Aureliano Bombarely, profile picture
Uploaded byAureliano Bombarely
18,679 views

Genome Assembly

The document provides an overview of plant genome sequence assembly, including: 1) A brief history of sequencing technologies and their improvements over time, from Sanger sequencing to newer technologies producing longer reads. 2) Key steps in a sequencing project including read processing, filtering, and corrections before assembly into contigs and scaffolds using appropriate software. 3) Factors to consider for experimental design and assembly optimization such as sequencing depth, library types, and software choices depending on the genome and data characteristics.

Embed presentation

Downloaded 541 times
Insights into Plant Genome Sequence Assembly. by Aureliano Bombarely ab782@cornell.edu
1. A brief history of the sequence assembly. 2. Sequencing, tools and computers. 3. Things that you should know about genomes.
1. A brief history of the sequence assembly. 2. Sequencing, tools and computers. 3. Things that you should know about genomes.
1. A brief history of the sequence assembly. http://www.ncbi.nlm.nih.gov/About/primer/genetics_genome.html
Genome = N x Sequence of DNA Gentile F. et al. Direct Imaging of DNA Fibers:TheVisage of Double Helix Nano Lett., 2012, 12 (12), pp 6453–6458 DNA sequencing ??? 1. A brief history of the sequence assembly.
DNA Sequencing: “Process of determining the precise order of nucleotides within a DNA molecule.” -Wikipedia ddATP ddGTP ddCTP ddTTP Taq-Polymerase STOP time 2) Chromatographic Separation GTCACCCTGAAT 1) PCR with ddNTPs 3) Chromatogram Read DNA Sanger Sequencing 1. A brief history of the sequence assembly.
Gentile F. et al. Direct Imaging of DNA Fibers:TheVisage of Double Helix Nano Lett., 2012, 12 (12), pp 6453–6458 ACGCGGTGACGTTGTCGA ACGCGTTTTGTTGTGG ACGATTTAAATGACGTTGTCGA ACGCGGTGACGTTGTCGA ACGCGTTTTGTTGTGG ACCCCGACGTTGTCGA DNA sequencing ACGCGTTTTGTTGTGG ACCCCTGGGGGGGTTGTCGA Fragments Sequence assembly ACGCGTTTTGTTGTGGTGGCCACACCACGCAGTGACGGAGATAACGGCGAGAGCATGGACGGAGGATGAGGATGG 1. A brief history of the sequence assembly.
Sequence assembly = Resolve a puzzle and rebuild a DNA sequence from its pieces (fragments) Image courtesy of iStock photo 1. A brief history of the sequence assembly.
Resolve a puzzle and rebuild a DNA sequence from its pieces (fragments) BACs BAC-by-BAC sequencing Whole Genome Shotgun (WGS) sequencing 1. A brief history of the sequence assembly.
MS2Bacteriophage(3.658Kb)1977 1978 1979 1980 1981 1982 1983 Epstein-BarrVirus(170Kb)1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 Haemophilusinfluenzae(1.83Mb)1995 Saccharomycescerevisiae(12.1Mb)1996 1997 Caenorhabditiselegans(100Mb)1998 1999 Arabidopsisthaliana(157Mb)2000 Homosapiens(3.2Gb)2001 Oryzasativa(420Mb)2002 2003 2004 2005 Populustrichocarpa(550Mb)2006 Vitisvinifera(487Mb)2007 Physcomitrela(480Mb);Caricapapaya(372Mb)2008 Sorghumbicolor(730Mb);ZeaMays(2.3Gb);Cucumissativus(367Mb)2009 Ectocarpus(214Mb);Malusxdomestica(742Mb);Glycinemax(1.1Gb)2010 Pigeonpea,Potato,Cannabis,A.lyrata,Cacao,Strawberry,Medicago2011 Mei,Benthamiana,Tomato,Setaria,Melon,Flax,T.salsuginea,Banana,Cotton,Orange,Pear2012 Sanger Sequencing 454 Solexa / Illumina SOLiD IonT PB 1. A brief history of the sequence assembly.
1977MS2Bacteriophage(3.658Kb)1977 N O SO FT W A R E U SED 1. A brief history of the sequence assembly.
Haemophilusinfluenzae(1.83Mb)1995 Software: TIGR ASSEMBLER Hardware: SPARCenter 2000 (512 Mb RAM) 1. A brief history of the sequence assembly.
Haemophilusinfluenzae(1.83Mb)1995 Software: TIGR ASSEMBLER Smith-Waterman alignments Sutton GG. et al.TIGR Assembler:A New Tool to Assembly Large Shotgun Sequencing Projects Genome Science and Technology, 1995,1:9-19 O V ER LA PPIN G M ET H O D O LO G Y 1. A brief history of the sequence assembly.
http://www.ebi.ac.uk/training/online/course/nucleotide-sequence-data-resources-ebi/what-ena/how-sequence-assembled 1. A brief history of the sequence assembly.
Homosapiens(3.2Gb)2001 BAC-by-BAC sequencing Whole Genome Shotgun (WGS) sequencing International Human Genome Sequencing Consortium. Initial Sequencing and Analysis of the Human Genome. Nature. 2001. 409:860-921 Venter JC. et al. The Sequence of the Human Genome. Science. 2001. 291:1304-1351 O V ER LA PPIN G M ET H O D O LO G Y 1. A brief history of the sequence assembly.
Homosapiens(3.2Gb)2001 Whole Genome Shotgun (WGS) sequencing Venter JC. et al. The Sequence of the Human Genome. Science. 2001. 291:1304-1351 Software: WGAASSEMBLER (CABOG) Hardware: 40 machines AlphaSMPs (4 Gb RAM/each and 4 cores/each, total=160 Gb RAM and 160 cores); 5 days. 1. A brief history of the sequence assembly.
Ailuropodamelanoleura(2.3Gb)2009 Whole Genome Shotgun (WGS) sequencing Li R. et al.The Sequence and the Novo Assembly of the Giant Panda Genome. Nature. 2009. 463:311-317 Software: SOAPdenovo Hardware: Supercomputer with 32 cores and 512 Gb RAM. B R U IJN G R A PH S M ET H O D O LO G Y 1. A brief history of the sequence assembly.
1. A brief history of the sequence assembly. What is a Kmer ? ATGCGCAGTGGAGAGAGAGCGATG Sequence A with 25 nt Specific n-tuple or n-gram of nucleic acid or amino acid sequences. -Wikipedia ordered list of elements contiguous sequence of n items from a given sequence of text 5 Kmers of 20-mer ATGCGCAGTGGAGAGAGAGC TGCGCAGTGGAGAGAGAGCG GCGCAGTGGAGAGAGAGCGA CGCAGTGGAGAGAGAGCGAT GCAGTGGAGAGAGAGCGATG N_kmers = L_read - Kmer_size
Compeau PEC. et al. How to apply de Bruijn graphs to genome assembly. Nature Biotech. 2011. 29:287-291 1. A brief history of the sequence assembly.
OLC (Overlap-layout-consensus) algorithm is more suitable for the low-coverage long reads, whereas the DBG (De-Bruijn-Graph) algorithm is more suitable for high-coverage short reads and especially for large genome assembly 1. A brief history of the sequence assembly.
1. A brief history of the sequence assembly. 2. Sequencing, tools and computers. 3. Things that you should know about genomes.
2. Sequencing, tools and computers. 2.0 Overview of a Sequencing Project: Assembly Technology Library Preparation Sequencing Amount Fastq raw Fastq Processed Contigs Scaffolds Decisions during the Experimental Design Software Identity %, Kmer ... Post-assembly Filtering Decisions during the Assembly Optimization Reads processing and filtering 1. Low quality reads (qscore) (Q30) 2. Short reads (L50) 3. PCR duplications (Only Genomes). 4. Contaminations. 5. Corrections Consensus Scaffolding Scaffolds Gap Filling
2. Sequencing, tools and computers. 2.0 Overview of a Sequencing Project: Assembly Technology Library Preparation Sequencing Amount Decisions during the Experimental Design
2. Sequencing, tools and computers. 2.1 Technologies Run Time Sequence Length Reads/Run Total nucleotides sequenced per run Capillary Sequencing (ABI37000) ~2.5 h 800 bp 386 0.308 Mb 454 Pyrosequencing (GS FLX Titanium XL+) ~23 h 700 bp 1,000,000 700 Mb (0.7 Gb) Illumina (HiSeq X Ten) 72 h (3 days) 2 x 150 bp 6,000,000,000 1,600,000,000 Mb (1,600 Gb) Illumina (MiSeq) 65 h 2 x 300 bp 2 x 22,000,000 13,500 Mb (13.5 Gb) SOLID (5500xl system) 120 h (7 days) 2 x 60 bp 400,000,000 300,000 Mb (300 Gb) Ion Torrent (Ion Proton I) 2 h 200 bp 60,000,000 10,000 Mb (10 Gb) PacBio (PacBioRS II) 1.5 h ~8,500 bp 50,000 375 Mb (0.37 Gb)
2. Sequencing, tools and computers. Strenghs Weaknesses 454 Pyrosequencing (GS FLX Titanium XL+) - Long reads (450/700 bp). - Long insert for mate pair libraries (20Kb). - Low observed raw error rate (0.1%) - Low percentage of PCR duplications for mate pair libraries - Homopolymer error. - Low sequence yield per run (0.7 Gb). - Preferred assembler (gsAssembler) uses overlapping methodology. Illumina (HiSeq 2500) - High sequence yield per run (600 Gb) - Low observed raw error rate (0.26%) - High percentage of PCR duplications for mate pair libraries. - Long run time (11 days) - High instrument cost (~ $650K) Illumina (MiSeq) - Medium read size (250 bp) - Faster run than Illumina HiSeq - Medium sequence yield per run (8.5 Gb) SOLID (5500xl system) - 2-base encoding reduce the observed raw error rate (0.06%) - 2-base color coding makes difficult the sequence manipulation and assembly. - Short reads (75 bp) Ion Torrent (Ion Proton I) - Fast run (2 hours) - Low instrument cost ($80K). - Medium read size (200 bp) - Medium sequence yield per run (10 Gb) - Medium observed raw error rate (1.7%) PacBio (PacBioRS) - Long reads (3000 bp) - Fast run (2 hours) - Really high observed raw error rate (12.7%) - High instrument cost (~ $700K) - No pair end/mate pair reads 2.1 Technologies
