Institute of Biomedical Sciences University of São Paulo

Institute of Biomedical SciencesUniversity of São Paulo

Arthur Gruber Coccilab – ICB/USP

DNA Assembly and Mapping

Next-generation sequencing platforms

• Mid 2000’s: next-generation sequencers (NGS) were developed

• 2004 – 454 (Roche, formerly 454 Life Sciences)

• 2006 – Illumina (formerly Solexa)

• 2008 – SOLiD (Life Technologies, formerly Applied Biosystems)

• 2011 –Ion Torrent /Proton (Life Technologies)

• 2011 – PacBio RS (Pacific Biosciences)• Massively parallel sequencing - tipo shotgun (random fragments)• Generate millions of sequences in one single run at a low cost per

base

Data generation x cost

Moore LawCost per MB of sequence

Source: Sboner et al. (2011) - Genome Biol. 12 (8): 125

Coccilab – ICB/USP

Evolution of sequencing costs

DNA Assembly

An estimate of the evolution of sequencing costs over the last 10 years. Costs are given for sequencing a megabase using a logarithmic scale. This curve is adapted from [15]. Time of introduction of new technologies is indicated.

Source: Delseny et al. (2010). Plant Science 179 (5): 407–422

NGS – Lower cost and greater data generation

Source: Sboner et al. (2011) - Genome Biol. 12 (8): 125


Source: Glen (2011). Mol Ecol Resources 11: 759–769






Different types of sequencing methods

A flow chart of the different types of sequencing methods


454 Workflow

Source:Mardis. (2008). Annu. Rev. Genomics Hum. Genet. 9: 387–402

Illumina Workflow

Source: Mardis. (2008). Annu. Rev. Genomics Hum. Genet. 9: 387–402

SOLiD Workflow

Source:Mardis. (2008). Annu. Rev. Genomics Hum. Genet. 9: 387–402


NGS platforms – applications

DNA Assembly

Source: Homer et al. (2009). Brief Bioinformatics II (2): 181-197.


NGS platforms – applications

DNA Assembly


Tool Website Category Platform

Sequence assembly

• Current sequencing platform can only generate sequence reads of dozens of bp (so called short reads) or some hundreds of reads (Sanger, 454, Ion Torrent, PacBio)

• Computational tools are necessary to assemble the sequence reads into a larger sequence segment/genome

• Sequence assemblers use two different approaches to assemble reads:• Overlap layout consensus• de Bruijn graphs

1

2

Schatz et al. (2010) - Assembly of large genomes using second-generation sequencing


K-mer graph

DNA Assembly

Source: Miller et al. (2010). Genomics 95: 315-327

A pair-wise overlap represented by a K-mer graph.

(a) Two reads have an error-free

overlap of 4 bases.

(b) One K-mer graph, with K=4, represents both reads. The pair-wise alignment is a by-product of the graph construction.

(c) The simple path through the graph implies a contig whose consensus sequence is easily reconstructed from the path.


Complexity in K-mer graphs

DNA Assembly

Source: Miller et al. (2010). Genomics 95: 315-327

Complexity in K-mer graphs can be diagnosed with read multiplicity information. In these graphs, edges represented in more reads are drawn with thicker arrows.

(a) An errant base call toward the end of a read causes a “spur” or short dead-end branch. The same pattern could be induced by coincidence of zero coverage after polymorphism near a repeat.

(b) An errant base call near a read middle causes a “bubble” or alternate path. Polymorphisms between donor chromosomes would be expected to induce a bubble with parity of read multiplicity on the divergent paths.

(c) Repeat sequences lead to the “frayed rope” pattern of convergent and divergent paths.

de Bruijn Graphs

Advantages:• Can deal with large amounts of data, consolidates redundant reads (high coverage)

in a very efficient way • Sequencing errors are promptly identified from the topology of the graph and k-mer

coverage

de BRUIJN Graph

Erro

Edge formation in the graph

Evaluating assemblies

• Size of Largest Contig• Number of contigs > n length• N50

Given a set of sequences of varying lengths, the N50 length is defined as the length N for which half of all bases in the sequences are in a sequence of length L < N. In other words, N50 is the contig length such that using equal or longer contigs produces half the bases of the genome. Therefore, the number of bases from of all sequences shorter than the N50 will equal the number of bases from all sequences longer than the N50.

