User guide for mapping-by-sequencing in
Contents
1. number of alignments per marker followed a coverage dis tribution assessed on real resequencing experiments using Illumina sequencing Deriving the coverage distribution from real sequencing experiments has the advantage that it considers all factors that contribute to the variation in sequence coverage Perhaps most prominently several dif ferent groups have demonstrated that local GC content is correlated with sequence coverage for example 19 20 which is consequently also represented in our coverage landscape Moreover within a recent study we rigorously assessed the sequencing error rate of sequence reads aligned to marker positions 7 where the actual per base sequencing error rate was between 0 09 and 0 21 after quality filtering In order to avoid overly optimistic simula tion we assumed a sequencing error rate of 0 3 in our simulations Based on these assumptions each of the simu lated read alignments was then assigned to a parental allele following a multinomial distribution based on local allele frequencies within the bulked segregants and Illu mina sequencing specific error rate 21 Materials and methods Most important however might be realistic simulations of recombinant genomes that greatly rely on frequency and location of recombination Thus we based our simu lations on experimentally determined recombination fre quencies derived from a F population established by crossing two diverged Arabidopsis accessi2. F populations derived from backcrossing the mutant plant to the non mutagenized progenitor as mapping populations 5 6 13 Within backcross popula tions all mutagen induced mutations segregate except for the causal and closely linked mutations which are fixed in the mutant pool by selecting for the mutant phenotype Thus selection for fixed differences between the mutant pool and its genetic background reduces the number of putative causal changes considerably To quantify results of each simulation we used the number of homozygous differences between the mutant pool and the background However the absolute number of homozygous mutations greatly depends on the definition and settings of parameters used for their identification As sequencing errors can introduce wild type alleles at otherwise homozygous loci selecting only those posi tions without reads that support the wild type allele excludes some of the real homozygous mutations On the other hand including positions with support for wild type alleles will introduce false positives In order to allow comparisons across samples we defined and applied thresholds which are adjusted to pool size and sequencing coverage Materials and methods Back crossing was simulated by crossing a single mutant plant to its isogenic parent followed by one generation of inbreeding to establish a BCF mapping population see I in Figure 1 James et al Genome Biology 2013 14 R61 http genome
3. ping by sequencing of 81 000 BCF and BC3F popula tions were compared to the prior analysis of BCF pools As expected additional backcrosses reduced the variation of CAMs in pools with a few plants Figure 4 In particu lar when genome wide coverage or the number of mutants was limited additional rounds of backcrossing helped to reduce the number of CAMs However pools with a rea sonable number of recombinants sequenced with sufficient coverage did not improve with additional backcrosses James et al Genome Biology 2013 14 R61 http genomebiology com 2013 14 6 R61 a t fi ii lon 100 o eee yi limy ece T T Titi i 50 Page 6 of 13 BC F 25X 75X 100x 200x Number of genomes 75 d UM diy oh e 5 TTEN 11 HT i Mi dia de ii Number of genomes Figure 3 Results of mapping by sequencing with backcross populations A Pools of 3 to 70 BC F gt individuals colored blocks were sequenced with increasing coverage ranging from 5x to 200x For each combination of pool size and coverage we simulated 500 independent populations and performed a mapping by sequencing analysis on each of them Average number of candidate mutations with one standard deviation is shown on the y axis The initial number of mutations per genome was 1 400 B Zoom in on the framed region in panel A Pools with three recombinants are not shown Backcross populations are usually derived from one sing
4. 61 Cite this article as James et al User guide for mapping by sequencing Submit your next manuscript to BioMed Central in Arabidopsis Genome Biology 2013 14 R61 and take full advantage of e Convenient online submission e Thorough peer review e No space constraints or color figure charges e Immediate publication on acceptance e Inclusion in PubMed CAS Scopus and Google Scholar e Research which is freely available for redistribution Submit your manuscript at C www biomedcentral com submit BioMed Central
5. F individuals colored blocks were sequenced with increasing coverage ranging from 5x to 200x For each combination of pool size and coverage we simulated 500 independent populations and performed a mapping by sequencing analysis on each of them Average number of candidate mutations with one standard deviation is shown on the y axis The initial number of mutations per genome was 1 400 B Outcome of the same analysis with BCF recombinants interfere with genetic mapping In order to study the in the phenotype can have severe effect on mapping by effect of mis scored recombinants we simulated differ sequencing based on backcross populations ent rates of mis scored plants ranging from 1 to 6 within a population of 50 BCF mutants sequenced at Direct sequencing of mutant genomes 50x Materials and methods Compared to previous As an alternative to bulk segregant analysis individual results pools with 1 to 2 false scored plants yielded mutant genomes can be sequenced directly see III in 82 and 145 more CAMs respectively Figure 3 in Figure 1 However the large number of background Additional file 1 This illustrates that even small errors mutations interferes with the unambiguous identification James et al Genome Biology 2013 14 R61 http genomebiology com 2013 14 6 R61 of causal mutations Backcrossing removes some of these background mutations 14 15 Here we analyzed mutant genomes after one to three rounds of bac