2. Sequencing, tools and computers. ★ Library types (orientations): • Single reads • Pair ends (PE) (150-800 bp insert size) • Mate pairs (MP) (2-40 Kb insert size) F F F F R R R 454/Roche Illumina Illumina 2.2 Libraries
2. Sequencing, tools and computers. • Why is important the pair information ? F - novo assembly: Consensus sequence (Contig) Reads Scaffold (or Supercontig) Pair Read information NNNNN Genetic information (markers) Pseudomolecule (or ultracontig) NNNNN NN 2.2 Libraries
2. Sequencing, tools and computers. 2.3 Sequencing Amount * Slide from MC. Schatz
2. Sequencing, tools and computers. 2.3 Sequencing Amount Depending of the genome complexity, technology used and assembler: • More is better (if you have enough computational resources). - Sanger > 10X (less for BACs-by-BACs approaches). - 454 > 20X - Illumina > 100X • Polyploidy or high heterogozyty increase the amount of reads needed. • The use of different library types (pair ends and mate pairs with different insert sizes is essential). • Longer reads is preferable.
2. Sequencing, tools and computers. 2.0 Overview of a Sequencing Project: Assembly Fastq raw Fastq Processed Contigs Scaffolds Software Decisions during the Assembly Optimization Reads processing and filtering 1. Low quality reads (qscore) (Q30) 2. Short reads (L50) 3. PCR duplications (Only Genomes). 4. Contaminations. 5. Corrections Consensus Scaffolding Scaffolds Gap Filling
2. Sequencing, tools and computers. 2.0 Overview of a Sequencing Project: Assembly Fastq raw Fastq Processed Software Decisions during the Assembly Optimization Reads processing and filtering 1. Low quality reads (qscore) (Q30) 2. Short reads (L50) 3. PCR duplications (Only Genomes). 4. Contaminations. 5. Corrections
2. Sequencing, tools and computers. 2.4 Tools: Read Quality Evaluation a) Length of the read. b) Bases with qscore > 20 or 30. •FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
2. Sequencing, tools and computers. 2.4 Tools: Read Trimming and Filtering • Fastx-Toolkit (http://hannonlab.cshl.edu) • Ea-Utils (http://code.google.com/p/ea-utils/) • PrinSeq (http://edwards.sdsu.edu/cgi-bin/prinseq/prinseq.cgi) Software Multiplexing Trimming/Filtering Fastx-Toolkit fastx_barcode_splitter fastq_quality_filter Ea-Utils fastq-multx fastq-mcf PrinSeq PrinSeq PrinSeq 1. Adaptor removal. 2. Low quality reads (qscore) (Q30) 3. Short reads (L50) 4. PCR duplications (Only Genomes, Use PrinSeq).
2. Sequencing, tools and computers. 2.4 Tools: Contaminations 5. Contaminations Contaminations can be removed mapping the reads against a reference with the contaminants such as E. coli and human genomes.The most common tools are Bowtie or BWA (for short reads) and Blast (for long reads).
2. Sequencing, tools and computers. 2.4 Tools: Read Corrections 6. Read Corrections Read corrections are generally based in the Kmer analysis. Medvedev P. et al. Error correction of high-throughput sequencing datasets with non-uniform coverage Bioinformatics. 2011 27 (13):i137-i141
2. Sequencing, tools and computers. 2.4 Tools: Read Corrections 6. Read Corrections • Quake (http://www.cbcb.umd.edu/software/quake/index.html) • Reptile (http://aluru-sun.ece.iastate.edu/doku.php?id=software) • ECHO (http://uc-echo.sourceforge.net/) • Corrector (http://soap.genomics.org.cn/soapdenovo.html) Usual Suspects:
2. Sequencing, tools and computers. 2.0 Overview of a Sequencing Project: Assembly Fastq Processed Contigs Scaffolds Software Decisions during the Assembly Optimization Consensus Scaffolding
2. Sequencing, tools and computers. Type Technology Used Features Link Arachne Overlap-layout-consensus Sanger, 454 Highly configurable http://www.broadinstitute.org/crd/wiki/ index.php/Main_Page CABOG Overlap-layout-consensus Sanger, 454, Illumina Highly configurable http://sourceforge.net/apps/mediawiki/wgs- assembler/index.php?title=Main_Page MIRA Overlap-layout-consensus Sanger, 454 Highly configurable http://sourceforge.net/apps/mediawiki/mira- assembler gsAssembler Overlap-layout-consensus Sanger, 454 Easy to use http://454.com/products/analysis-software/ index.asp iAssembler Overlap-layout-consensus Sanger, 454 Improves MIRA http://bioinfo.bti.cornell.edu/tool/iAssembler ABySS Bruijn graph 454 or Illumina Easy to use http://www.bcgsc.ca/platform/bioinfo/software/ abyss ALLPATH-LG Bruijn graph 454 or Illumina Good results http://www.broadinstitute.org/software/allpaths- lg/blog Ray Bruijn graph 454 or Illumina Slow but use less memory http://denovoassembler.sf.net/ SOAPdenovo Bruijn graph 454 or Illumina Fastest http://soap.genomics.org.cn/soapdenovo.html Velvet Bruijn graph 454 or Illumina or SOLiD SOLiD http://www.ebi.ac.uk/~zerbino/velvet/ 2.4 Tools: Assemblers
2. Sequencing, tools and computers. Type Technology Used Features Link Arachne Overlap-layout-consensus Sanger, 454 Highly configurable http://www.broadinstitute.org/crd/wiki/ index.php/Main_Page CABOG Overlap-layout-consensus Sanger, 454, Illumina Highly configurable http://sourceforge.net/apps/mediawiki/wgs- assembler/index.php?title=Main_Page MIRA Overlap-layout-consensus Sanger, 454 Highly configurable http://sourceforge.net/apps/mediawiki/mira- assembler gsAssembler Overlap-layout-consensus Sanger, 454 Easy to use http://454.com/products/analysis-software/ index.asp iAssembler Overlap-layout-consensus Sanger, 454 Improves MIRA http://bioinfo.bti.cornell.edu/tool/iAssembler ABySS Bruijn graph 454 or Illumina Easy to use http://www.bcgsc.ca/platform/bioinfo/software/ abyss ALLPATH-LG Bruijn graph 454 or Illumina Good results http://www.broadinstitute.org/software/allpaths- lg/blog Ray Bruijn graph 454 or Illumina Slow but use less memory http://denovoassembler.sf.net/ SOAPdenovo Bruijn graph 454 or Illumina Fastest http://soap.genomics.org.cn/soapdenovo.html Velvet Bruijn graph 454 or Illumina or SOLiD SOLiD http://www.ebi.ac.uk/~zerbino/velvet/ 2.4 Tools: Assemblers
2. Sequencing, tools and computers. 2.4 Tools: Assemblers ... but there are more assemblers and information... Take a look to SeqAnswers http://seqanswers.com/wiki/Special:BrowseData/Bioinformatics_application? Bioinformatics_method=Assembly&Biological_domain=De-novo_assembly Also highly recommendable:
2. Sequencing, tools and computers. 2.0 Overview of a Sequencing Project: Assembly Scaffolds Software Decisions during the Assembly Optimization Scaffolds Gap Filling
2. Sequencing, tools and computers. Type Technology Used Features Link GapCloser Overlap-layout-consensus Illumina C++ Free http://soap.genomics.org.cn/soapdenovo.html GapFiller Overlap-layout-consensus Any Perl Commercial* http://www.baseclear.com/landingpages/ basetools-a-wide-range-of-bioinformatics- solutions/gapfiller/ PBSuite Overlap-layout-consensus 454, PacBio Python, Long reads http://sourceforge.net/p/pb-jelly/wiki/ 2.4 Tools: Gap Filling
2. Sequencing, tools and computers. 2.5 Computers Bigger is better: • How much do you need depends of: ➡ how big is your genome ? ~ Human size (3Gb) require ~256 Gb to 1 Tb ➡ how many reads do you have, ? More reads, more memory and hard disk. ➡ what software are you going to use ? OLC uses more memory and time than DBG. ➡ what parameters are you going to use ? Bigger Kmers, more memory. Four options: 1. Buy your own server (512 Gb, 4.5 Tb, 64 cores; ~ $15,000). 2. Rent a server for ~ 1 month (same specs. $1.5/h; ~$1,000)(CBSU). 3. Use a supercomputing center associated with NSF, NIH, USDA... where they offer reduced prices (iPlant, Indiana University....). 4. Collaborate with some group with a big server.
2. Sequencing, tools and computers. 2.6 Assembly evaluation During the assembly optimization will be generated several assemblies.The most used parameters to evaluate the assembly are: 1. Total Assembly Size, How far is this value from the estimated genome size 2. Total Number of Sequences (Scaffold/Contigs) How far is this value from the number of chromosomes. 3. Longest scaffold/contig 4. Average scaffold/contig size 5. N50/L50 (or any other N/L) Number sequence (N) and minimum size of them (L) that represents the 50% of the assembly if the sequences are sorted by size, from bigger to smaller.
2. Sequencing, tools and computers. 2.6 Assembly evaluation N50/L50 Total assembly size: 1000 Mb 93 87 75 68 62 56 50 44 37 30 25 20 18 Sequences order by descending size (Mb)
2. Sequencing, tools and computers. 2.6 Assembly evaluation N50/L50 Total assembly size: 1000 Mb 93 87 75 68 62 56 50 44 37 30 25 20 18 Sequences order by descending size (Mb) 50 % assembly: 500 Mb N50 N50 = 7 sequences L50 = 50 Mb
2. Sequencing, tools and computers. 2.6 Assembly evaluation N90/L90 Total assembly size: 1000 Mb 93 87 75 68 62 56 50 44 37 30 25 20 18 Sequences order by descending size (Mb) 90 % assembly: 900 Mb N90 N90 = 29 sequences L90 = 12.5 Mb