Evaluating assemblies

• N50Contig or scaffold N50 is a weighted median statistic such that 50% of the entire assembly is contained in contigs or scaffolds equal to or larger than this value

Some definitions

• ContigA sequence contig is a contiguous, overlapping sequence read resulting from the reassembly of the small DNA fragments generated by sequencing strategies• ScaffoldUsing paired-end sequencing technology, the distance between both sequence ends of a fragment is known. This gives additional information about the orientation of contigs constructed from these reads and allows for their assembly into scaffolds.

Libraries for NGS platforms


Paired-end technology

DNA Assembly


A) Schematic drawing of the paired-end technology. Adaptors and genome fragments are represented respectively by the black and grey lines.

B) B) Strategy for sequencing large DNA fragments: short reads are assembled into contigs. A high coverage is required. In the next steps, paired-ends derived from larger fragments are used to assemble contigs into scaffolds.

Contigs and scaffolds

An example of a real file 454 data

An example of real file 454 data

An example of real file 454 data


NGS platforms – performances and features

DNA Assembly



Comparison of De Novo Genome Assemblers

DNA Assembly

Source: Zhang et al. (2011). PLoS ONE 6 (3): e17915.



DNA Assembly


Accuracy and integrity for 36-mer datasets assembly. The quality of consequential contigs is shown with:

(A) the accuracy of assembled contigs

(B) the genome coverage of the assembled contigs. No data is shown when the RAM is insufficient or the assembly tool is not suitable for the dataset.



DNA Assembly


Accuracy and integrity for 75-mer datasets assembly. The quality of consequential contigs is shown with:

(A) the accuracy of assembled contigs

(B) the genome coverage of the assembled contigs. No data is shown when the RAM is insufficient or the assembly tool is not suitable for the dataset.



DNA Assembly


Statistics for assembled contigs of 36-mer short reads. Indicatrix that illustrates the feature of size distribution are adopted for analysis. ‘‘#’’ denotes the RAM of machine is not enough, and ‘‘N/A’’ means the data is not available. The N50 size and N80 size represent the maximum read length for which all contigs greater than or equal to the threshold covered 50% or 80% of the reference genome.



DNA Assembly


Statistics for assembled contigs of 75-mer short reads. Indicatrix that illustrates the feature of size distribution are adopted for analysis. ‘‘#’’ denotes the RAM of machine is not enough, and ‘‘N/A’’ means the data is not available. The N50 size and N80 size represent the maximum read length for which all contigs greater than or equal to the threshold covered 50% or 80% of the reference genome.


Genomes assembled de novo exclusively from Illumina short sequence reads

DNA Assembly

Organisms:

• Turkey (Meleagris gallopavo)• Giant panda (Ailuropoda melanoleuca)• Bacillus subtilis 168• Bacillus subtilis natto• Pseudomonas syringae pv. tabaci 11528• Pseudomonas syringae pv. syringae Psy642• Pseudomonas syringae pv. tomato T1• Pseudomonas syringae pv. Aesculi• Apple scab (Ventura inaequalis)• Pine (Pinus species) chloroplast

Paszkiewicz & Studholme (2010). Brief Bioinform 11 (5): 457-472.


Assembly results using real illumina single-end and paired-end reads from SRA

DNA Assembly

Source: Bao et al. (2011). Journal of Human Genetics 56: 406–414.


Biases in real short-read sequence data

DNA Assembly

Source: Paszkiewicz & Studholme (2010). Brief Bioinform 11 (5): 457-472.

(A) Illustrates the depth of coverage by aligned reads over the 6 Mb circular chromosome. Coverage is shallower around the 3 Mb region than it is near the origin of replication (position 0)

(B) Illustrates the expected frequency distribution of alignment depth, assuming random sampling of the genome

(C) Illustrates the observed frequency distribution of alignment depth, which is broader than the expected distribution, indicating greater variance due to biased sampling.


