC %
- 59. OtherNumber of N’s per 100 kbpGC %Distributions of GC % in small windows:GC=37GC=44GC=41GC=...
- 60. Other
- 61. Reference-based metricsA lot of metricsAccurate assessment
- 62. Basic reference statisticsReference lengthReference GC %Number of chromosomes
- 63. Basic reference statisticsNGx, LGxNG50 = 40LG50 = 4
- 64. Basic reference statisticsNGx, LGxNG50 = 40LG50 = 4
- 65. Basic reference statisticsNGx, LGxNG50 = 40 40LG50 = 4 4
- 66. Alignment statisticsAssemblyReference genome
- 67. Alignment statistics
- 68. Genome fraction %Alignment statistics
- 69. Genome fraction %Duplication ratioAlignment statistics
- 70. Genome fraction %Duplication ratioNumber of gapsAlignment statistics
- 71. Genome fraction %Duplication ratioNumber of gapsLargest alignment lengthAlignment statistics
- 72. Genome fraction %Duplication ratioNumber of gapsLargest alignment lengthNumber of unaligned contigs (full & partial)Alignment statistics
- 73. Genome fraction %Duplication ratioNumber of gapsLargest alignment
- 74. Alignment statisticsGenome fraction %Duplication ratioNumber of gapsLargest
- 75. MisassembliesContigReference genomeChromosome 1Chromosome 2
- 76. MisassembliesContigReference genomeChromosome 1Chromosome 2Relocation> 1kbpChromosome 2Chromosome 1InversionChromosome 2Chromosome 1TranslocationChromosome 2Chromosome 1
- 77. There is no best metricNB!
- 78. NA50Assembly AAssembly B200100
- 79. NA50Assembly AReference genomeAssembly B200100
- 80. NA50Assembly AReference genomeAssembly B200100N50 = 200# misassemblies = 2N50 = 100# misassemblies = 0
- 81. NA50Assembly AReference genomeAssembly B200100N50 = 200# misassemblies
- 82. QUality ASsesment Tool for Genome Assemblies
- 83. QUASTAssembly statistics Basic statisticsReference-based evaluationSimple de novo evaluationAvailable as a web-based and a command line toolquast.sf.net
- 84. QUAST: console toolquast.pyquast.py --help
- 85. QUAST basicsquast.pyquast.py --helpquast.py contigs.fastaquast.py [options] contigs.fastaquast.py -o out_dir contigs.fasta
- 86. Reference optionsReference genome-R reference.fastaGene annotation-G genes.gff Operon annotation-O operons.gff
- 87. QUAST outputReports in different formatsPlain text tableTab
- 88. Contig alignment viewerAll alignments for each contigMisassembly details Contig ordering along the genomeOverlaps / gaps
- 89. Contig alignment viewer
- 90. Contig size viewerContigs ordered from longest to
- 91. Contig size viewer
- 92. De novo evaluation
- 93. Read-based statisticsNumber of aligned/unaligned reads % of assembly covered by reads
- 94. Read-based statisticsNumber of aligned/unaligned reads % of
- 95. Annotation-based statisticsNumber of ORFs
- 96. Annotation-based statisticsNumber of ORFsNumber of gene/operon-like regionsGeneMarkS (Borodovsky et al.)GlimmerHMM (Majoros et al.)
- 97. Annotation-based statisticsNumber of ORFsNumber of gene/operon-like regionsGeneMarkS
- 98. Thank you!Questions?
- 99. Скачать презентанцию
Introduction
Слайды и текст этой презентации
Слайд 5Why to assemble?
Sequencing data
Billions of short reads
Sequencing errors
Contaminants
Assembly
Corrects sequencing
errors
errors
Слайд 12Genomic repeats
TATTCTTC
CTTCCACG
CACGTAGG
GGCCTTCCCTTCCACG
CACGCTTCG
TATTCTTCCACGTAGGGCCTTCCACGCTTCG
Слайд 22Input data options
Unpaired reads
Illumina unpaired
-s single.fastq
-s single1.fastq -s single2.fastq ...
Слайд 23Input data options
Paired-end reads
Interlaced pairs in one file
>left_read_id
ACGTGCAGG…
>right_read_id
GCTTCGAGG…
Separate files
file1.fastq file2.fastq
>left_read_id >right_read_id
ACGTGCAGG… GCTTCGAGG…
Слайд 24Input data options
Paired-end reads
Interlaced pairs in one file
--pe1-12 file.fastq
Separate files
--pe1-1
file1.fastq --pe1-2 file2.fastq
Слайд 25Input data options
Paired-end reads
Interlaced pairs in one file
--pe1-12 file.fastq
Separate files
--pe1-1
file1.fastq --pe1-2 file2.fastq
--pe1-s unpaired.fastq
Слайд 26SPAdes performance options
Number of threads
-t N
Maximal available RAM (GB)
SPAdes will
terminate if exceeded
-m M
Слайд 27Pipeline options
Run only assembler (input reads are already corrected or
quality-trimmed)
--only-assembler
Слайд 28Input data options
Mate-pair reads
Cannot be used separately
Interlaced pairs in
one file
--mp1-12 mp.fastq
Separate files
--mp1-1 mp1.fastq --mp1-2 mp2.fastq
Слайд 29Hybrid assembly options
PacBio CLR
--pacbio pb.fastq
Oxford Nanopore reads
--nanopore nanopore_reads.fastq
Слайд 33Which assembler to use?
ABySS
ALLPATHS-LG
CLC
IDBA-UD
MaSuRCA
MIRA
Ray
SOAPdenovo
SPAdes
Velvet
and many more...
Слайд 34Which assembler to use?
Different technologies (Illumina, 454, IonTorrent, ...)