6. James et al Genome Biology 2013 14 R61 http genomebiology com 2013 14 6 R61 Genome Biology User guide for mapping by sequencing in Arabidopsis Geo Velikkakam James Vipul Patel Karl JV Nordstrom Jonas R Klasen Patrice A Salom Detlef Weigel and Korbinian Schneeberger Abstract Mapping by sequencing combines genetic mapping with whole genome sequencing in order to accelerate mutant identification However application of mapping by sequencing requires decisions on various practical settings on the experimental design that are not intuitively answered Following an experimentally determined recombination landscape of Arabidopsis and next generation sequencing specific biases we simulated more than 400 000 mapping by sequencing experiments This allowed us to evaluate a broad range of different types of experiments and to develop general rules for mapping by sequencing in Arabidopsis Most importantly this informs about the properties of different crossing scenarios the number of recombinants and sequencing depth needed for successful mapping experiments Keywords Mapping by sequencing SHOREmapping genetic mapping whole genome sequencing Arabidopsis thaliana Background Forward genetic screens remain one of the major genetic tools to uncover gene function in plants as well as in other organisms However genetic mapping the process that links a phenotype to its causal mutation is tedious and time
7. acted by an elevated recombination fre quency the interval sizes for barley were drastically increased which is in agreement with its large genome size The outcome of the simulations relies in large parts on the recombination frequency and hotspots and differ ent varieties might feature different recombination land scapes Thus in order to study mapping by sequencing in different species or other crossing schemes we pro pose to repeat the simulations with the appropriate recombination frequency and landscape Our Pop seq simulator is available for download and will ease such an application see Materials and methods Materials and methods Simulation of recombined individuals and mapping populations Simulation of in silico mutant genomes started by creat ing an initial mutant genome with 700 or 1 400 randomly James et al Genome Biology 2013 14 R61 http genomebiology com 2013 14 6 R61 placed homozygous mutations respectively One of those was randomly selected as causal mutation Wild type genomes were simulated without any mutation In order to simulate offspring genomes we combined recombined haploid genomes from one or two virtual parents Offspring genomes were used as parents for further crosses The actual number of recombination per meiosis for each chromosome was randomized based on the distribution of recombination events in Arabidopsis these empirical determined recombination frequencies were derived from a cro
8. binants reduced the average number of homozygous candidate mutations Sequencing pools with 30 recombinants at coverage of 25x revealed 43 18 CAMs on average Like for outcross popula tions the variation of CAMs was high in pools with few recombinants but got reduced in larger pools In great contrast to outcross populations we observed immediate saturation of the number of CAMs while increasing pool size For example pools with 20 mutants sequenced at a coverage level of 20x revealed 56 22 CAMS on average Pools with 70 mutants which were sequenced with the same sequencing effort revealed almost the same number In general for coverage levels of lt 25x we observed no reduction in the number of CAMs when increasing the pools beyond 20 recombi nants This suggests that low fold sequencing lacks the power to make use of the compliment of recombination in the pool and more sequencing is required to exploit all recombination events In agreement we still observed a decrease in CAMs for deeply sequenced samples 200x when increasing pool size from 60 to 70 This illustrates the mutual importance of both pool size and coverage This trend was also observed when introducing a low mutation load Figure S1 in Additional file 1 Effects of successive backcrossing In a series of simulations we increased the number of backcross generations up to three before establishing a mapping population see II in Figure 1 In total map
9. biology com 2013 14 6 R61 9 oa a E i a O xe O Page 5 of 13 100x Size of mapping interval Mb l a Sfi liii ii Number of genomes Figure 2 Results of mapping by sequencing with outcross populations Pools of 40 to 400 individuals colored blocks were sequenced with increasing coverage ranging from 5x to 200x For each combination of pool size and coverage we simulated 500 independent populations and performed a mapping by sequencing analysis on each of them Average mapping interval size and its standard deviation as well as the imputed number of candidate mutations within mapping region are shown on right and left y axis respectively The initial number of mutations per genome was 1 400 The interplay of pool size and genome wide coverage in BCF populations We simulated BCF populations with 3 to 70 mutants for high and low mutation rates separately Sequencing was simulated at different coverage levels ranging from 5x to 200x For each combination of pool size and cover age level we simulated 500 independent mapping popu lations and scored the number of homozygous mutations Figure 3 Mutations that are not fixed but are close to fixation have a high probability to appear as fixed in the sequencing data This effect becomes stronger at low coverage levels where the reduced number of reads does not allow identifying low frequencies of wild type alleles As expected more recom