Limitations of next-generation genome sequence assembly

DNA Assembly

Source: Alkan et al. (2010). Nat Methods 8(1): 61-65.

Limitations:

• NGS technologies typically generate shorter sequences with higher error rates from relatively short insert libraries

• Assembly of longer repeats and duplications will suffer from this short read length

• Assembly methods for short reads are based on de Bruijn graph and Eulerian path approaches, which have difficulty in assembling complex regions of the genome.

• DNA contamination or insertion polymorphism?


Limitations of next-generation genome sequence assembly

DNA Assembly

Source: Alkan et al. (2010). Nat Methods 8(1): 61-65.

Limitations:

• Repeat content• WGS-based de novo sequence assembly algorithm will collapse

identical repeats, resulting in reduced or lost genomic complexity.• Missing and fragmented genes


Data generation and analysis steps of a typical RNA-seq experiment.

Source: Martin & Wang. (2011). Nature Reviews Genetics 12, 671-682.


Reference-based transcriptome assembly strategy

DNA Assembly



Overview of the de novo transcriptome assembly strategy



Alternative approaches for combined transcriptome assembly

DNA Assembly



Software for transcriptome assembly

DNA Assembly



Splice-aware short-read aligners

DNA Assembly



Mapping reads onto a reference sequence

DNA Assembly

Programs:

• Bowtie is an ultrafast, memory-efficient short read aligner. It aligns short DNA sequences (reads) to the human genome at a rate of over 25 million 35-bp reads per hour.

• Available at http://bowtie-bio.sourceforge.net/index.shtml• SHRiMP is a software package for aligning genomic reads against a

target genome. Available at http://compbio.cs.toronto.edu/shrimp/• BarraCUDA - an ultra fast short read sequence alignment software

using GPUs. • Available at

http://www.many-core.group.cam.ac.uk/projects/lam.shtml • Burrows-Wheeler Aligner (BWA) is an efficient program that aligns

relatively short nucleotide sequences against a long reference sequence such as the human genome.

• Available at http://bio-bwa.sourceforge.net/

http://bowtie-bio.sourceforge.net/index.shtml






Mapping reads onto a reference sequence

Programs:

• BLAT is a bioinformatics software a tool which performs rapid mRNA/DNA and cross-species protein alignments

• Available at http://www.kentinformatics.com/products.html• BFAST facilitates the fast and accurate mapping of short reads to

reference sequences. Some advantages of BFAST include: • Speed: enables billions of short reads to be mapped quickly.• Accuracy: A priori probabilities for mapping reads with defined set

of variants.• An easy way to measurably tune accuracy at the expense of

speed.• Available at

http://sourceforge.net/apps/mediawiki/bfast/index.php?title=Main_Page

http://www.kentinformatics.com/products.html

http://www.kentinformatics.com/products.html


Visualizing reads mapped onto a reference sequence

Programs:

• TABLET - lightweight, high-performance graphical viewer for next generation sequence assemblies and alignments.

• Available at http://bioinf.scri.ac.uk/tablet/index.shtml• IGV - Integrative Genomics Viewer - a high-performance visualization

tool for interactive exploration of large, integrated genomic datasets. • Available at http://www.broadinstitute.org/igv/

http://bioinf.scri.ac.uk/tablet/index.shtml

http://bioinf.scri.ac.uk/tablet/index.shtml


TABLET - graphical viewer


Integrative Genomics Viewer (IGV)


Data formats - SOLiD

Color Space:

• Also known as 2-base (Di-Base) encoding, is based on ligation sequencing rather than sequencing by synthesis.

• Each base in this sequencing method is read twice. This changes the color of two adjacent color space calls, therefore in order to miscall a SNP, two adjacent colors must be miscalled.

• Requires specific software to manipulate the data. Most assemblers are not designed to deal with color space.



