RNA sequencing with the MinION at Genoscope
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1 RNA sequencing with the MinION at Genoscope Jean-Marc December 13, 2017 RNA workshop, Genoscope
2 Overview Genoscope Overview MinION sequencing at Genoscope RNA-Seq using the Oxford Nanopore technology PAGE 2
3 Genoscope Overview French National Sequencing Center lead by Patrick Wincker, created in 1997 and part of the CEA since Provide high-throughput sequencing data to the Academic community, and carry out in-house genomic projects Focus on biodiversity : de novo sequencing and metagenomic projects (TaraOceans) But. it's not enough to just know one individual s DNA. A single reference genome is not compatible with resequencing approaches Triticum sp (wheat) Quercus robur (oak) Musa acuminata (banana) Flickr/chaojikazu Brasssica napus (seed rape) PAGE 3
4 Genome Assembly Difficulties Repeat R1 Repeat R2 Repeat R3 Genome Short reads sequencing Contig 3 Contig 4 Contig 2 Contig graph Contig 1 Contig 5 => Repetitive regions lead to fragmented assemblies and under-estimate repeat content PAGE 4
5 Sequencing capacities 2 Illumina HiSeq Illumina HiSeq MiSeq 6 Oxford Nanopore MkI 1 PromethION 1 Irys System PAGE 5
6 MinION sequencing at Genoscope 6 MinION devices >800 flowcells; >50 different organisms; ~700Gb of ONT reads ; DNA and RNA samples de novo assembly (22 yeast strains ~12Mb, 4 fungi genomes ~30Mb, several bacterial genomes, >10 plant genomes of Mb) and gene prediction Software development for the automation : management of the data flow, storing metrics in our LIMS Benchmark several DNA preparation protocols to obtain longer reads (size-selection using the blue pippin) PAGE 6
7 Nanopore : a fast evolving technology Yield improvement : ~100Mb to >1Gb but the throughput of R9.5 flowcells seems to be more erratic PAGE 7
8 Nanopore : a fast evolving technology The throughput dropped off in the last months, we now used R9.4 in production PAGE 8
9 # of active pores / flowcell Nanopore : a fast evolving technology The flowcell quality seems to be one of the issue PAGE 9
10 Nanopore : a fast evolving technology Improvement of the DNA translocation speed through the pore RNA direct sequencing (~70bp/s) PAGE 10
11 Nanopore : a fast evolving technology Average quality and error rate improvement PAGE 11
12 Nanopore : a fast evolving technology Today error rate is even lower (in average 14% for 1D reads and <7% for 1D² reads), => basecaller is a key component in the error rate drop off R9.4 R9.5 1Dsquare Distribution of identity percent based on yeast reference (S288C). Alignments were performed using bwa-mem 1D 2 is a real improvement in the error rate, unfortunately we get only up to 30% of 1D 2 reads PAGE 12
13 Nanopore : a fast evolving technology The device is able to sequence very long DNA fragments (>100Kb) ~400 high quality reads with alignment length > 100Kbp => ~4X of yeast genome Nb bases Nb sequences Max length (bp) N50 (bp) Nb seq. > 50kb Nb seq. > 100kb PAGE 13
14 Nanopore : a fast evolving technology read with the longest alignment for each chromosome Smallest chromosomes 1 and 6 are obtained in a single nanopore read! PAGE 14
15 Nanopore : a fast evolving technology Chromosomes can be captured entirely, the example read span chromosome 1 from telomere to telomere ccaca.cca.cacccacacacccacacaccacaccacacaccacaccac cattagcttcgttccagt nt.. accccccacaccacccaccacacccacacccaccacccac.cacccacac.ccaca.cac.caccacaccac ^ ^ ^ ^ ^ ^ Chr I (230,217bp) 220,227bp nanopore read ; identity alignment ~ 90% ggtgtgggtgtggtgtggtgtgtgggtgtggtgtgggtgtggtgtgtgtg ggtgtaggtgtggtgtggtgtgtgggtgtggtgtg.gtgtggtgtgggtgtgggtgtattgtgggtgtgg nt.. gtgtgggtgtgggtgtgtgtggt ^ ^ ^ The nanopore read is smaller than the chromosome due to deletions PAGE 15
16 Nanopore : a fast evolving technology High error rate in homopolymers is still an issue for de novo sequencing projects, however the R9.5 release (and scrappie) really improve the basecalling of homopolymers. It is still impossible to generate high quality consensus using nanopore only strategy. PAGE 16
17 cdna-seq and RNA-Seq using the Oxford Nanopore technology Jean-Marc December 13, 2017 CEA 10 AVRIL 2012 PAGE 17 RNA workshop, Genoscope
18 Comparison Nanopore / Illumina A typical cdna-seq experiment generates around 2M of reads, in comparison RNA-Seq experiments generate less reads (450bp/s vs 70bp/s) PAGE 18