10. consuming Recently the combination of bulk segregant analysis and whole genome resequencing has proven to radically speed up this process 1 This speed up was gained by whole genome sequencing of bulked recombinants and a subsequent analysis for local skews in the parental allele frequencies which are introduced through pheno typic selection for mutant phenotypes This directs the analysis to an approximate mapping interval that can be screened for underlying mutations using the exact same sequencing data Several analysis pipelines have been introduced 1 9 and were already applied to various model species including plants yeast nematodes mam mals and invertebrates 10 12 Different types of crossing schemes for mapping by sequencing have been suggested The very first mapping Correspondence schneeberger mpipz mpg de Max Planck Institute for Plant Breeding Research Department for Plant Developmental Biology 50829 Cologne Germany Full list of author information is available at the end of the article C BioMed Central by sequencing experiments were performed on pooled gen omes of mutant recombinants that were generated by crossing the mutants to diverged strains followed by one round of selfing 1 11 Recently several groups suggested using backcrossed instead of outcrossed individuals as map ping populations as mutagen induced changes segregate like natural polymorphisms Even though there is no prior know
11. dom align ments locations were chosen The read length defined the end of the alignment The actual location of the read pair was defined by read length and insert site If the simulated alignments overlapped with one or more markers the alignment was scored as informative Implementation of simulation pipeline Our pipeline for simulating recombinant genomes and their sequencing Pop seq simulator is available at sour ceforge 38 Recombination frequency and landscape are specified by configuration files which we provide for all simulations performed in this study Additional data files The following additional data are available with the online version of this article Additional file 1 describes supplementary figures List of abbreviations Bp base pair CAMs candidate mutations dCARE deep candidate resequencing FNR fast neutron radiated Gb giga bases kb kilo bases Mb mega bases Competing interests The authors declare that they have no competing interests Authors contributions KS and GVJ designed the study GVJ VP KIVN and KS implemented the simulation pipeline GVJ JK and KS performed data analysis PS and DW provided biological resources KS and GVJ wrote the manuscript All authors read and approved the final manuscript Authors information KS and DW published the first mapping by sequencing analysis and software for next generation sequencing based bulk segregant analyses Their labs perform mapp
12. e mapping interval Simulation of mapping populations with 400 mutants that were sequenced with an average coverage 200x at the enriched regions resulted in map ping intervals with an average size of 3 2 Mb Conclusions Identification of a wide range of mutagen induced pheno types founded the basis of genetic research in Arabidopsis in the last century 35 Next generation sequencing now revived and accelerated this successful concept of forward genetics as it allows for instant identification of CAMs This speed up in genetic mapping now opens new avenues even for more complex phenotypes We have addressed several questions about the design of mapping by sequen cing in a large simulation study and summarize our sugges tions for reasonable and successful experiments Table 1 In silico simulations have the tendency to be too optimis tic in their predictions as simulations only consider known variables but additional noise which is common to real data might not be simulated appropriately Differences between real mapping by sequencing experiments and our simulations include differences in the number of recombi nation and mutations As recombination events can be influenced by changes in the environment as well as the genetic background our empirical data which was based on limited environments and genetic backgrounds will not always fit the real recombination landscape 36 However variation in recombination is subtle and our resu
13. e observed very similar trends for mapping by sequencing in rice as compared to Arabidopsis Figure S4 and S5 Changes in the genome wide coverage affected the outcome of backcross populations more than outcross populations and pools with very low number of recombi nants drastically suffered from the lack of recombination Outcross populations with 150 mutant recombinants sequenced with not more than 20x featured lt 3 CAMs on average in our simulations In contrast backcross popula tions consisting of 50 mutants which were sequenced at a genome wide coverage of 50 yielded around 10 CAMs on average In general the greater genome size of rice as compared to Arabidopsis was counteracted by an enriched recom bination frequency allowing for similar conclusions on the experimental design in rice as in Arabidopsis Mapping by sequencing based on targeted enrichment sequencing As of today the large genome sizes of crop species like the one of Hordeum vulgare barley make whole gen ome resequencing as part of mapping by sequencing an expensive and risky approach To address this general problem genome complexity reduction methods like transcriptome sequencing restriction site associated DNA sequencing or targeted enrichment sequencing have been proposed 29 32 For example targeted enrichment sequencing has been proven to be suitable for mapping by sequencing already 7 Here we simulated targeted enrichment sequencing of approximate
14. ead pairs span recombination events If two sin gle end reads overlap with markers both contribute to the estimation of allele frequencies as they have been sampled independently It is thus not obvious whether paired end or single end sequencing is advantageous for mapping by sequencing We have compared the efficiency of single and paired end reads by counting the number of randomly gener ated read or read pair alignments that overlap with pre defined marker and CAMs sets A read respectively a pair was scored as informative if it was uniquely aligned to at least one or more markers The lengths of the simulated reads ranged from 50 bp to 750 bp to cover a wide range of next generation sequencing read lengths Figure 5 Reads which align equally well to multiple regions in the genome are excluded for further analysis Increased read length span some of the short repeats and thus allows aligning more reads uniquely 23 24 For the analysis of mapping by sequencing with out cross populations we defined two sets with 291 973 and 281 668 markers for single end and paired end sequen cing respectively in order to take the different mapping Page 8 of 13 Paired end Single end Percent informative reads O o S S S S S S S S D K E S O SF OC KM Read length Figure 5 Percentage of informative reads for different sequencing read lengths and types Reads or read pairs respectively can only contribute to mapping by sequencin
