Next Generation Sequencing data Analysis at Genoscope
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1 Next Generation Sequencing data Analysis at Genoscope
2 Genoscope (National Sequencing center) Among the largest sequencing center in Europe Part of the CEA Institut de Génomique since 05/2007 Provide high-throughput sequencing data to the French Academic community, and carry out in-house genomic projects Involved in large genome projects : human genome project, arabidopsis, rice, Coordination of large genome projects : tetraodon, paramecium, vitis, oikopleura, and as well fungal genomes (botrytis, tuber) and prokaryotic genomes
3 Genoscope (National Sequencing center) Sequencing capacity : 4 ABI /Roche Titanium 3 Illumina GAIIx 3 Illumina HiSeq 2000 Linux AMD x86 64 bits 46 serveurs totalisant 348 CPUs : 13 serveurs à 4 coeurs/machine (16Go) 31 serveurs à 8 cœurs:machine (64Go) 1 serveur à 16 cœurs (256Go) 1 serveur à 32 cœurs (1To) Utilisation du CCRT (CEA) : Cluster Hybride Bull cœurs Intel et cœurs nvidia
4 Sequencing technologies 454 / Roche Genome Sequence FLX Gs20 20Mb / run 100bp / read GSFLX Standard 100Mb / run 250bp / read GSFLX Titanium 500Mb / run 500bp / read GSFLX+ Titanium 750Mb / run 700bp / read Actual version (GSFLX Titanium) : Majority of 500bp reads Around reads / run and 500Mbp / run Run duration : 8h High error rate in homopolymer sequences Good assemblies at 20X of coverage No cloning biases
5 Sequencing technologies Illumina / Solexa Genetic Analyzer GA 1 1Gb / run 32bp / read GA II 8Gb / run 50bp / read Paired reads GA IIx 90 Gb / run 150bp / read 14 days / run Paired reads and mate pairs Hi-Seq Gb / run 100bp / read 8 days / run Paired reads and mate pairs Actual version (HiSeq 2000): Reads of 101bp Around 6000M reads / run and 600Gbp / run 8 days / run very low error rate (70% of perfect reads) No indels => good complementarity with the 454 technology No coverage gaps Price
6 A reference genome for NGS tests GC% = % repetitive DNA Manually finished sequence
7 Data quality and sequencers performance 454 / Roche Genome Sequence FLX Test a new sequencing technology on a wellknown bacteria : Acinetobacter baylyi (3,5Mb) Attempt to characterize the cause of the error rate ~ reads ; Cumulative size of 130Mb 99,9% of aligned reads Average error rate : 0,55% 37% deletions, 53% insertions, 10% substitutions Errors accumulated around homopolymers => error rate is not constant
8 Sequencing technologies Illumina / Solexa Genetic Analyzer 1 lane on Acinetobacter baylyi (3,5Mb) 11,4M reads cumulative size of 900Mb 98,5% aligned reads Average error rate : 0,38% 3% deletions, 2% insertions, 95% substitutions.
9 Sequencing technologies GS FLX base calls : initial version GS FLX base calls : upgrade March 2008 Solexa base calls GA I Solexa base calls GA II
10 Sequencing technologies
11 Whole genome shotgun sequencing
12 Whole genome shotgun sequencing
13 Microbial Genome Sequencing Evolution Propose a strategy to sequence prokaryotic genomes, accounting for assembly quality and costs Mixing 454 and illumina technologies to obtain high quality drafts (454 provides long reads and illumina low error rate) (15X) + Sanger (4X) Sanger - 12X ~23 projects ~72 projects 454 (20X) + Sanger (1X) + Solexa (~50X) ~12 projects 454 (20X) + Solexa (~50X) 66 projects Phrap Arachne Newbler Velvet Solexa (~150X) 82 projects
14 Prokaryotic genomes sequencing Sanger unpaired + paired illumina GAI Illumina 76bp + Illumina MP 10Kb 52bp Coverage 7.4X 25X and 50X 100X and 50X Assembler Arachne (Broad Institute) Newbler (454 / Roche) Velvet (EBI) # of contigs Contigs N50 (Kb) # of scaffolds Scaffolds N50 (Kb) 2,200 3,600 3,601 Assembly size 3.417Mb Mb Mb (% of reference) (95%) (98%) 58K N (100%) 11K N Misassemblies # of errors 3,442 <127 1 error / 28Kb error / 17Kb Substitutions 2, Insertions / Deletions
15 Eukaryotic Genome Sequencing Evolution Extend prokaryotic strategy to eukaryotic genomes Sanger sequencing is still used to sequence long DNA fragments : >20Kb, BAC ends, Sanger - 10X - >12 projects tetraodon, paramecium, vitis, meloidogyne, tuber, blastocystis, chondrus, podospora, oikopleura, ectocarpus 454 (~15-20X) + Sanger BAC ends + Solexa (~50X) 9 projects : cocoa, banana, trypanosome, citrus, clytia, adineta, coffee, trout, colza Arachne Newbler
16 Eukaryotic Genome Annotation Automatic Annotation : Definition of exon/intron structures based on genomic sequences provided by the assembly step. The main goal is to produce a reference set of gene models. Data Distribution Genome browser Web site Submission Formatting etc Masking Data Collection Integration Post Annotation Analysis known repeats Ab initio repeats detection cdnas mapping Public protein mapping Public ESTs mapping Ab initio genes prediction Gene models prediction Proteic domains detection Paralogs/Orthologs definition Enzymatic activity detection Metabolic pathway inference