Genome type
and size (bacteria, insects, mammals, plants, ...)
Type of prepared libraries
(single reads, paired-end, mate-pairs, combinations)Type of data (multicell, metagenomic, single-cell)
Слайд 36Which assembler to use?
Assemblathon 1 & 2
Simulated and real datasets
More
than 30 teams competing
Independent studies
Papers (GAGE, GAGE-B, GABenchToB)
Web-sites (nucleotid.es, …)
Surveys
Genome assembly evaluation tools
QUAST
GAGE
Слайд 37Assembly evaluation
Basic evaluation
No extra input
Very quick
Reference-based evaluation
A lot of metrics
Very
accurate
De novo evaluation
Advanced analysis of de novo assemblies
Слайд 42Contig sizes
Number of contigs
Number of large contigs (i.e. > 1000
bp)
Largest contig length
Total assembly length
Слайд 43N50
The maximum length X for which the collection of all
contigs of length >= X covers at least 50% of
the assembly
Слайд 44N50
The maximum length X for which the collection of all
contigs of length >= X covers at least 50% of
the assembly
Слайд 45N50
The maximum length X for which the collection of all
contigs of length >= X covers at least 50% of
the assembly
Слайд 46N50
The maximum length X for which the collection of all
contigs of length >= X covers at least 50% of
the assembly
Слайд 47N50
The maximum length X for which the collection of all
contigs of length >= X covers at least 50% of
the assembly
Слайд 48N50
The maximum length X for which the collection of all
contigs of length >= X covers at least 50% of
the assembly
Слайд 49N50
The maximum length X for which the collection of all
contigs of length >= X covers at least 50% of
the assembly
Слайд 50N50
The maximum length X for which the collection of all
contigs of length >= X covers at least 50% of
the assemblyN50 = 60
Слайд 51L50
The minimum number X such that X longest contigs cover
at least 50% of the assembly
L50 = 3
Слайд 52L50
The minimum number X such that X longest contigs cover
at least 50% of the assembly
L50 = 3
Слайд 59Other
Number of N’s per 100 kbp
GC %
Distributions of GC %
in small windows:
GC=37
GC=44
GC=41
GC=...
Слайд 71Genome fraction %
Duplication ratio
Number of gaps
Largest alignment length
Alignment statistics
Слайд 72Genome fraction %
Duplication ratio
Number of gaps
Largest alignment length
Number of unaligned
contigs (full & partial)
Alignment statistics
Слайд 73Genome fraction %
Duplication ratio
Number of gaps
Largest alignment length
Number of unaligned
contigs (full & partial)
Number of mismatches/indels per 100 kbp
Alignment statistics
Слайд 74Alignment statistics
Genome fraction %
Duplication ratio
Number of gaps
Largest alignment length
Number of
unaligned contigs (full & partial)
Number of mismatches/indels per 100 kbp
Number
of genes/operons (full & partial)
Слайд 76Misassemblies
Contig
Reference genome
Chromosome 1
Chromosome 2
Relocation
> 1kbp
Chromosome 2
Chromosome 1
Inversion
Chromosome 2
Chromosome 1
Translocation
Chromosome 2
Chromosome
Слайд 80NA50
Assembly A
Reference genome
Assembly B
200
100
N50 = 200
# misassemblies = 2
N50 =
100
# misassemblies = 0
Слайд 81NA50
Assembly A
Reference genome
Assembly B
200
100
N50 = 200
# misassemblies = 2
NA50 =
100
N50 = 100
# misassemblies = 0
NA50 = 100
Слайд 83QUAST
Assembly statistics
Basic statistics
Reference-based evaluation
Simple de novo evaluation
Available as a
web-based and a command line tool
quast.sf.net
Слайд 85QUAST basics
quast.py
quast.py --help
quast.py contigs.fasta
quast.py [options] contigs.fasta
quast.py -o out_dir contigs.fasta
![QUAST basicsquast.pyquast.py --helpquast.py contigs.fastaquast.py [options] contigs.fastaquast.py -o out_dir contigs.fasta](/img/thumbs/417832c044ff54e0beebe4050ee70cb2-800x.jpg "Genome assembly with SPAdes QUAST basicsquast.pyquast.py --helpquast.py contigs.fastaquast.py [options] contigs.fastaquast.py -o out_dir contigs.fasta")
Слайд 86Reference options
Reference genome
-R reference.fasta
Gene annotation
-G genes.gff
Operon annotation
-O operons.gff
Слайд 87QUAST output
Reports in different formats
Plain text table
Tab separated values (Excel,
Google Spreadsheets)
Interactive HTML
Plots (PDF/PNG/SVG)
Nx, NGx, NAx
Genes
Cumulative length
Interactive contig viewers (Icarus)
Contig
alignment viewerContig size viewer
Слайд 88Contig alignment viewer
All alignments for each contig
Misassembly details
Contig ordering
along the genome
Overlaps / gaps
Слайд 90Contig size viewer
Contigs ordered from longest to shortest
N50, N75 (NG50,
NG75)
Filtration by contig size
Gene prediction results
Available without a reference
Слайд 94Read-based statistics
Number of aligned/unaligned reads
% of assembly covered by
reads
Points with low coverage
Points with multiple read clipping
Points with incorrect
insert sizes
Слайд 96Annotation-based statistics
Number of ORFs
Number of gene/operon-like regions
GeneMarkS (Borodovsky et al.)
GlimmerHMM
(Majoros et al.)
Слайд 97Annotation-based statistics
Number of ORFs
Number of gene/operon-like regions
GeneMarkS (Borodovsky et al.)
GlimmerHMM
(Majoros et al.)
Number of conservative genes
BUSCO (Simão et al.)
CEGMA (Korf
et al., no longer supported)