15. ed end Single end As an alternative to bulk segregant analysis we also analyzed direct sequencing of individual genomes of backcross populations Each successive backcross reduces the foreground genome and the number of CAMs How ever it requires multiple backcross generations before the number of CAMs is as low as in bulk segregant ana lyses and still tedious rough mapping may be required 15 Our study suggests that multiple rounds of back crosses can be avoided by pooling multiple genomes The genome wide mutation rate of radiation mutants is reported to be significantly lower as compared to che mically induced mutants 37 Direct sequencing of mutants with fewer but putatively more severe muta tions can simplify the interpretation of whole genome analysis of directly sequenced mutant genomes Another way to reduce the number of CAMs is sequen cing multiple independent alleles of the same mutant which was shown recently for the first time 16 This strat egy does not reduce the number of CAMs in individual genomes but most of the CAMs can be ignored as they affect genes only in one of the alleles The chance of finding unrelated genes with mutations in different alleles is very low 16 We have extended our analysis on the design of map ping by sequencing experiments to rice and barley which involved the simulation of targeted enrichment sequencing While the larger genome of rice as compared with Arabi dopsis was counter
16. g experiments requires establishment and sequencing of thousands of mapping populations which is practically not feasible in plants In contrast in silico simulations do allow for the generation of many experiments with the potential caveat that they rely on prior assumptions In particular genuine simulations of mapping by sequencing experiments require realistic assumptions about mutation load next generation sequencing and meiotic recombination The most commonly used mutagen for Arabidopsis is ethyl methanesulfonate EMS a chemical mutagen that predominantly introduces C to T and G to A changes There are various reports about the frequency of EMS induced mutations including one change in 112 to one change in 171 kb 15 18 indicating a dosage dependency of the mutation rate which suggests that the actual fre quency range is likely to be much wider In order to explore the effects of different mutation rates we simu lated low 700 changes and high 1 400 changes rates of mutations that were randomly introduced into the genome Similarly realistic simulations of next generation sequencing rely on correct assumptions about the number Page 2 of 13 of short read alignments per reference position from here on referred to as coverage and sequencing errors As we were only interested in coverage at marker loci we simu lated whole genome sequencing by randomizing the num ber of read alignments at each marker The absolute
17. g if their alignments overlap with at least one marker or mutations informative reads The number of informative reads from single end and paired end sequencing are shown in purple and blue respectively The lower graphs refer to a mutation density which is typical for backcross populations here 1 400 mutations per mutant genome The upper graphs refer to the number of marker in outcross populations 281 668 and 291 973 for single end and paired end sequencing properties into account Depending on the read length paired end sequencing featured between 25 and 78 more informative read pairs Consequently it would require between 25 and 78 more single end reads in order to end up with the same number of informative reads This calculation allows for a cost comparison of mapping by sequencing for single and paired end sequencing based on actual sequencing costs However as paired end sequencing enables the analysis of parts of the otherwise inaccessible DNA it might be advanta geous to sequence both ends even if this would be more expensive In particular if combined with mutation identification paired end sequencing has a higher chance not to miss the causal mutation We repeated this exercise for mapping by sequencing based on back cross populations that were simulated with 1 400 muta tions in the genome Here paired end sequencing featured between 95 and 119 more informative read pairs Application of mapping by sequenc
18. gton JC Identification of MIR390a precursor processing defective mutants in Arabidopsis by direct genome sequencing Proc Natl Acad Sci USA 2010 107 466 471 Birkeland SR Jin N Ozdemir AC Lyons RH Weisman LS Wilson TE Discovery of mutations in Saccharomyces cerevisiae by pooled linkage analysis and whole genome sequencing Genetics 2010 186 1127 1137 Lindner H Raissig MT Sailer C Shimosato Asano H Bruggmann R Grossniklaus U SNP Ratio Mapping SRM identifying lethal alleles and mutations in complex genetic backgrounds by next generation sequencing Genetics 2012 191 1381 1386 Zuryn S Le Gras S Jamet K Jarriault S A Strategy for Direct Mapping and Identification of Mutations by Whole Genome Sequencing Genetics 2010 186 427 430 Ashelford K Eriksson ME Allen CM D Amore R Johansson M Gould P Kay S Millar AJ Hall N Hall A Full genome re sequencing reveals a novel circadian clock mutation in Arabidopsis Genome Biol 2011 12 R28 Nordstrom KJV Albani MC James GV Gutjahr C Hartwig B Turck F Paszkowski U Coupland G Schneeberger K Mutation identification by direct comparison of whole genome sequencing data from mutant and wild type individuals using k mers Nat Biotechnol 2013 31 325 330 Salom PA Bomblies K Fitz J Laitinen RAE Warthmann N Yant L Weigel D The recombination landscape in Arabidopsis thaliana F2 populations Heredity Edinb 2012 108 447 455 Jander G Norris SR Rounsley SD Bush DF Le