P. axillaris Current Assembly Peaxi v1.6.2 Dataset Contigs Scaffolds Total assembly size (Gb) 1.22 1.26 Total assembly sequences 109,892 83,639 Longest sequence (Mb) 0.57 8.56 Sequence length mean (Kb) 11.08 15.05 N90 (sequences) 13,481 1,051 L90 (Kb) 22.28 295.75 N50 (sequences) 3,943 309 L50 (Kb) 95.17 1,236.73 2. Sequencing, tools and computers. 2.6 Assembly evaluation
Whole Genome Representation Sequence Status Genes Usability Incomplete for non- repetitive regions Small scaffolds and contigs Incomplete genes Markers development Complete for non- repetitive regions Medium scaffolds and contigs Complete but 1-2 genes/contig Gene mining Complete for non- repetitive regions Large scaffolds and contigs Several dozens of genes/contig Microsynteny Complete for almost the whole genome Pseudomolecules Hundreds of genes/ contig Any (Synteny, Candidate gene by QTLs) Complete genome Pseudomolecules Thousands of genes/ contig5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages
5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages Arabidopsis thaliana (TAIR10) (5 chromosomes with 119 Mb) (96 gaps with 185 Kb total) Solanum lycopersicum (v2.5) (12 chromosomes with 824 Mb) (26,866 gaps with 82 Mb total) Petunia axillaris (v1.6.2) (83,639 scaffolds with 1.26 Gb) (26,268 gaps with 41 Mb total) Nicotiana benthamiana (v0.4.4) (141,339 scaffolds with 2.63 Gb*) (337,433 gaps with 162 Mb total) *0.61-0.67 Gb no assembled
Whole Genome Representation Sequence Status Genes Usability Incomplete for non- repetitive regions Small scaffolds and contigs Incomplete genes Markers development Complete for non- repetitive regions Medium scaffolds and contigs Complete but 1-2 genes/contig Gene mining Complete for non- repetitive regions Large scaffolds and contigs Several dozens of genes/contig Microsynteny Complete for almost the whole genome Pseudomolecules Hundreds of genes/ contig Any (Synteny, Candidate gene by QTLs) Complete genome Pseudomolecules Thousands of genes/ contig5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages ?
Whole Genome Representation Sequence Status Genes Usability Incomplete for non- repetitive regions Small scaffolds and contigs Incomplete genes Markers development Complete for non- repetitive regions Medium scaffolds and contigs Complete but 1-2 genes/contig Gene mining Complete for non- repetitive regions Large scaffolds and contigs Several dozens of genes/contig Microsynteny Complete for almost the whole genome Pseudomolecules Hundreds of genes/ contig Any (Synteny, Candidate gene by QTLs) Complete genome Pseudomolecules Thousands of genes/ contig5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages Genome size vs assembly size
Whole Genome Representation Sequence Status Genes Usability Incomplete for non- repetitive regions Small scaffolds and contigs Incomplete genes Markers development Complete for non- repetitive regions Medium scaffolds and contigs Complete but 1-2 genes/contig Gene mining Complete for non- repetitive regions Large scaffolds and contigs Several dozens of genes/contig Microsynteny Complete for almost the whole genome Pseudomolecules Hundreds of genes/ contig Any (Synteny, Candidate gene by QTLs) Complete genome Pseudomolecules Thousands of genes/ contig5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages Cytogenetic data (Flow cytometry) ✴ http://data.kew.org/cvalues Example: Petunia axillaris genome size 1.37 Gb (White and Rees. 1987)
Whole Genome Representation Sequence Status Genes Usability Incomplete for non- repetitive regions Small scaffolds and contigs Incomplete genes Markers development Complete for non- repetitive regions Medium scaffolds and contigs Complete but 1-2 genes/contig Gene mining Complete for non- repetitive regions Large scaffolds and contigs Several dozens of genes/contig Microsynteny Complete for almost the whole genome Pseudomolecules Hundreds of genes/ contig Any (Synteny, Candidate gene by QTLs) Complete genome Pseudomolecules Thousands of genes/ contig5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages Sequencing data (Kmer count) • Jellyfish (http://www.genome.umd.edu/ jellyfish.html) • BFCounter (http:// pritchardlab.stanford.edu/bfcounter.html)
0 10 20 30 40 50 60 0e+002e+074e+076e+078e+071e+08 63 Kmer Frequency for Petunia axillaris Coverage KmerCount peak = 31 Estimated genome size = 1.38 Gb Possiblesequencingerrors Sequencing data (Kmer count) 2. Sequencing, tools and computers. G = (N − B) * K / D * 2 G = genome size N = total kmers B = possible kmers with errors K = kmer length D = kmer peak 2.7 Assembly stages
Estimated genome size for P. axillaris: • Flow Cytometry (White & Rees,1987)= 1.37 Gb • Kmer Count* = 1.38 Gb • Assembly size (scaffolds v1.6.2) = 1.26 Gb • Assembly size (contigs v1.6.2) = 1.22 Gb Estimated genome not assembled = 110 - 120 Mb 2. Sequencing, tools and computers. 2.7 Assembly stages
1. A brief history of the sequence assembly. 2. Sequencing, tools and computers. 3. Things that you should know about genomes.
3. Things that you should know about genomes. 1. They have variable size, for example in angiosperm plants they range from 63 Mb (Genlisea margaretae) to 150 Gb (Paris japonica) with an average of 5.6 Gb. More data at: ✴ Plants: http://data.kew.org/cvalues/ ✴ Animals: http://www.genomesize.com/ 2. They can be polyploids. It means that homoeologus regions with highly similar will collapse during the assembly. 3. They can be highly heterozygous and polymorphic. In this case some of the alleles will collapse, some of them not.The effective coverage will lower than expected. 4. They can have recent whole genome duplication (or triplication) events. Paralogous genes may collapse during the assembly. 5. Repeats, most of the genomes are full of repeats and they are difficult to resolve. By default assemblers throw them away based in the Kmer histogram.
3. Things that you should know about genomes. 3.1 Collapsing problem CACTTGACGACATGACG CTTGACGACATGACGAC CCCTTGACGACATGACG CGCCCTTGACGACATGA Gene 1 A Gene 1 B CGCCCTTGACGACATGACGACA Collapsed consensus Gene 1 A + Gene 1 B ✴Polyploidy ✴Whole Genome Duplication Gene 1 Gene 1 A Gene 1 B Schmutz J et al. Genome Sequence of the Paleoploid Soybean. Nature 2010 463:178-183
3. Things that you should know about genomes. 3.1 Collapsing problem ✴Whole Genome Duplication http://chibba.agtec.uga.edu/duplication/index/home
3. Things that you should know about genomes. 3.1 Collapsing evaluation CACTTGACGACATGACG CTTGACGACATGACGAC CCCTTGACGACATGACG CGCCCTTGACGACATGA CGCCCTTGACGACATGACGACA Consensus Reads Read collapsing during the assembly process can be evaluated mapping reads back to the consensus sequence and analyzing SNPs. CACTTGACGACATGA SNPs = Heterozygous Positions (AB) + Homoeologs Collapsing (ABBB,AABB,ABBB) + Paralogs Collapsing (variable)
3. Things that you should know about genomes. 3.1 Collapsing solutions 1. Sequence the two progenitors and use them as a reference. ✓Approach used previously (cotton genome; Patterson A. et. al 2012). ❖ Information not contained in the references will be lost. ❖ Progenitors are not always available. 2. Use single molecule technologies such as PacBio or Moleculo ✓ Best approach because it will give the right phase for long sequence chunks. It exists software to integrate big sequences (minimus2...) ❖ Experimental and probably expensive. 3. Assembly everything with the higher Kmer, evaluate the collapsed regions and rescue the haplotype/region using SNPs and pair end read information. ✓ Cheap, easy to apply for the current technologies. ❖ No software available
3. Things that you should know about genomes. 3.2 Repeats and assemblers 5. Repeats, most of the genomes are full of repeats and they are difficult to resolve. By default some assemblers throw them away based in the kmer histogram. For example, SOAPdenovo throw away anything up to 255. It is convenient to do a Kmer analysis using Jellyfish (or other Kmer counter) before do any assembly to analyze contaminations (bacterial contaminations may appears with high Kmers content) and repeats. Software to analyze repeats based in sequence graphs: RepeatExplorer (http://repeatexplorer.umbr.cas.cz/) Novak, P., Neumann, P. & Macas, J. Graph-based clustering and characterization of repetitive sequences in next- generation sequencing data. Bmc Bioinformatics 11, 378 (2010)
Acknowledgements: Petunia Genome Sequencing Consortium Petunia NIU (USA) Tom Sims Mitrick Jones BTI (USA) Lukas Mueller Aureliano Bombarely UNIBE (Switzerland) Cris Kuhlemeier Remy Bruggmann Michel Moser UV (Italy) Massimo Delledonne Mario Pezzotti RU andVU (Netherlands) Tom Gerats. Francesca.Quattrochio BGI (China)

