New Results

Extensive gene duplication in Arabidopsis revealed by pseudo-heterozygosity

Benjamin Jaegle, Luz Mayela Soto-Jiménez, Robin Burns, Fernando A. Rabanal, Magnus Nordborg
doi: https://doi.org/10.1101/2021.11.15.468652

Abstract

Background It is becoming apparent that genomes harbor massive amounts of structural variation, and that this variation has largely gone undetected for technical reasons. In addition to being inherently interesting, structural variation can cause artifacts when short-read sequencing data are mapped to a reference genome. In particular, spurious SNPs (that do not show Mendelian segregation) may result from mapping of reads to duplicated regions. Recalling SNP using the raw reads of the 1001 Arabidopsis Genomes Project we identified 3.3 million heterozygous SNPs (44% of total). Given that Arabidopsis thaliana (A. thaliana) is highly selfing, we hypothesized that these SNPs reflected cryptic copy number variation, and investigated them further.

Results While genuine heterozygosity should occur in tracts within individuals, heterozygosity at a particular locus is instead shared across individuals in a manner that strongly suggests it reflects segregating duplications rather than actual heterozygosity. Focusing on pseudo-heterozygosity in annotated genes, we used GWAS to map the position of the duplicates, identifying 2500 putatively duplicated genes. The results were validated using de novo genome assemblies from six lines. Specific examples included an annotated gene and nearby transposon that, in fact, transpose together.

Conclusions Our study confirms that most heterozygous SNPs calls in A. thaliana are artifacts, and suggest that great caution is needed when analysing SNP data from short-read sequencing. The finding that 10% of annotated genes are copy-number variables, and the realization that neither gene- nor transposon-annotation necessarily tells us what is actually mobile in the genome suggest that future analyses based on independently assembled genomes will be very informative.

Introduction

With the sequencing of genomes becoming routine, it is evident that, besides single nucleotide polymorphisms (SNPs), structural variants (SVs) play a major role in genome variation (Alkan, Coe, and Eichler 2011). There are many kinds of SVs, e.g., indels, inversions, and transpositions. Of particular interest from a functional point of view is gene duplication, leading to copy number variation (CNV).

Before Next-Generation Sequencing (NGS) was available, genome-wide detection of CNVs was achieved using DNA-microarrays. These methods had severe weaknesses, leading to low resolution and problems detecting novel and rare mutations. (Carter 2007; Snijders et al. 2001). With the development of NGS, our ability to detect CNVs increased dramatically, using tools based on split reads, sequencing coverage, or even de novo assembly (Shendure and Ji 2008; Zhao et al. 2013). In mammals, many examples of CNVs with a major phenotypic effect have been found (Gonzalez et al. 2005; Perry et al. 2007; Handsaker et al. 2011). One example is the duplication of MWS/MLS associated with better trichromatic color vision (Miyahara et al. 1998).

While early investigation of CNV focused on mammals, several subsequent studies have looked at plant genomes. In Brassica rapa, gene CNV has been shown to be involved in morphological variation (Lin et al. 2014) and an analysis of the poplar pan genome revealed at least 3000 genes affected by CNV (Pinosio et al. 2016). It has also been shown that variable regions in the rice genome are enriched in genes related to defence to biotic stress. (Yao et al. 2015). More recently, the first chromosome-level assemblies of seven accessions of A. thaliana based on long-read sequencing were released (Jiao and Schneeberger 2019), demonstrating that a large proportion of the genome is structurally variable. Similar studies have also been carried out in maize (C. Li et al. 2020; Hufford et al. 2021), tomato (Alonge et al. 2020), rice (Zhou et al. 2020) and soybean (Y. Liu et al. 2020). These approaches are likely to provide a more comprehensive picture than short-read sequencing, but are also far more expensive.

In 2016, the 1001 Genomes Consortium released short-read sequencing data and SNP calls for 1135 A. thaliana accessions (1001 Genomes Consortium 2016). Several groups have used these data to identify large numbers of structural variants using split reads (Göktay, Fulgione, and Hancock 2020; Zmienko et al. 2020; D.-X. Liu et al. 2021). Here we approach this from a different angle. Our starting point is the startling observation that recalling SNPs using the raw reads of the 1001 Genomes data set we identified 3.3 million (44% of total) putatively heterozygous SNPs. In a highly selfing organism, this is obviously highly implausible, and these SNPs were flagged as spurious, presumably products of cryptic CNV, which can generate “pseudo-SNPs” (Ranade et al. 2001; Hurles 2002) when sequencing reads from non-identical duplicates are (mis-)mapped to a reference genome that does not contain the duplication. Note that allelic SNP differences are expected to exist ab initio in the population, leading to instant pseudo-heterozygosity as soon as the duplicated copy recombines away from its template. In this paper we return to these putative pseudo-SNPs and show that they are indeed largely due to duplications, the position of which can be precisely mapped using GWAS. Our approach is broadly applicable, and we demonstrate that it can reveal interesting biology.