17 Eukaryotic Genome Annotation Mapping of expressed sequences Generic pipeline to align mrnas and proteins to a genome assembly. Genome Sequences Data flow is split to take advantage of our computing cluster Split Data cdnas reads are mapped on the assembly using Blat (all matches with a score within 90% of the best score are retained) and est2genome. Protein sequences (UniProt) are aligned against the assembly using Blat (filtered on the alignment length and score) and genewise. Sequences localization on genome with fast algorithm (BLAT) Filter Spliced alignments (est2genome, genewise, exonerate) Candidate gene models
18 Eukaryotic Genome Annotation Data integration Resources describes previously are intergated using the GAZE (R. Durbin, K. Howe & T. Chothia) software to build a reference set of gene models. Repeats Ab initio models Proteins Individual predictions are broken down into segments : intron, exon, intergenic. Each segment is given a score reflecting our confidence in the data. cdnas Data integration (GAZE) ESTs Whole genome is scanned to find signals : splices sites, start and stop codons An automaton describes the gene structure. Gene models Compute optimal gene structure
19 Annotating genomes using RNA-Seq Goal : annotate eukaryotic genomes using transcriptomic data from ultra-high throughput sequencers : Illumina and Solid Difficulties : Predict complete gene structures with 40 bp reads Align short reads to exon/exon junctions (mapping algorithms allow a limited number of gaps during alignments).!" #$%& ' %((#)
20 Annotating genomes using RNA-Seq Map reads onto the target genome Mapped reads are contiged to build covtigs For each covtig, extract a list of candidates exons 1. covtigs construction genome mapped reads 2. candidate exons covtig 100 nt coverage depth ag gt threshold covtigs Step 1. covtigs construction forward and reverse candidate exons Step 2. extraction of candidate exons
21 Annotating genomes using RNA-Seq unmapped reads word dictionary k-mer 1 X 1 Step 3: Validation of exon/exon junctions Validation of junctions between candidate exons using a word dictionary built from the unmapped reads.. k-mer 2 k-mer n X 2 X n verify words existence in the dictionary candidate exons gt ag validated junction covtig1...ggtgttcactacttacccatgt...agatctacacacttttagaagcctgaaag... covtig2 K-mers TTACCCAT ATCTACACACTTTTAGA CTTACCCAT ATCTACACACTTTTAG ACTTACCCAT ATCTACACACTTTTA TACTTACCCAT ATCTACACACTTTT CTACTTACCCAT ATCTACACACTTT ACTACTTACCCAT ATCTACACACTT CACTACTTACCCAT ATCTACACACT TCACTACTTACCCAT ATCTACACAC TTCACTACTTACCCAT ATCTACACA GTTCACTACTTACCCAT ATCTACAC Junction validation Each unmapped read is split into overlapping k-mers used to create the word dictionary Unmapped reads TGTTCACTACTTACCCATATCTACACACTTTTAGAA TGTTCACTACTTACCCATATCTACA TCACTACTTACCCATATCTACACACTTTTAGAAGCC GTTCACTACTTACCCATATCTACAC GTTCACTACTTACCCATATCTACACACTTTTAGAAG TTCACTACTTACCCATATCTACACA TTCACTACTTACCCATATCTACACACTTTTAGAAGC TCACTACTTACCCATATCTACACAC TGTTCACTACTTACCCATATCTACACACTTTTAGAA CACTACTTACCCATATCTACACACT GTTCACTACTTACCCATATCTACACACTTTTAGAAG... GTGTTCACTACTTACCCATATCTACACACTTTTAGA
22 Annotating genomes using RNA-Seq 4. graph of candidate exons linked by validated junctions Open Reading Frame G-Mo.R-Se models 5. model construction and coding sequence detection M1 M 2 M3 M 4 M5 M 6 M 7 Real transcripts T 1 T 2 T 3 T 4 T 5
23 Annotating genomes using RNA-Seq G-Mo.R-Se (Gene MOdeling using Rna-Seq), is downloadable from Genoscope website : Method set-up to annotate the vitis genome (500Mb) Around 175 million of illumina reads 4 tissues : leaf, root, stem and callus 140 million of uniquely aligned reads (73,5Mb) around covtigs (38,5Mb) transcript models ( loci), and with a plausible CDS ( loci) method used for current and coming projects : banana, colza, trout,
24 Annotating genomes using RNA-Seq
25 Current and future works The Next-gen sequencing brings diversification of applications and projects : de novo sequencing, metagenomic, genome annotation, re-sequencing, functional genomic, identification of mutations and structural variations Increase of number and size of projects : need to set-up new file formats to reduce disk storage. Fastq format is not suitable to avoid redundancy. Because of next-generation sequencing (NGS), the cost of sequencing a base is dropping faster than the cost of storing a byte. Scales are logarithmic and not corrected for inflation or costs of personnel, overheads and depreciation. Image is reprinted from Stein, L.D. Genome Biol. 11, 207 (2010).
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