More Related Content

PDF
Basics of Genome Assembly
byJosé Héctor Gálvez
 
PPTX
2 whole genome sequencing and analysis
bysaberhussain9
 
PDF
Genomic Data Analysis
byData Driven Innovation
 
PPT
Genome assembly
byMalla Reddy College of Pharmacy
 
PDF
De novo genome assembly - T.Seemann - IMB winter school 2016 - brisbane, au ...
byTorsten Seemann
 
PPTX
Gemome annotation
byTajammal Daultana
 
PPTX
NGS data formats and analyses
byrjorton
 
PPT
Genomics and bioinformatics
bySenthil Natesan
 
Basics of Genome Assembly
byJosé Héctor Gálvez
 
2 whole genome sequencing and analysis
bysaberhussain9
 
Genomic Data Analysis
byData Driven Innovation
 
Genome assembly
byMalla Reddy College of Pharmacy
 
De novo genome assembly - T.Seemann - IMB winter school 2016 - brisbane, au ...
byTorsten Seemann
 
Gemome annotation
byTajammal Daultana
 
NGS data formats and analyses
byrjorton
 
Genomics and bioinformatics
bySenthil Natesan
 

What's hot

PDF
Gene prediction methods vijay
byVijay Hemmadi
 
PPTX
Expressed sequence tag (EST), molecular marker
byKAUSHAL SAHU
 
PPTX
SAGE (Serial analysis of Gene Expression)
bytalhakhat
 
PPT
Est database
byAmit Ruchi Yadav
 
PPSX
Protein Databases
byDepartment of Bioinformatics, Noorul Islam College of Arts and Science, Kumaracoil
 
PPTX
The Gene Ontology & Gene Ontology Annotation resources
byMelanie Courtot
 
PDF
Gene prediction method
byNusrat Gulbarga
 
PPT
Introduction to Biological databases
byDr.K.RameshKumar, Assistant Professor,Vivekananda College,Tiruvedakam West, Madurai
 
PPTX
Orthologs,Paralogs & Xenologs
byOsamaZafar16
 
PPTX
Express sequence tags
byDhananjay Desai
 
PDF
Transcriptomics and metabolomics
bySukhjinder Singh
 
PPTX
Shotgun and clone contig method
byDr. Naveen Gaurav srivastava
 
PPTX
genomic comparison
bycomsats university of science information technology
 
PPTX
Kegg
bymsfbi1521
 
PPTX
Genomics(functional genomics)
byIndrajaDoradla
 
PDF
FASTA
byThapar Institute of Engineering & Technology, Patiala, Punjab, India
 
PPTX
Protein 3 d structure prediction
bySamvartika Majumdar
 
PPT
Sequence file formats
byAlphonsa Joseph
 
PPTX
Sequence comparison techniques
byruchibioinfo
 
PPTX
Pymol
byBioCode Ltd
 
Gene prediction methods vijay
byVijay Hemmadi
 
Expressed sequence tag (EST), molecular marker
byKAUSHAL SAHU
 
SAGE (Serial analysis of Gene Expression)
bytalhakhat
 
Est database
byAmit Ruchi Yadav
 
Protein Databases
byDepartment of Bioinformatics, Noorul Islam College of Arts and Science, Kumaracoil
 
The Gene Ontology & Gene Ontology Annotation resources
byMelanie Courtot
 
Gene prediction method
byNusrat Gulbarga
 
Introduction to Biological databases
byDr.K.RameshKumar, Assistant Professor,Vivekananda College,Tiruvedakam West, Madurai
 
Orthologs,Paralogs & Xenologs
byOsamaZafar16
 
Express sequence tags
byDhananjay Desai
 
Transcriptomics and metabolomics
bySukhjinder Singh
 
Shotgun and clone contig method
byDr. Naveen Gaurav srivastava
 
genomic comparison
bycomsats university of science information technology
 
Kegg
bymsfbi1521
 
Genomics(functional genomics)
byIndrajaDoradla
 
FASTA
byThapar Institute of Engineering & Technology, Patiala, Punjab, India
 
Protein 3 d structure prediction
bySamvartika Majumdar
 
Sequence file formats
byAlphonsa Joseph
 
Sequence comparison techniques
byruchibioinfo
 
Pymol
byBioCode Ltd
 

Similar to Genome Assembly

PDF
Genome Assembly 2018
byAureliano Bombarely
 
PPTX
Sequence assembly
byDr. Naveen Gaurav srivastava
 
PPTX
NEXT GENERATION SEQUENCING
byBilal Nizami
 
PPT
Assembly and finishing
byNikolay Vyahhi
 
PDF
Genome assembly: the art of trying to make one big thing from millions of ver...
byKeith Bradnam
 
PDF
Genome Assembly: the art of trying to make one BIG thing from millions of ver...
byKeith Bradnam
 
PPTX
from genome sequencing to genome assembly
byPierluigi10
 
PDF
Computational methods for understanding bacterial and archaeal genomes Lectur...
byMona Othman Albureikan / King Abdulaziz University
 
PDF
DNA SEQUENCING_BASICS_NGS_SANGER_NGS_SLIDES
bylfr20160811
 
PPTX
Lecture-1_NGS.pptx important document it
bysiddheshmhatredot
 
PPT
DNA SEQUENCING TECHNIQUES AND TYPES OF SEQUENCINGppt
byhemantshiv1985
 
PPTX
Metagenome Sequence Assembly (CABBIO 20150629 Buenos Aires)
bybedutilh
 
PPTX
2011 jeroen vanhoudt_ngs
byDin Apellidos
 
PPT
20100516 bioinformatics kapushesky_lecture08
byComputer Science Club
 
PPTX
lecture 4 genomic and transcriptomic (1).pptx
bymonearaalotaby
 
PDF
De Novo
byAulidya Habibah Adnan
 
PPTX
Genome Assembly copy
byPradeep Kumar
 
PPTX
20150601 bio sb_assembly_course
byhansjansen9999
 
PPTX
Rnaseq forgenefinding
bySucheta Tripathy
 
PPTX
Hamas 1
byAhmad Ali khan
 
Genome Assembly 2018
byAureliano Bombarely
 
Sequence assembly
byDr. Naveen Gaurav srivastava
 
NEXT GENERATION SEQUENCING
byBilal Nizami
 
Assembly and finishing
byNikolay Vyahhi
 
Genome assembly: the art of trying to make one big thing from millions of ver...
byKeith Bradnam
 
Genome Assembly: the art of trying to make one BIG thing from millions of ver...
byKeith Bradnam
 
from genome sequencing to genome assembly
byPierluigi10
 
Computational methods for understanding bacterial and archaeal genomes Lectur...
byMona Othman Albureikan / King Abdulaziz University
 