19 Nanopore : a fast evolving technology Dataset used to perform comparisons Brain sample FC release R9.4 Brain sample FC release R9.5 Brain sample FC release HiSeq 4000 Nb sequences Nb sequences Nb sequences 59M Nb bases Nb bases Nb bases 17Gb N50 (bp) Liver sample FC release R9.4 N50 (bp) Liver sample FC release R9.5 N50 (bp) 150 Liver sample FC release HiSeq 4000 Nb sequences Nb sequences Nb sequences 45M Nb bases Nb bases Nb bases 13Gb N50 (bp) cdna sequencing N50 (bp) Direct RNA N50 (bp) 150 PAGE 19
20 Nanopore : a fast evolving technology Mapping of reads against RefSeq genes (refseq109) and the mouse genome (GRCm38) Alignment against GRCm38 using minimap2 (36 cores) Number of reads Mapped reads Mapped bases (of aligned reads) Elapsed time (sec) 1D cdna % 89.6% 396 RNA direct % 82.8% 20 Alignment against RefSeq 105 using bwa-mem (8 cores) Number of reads Mapped reads Mapped bases (of aligned reads) Elapsed time (sec) rrna Mitochondrial 1D cdna % 64.2% % 15.8% RNA direct % 75.2% % 18.5% PAGE 20
21 Comparison Nanopore / Illumina Number of RefSeq genes seen by each sequencing technology Brain sample Liver sample PAGE 21
22 Comparison Nanopore / Illumina A gene can be covered entirely by a single read 50 illumina reads are aligned and partially cover the gene The entire gene is covered by a single nanopore read PAGE 22
23 Comparison Nanopore / Illumina As expected, less nanopore reads are needed to cover RefSeq genes, when we need at least 500 illumina reads to cover 75% of a given gene, 10 nanopore reads are sufficients PAGE 23
24 Expression levels (brain and liver samples) are correlated between Illumina and Nanopore experiments Comparison Nanopore / Illumina PAGE 24
25 Are all reads full-length RNA? A small proportion of reads are full-length RNA, in average a cdna and RNA read cover 55% and 47% respectively of a RefSeq gene PAGE 25
26 TeloPrime amplification kit We tested the TeloPrime amplification kit from Lexogen Based on Lexogen s unique Cap-Dependent Linker Ligation (CDLL) and long reverse transcription (long RT) technology, it is highly selective for fulllength RNA molecules that are both capped and polyadenylated. 2 sequencing runs from brain and liver samples Brain sample FC release R9.5 Nb sequences Nb bases N50 (bp) Liver sample FC release R9.5 Nb sequences Nb bases N50 (bp) 896 PAGE 26
27 TeloPrime amplification kit TeloPrime reads better cover RefSeq genes, compared to cdna and RNA sequencing. in average a TeloPrime read cover 80% of a RefSeq gene PAGE 27
28 TeloPrime amplification kit Even with a higher number of reads, TeloPrime reads spread over a limited number of genes (~8k vs ~21k using 1D protocol) Nanopore cdna FC release R9.4 Nb sequences Nb bases N50 (bp) Direct RNA FC release R9.5 Nb sequences Nb bases N50 (bp) TeloPrime FC release R9.5 Nb sequences Nb bases N50 (bp) PAGE 28
29 TeloPrime amplification kit We need to sequence at a higher depth with the TeloPrime amplification kit to be able to catch a high proportion of RefSeq genes PAGE 29
30 Conclusion Today the throughput of the MinION device is sufficient for profiling eukaryotic gene expression, gene prediction can take advantage of long reads to avoid transciptome assembly The potential of the device to sequence long reads is impressive, sequencing of large eukaryotic genomes is now possible even with the MinION device Error rate is acceptable for de novo sequencing projects (a high proportion of reads with less than 10% of errors), still an issue with homopolymers Need to improve the wetlab part to increase the proportion of full-length reads, TeloPrime kit seems to bring a real improvement PAGE 30
31 Acknowledgements Genoscope labs Bioinformatic : Corinne Da Silva, Stefan Engelen, Benjamin Istace and Marion Dubarry Nanopore Sequencing : Corinne Cruaud, Odette Beluche, Emilie Payen, Thomas Guérin and Arnaud Lemainque R&DBioSeq Team Members of the ASTER project Funding agencies : CEA, Genoscope, France Génomique and ANR PAGE 31
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Gene Information Gene Name Gene Symbol Organism Gene Summary Gene Aliases RefSeq Accession No. UniGene ID Ensembl Gene ID mcf.2 transforming sequence-like Mcf2l Mouse Description Not Available C130040G20Rik,
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Gene Information Gene Name keratin 78 Gene Symbol Organism Gene Summary Gene Aliases RefSeq Accession No. UniGene ID Ensembl Gene ID KRT78 Human This gene is a member of the type II keratin gene family