19. here one single wild type chro mosome is present defined as j mx2 1 sf n 2 where m and n are the number of mis scored and total mutants in the pool respectively For the second threshold we calculated the mutant allele frequency as estimated by the short read alignments where one alignment is sampled from a non mutant chromosome defined as r 1 BH C p where c is the actual coverage at position p and e is the estimated sequencing error frequency of 0 3 7 Only mutations with mutant allele frequencies greater than gy and ry have been considered as homozygous mutations High quality marker selection For all simulations based on outcross populations we defined a high quality marker set based on resequencing data of Arabidopsis Ler 21 All SNPs with a resequen cing quality score lt 25 were discarded as well as SNPs that overlap with regions with different copy number between the parents as predicted by the resequencing Further we iteratively removed SNPs which were closer than 50 bp This yielded 291 973 high quality markers James et al Genome Biology 2013 14 R61 http genomebiology com 2013 14 6 R61 Comparison of single end and paired end sequencing Single and paired end sequencing was simulated with reads ranging from 50 bp to 750 bp in length Insert length for paired end sequencing was simulated with three times the read length For each combination of read length and sequencing type 100 000 ran
20. hisms and those in repetitive regions from the complete set of differences between Arabidopsis accessions Columbia Col 0 and Landsberg erecta Ler 21 Material and methods Interplay of pool size and genome wide coverage Outcross populations were simulated with 40 to 400 mutant genomes Next generation sequencing was simu lated at various genome wide coverage levels ranging from 5x to 200x Each combination of pool size and cov erage was independently repeated for 500 times For each dataset we performed a SHOREmap analysis and assessed the size of the final mapping intervals Figure 2 Overall the sizes of the mapping intervals were remarkably vari able This variation was lower for pools with more recombinants as compared to pools with fewer recombi nants As expected the number of recombinants also strongly influenced mapping resolution For example at an average genome wide coverage level of 15x pools with 200 recombinants yielded an average interval size of 381 222 kb whereas pools with 50 recombinants gen erated interval sizes of 783 567 kb on average Like in conventional mapping experiments the decrease in the size of the mapping interval was not linear The first indi cation of saturation was observed at a sequencing cover age of 5x to 15x where increasing the pool size beyond 350 recombinants did not improve the interval size In contrast to pool size coverage alone had only a small effect on size and variat
21. ing by sequencing analyses on a regular basis Acknowledgements The authors would like to thank Fernando Andr s Lalaguna for the helpful discussions This work was funded by the Gottfried Wilhelm Leibniz Award of the DFG and Max Planck Society D W Authors details Max Planck Institute for Plant Breeding Research Department for Plant Developmental Biology 50829 Cologne Germany Department of Biological Sciences Institute of Genetics Epigenetics University of Saarland Campus Saarbr cken 66123 Saarbr cken Germany Max Planck Institute for Developmental Biology Department of Molecular Biology 72076 T bingen Germany Received 28 February 2013 Revised 3 May 2013 Accepted 17 June 2013 Published 17 June 2013 References 1 Schneeberger K Ossowski S Lanz C Juul T Petersen AH Nielsen KL Jorgensen J E Weigel D Andersen SU SHOREmap simultaneous mapping 20 22 Page 12 of 13 and mutation identification by deep sequencing Nat Methods 2009 6 550 551 Doitsidou M Poole RJ Sarin S Bigelow H Hobert O C elegans mutant identification with a one step whole genome sequencing and SNP mapping strategy PLoS One 2010 5 e15435 Austin RS Vidaurre D Stamatiou G Breit R Provart NJ Bonetta D Zhang J Fung P Gong Y Wang PW McCourt P Guttman DS Next Generation Mapping of Arabidopsis Genes Plant J 2011 67 715 725 Uchida N Sakamoto T Kurata T Tasaka M Identification of EMS induced causal mutatio
22. ing simulations to model crop species Mapping by sequencing has already been successfully applied to crop species like rice and polyploid wheat 5 16 25 As the size of some of the crop genomes can James et al Genome Biology 2013 14 R61 http genomebiology com 2013 14 6 R61 be as large as multiple Gb an informed decision on the experimental design of mapping by sequencing seams even more important for such species Here we explored the power of Pop seq simulator to address questions about the experimental design of mapping by sequencing experiments in rice and barley where map ping by sequencing starts to become part of standard molecular toolbox Mapping by sequencing in the crop model species rice First we estimated the recombination frequency and land scape of rice by combining two publically available rice RIL populations 26 27 Further we selected a publically available set of 139 244 marker for the simulation of out cross populations markers 28 Similarly to Arabidopsis we randomly introduced 2 222 mutations 1 every 171 kb of which one was selected to be causal Based on this we simulated mapping by sequencing using both outcross and BCF backcross populations with 50 to 400 and 10 to 80 mutant genomes respectively Sequencing of these pooled genomes was simulated at various genome wide coverage levels ranging from 10x to 100x Each combina tion of pool size and coverage was simulated for 300 times Overall w
23. ion of mapping intervals Pools of 100 recombinants which were sequenced at 15x yielded an average interval size of around 500 310 kb as com pared to 419 298 kb at a coverage of 200x The reason Page 4 of 13 for the weak impact of coverage on the size of the map ping interval is the large number of markers which are distributed throughout the genome and allow for an accu rate assessment of allele frequencies even at low coverage levels Assuming 1 400 mutagen induced mutations per gen ome the average number of CAMs was around five for pools of gt 100 recombinants sequenced at an average genome wide coverage of 25x In practical application additional prioritization by functional annotation and location of mutations in the interval has the potential to reduce this low number of CAMs to one outstanding candidate only 1 Mapping by sequencing with backcross populations Conventional genetic mapping requires a cross of the mutant to a diverged genome In addition to genetic variation this introduces phenotypic variation which can interfere with the recognition of subtle phenotypes Moreover if the mutagenesis was performed in a com plex transgenic or otherwise mutagenized background this background needs to be introgressed into the diverged genome if tedious genotyping for the presence of first site mutations within all recombinants is to be avoided In order to bypass these obstacles it has been sug gested to use