DNA SEQUENCING_BASICS_NGS_SANGER_NGS_SLIDES
bylfr20160811
 
Lecture-1_NGS.pptx important document it
bysiddheshmhatredot
 
DNA SEQUENCING TECHNIQUES AND TYPES OF SEQUENCINGppt
byhemantshiv1985
 
Metagenome Sequence Assembly (CABBIO 20150629 Buenos Aires)
bybedutilh
 
2011 jeroen vanhoudt_ngs
byDin Apellidos
 
20100516 bioinformatics kapushesky_lecture08
byComputer Science Club
 
lecture 4 genomic and transcriptomic (1).pptx
bymonearaalotaby
 
De Novo
byAulidya Habibah Adnan
 
Genome Assembly copy
byPradeep Kumar
 
20150601 bio sb_assembly_course
byhansjansen9999
 
Rnaseq forgenefinding
bySucheta Tripathy
 
Hamas 1
byAhmad Ali khan
 

More from Aureliano Bombarely

PDF
RNAseq Analysis
byAureliano Bombarely
 
PDF
GoTermsAnalysisWithR
byAureliano Bombarely
 
PDF
Genomic Data Analysis: From Reads to Variants
byAureliano Bombarely
 
PDF
PlastidEvolution
byAureliano Bombarely
 
PDF
BasicLinux
byAureliano Bombarely
 
PDF
Introduction2R
byAureliano Bombarely
 
PDF
Lesson mitochondria bombarely_a20180927
byAureliano Bombarely
 
PDF
BasicGraphsWithR
byAureliano Bombarely
 
PDF
PerlScripting
byAureliano Bombarely
 
PDF
PerlTesting
byAureliano Bombarely
 
RNAseq Analysis
byAureliano Bombarely
 
GoTermsAnalysisWithR
byAureliano Bombarely
 
Genomic Data Analysis: From Reads to Variants
byAureliano Bombarely
 
PlastidEvolution
byAureliano Bombarely
 
BasicLinux
byAureliano Bombarely
 
Introduction2R
byAureliano Bombarely
 
Lesson mitochondria bombarely_a20180927
byAureliano Bombarely
 
BasicGraphsWithR
byAureliano Bombarely
 
PerlScripting
byAureliano Bombarely
 
PerlTesting
byAureliano Bombarely
 

Recently uploaded

PDF
PULSE according to Physiology special pdf for all medical sectors students & ...
byhttps://www.facebook.com/profile.php?id=61578457231735
 
PPTX
special senses-eye, ear, nose, tongue.pptx
byAshish Umale
 
PPTX
OCCULTISM IN JEHOVAH'S WITNESSES' ARTWORK
byDaniel Barnea
 
PDF
Overview of Ethiopian Agriculture: Systems and Economic Impact.pdf
byinebaseifu
 
PDF
2.India-and-Her-Neighbours ppt.pdf/Social science Grade 7 part -2 By:K SANDE...
bySandeep Swamy
 
PDF
30 Ways Teachers Use AI in 2026 – Practical Classroom Workflows with Monsha f...
byMonsha.AI
 
PPTX
Regenerate invoice in odoo 19 Accounting
byCeline George
 
PPTX
Overview of SEO optimiser in Odoo 19 Website
byCeline George
 
PPTX
Complete and Detailed Overview of Odoo 18 Contact
byCeline George
 
PPTX
ANTI- RHEUMATICS CHAPTER NO.05 PHARMACOGNOSY
byGanu Gavade
 
PDF
eSAT-for-Teachers-2025-2028-ver.2-Nov2025 idp.pdf
byJunmelValientes
 
PPTX
SIZE REDUCTION Prepared By: Ms. Vaishnavi Ghormade
byvaishnavighormade199
 
PDF
1. AI for E-content Development: What and Why, 2. E- content Development thro...
byProf. Vinod Kumar Kanvaria
 
PPTX
THEORIES OF LEARNING Shilpa Hotakar.pptx
bySHILPA HOTAKAR
 
PPTX
Blood, Blood Composition, Blood Groups, Disorders of Blood.pptx
byAshish Umale
 
PPTX
History in your Hands Class 5 January 2026 online slides.pptx
byEilsONeill
 
PPTX
OverView of AI in Products in Odoo 19
byCeline George
 
PPTX
Report for Prepare Time Management in Odoo 19 POS
byCeline George
 
PPTX
DIGITAL TRANSFORMATION MODULE KEY POINTS
byLisa Harris
 
PPTX
Standard Development Principles in Assessing the Quality of Opinion Editorial...
byastairelyxx
 
PULSE according to Physiology special pdf for all medical sectors students & ...
byhttps://www.facebook.com/profile.php?id=61578457231735
 
special senses-eye, ear, nose, tongue.pptx
byAshish Umale
 
OCCULTISM IN JEHOVAH'S WITNESSES' ARTWORK
byDaniel Barnea
 
Overview of Ethiopian Agriculture: Systems and Economic Impact.pdf
byinebaseifu
 
2.India-and-Her-Neighbours ppt.pdf/Social science Grade 7 part -2 By:K SANDE...
bySandeep Swamy
 
30 Ways Teachers Use AI in 2026 – Practical Classroom Workflows with Monsha f...
byMonsha.AI
 
Regenerate invoice in odoo 19 Accounting
byCeline George
 
Overview of SEO optimiser in Odoo 19 Website
byCeline George
 
Complete and Detailed Overview of Odoo 18 Contact
byCeline George
 
ANTI- RHEUMATICS CHAPTER NO.05 PHARMACOGNOSY
byGanu Gavade
 
eSAT-for-Teachers-2025-2028-ver.2-Nov2025 idp.pdf
byJunmelValientes
 
SIZE REDUCTION Prepared By: Ms. Vaishnavi Ghormade
byvaishnavighormade199
 
1. AI for E-content Development: What and Why, 2. E- content Development thro...
byProf. Vinod Kumar Kanvaria
 
THEORIES OF LEARNING Shilpa Hotakar.pptx
bySHILPA HOTAKAR
 
Blood, Blood Composition, Blood Groups, Disorders of Blood.pptx
byAshish Umale
 
History in your Hands Class 5 January 2026 online slides.pptx
byEilsONeill
 
OverView of AI in Products in Odoo 19
byCeline George
 
Report for Prepare Time Management in Odoo 19 POS
byCeline George
 
DIGITAL TRANSFORMATION MODULE KEY POINTS
byLisa Harris
 
Standard Development Principles in Assessing the Quality of Opinion Editorial...
byastairelyxx
 