Analysis

Massive pseudo-heterozygosity in the 1001 Genomes data

Given that A. thaliana is highly selfing, a large fraction (44%) of heterozygous SNPs is inherently implausible. Two other lines of evidence support the conclusion that they are spurious. First, genuine residual heterozygosity would appear as large genomic tracts of heterozygosity in individuals with recent outcrossing in their ancestry. Being simply a product of recombination and Mendelian segregation, which tracts remain heterozygous is random, and there is no reason two individuals would share tracts unless they are very closely related. The observed pattern is completely the opposite. While a small number of individuals do show signs of recent outcrossing, this is quite rare (as expected given the low rate of outcrossing in this species, and the fact that the sequenced individuals were selected to be completely inbred).

Instead we find that the same SNP are often heterozygous in multiple individuals. Although the population frequency of heterozygosity at a given SNP is typically low (Supplemental Figure 1), over a million heterozygous SNPs are shared by at least 5 accessions, and a closer look at the pattern of putative heterozygosity usually reveals short tracts of shared heterozygosity that would be vanishingly unlikely under residual heterozygosity, but would be expected if the tract represents a shared duplication, and heterozygosity is in fact pseudo-heterozygosity due to mis-mapped reads (Figure 1).

Figure 1:
Figure 1:

Pseudo-heterozygosity in the 1001 Genomes dataset. (A) Cartoon illustrating how a duplication can generate pseudo-SNPs when mapping to a reference genome that does not contain the duplication. (B) Genomic density of transposons, genes, and shared heterozygous SNPs. (C) The pattern of putative heterozygosity around AT1G31910 for the 1057 accessions. Dots in the plot represent putative heterozygosity.

Furthermore, the density of shared heterozygous SNPs is considerably higher around the centromeres (Figure 1), which is again not expected under random residual heterozygosity, but is rather reminiscent of the pattern observed for transposons, where it is interpreted as the result of selection removing insertions from euchromatic regions, leading to a build-up of common (shared) transposon insertions near centromere (Quadrana et al. 2016). As we shall see below, it is likely that transposons play an important role in generating cryptic duplications leading to pseudo-heterozygosity.

Despite the evidence for selection against these putative duplications, we found 2570 genes containing pseudo-SNPs segregating at 5% or more in the population (Supplemental Figure 2). Gene-ontology analysis of these genes reveals an enrichment for biological processes involved in response to UV-B, bacteria or fungi (Supplemental Figure 3). In the following sections, we investigate these putatively duplicated genes further.

Mapping common duplications using genome-wide association

If heterozygosity is caused by the presence of cryptic duplications in non-reference genomes, it should be possible to map the latter using GWAS and heterozygosity as a “phenotype” (Imprialou et al 2017). We did this for each of a total of 26647 SNPs exhibiting heterozygosity within the genes described above.

Of the 2570 genes that showed evidence of duplication, 2511 contained at least one major association (using significance threshold of p < 10−20; see Methods). For 708 genes, the association was more than 50 kb away from the pseudo-SNP used to define the phenotype, and for 175 it was within 50 kb. We will refer to these as trans- and cis-associations, respectively. The majority of genes, 1628, had both cis- and trans-associations (Figure 2).

Figure 2:
Figure 2:

GWAS of putative duplications A: Schematic representation of the principle of how GWAS can be used to detect the position of the duplicated genes based on linkage disequilibrium (LD). As phenotype, heterozygosity at the position of interest is coded as 1 (present) or 0 (absent). As genotype, the SNPs matrix of the 1001 genome dataset was used (with heterozygous SNPs filtered out). Color gradients represent the strength of LD around the two loci. In this example the reference genome does not contain locus2. (B) GWAS results for three different genes with evidence of duplication. The grey boxes represent the centromere of each chromosome. The red lines indicate the position of the pseudo-SNP used for each GWAS and the thick grey lines indicate the centromeres. The top plot shows a trans-association, the bottom a cis-association, and the middle shows a case with both (cis plus two trans). The precise coordinates in base-pair on chromosome 1 are 6013323, 7068307 and 7204308. (C) All 26647 GWAS results summary.