24. ions Mapping by sequencing with outcross populations is based on mutant allele frequencies assessed at large scale marker sets leading to the identification of mapping James et al Genome Biology 2013 14 R61 http genomebiology com 2013 14 6 R61 intervals Such regions can then be screened for novel mutagen induced changes using the same whole genome sequencing data see I in Figure 1 Usually a rough identification of linked regions suffices as even in larger regions sequencing data can easily be screened for CAMs In order to evaluate this process we used the mapping by sequencing analysis pipeline SHOREmap which implements a likelihood ratio test statistics that converts mapping by sequencing data into confidence mapping intervals 7 These mapping intervals represent the region in which causal candidates reside at a given confidence level P here P 0 99 As we assume that mutations are randomly introduced into the genome the number of CAMs is linearly correlated with the length of mapping intervals which we used to quantify the out come of a mapping by sequencing experiment Though marker density positively impacts on mapping resolution inclusion of markers that cannot be accessed with the actual sequencing methods or that have been falsely included can have severe local effects on the precise determination of mapping intervals 7 The marker set we used consisted of 291 973 markers after discarding closely linked polymorp
25. kcross ing Mutants that are selected from backcross popula tions will generally yield fewer CAMs The theoretical fraction of the recurrent parental genome after n rounds n 1 __ 1 of backcrossing is 22 Our simulated popula tions closely followed the expected percentage and showed an average reduction of foreground genome by 12 8 and 6 8 in BC and BC respectively As expected direct sequencing yielded more CAMs than in our bulk segregant analyses For example across all cov erage levels pools with no more than three BC F mutant individuals showed less than half of the CAMs as compared to direct sequencing of BCF individuals illustrating the power of bulk segregant analysis Paired end versus single end sequencing Many of the new sequencing technologies allow sequen cing of one or both ends of DNA clones Paired end sequencing enables access to the borders of repetitive sequences which increases the number of markers and mutations that can be analyzed Even though single end sequence reads might not be able to explore the same genomic space as paired end sequence reads they are independent of each other In bulk segregant sequen cing independent reads are counted to estimate allele frequencies see IV in Figure 1 If both reads of a pair align to different markers they cannot contribute twice to the estimation of allele frequencies as they carry the same genetic background ignoring the very rare cases where r
26. le mutant plant and its isogenic parent However generation of a backcross population based on multiple mutant siblings all of which are crossed to their iso genic parent introduces additional variation around the causal locus Here we simulated the generation of back cross populations with three mutant siblings and com pared the mapping outcome to our previous results which were based on one mutant parent only Figure S2 in Additional file 1 The improvement in mapping reso lution was very limited and restricted to pools with few mutants only Effects of mis scored plants Phenotyping complex or subtle phenotypes can lead to mis scored plants Such plants introduce wild type alleles at the causal candidate locus and severely James et al Genome Biology 2013 14 R61 Page 7 of 13 http genomebiology com 2013 14 6 R61 400 300 200 Candidate mutations 100 400 300 Candidate mutations O O 100 BC F 5x 10x BX 20x asx _ 50x 75x 100x 200x J e e e e lili EO jiii im litem Lo iTr tig geese AL etter LT l lill a OT eee oT eTe pd l Tasos dtd It eee lise e Iie is Number of genomes BCF 5x 10x ie O _50x_ 75X 100x in I o wm a i pe Jim teeh O iu That Ties Tse Tis ie Number of genomes Figure 4 Effect of coverage and pool size on BCF and BCF backcross populations A Pools of 3 to 70 BC
27. ledge about their distribution or location mutagen induced changes can be identified within whole genome sequencing data and subsequently used for mapping 5 6 13 Similarly direct sequencing of an individual mutant recombinant as suggested for Caenorhabditis ele gans and later for Arabidopsis thaliana Arabidopsis will allow for a rough mapping of the causal mutation 14 15 Although multiple rounds of backcrossing are usually not sufficient to considerably minimize the size of linked regions around causal mutations this strategy has the advantage to characterize the complete genome of a mutant recombinant Alternatively direct sequencing of two or more independently generated alleles of the same mutant followed by a subsequent search for genes that carry muta tions in all mutant alleles is powerful enough to unambigu ously identify the causal mutation 16 Irrespective of the actual strategy application of map ping by sequencing involves decisions on the experimental 2013 James et al licensee BioMed Central Ltd This is an open access article distributed under the terms of the Creative Commons Attribution License http creativecommons org licenses by 2 0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited James et al Genome Biology 2013 14 R61 http genomebiology com 2013 14 6 R61 makeup for instance the size of the mapping population as well a