SOLiD 4 – data is provided as *csfasta and *.qual

csfasta: >1_7_80_F3T223003300123201021020110010200020002200000000300000000001000020000110002200>1_7_157_F3T120030200320003020020010020100300003100031000300001000000000010000000000000>1_7_202_F3T230020100031001030000230000000200003100000000000003000000000010000000000000

qual: >1_7_80_F340 42 16 4 42 4 7 32 4 42 4 27 36 4 42 4 16 42 4 42 4 27 35 4 4 4 27 35 4 4 7 27 4 4 4 4 27 4 4 4 4 22 4 4 4 4 4 4 4 4 4 4 4 4 4 4 16 4 4 4 4 16 4 4 4 4 16 11 4 4 4 22 7 4 4 >1_7_157_F340 42 4 4 42 42 40 4 4 40 42 32 4 4 42 4 7 4 4 7 4 4 36 4 4 40 4 16 4 4 36 4 4 4 4 42 4 4 4 4 42 4 4 4 4 36 4 4 4 4 36 4 4 4 4 4 7 4 4 4 4 4 4 4 4 4 7 4 4 4 4 4 4 4 4 >1_7_202_F342 42 4 4 42 42 42 4 4 42 40 35 4 4 42 4 27 4 4 40 4 36 42 4 4 36 4 42 4 4 27 4 4 4 4 16 4 4 4 4 7 7 4 4 4 16 4 4 4 4 4 4 4 4 4 4 7 4 4 4 4 16 4 4 4 4 4 4 4 4 4 11 4 4 4



Color space can be converted into DE (Double encoding)

Life Technologies provides a set of scripts (SOLiD™ de novo accessory tools 2.0) for conversion and data usage with Velvet assembler.

• The program prepares reads in the format accepted by Velvet assembly engine.

• The program removes first base and first color, double encodes reads (i.e., 0 for A,1 for C,2 for G,3 for T).

• After running the assembler, the DE contigs must be converted into base space.



WSQ:

• Extensible Sequence (XSQ) format introduced with the 5500 series SOLiD Sequencer.

• Developed to store each call and quality value in a single byte, which results in file sizes that are up to 75% smaller.

• Binary format – can be converted into csfasta/qual and *.fastq formats using the SOLiDTM System XSQ Tools (available at Life Technologies)


Data formats – 454 platform

FASTA and QUAL:

• Files can be provided in FASTA (*.fna) and QUAL (*.qual) formats.

SFF:

• Standard Flowgram Format - equivalent of the scf/ab1/trace file for Sanger sequencing, contains information on the signal strength for each flow.

• Binary format – can be converted into FASTA/QUAL using a python script (sff_extract) or using sff2fastq script.


Data formats – Illumina

FASTAQ

• Originally developed at the Wellcome Trust Sanger Institute to bundle a FASTA sequence and its quality data.

• FASTQ format is a text-based format for storing both a biological sequence (usually nucleotide sequence) and its corresponding quality scores.

• Adopted by the Illumina Genome Analyzer.• FASTQ has become an almost universal format. It is accepted by

many assemblers (e.g. Edena, Euler, Velvet, ABySS, etc. ) and sequence mapping programs (e.g. Bowtie, BFAST, SHRIMP, MOSAIK, etc.)

• FASTQ can be converted into FASTA using the FASTX-Toolkit.



FASTAQ

• Both the sequence letter and quality score are encoded with a single ASCII character for brevity.

• Line 1 begins with a '@' character and is followed by a sequence identifier and an optional description (like a FASTA title line).

• Line 2 is the raw sequence letters. • Line 3 begins with a '+' character and is optionally followed by the

same sequence identifier (and any description) again. • Line 4 encodes the quality values for the sequence in Line 2, and

must contain the same number of symbols as letters in the sequence.

@SEQ_ID

GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT+!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65



FASTAQ Encoding

• Sanger and Illumina use slightly different base quality calculations.

• SangerQsanger = -10 log10p

• Illumina (prior to version 1.3)Qillumina = -10 log10 [ p /(1-p)]

• Solexa/Illumina 1.0 format can encode a quality score from -5 to 62 using ASCII 59 to 126 (Solexa+64).

• Sanger format uses Phred quality from 0 to 93 using ASCII characters 33 to 126 (Phred+33):

!"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~ | | | | | |33 59 64 73 104 126

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Institute of Biomedical Sciences University of São Paulo