Genome Assembly

  • 1.
    Insights into PlantGenome Sequence Assembly. by Aureliano Bombarely ab782@cornell.edu
  • 2.
    1. A briefhistory of the sequence assembly. 2. Sequencing, tools and computers. 3. Things that you should know about genomes.
  • 3.
    1. A briefhistory of the sequence assembly. 2. Sequencing, tools and computers. 3. Things that you should know about genomes.
  • 4.
    1. A briefhistory of the sequence assembly. http://www.ncbi.nlm.nih.gov/About/primer/genetics_genome.html
  • 5.
    Genome = Nx Sequence of DNA Gentile F. et al. Direct Imaging of DNA Fibers:TheVisage of Double Helix Nano Lett., 2012, 12 (12), pp 6453–6458 DNA sequencing ??? 1. A brief history of the sequence assembly.
  • 6.
    DNA Sequencing: “Process ofdetermining the precise order of nucleotides within a DNA molecule.” -Wikipedia ddATP ddGTP ddCTP ddTTP Taq-Polymerase STOP time 2) Chromatographic Separation GTCACCCTGAAT 1) PCR with ddNTPs 3) Chromatogram Read DNA Sanger Sequencing 1. A brief history of the sequence assembly.
  • 7.
    Gentile F. etal. Direct Imaging of DNA Fibers:TheVisage of Double Helix Nano Lett., 2012, 12 (12), pp 6453–6458 ACGCGGTGACGTTGTCGA ACGCGTTTTGTTGTGG ACGATTTAAATGACGTTGTCGA ACGCGGTGACGTTGTCGA ACGCGTTTTGTTGTGG ACCCCGACGTTGTCGA DNA sequencing ACGCGTTTTGTTGTGG ACCCCTGGGGGGGTTGTCGA Fragments Sequence assembly ACGCGTTTTGTTGTGGTGGCCACACCACGCAGTGACGGAGATAACGGCGAGAGCATGGACGGAGGATGAGGATGG 1. A brief history of the sequence assembly.
  • 8.
    Sequence assembly = Resolve apuzzle and rebuild a DNA sequence from its pieces (fragments) Image courtesy of iStock photo 1. A brief history of the sequence assembly.
  • 9.
    Resolve a puzzleand rebuild a DNA sequence from its pieces (fragments) BACs BAC-by-BAC sequencing Whole Genome Shotgun (WGS) sequencing 1. A brief history of the sequence assembly.
  • 10.
    MS2Bacteriophage(3.658Kb)1977 1978 1979 1980 1981 1982 1983 Epstein-BarrVirus(170Kb)1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 Haemophilusinfluenzae(1.83Mb)1995 Saccharomycescerevisiae(12.1Mb)1996 1997 Caenorhabditiselegans(100Mb)1998 1999 Arabidopsisthaliana(157Mb)2000 Homosapiens(3.2Gb)2001 Oryzasativa(420Mb)2002 2003 2004 2005 Populustrichocarpa(550Mb)2006 Vitisvinifera(487Mb)2007 Physcomitrela(480Mb);Caricapapaya(372Mb)2008 Sorghumbicolor(730Mb);ZeaMays(2.3Gb);Cucumissativus(367Mb)2009 Ectocarpus(214Mb);Malusxdomestica(742Mb);Glycinemax(1.1Gb)2010 Pigeonpea,Potato,Cannabis,A.lyrata,Cacao,Strawberry,Medicago2011 Mei,Benthamiana,Tomato,Setaria,Melon,Flax,T.salsuginea,Banana,Cotton,Orange,Pear2012 Sanger Sequencing 454 Solexa /Illumina SOLiD IonT PB 1. A brief history of the sequence assembly.
  • 11.
  • 12.
    Haemophilusinfluenzae(1.83Mb)1995 Software: TIGR ASSEMBLER Hardware: SPARCenter 2000(512 Mb RAM) 1. A brief history of the sequence assembly.
  • 13.
    Haemophilusinfluenzae(1.83Mb)1995 Software: TIGR ASSEMBLER Smith-Watermanalignments Sutton GG. et al.TIGR Assembler:A New Tool to Assembly Large Shotgun Sequencing Projects Genome Science and Technology, 1995,1:9-19 O V ER LA PPIN G M ET H O D O LO G Y 1. A brief history of the sequence assembly.
  • 14.
  • 15.
    Homosapiens(3.2Gb)2001 BAC-by-BAC sequencing Whole GenomeShotgun (WGS) sequencing International Human Genome Sequencing Consortium. Initial Sequencing and Analysis of the Human Genome. Nature. 2001. 409:860-921 Venter JC. et al. The Sequence of the Human Genome. Science. 2001. 291:1304-1351 O V ER LA PPIN G M ET H O D O LO G Y 1. A brief history of the sequence assembly.
  • 16.
    Homosapiens(3.2Gb)2001 Whole Genome Shotgun(WGS) sequencing Venter JC. et al. The Sequence of the Human Genome. Science. 2001. 291:1304-1351 Software: WGAASSEMBLER (CABOG) Hardware: 40 machines AlphaSMPs (4 Gb RAM/each and 4 cores/each, total=160 Gb RAM and 160 cores); 5 days. 1. A brief history of the sequence assembly.
  • 17.
    Ailuropodamelanoleura(2.3Gb)2009 Whole Genome Shotgun(WGS) sequencing Li R. et al.The Sequence and the Novo Assembly of the Giant Panda Genome. Nature. 2009. 463:311-317 Software: SOAPdenovo Hardware: Supercomputer with 32 cores and 512 Gb RAM. B R U IJN G R A PH S M ET H O D O LO G Y 1. A brief history of the sequence assembly.
  • 18.
    1. A briefhistory of the sequence assembly. What is a Kmer ? ATGCGCAGTGGAGAGAGAGCGATG Sequence A with 25 nt Specific n-tuple or n-gram of nucleic acid or amino acid sequences. -Wikipedia ordered list of elements contiguous sequence of n items from a given sequence of text 5 Kmers of 20-mer ATGCGCAGTGGAGAGAGAGC TGCGCAGTGGAGAGAGAGCG GCGCAGTGGAGAGAGAGCGA CGCAGTGGAGAGAGAGCGAT GCAGTGGAGAGAGAGCGATG N_kmers = L_read - Kmer_size
  • 19.
    Compeau PEC. etal. How to apply de Bruijn graphs to genome assembly. Nature Biotech. 2011. 29:287-291 1. A brief history of the sequence assembly.
  • 20.
    OLC (Overlap-layout-consensus) algorithmis more suitable for the low-coverage long reads, whereas the DBG (De-Bruijn-Graph) algorithm is more suitable for high-coverage short reads and especially for large genome assembly 1. A brief history of the sequence assembly.
  • 21.
    1. A briefhistory of the sequence assembly. 2. Sequencing, tools and computers. 3. Things that you should know about genomes.
  • 22.
    2. Sequencing, toolsand computers. 2.0 Overview of a Sequencing Project: Assembly Technology Library Preparation Sequencing Amount Fastq raw Fastq Processed Contigs Scaffolds Decisions during the Experimental Design Software Identity %, Kmer ... Post-assembly Filtering Decisions during the Assembly Optimization Reads processing and filtering 1. Low quality reads (qscore) (Q30) 2. Short reads (L50) 3. PCR duplications (Only Genomes). 4. Contaminations. 5. Corrections Consensus Scaffolding Scaffolds Gap Filling
  • 23.
    2. Sequencing, toolsand computers. 2.0 Overview of a Sequencing Project: Assembly Technology Library Preparation Sequencing Amount Decisions during the Experimental Design
  • 24.
    2. Sequencing, toolsand computers. 2.1 Technologies Run Time Sequence Length Reads/Run Total nucleotides sequenced per run Capillary Sequencing (ABI37000) ~2.5 h 800 bp 386 0.308 Mb 454 Pyrosequencing (GS FLX Titanium XL+) ~23 h 700 bp 1,000,000 700 Mb (0.7 Gb) Illumina (HiSeq X Ten) 72 h (3 days) 2 x 150 bp 6,000,000,000 1,600,000,000 Mb (1,600 Gb) Illumina (MiSeq) 65 h 2 x 300 bp 2 x 22,000,000 13,500 Mb (13.5 Gb) SOLID (5500xl system) 120 h (7 days) 2 x 60 bp 400,000,000 300,000 Mb (300 Gb) Ion Torrent (Ion Proton I) 2 h 200 bp 60,000,000 10,000 Mb (10 Gb) PacBio (PacBioRS II) 1.5 h ~8,500 bp 50,000 375 Mb (0.37 Gb)
  • 25.
    2. Sequencing, toolsand computers. Strenghs Weaknesses 454 Pyrosequencing (GS FLX Titanium XL+) - Long reads (450/700 bp). - Long insert for mate pair libraries (20Kb). - Low observed raw error rate (0.1%) - Low percentage of PCR duplications for mate pair libraries - Homopolymer error. - Low sequence yield per run (0.7 Gb). - Preferred assembler (gsAssembler) uses overlapping methodology. Illumina (HiSeq 2500) - High sequence yield per run (600 Gb) - Low observed raw error rate (0.26%) - High percentage of PCR duplications for mate pair libraries. - Long run time (11 days) - High instrument cost (~ $650K) Illumina (MiSeq) - Medium read size (250 bp) - Faster run than Illumina HiSeq - Medium sequence yield per run (8.5 Gb) SOLID (5500xl system) - 2-base encoding reduce the observed raw error rate (0.06%) - 2-base color coding makes difficult the sequence manipulation and assembly. - Short reads (75 bp) Ion Torrent (Ion Proton I) - Fast run (2 hours) - Low instrument cost ($80K). - Medium read size (200 bp) - Medium sequence yield per run (10 Gb) - Medium observed raw error rate (1.7%) PacBio (PacBioRS) - Long reads (3000 bp) - Fast run (2 hours) - Really high observed raw error rate (12.7%) - High instrument cost (~ $700K) - No pair end/mate pair reads 2.1 Technologies
  • 26.
    2. Sequencing, toolsand computers. ★ Library types (orientations): • Single reads • Pair ends (PE) (150-800 bp insert size) • Mate pairs (MP) (2-40 Kb insert size) F F F F R R R 454/Roche Illumina Illumina 2.2 Libraries
  • 27.
    2. Sequencing, toolsand computers. • Why is important the pair information ? F - novo assembly: Consensus sequence (Contig) Reads Scaffold (or Supercontig) Pair Read information NNNNN Genetic information (markers) Pseudomolecule (or ultracontig) NNNNN NN 2.2 Libraries
  • 28.
    2. Sequencing, toolsand computers. 2.3 Sequencing Amount * Slide from MC. Schatz
  • 29.