28. lts are in agreement with published mapping by sequencing experi ments 1 3 James et al Genome Biology 2013 14 R61 http genomebiology com 2013 14 6 R61 Page 10 of 13 Table 1 Suggestions for the design of mapping by sequencing experiments Outcross populations Generation F gt Mutants n Optimal coverage gt BG F gt Approximately 150 Approximately 50 Approximately 50 Paired end Sequencing type Paired end Depending on mutation rate We have analyzed the effect of genome wide coverage and recombinants on the performance of mapping by sequencing Our results revealed that coverage and recombinants are interconnected and rely on the crossing scheme Mapping by sequencing with backcross popula tions requires higher coverage for optimal mapping results as compared to outcross populations This differ ence comes from the drastic difference in the number of markers or mutations between these two types of popu lations Outcross populations contain hundreds of thou sands of natural markers which are much denser than the expected recombination frequency Thus sliding window like approaches can combine the information from neighboring markers and establish precise allele frequency estimates Even though the average coverage for sliding window based methods can be low subse quent identification of mutations limits the minimal cov erage required of such experiments In order to avoid the risk of missing the causal mu
29. ly 60 Mb of the barley genome This includes the simulation of deep sequencing at selected regions of the genome but at the same time the simulation excludes Page 9 of 13 the rest of the genome from sequencing Even though enrichment sequencing has a high chance to exclude the causal mutation from the actual sequencing data map ping by sequencing based on enrichment sequencing will guide subsequent fine mapping efforts The design of the enrichment reduced the set of gen ome wide marker as defined between the two cultivars Morex and Barke from 11 371 643 to 164 492 markers which are accessible through our enrichment sequencing 33 Mapping populations were simulated with 50 to 600 mutant plants selected from F outcross populations and were based on the recombination frequency and land scape for barley as observed in the Oregon Wolfe Barley mapping population 34 Sequencing was simulated at coverage levels of 100x to 1 000x reflecting the high cov erage gained in enriched regions Each combination of pool size and coverage was simulated for 300 times Overall the reduced recombination frequency in barley as compared to the other species resulted in large map ping intervals Figure S6 Similarly to the observations for the other two species increased coverage had only a minor effect on the results of outcross population based mapping by sequencing but an increase in the number of mutants can have a strong effect on the size of th
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32. ons 17 These data reveals the number of recombination in single crosses as well as their distribution over the physical range of the chromosomes We used the frequency of recombination events along the chromosomes as a probability function after which recombination location and frequency were simulated Materials and methods This method for in silico simulation of recombination breakpoint events can be applied to any type of crossing regime In this study we focused on three different types of mapping by sequencing scenarios Figure 1 First we simulated F mapping populations generated by crossing a mutant plant to a non mutagenized accession with a diverged background followed by selfing of the F hybrid as performed by 1 3 11 We refer to these classical map ping populations as outcross populations In outcross populations natural sequence variations along with muta gen induced changes serve as genetic markers A second type of population was simulated by backcrossing the mutant plant to the non mutagenized progenitor followed by selfing of the hybrid as performed by 5 6 We refer to these mapping populations as backcross populations in which only mutagen induced changes serve as markers James et al Genome Biology 2013 14 R61 http genomebiology com 2013 14 6 R61 a a Self Mutant M Mutant M3 Outcross populations a Accession F1 Self or ame Page 3 of 13 1 Outcro
33. osition based on the observed coverage in real resequencing experiments of Arabidopsis wild type 21 The value describes the ratio of observed coverage at single marker in relation to the genome wide average Actual number of reads at each marker position c per sequencing simulation was then calculated by ci Multinomial m n n2 ng Page 11 of 13 where m n and k are the total number of reads nor malized coverage probability per marker and the total number of markers respectively Then we used the allele frequency a and a gt within the population under investigation and assigned each read r to one of the parental alleles by ri Trinomial c a1 42 5 where a d2 and s are the allele frequency of mutant allele frequency of wild type and sequencing error We used a constant sequencing error rate of 0 3 7 The fre quency of different types of sequencing errors in Illumina sequencing is non randomly distributed 19 however as this would have a limited impact on our simulations we did not address this fact here Selection of homozygous mutations Predictions of homozygous mutations are affected by pool size and coverage In order to define a uniform threshold for the detection of homozygous mutations across all dee ply and shallowly sequenced pools with a few or many plants we introduced two thresholds respecting pool size and sequencing coverage First we calculated the mutant allele frequency at loci w
34. s the amount of next generation sequencing data Since both are directly related to time and financial effort it is important to optimize the setup of mapping by sequencing experiments The lack of general guidelines describing an optimal design might lead to conservative decisions that prime an unnecessarily high number of individuals and sequencing coverage Within this study we establish a guideline for mapping by sequencing for Arabidopsis Following an experimen tally established recombination landscape 17 we simu lated next generation sequencing of gt 400 000 mapping by sequencing experiments to analyze the differences in the design of mapping populations in relation to the num ber of candidate mutations identified in the course of such an experiment Furthermore we evaluated the impact of technical aspects such as read length and read pairing on mapping by sequencing Even though our simulations were focused on Arabi dopsis our simulation pipeline called Pop seq simulator is generic and can be applied to other species as well as other mapping or sequencing strategies In the last sec tion we describe the extension of our analysis on the experimental design of mapping by sequencing to two crop model species rice and barley in which next gen eration sequencing based mapping becomes tangible reality Results and Discussion In silico mapping by sequencing experiments Assessing different types of mapping by sequencin
