Genome assembly with SPAdes презентация

Genome assembly with SPAdes Center for Algorithmic Biotechnology SPbU

Introduction

Why to assemble?

Why to assemble? Sequencing data Billions of short reads Sequencing errors Contaminants

Why to assemble? Sequencing data Billions of short reads Sequencing errors Contaminants Assembly Corrects sequencing errors Much longer sequences Each genomic region is presented only once May introduce errors

Assembly basics

Assembly in a perfect world

Assembly in real world

De novo whole genome assembly

De novo whole genome assembly

Genomic repeats TATTCTTCCACGTAGGGCCTTCCACGCTTCG

Genomic repeats TATTCTTC CTTCCACG CACGTAGG GGCCTTCC CTTCCACG CACGCTTCG TATTCTTCCACGTAGGGCCTTCCACGCTTCG

Genomic repeats TATTCTTC CTTCCACG CACGTAGG GGCCTTCC CTTCCACG CACGCTTCG

Genomic repeats TATTCTTCCACGTAGG GGCCTTCCACGCTTCG TATTCTTCCACGCTTCG GGCCTTCCACGTAGG

Genomic repeats TATTCTTCCACGTAGG ACGTAGGGCCTT GCCTTCCACGCTTCG TATTCTTCCACGTAGGGCCTTCCACGCTTCG

Genomic repeats TATTCTTCCACGTAGG ACGTAGGGCCTT GCCTTCCACGCTTCG

SPAdes assembler

SPAdes first steps spades.py

SPAdes first steps spades.py spades.py --help spades.py --test

SPAdes first steps spades.py spades.py --help spades.py --test -o <output_dir>

Input data formats FASTA: .fasta / .fa FASTQ: .fastq / .fq Gzipped: .gz

Input data options Unpaired reads Illumina unpaired -s single.fastq -s single1.fastq -s single2.fastq ...

Input 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…

Input data options Paired-end reads Interlaced pairs in one file --pe1-12 file.fastq Separate files --pe1-1 file1.fastq --pe1-2 file2.fastq

Input 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

SPAdes performance options Number of threads -t N Maximal available RAM (GB) SPAdes will terminate if exceeded -m M

Pipeline options Run only assembler (input reads are already corrected or quality-trimmed) --only-assembler

Input 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

Hybrid assembly options PacBio CLR --pacbio pb.fastq Oxford Nanopore reads --nanopore nanopore_reads.fastq

Restarting SPAdes SPAdes / system crashed --continue -o your_output_dir

Genome assembly evaluation with QUAST Center for Algorithmic Biotechnology SPbU

In reality

Which assembler to use? ABySS ALLPATHS-LG CLC IDBA-UD MaSuRCA MIRA Ray SOAPdenovo SPAdes Velvet and many more...

Which 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)

There is no best assembler

Which 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

Assembly 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

Basic statistics

Contig sizes Number of contigs

Contig sizes Number of contigs Number of large contigs (i.e. > 1000 bp)

Contig sizes Number of contigs Number of large contigs (i.e. > 1000 bp) Largest contig length

Contig sizes Number of contigs Number of large contigs (i.e. > 1000 bp) Largest contig length Total assembly length

N50 The maximum length X for which the collection of all contigs of length >= X covers at least 50% of the assembly

N50 The maximum length X for which the collection of all contigs of length >= X covers at least 50% of the assembly

N50 The maximum length X for which the collection of all contigs of length >= X covers at least 50% of the assembly

N50 The maximum length X for which the collection of all contigs of length >= X covers at least 50% of the assembly

N50 The maximum length X for which the collection of all contigs of length >= X covers at least 50% of the assembly

N50 The maximum length X for which the collection of all contigs of length >= X covers at least 50% of the assembly

N50 The maximum length X for which the collection of all contigs of length >= X covers at least 50% of the assembly

N50 The maximum length X for which the collection of all contigs of length >= X covers at least 50% of the assembly

L50 The minimum number X such that X longest contigs cover at least 50% of the assembly

L50 The minimum number X such that X longest contigs cover at least 50% of the assembly

N50-variations N25, N75 L25, L75

N50-variations N25, N75 L25, L75

N50-variations N25, N75 L25, L50, L75

N50-variations N25, N75 L25, L50, L75 Nx, Lx

Other Number of N’s per 100 kbp

Other Number of N’s per 100 kbp GC %

Other Number of N’s per 100 kbp GC % Distributions of GC % in small windows:

Other

Reference-based metrics

Basic reference statistics Reference length Reference GC % Number of chromosomes

Basic reference statistics NGx, LGx

Basic reference statistics NGx, LGx

Basic reference statistics NGx, LGx

Alignment statistics

Alignment statistics

Alignment statistics Genome fraction %

Alignment statistics Genome fraction % Duplication ratio

Alignment statistics Genome fraction % Duplication ratio Number of gaps

Alignment statistics Genome fraction % Duplication ratio Number of gaps Largest alignment length

Alignment statistics Genome fraction % Duplication ratio Number of gaps Largest alignment length Number of unaligned contigs (full & partial)

Alignment statistics Genome fraction % Duplication ratio Number of gaps Largest alignment length Number of unaligned contigs (full & partial) Number of mismatches/indels per 100 kbp

Alignment 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)

Misassemblies

Misassemblies

There is no best metric

NA50

NA50

NA50

NA50

QUality ASsesment Tool for Genome Assemblies

QUAST Assembly statistics Basic statistics Reference-based evaluation Simple de novo evaluation Available as a web-based and a command line tool quast.sf.net

QUAST: console tool quast.py quast.py --help

QUAST basics quast.py quast.py --help quast.py contigs.fasta quast.py [options] contigs.fasta quast.py -o out_dir contigs.fasta

Reference options Reference genome -R reference.fasta Gene annotation -G genes.gff Operon annotation -O operons.gff

QUAST 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 viewer Contig size viewer

Contig alignment viewer All alignments for each contig Misassembly details Contig ordering along the genome Overlaps / gaps

Contig alignment viewer

Contig size viewer Contigs ordered from longest to shortest N50, N75 (NG50, NG75) Filtration by contig size Gene prediction results Available without a reference

Contig size viewer

De novo evaluation

Read-based statistics Number of aligned/unaligned reads % of assembly covered by reads

Read-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

Annotation-based statistics Number of ORFs

Annotation-based statistics Number of ORFs Number of gene/operon-like regions GeneMarkS (Borodovsky et al.) GlimmerHMM (Majoros et al.)

Annotation-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)

Thank you! Questions?