    2. Sequencing, toolsand computers. 2.3 Sequencing Amount Depending of the genome complexity, technology used and assembler: • More is better (if you have enough computational resources). - Sanger > 10X (less for BACs-by-BACs approaches). - 454 > 20X - Illumina > 100X • Polyploidy or high heterogozyty increase the amount of reads needed. • The use of different library types (pair ends and mate pairs with different insert sizes is essential). • Longer reads is preferable.
  • 30.
    2. Sequencing, toolsand computers. 2.0 Overview of a Sequencing Project: Assembly Fastq raw Fastq Processed Contigs Scaffolds Software Decisions during the Assembly Optimization Reads processing and filtering 1. Low quality reads (qscore) (Q30) 2. Short reads (L50) 3. PCR duplications (Only Genomes). 4. Contaminations. 5. Corrections Consensus Scaffolding Scaffolds Gap Filling
  • 31.
    2. Sequencing, toolsand computers. 2.0 Overview of a Sequencing Project: Assembly Fastq raw Fastq Processed Software Decisions during the Assembly Optimization Reads processing and filtering 1. Low quality reads (qscore) (Q30) 2. Short reads (L50) 3. PCR duplications (Only Genomes). 4. Contaminations. 5. Corrections
  • 32.
    2. Sequencing, toolsand computers. 2.4 Tools: Read Quality Evaluation a) Length of the read. b) Bases with qscore > 20 or 30. •FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)
  • 33.
    2. Sequencing, toolsand computers. 2.4 Tools: Read Trimming and Filtering • Fastx-Toolkit (http://hannonlab.cshl.edu) • Ea-Utils (http://code.google.com/p/ea-utils/) • PrinSeq (http://edwards.sdsu.edu/cgi-bin/prinseq/prinseq.cgi) Software Multiplexing Trimming/Filtering Fastx-Toolkit fastx_barcode_splitter fastq_quality_filter Ea-Utils fastq-multx fastq-mcf PrinSeq PrinSeq PrinSeq 1. Adaptor removal. 2. Low quality reads (qscore) (Q30) 3. Short reads (L50) 4. PCR duplications (Only Genomes, Use PrinSeq).
  • 34.
    2. Sequencing, toolsand computers. 2.4 Tools: Contaminations 5. Contaminations Contaminations can be removed mapping the reads against a reference with the contaminants such as E. coli and human genomes.The most common tools are Bowtie or BWA (for short reads) and Blast (for long reads).
  • 35.
    2. Sequencing, toolsand computers. 2.4 Tools: Read Corrections 6. Read Corrections Read corrections are generally based in the Kmer analysis. Medvedev P. et al. Error correction of high-throughput sequencing datasets with non-uniform coverage Bioinformatics. 2011 27 (13):i137-i141
  • 36.
    2. Sequencing, toolsand computers. 2.4 Tools: Read Corrections 6. Read Corrections • Quake (http://www.cbcb.umd.edu/software/quake/index.html) • Reptile (http://aluru-sun.ece.iastate.edu/doku.php?id=software) • ECHO (http://uc-echo.sourceforge.net/) • Corrector (http://soap.genomics.org.cn/soapdenovo.html) Usual Suspects:
  • 37.
    2. Sequencing, toolsand computers. 2.0 Overview of a Sequencing Project: Assembly Fastq Processed Contigs Scaffolds Software Decisions during the Assembly Optimization Consensus Scaffolding
  • 38.
    2. Sequencing, toolsand computers. Type Technology Used Features Link Arachne Overlap-layout-consensus Sanger, 454 Highly configurable http://www.broadinstitute.org/crd/wiki/ index.php/Main_Page CABOG Overlap-layout-consensus Sanger, 454, Illumina Highly configurable http://sourceforge.net/apps/mediawiki/wgs- assembler/index.php?title=Main_Page MIRA Overlap-layout-consensus Sanger, 454 Highly configurable http://sourceforge.net/apps/mediawiki/mira- assembler gsAssembler Overlap-layout-consensus Sanger, 454 Easy to use http://454.com/products/analysis-software/ index.asp iAssembler Overlap-layout-consensus Sanger, 454 Improves MIRA http://bioinfo.bti.cornell.edu/tool/iAssembler ABySS Bruijn graph 454 or Illumina Easy to use http://www.bcgsc.ca/platform/bioinfo/software/ abyss ALLPATH-LG Bruijn graph 454 or Illumina Good results http://www.broadinstitute.org/software/allpaths- lg/blog Ray Bruijn graph 454 or Illumina Slow but use less memory http://denovoassembler.sf.net/ SOAPdenovo Bruijn graph 454 or Illumina Fastest http://soap.genomics.org.cn/soapdenovo.html Velvet Bruijn graph 454 or Illumina or SOLiD SOLiD http://www.ebi.ac.uk/~zerbino/velvet/ 2.4 Tools: Assemblers
  • 39.
    2. Sequencing, toolsand computers. Type Technology Used Features Link Arachne Overlap-layout-consensus Sanger, 454 Highly configurable http://www.broadinstitute.org/crd/wiki/ index.php/Main_Page CABOG Overlap-layout-consensus Sanger, 454, Illumina Highly configurable http://sourceforge.net/apps/mediawiki/wgs- assembler/index.php?title=Main_Page MIRA Overlap-layout-consensus Sanger, 454 Highly configurable http://sourceforge.net/apps/mediawiki/mira- assembler gsAssembler Overlap-layout-consensus Sanger, 454 Easy to use http://454.com/products/analysis-software/ index.asp iAssembler Overlap-layout-consensus Sanger, 454 Improves MIRA http://bioinfo.bti.cornell.edu/tool/iAssembler ABySS Bruijn graph 454 or Illumina Easy to use http://www.bcgsc.ca/platform/bioinfo/software/ abyss ALLPATH-LG Bruijn graph 454 or Illumina Good results http://www.broadinstitute.org/software/allpaths- lg/blog Ray Bruijn graph 454 or Illumina Slow but use less memory http://denovoassembler.sf.net/ SOAPdenovo Bruijn graph 454 or Illumina Fastest http://soap.genomics.org.cn/soapdenovo.html Velvet Bruijn graph 454 or Illumina or SOLiD SOLiD http://www.ebi.ac.uk/~zerbino/velvet/ 2.4 Tools: Assemblers
  • 40.
    2. Sequencing, toolsand computers. 2.4 Tools: Assemblers ... but there are more assemblers and information... Take a look to SeqAnswers http://seqanswers.com/wiki/Special:BrowseData/Bioinformatics_application? Bioinformatics_method=Assembly&Biological_domain=De-novo_assembly Also highly recommendable:
  • 41.
    2. Sequencing, toolsand computers. 2.0 Overview of a Sequencing Project: Assembly Scaffolds Software Decisions during the Assembly Optimization Scaffolds Gap Filling
  • 42.
    2. Sequencing, toolsand computers. Type Technology Used Features Link GapCloser Overlap-layout-consensus Illumina C++ Free http://soap.genomics.org.cn/soapdenovo.html GapFiller Overlap-layout-consensus Any Perl Commercial* http://www.baseclear.com/landingpages/ basetools-a-wide-range-of-bioinformatics- solutions/gapfiller/ PBSuite Overlap-layout-consensus 454, PacBio Python, Long reads http://sourceforge.net/p/pb-jelly/wiki/ 2.4 Tools: Gap Filling
  • 43.
    2. Sequencing, toolsand computers. 2.5 Computers Bigger is better: • How much do you need depends of: ➡ how big is your genome ? ~ Human size (3Gb) require ~256 Gb to 1 Tb ➡ how many reads do you have, ? More reads, more memory and hard disk. ➡ what software are you going to use ? OLC uses more memory and time than DBG. ➡ what parameters are you going to use ? Bigger Kmers, more memory. Four options: 1. Buy your own server (512 Gb, 4.5 Tb, 64 cores; ~ $15,000). 2. Rent a server for ~ 1 month (same specs. $1.5/h; ~$1,000)(CBSU). 3. Use a supercomputing center associated with NSF, NIH, USDA... where they offer reduced prices (iPlant, Indiana University....). 4. Collaborate with some group with a big server.
  • 44.
    2. Sequencing, toolsand computers. 2.6 Assembly evaluation During the assembly optimization will be generated several assemblies.The most used parameters to evaluate the assembly are: 1. Total Assembly Size, How far is this value from the estimated genome size 2. Total Number of Sequences (Scaffold/Contigs) How far is this value from the number of chromosomes. 3. Longest scaffold/contig 4. Average scaffold/contig size 5. N50/L50 (or any other N/L) Number sequence (N) and minimum size of them (L) that represents the 50% of the assembly if the sequences are sorted by size, from bigger to smaller.
  • 45.
    2. Sequencing, toolsand computers. 2.6 Assembly evaluation N50/L50 Total assembly size: 1000 Mb 93 87 75 68 62 56 50 44 37 30 25 20 18 Sequences order by descending size (Mb)
  • 46.
    2. Sequencing, toolsand computers. 2.6 Assembly evaluation N50/L50 Total assembly size: 1000 Mb 93 87 75 68 62 56 50 44 37 30 25 20 18 Sequences order by descending size (Mb) 50 % assembly: 500 Mb N50 N50 = 7 sequences L50 = 50 Mb
  • 47.
    2. Sequencing, toolsand computers. 2.6 Assembly evaluation N90/L90 Total assembly size: 1000 Mb 93 87 75 68 62 56 50 44 37 30 25 20 18 Sequences order by descending size (Mb) 90 % assembly: 900 Mb N90 N90 = 29 sequences L90 = 12.5 Mb
  • 48.
    P. axillaris Current AssemblyPeaxi v1.6.2 Dataset Contigs Scaffolds Total assembly size (Gb) 1.22 1.26 Total assembly sequences 109,892 83,639 Longest sequence (Mb) 0.57 8.56 Sequence length mean (Kb) 11.08 15.05 N90 (sequences) 13,481 1,051 L90 (Kb) 22.28 295.75 N50 (sequences) 3,943 309 L50 (Kb) 95.17 1,236.73 2. Sequencing, tools and computers. 2.6 Assembly evaluation