35. sequencing data with bulked segregant analysis BSA to fine map genes in polyploid wheat BMC Plant Biol 2012 12 14 26 Huang X Feng Q Qian Q Zhao Q Wang L Wang A Guan J Fan D Weng Q Huang T Dong G Sang T Han B High throughput genotyping by whole genome resequencing Genome Res 2009 19 1068 1076 27 Harushima Y Yano M Shomura A Sato M Shimano T Kuboki Y Yamamoto T Lin SY Antonio BA Parco A A high density rice genetic linkage map with 2275 markers using a single F2 population Genetics 1998 148 479 494 28 McNally KL Childs KL Bohnert R Davidson RM Zhao K Ulat VJ Zeller G Clark RM Hoen DR Bureau TE Stokowski R Ballinger DG Frazer KA Cox DR Padhukasahasram B Bustamante CD Weigel D Mackill DJ Bruskiewich RM Ratsch G Buell CR Leung H Leach JE Genomewide SNP variation reveals relationships among landraces and modern varieties of rice Proc Natl Acad Sci USA 2009 106 12273 12278 29 Baird NA Etter PD Atwood TS Currey MC Shiver AL Lewis ZA Selker EU Cresko WA Johnson EA Rapid SNP discovery and genetic mapping using sequenced RAD markers PLoS One 2008 3 e3376 30 Elshire RJ Glaubitz JC Sun Q Poland JA Kawamoto K Buckler ES Mitchell SE A robust simple genotyping by sequencing GBS approach for high diversity species PLoS One 2011 6 e19379 31 Turner EH Lee C Ng SB Nickerson DA Shendure J Massively parallel exon capture and library free resequencing across 16 genomes Nat
36. ss between Arabidopsis Col 0 and Fei 0 17 It was calculated by X Trinomial n p1 p2 p3 where p p2 and p are the observed frequencies of none one and two or more recombination per chromo some per meiosis The location of each recombination was selected after the observed frequencies over each marker along the chromosome and placed in between two adjacent markers The probability of a recombina tion at position x between two adjacent markers was calculated by p mi l ee Oe Ks k P x i iF AL ety ety where i j k and are the marker base pair total number of markers and length between adjacent mar kers respectively Pim is the observed probability of recombination in between marker m and m _ The location of additional recombination events was mod eled after a gamma distribution in order to take crossing over interference into account Both gamma distribution parameters scale and shape were chosen such that the resulting distribution followed the empirical data Plants with homozygous causal mutations were labeled as mutant phenotypes Simulation of whole genome sequencing Accurate simulation of whole genome sequencing of bulks and individual genomes needs to consider the total number of alignments per marker the parental allele frequencies and sequencing errors To incorporate the variation in the number of alignments per marker we assigned a prior normalization n value to each mar ker p
37. ss or a backcross Backcross populations Wild type Il Perform Fy additional backcrosses Self Ili How many plants need to be pooled Mapping population Select mutant plants Individual genome Pooled genomes IV How many and what types of reads are needed Figure 1 Overview of different strategies for mapping by sequencing Establishing mutant pools or individual mutant recombinant genomes requires decisions on different aspects of mapping by sequencing Common questions are shown in red I Mutants can be crossed to diverged accessions or backcrossed to the wild type II The number of backcrosses and number of plants used as parents contribute to the outcome of mapping by sequencing Ill The number of mutant plants sampled from mapping population greatly impacts on the mapping results IV Finally the sequencing coverage as well as type of sequencing single end or paired end affects the outcome of mapping by sequencing In contrast to the previous two methods which make use of recombination the third type of simulation con stitutes direct sequencing of individual mutant genomes selected from the backcross populations as performed by 14 15 In the next sections we explore the consequences of dif ferent crossing schemes and the effect of pool size and coverage on the extent of the resulting mapping interval and on the number of candidate mutations CAMs Mapping by sequencing with outcross populat
38. tation it is not recom mended to target coverage levels of lt 25x for mapping by sequencing experiments As a consequence of the reduced analysis power map ping by sequencing with backcross populations cannot make use of the full complement of recombination In contrast to outcross populations coverage has a large impact on the final number of CAMs In order to prioritize the final set of CAMs their putative effects on genes can be examined An attractive additional way to further filter the final set of CAMs is generating more sequencing data Hartwig et al sug gested deep sequencing of PCR amplicons or deep can didate resequencing dCARE to locally increase the read coverage up to multiple thousands of reads 6 This has the advantage of avoiding expensive whole genome sequencing while providing an exact count on allele fre quencies at each CAM If dCARE is considered pooling many recombinants even more than the whole genome sequencing can resolve is recommended Irrespective of the demands in coverage and number of mutants decisions on the experimental setup should consider the phenotype Complex phenotypes which can be affected by the presence of natural variation will benefit from isogenic mapping strategies as mis scored phenotypes can have severe effects on the mapping result Backcross populations Direct sequencing Deep candidate resequencing dCARE BC 3F 5 n a 1 As many as possible 22 gt n a Pair
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