  • 49.
    Whole Genome Representation Sequence Status Genes Usability Incompletefor non- repetitive regions Small scaffolds and contigs Incomplete genes Markers development Complete for non- repetitive regions Medium scaffolds and contigs Complete but 1-2 genes/contig Gene mining Complete for non- repetitive regions Large scaffolds and contigs Several dozens of genes/contig Microsynteny Complete for almost the whole genome Pseudomolecules Hundreds of genes/ contig Any (Synteny, Candidate gene by QTLs) Complete genome Pseudomolecules Thousands of genes/ contig5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages
  • 50.
    5 4 3 2 1 2. Sequencing, toolsand computers. 2.7 Assembly stages Arabidopsis thaliana (TAIR10) (5 chromosomes with 119 Mb) (96 gaps with 185 Kb total) Solanum lycopersicum (v2.5) (12 chromosomes with 824 Mb) (26,866 gaps with 82 Mb total) Petunia axillaris (v1.6.2) (83,639 scaffolds with 1.26 Gb) (26,268 gaps with 41 Mb total) Nicotiana benthamiana (v0.4.4) (141,339 scaffolds with 2.63 Gb*) (337,433 gaps with 162 Mb total) *0.61-0.67 Gb no assembled
  • 51.
    Whole Genome Representation Sequence Status Genes Usability Incompletefor non- repetitive regions Small scaffolds and contigs Incomplete genes Markers development Complete for non- repetitive regions Medium scaffolds and contigs Complete but 1-2 genes/contig Gene mining Complete for non- repetitive regions Large scaffolds and contigs Several dozens of genes/contig Microsynteny Complete for almost the whole genome Pseudomolecules Hundreds of genes/ contig Any (Synteny, Candidate gene by QTLs) Complete genome Pseudomolecules Thousands of genes/ contig5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages ?
  • 52.
    Whole Genome Representation Sequence Status Genes Usability Incompletefor non- repetitive regions Small scaffolds and contigs Incomplete genes Markers development Complete for non- repetitive regions Medium scaffolds and contigs Complete but 1-2 genes/contig Gene mining Complete for non- repetitive regions Large scaffolds and contigs Several dozens of genes/contig Microsynteny Complete for almost the whole genome Pseudomolecules Hundreds of genes/ contig Any (Synteny, Candidate gene by QTLs) Complete genome Pseudomolecules Thousands of genes/ contig5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages Genome size vs assembly size
  • 53.
    Whole Genome Representation Sequence Status Genes Usability Incompletefor non- repetitive regions Small scaffolds and contigs Incomplete genes Markers development Complete for non- repetitive regions Medium scaffolds and contigs Complete but 1-2 genes/contig Gene mining Complete for non- repetitive regions Large scaffolds and contigs Several dozens of genes/contig Microsynteny Complete for almost the whole genome Pseudomolecules Hundreds of genes/ contig Any (Synteny, Candidate gene by QTLs) Complete genome Pseudomolecules Thousands of genes/ contig5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages Cytogenetic data (Flow cytometry) ✴ http://data.kew.org/cvalues Example: Petunia axillaris genome size 1.37 Gb (White and Rees. 1987)
  • 54.
    Whole Genome Representation Sequence Status Genes Usability Incompletefor non- repetitive regions Small scaffolds and contigs Incomplete genes Markers development Complete for non- repetitive regions Medium scaffolds and contigs Complete but 1-2 genes/contig Gene mining Complete for non- repetitive regions Large scaffolds and contigs Several dozens of genes/contig Microsynteny Complete for almost the whole genome Pseudomolecules Hundreds of genes/ contig Any (Synteny, Candidate gene by QTLs) Complete genome Pseudomolecules Thousands of genes/ contig5 4 3 2 1 2. Sequencing, tools and computers. 2.7 Assembly stages Sequencing data (Kmer count) • Jellyfish (http://www.genome.umd.edu/ jellyfish.html) • BFCounter (http:// pritchardlab.stanford.edu/bfcounter.html)
  • 55.
    0 10 2030 40 50 60 0e+002e+074e+076e+078e+071e+08 63 Kmer Frequency for Petunia axillaris Coverage KmerCount peak = 31 Estimated genome size = 1.38 Gb Possiblesequencingerrors Sequencing data (Kmer count) 2. Sequencing, tools and computers. G = (N − B) * K / D * 2 G = genome size N = total kmers B = possible kmers with errors K = kmer length D = kmer peak 2.7 Assembly stages
  • 56.
    Estimated genome sizefor P. axillaris: • Flow Cytometry (White & Rees,1987)= 1.37 Gb • Kmer Count* = 1.38 Gb • Assembly size (scaffolds v1.6.2) = 1.26 Gb • Assembly size (contigs v1.6.2) = 1.22 Gb Estimated genome not assembled = 110 - 120 Mb 2. Sequencing, tools and computers. 2.7 Assembly stages
  • 57.
    1. A briefhistory of the sequence assembly. 2. Sequencing, tools and computers. 3. Things that you should know about genomes.
  • 58.
    3. Things thatyou should know about genomes. 1. They have variable size, for example in angiosperm plants they range from 63 Mb (Genlisea margaretae) to 150 Gb (Paris japonica) with an average of 5.6 Gb. More data at: ✴ Plants: http://data.kew.org/cvalues/ ✴ Animals: http://www.genomesize.com/ 2. They can be polyploids. It means that homoeologus regions with highly similar will collapse during the assembly. 3. They can be highly heterozygous and polymorphic. In this case some of the alleles will collapse, some of them not.The effective coverage will lower than expected. 4. They can have recent whole genome duplication (or triplication) events. Paralogous genes may collapse during the assembly. 5. Repeats, most of the genomes are full of repeats and they are difficult to resolve. By default assemblers throw them away based in the Kmer histogram.
  • 59.
    3. Things thatyou should know about genomes. 3.1 Collapsing problem CACTTGACGACATGACG CTTGACGACATGACGAC CCCTTGACGACATGACG CGCCCTTGACGACATGA Gene 1 A Gene 1 B CGCCCTTGACGACATGACGACA Collapsed consensus Gene 1 A + Gene 1 B ✴Polyploidy ✴Whole Genome Duplication Gene 1 Gene 1 A Gene 1 B Schmutz J et al. Genome Sequence of the Paleoploid Soybean. Nature 2010 463:178-183
  • 60.
    3. Things thatyou should know about genomes. 3.1 Collapsing problem ✴Whole Genome Duplication http://chibba.agtec.uga.edu/duplication/index/home
  • 61.
    3. Things thatyou should know about genomes. 3.1 Collapsing evaluation CACTTGACGACATGACG CTTGACGACATGACGAC CCCTTGACGACATGACG CGCCCTTGACGACATGA CGCCCTTGACGACATGACGACA Consensus Reads Read collapsing during the assembly process can be evaluated mapping reads back to the consensus sequence and analyzing SNPs. CACTTGACGACATGA SNPs = Heterozygous Positions (AB) + Homoeologs Collapsing (ABBB,AABB,ABBB) + Paralogs Collapsing (variable)
  • 62.
    3. Things thatyou should know about genomes. 3.1 Collapsing solutions 1. Sequence the two progenitors and use them as a reference. ✓Approach used previously (cotton genome; Patterson A. et. al 2012). ❖ Information not contained in the references will be lost. ❖ Progenitors are not always available. 2. Use single molecule technologies such as PacBio or Moleculo ✓ Best approach because it will give the right phase for long sequence chunks. It exists software to integrate big sequences (minimus2...) ❖ Experimental and probably expensive. 3. Assembly everything with the higher Kmer, evaluate the collapsed regions and rescue the haplotype/region using SNPs and pair end read information. ✓ Cheap, easy to apply for the current technologies. ❖ No software available
  • 63.
    3. Things thatyou should know about genomes. 3.2 Repeats and assemblers 5. Repeats, most of the genomes are full of repeats and they are difficult to resolve. By default some assemblers throw them away based in the kmer histogram. For example, SOAPdenovo throw away anything up to 255. It is convenient to do a Kmer analysis using Jellyfish (or other Kmer counter) before do any assembly to analyze contaminations (bacterial contaminations may appears with high Kmers content) and repeats. Software to analyze repeats based in sequence graphs: RepeatExplorer (http://repeatexplorer.umbr.cas.cz/) Novak, P., Neumann, P. & Macas, J. Graph-based clustering and characterization of repetitive sequences in next- generation sequencing data. Bmc Bioinformatics 11, 378 (2010)
  • 64.
    Acknowledgements: Petunia Genome Sequencing Consortium Petunia NIU(USA) Tom Sims Mitrick Jones BTI (USA) Lukas Mueller Aureliano Bombarely UNIBE (Switzerland) Cris Kuhlemeier Remy Bruggmann Michel Moser UV (Italy) Massimo Delledonne Mario Pezzotti RU andVU (Netherlands) Tom Gerats. Francesca.Quattrochio BGI (China)