To validate these results we assembled 6 non-reference genomes de novo using long-read PacBio sequencing. The GWAS hits tells us where we should expect to find the duplication (the cause of pseudo-heterozygosity) using coordinates from the reference genome. We identified the homologous region of each non-reference genome, then used BLAST to search for evidence of duplication. Of the 398 genes predicted to have a duplication present in at least one of the 6 non-reference genomes, 235 (60%) were found to do so. The distribution of fragment sizes detected suggests that we capture a mixture of gene fragments and full genes (Supplemental Figure 4).

Rare duplications

The GWAS approach has no power to detect rare duplications, which is why we restricted the analysis above to pseudo-heterozygous SNPs seen in five or more individuals. Yet most are rarer: 40% are seen only in a single individual, and 16% are seen in two. As it turns out, many of these appear to be associated with more common duplications. Restricting ourselves to genes only, 11.4% of the singleton pseudo-heterozygous SNPs are found in the 2570 genes already identified using common duplications, a significant excess (p = 2.481877e-109). For doubletons, the percentage is 11.1% (p = 1.882515e-139). Whether they are caused by the same duplications, or reflect additional ones present at lower frequency is impossible to say. To confirm duplications more directly, we simply took the reads generating the singleton and doubleton pseudo-heterozygotes, and compared the result of mapping them to the reference genome, and to the right genome (from the same inbred line). A consequence of the reads mapping at different locations is that the mapping coverage around the pseudo-SNPs will be decreased when mapping to the PacBio genomes. As expected a high proportion of the SNPs tested have lower coverage when mapping to the PacBio genomes (Supplemental Figure 5-6). On top of the decrease in coverage we could also detect reads mapping to multiple locations as well as the disappearance of the Pseudo-SNPs. Overall, 41.5% of the doubletons tag regions that map in more regions in the PacBio genomes compared to the reference genome (Supplemental Figure 5-6, 7).

Local duplications

If duplications arise via tandem duplications, they will not give rise to pseudo-SNPs until the copies have diverged via mutations. This is in contrast to unlinked copies, which will lead to pseudo-SNPs due to standing allelic variation as soon as recombination has separated copy from original. We should thus expect the approach taken here to be biased against detecting local duplications. Nonetheless, GWAS revealed 175 genes with evidence only for a cis duplication. 28 of these were predicted to be present in at least one of the 6 PacBio assemblies, and 16 could be confirmed to have local variation of copy number compare to the reference. (Figure 4A).

The local structure of the duplications can be complex. An example is provided by the gene AT1G31390, annotated as a member of MATH/TRAF-domain genes, and which appears to be present in 4 tandem copies in the reference genome, but which is highly variable between accessions, with one of our accessions carrying at least 6 copies (Figure 4B). However, there are no copies elsewhere in any of the new genomes (Supplemental Figure 8).

Transposon-driven duplications

Transposons are thought to play a major role in gene duplications, moving genes or gene fragments around the genome (Woodhouse, Pedersen, and Freeling 2010). While confirming the trans duplications in the PacBio genomes, we found a beautiful example of this process. The gene AT1G20400 (annotated to encode a myosin heavy chain like protein, confirmed by expression data) was predicted to have multiple trans-duplications. The 944 bp coding region contains 125 putatively heterozygous SNPs with striking haplotype structure clearly suggesting structural variation (Figure 4C). We were able to identify the duplication predicted by GWAS in the 6 PacBio genomes (Figure 4). Three of the newly assembled genomes have only one copy of the gene, just like the reference genome, but one accession has 2 copies, and two have 4 copies. However, none of the 6 new genomes has a copy in the same place as in the reference genome (Supplemental Figure 9).

In the reference genome, AT1G20400 is closely linked to AT1G203090, which is annotated as a Gypsy transposable element. This element also contains many pseudo-SNPs, and GWAS revealed duplication sites overlapping those for AT1G20400 (Figure 3B). This suggested that the putative gene and putative Gypsy element transpose together, i.e. that both are misannotated, and that the whole construct is effectively a large transposable element. Further analysis of the PacBio genomes confirmed that AT1G20400 and AT1G20390 were always found together, and we were also able to find conserved Long Terminal Repeat sequences flanking the whole construct, as would be expected for a retrotransposon (Supplementary Figure 10-11). Available bisulfite sequencing data (Kawakatsu et al. 2016) showed that the whole region is heavily methylated, as expected for a transposon (Figure 3). We tried mapping the bisulfite reads to the appropriate genome for the respective accesions, but the coverage was too low and noisy to observe a difference in methylation between the multiple insertions (Supplemental Figure 12).

Figure 3:
Figure 3:

Confirmation of tandem duplications. (A) The distribution of estimated copy number (based on sequencing coverage) across 6 PacBio genomes for 28 genes predicted to be involved in tandem duplications based on the analyses of this paper. (B) The duplication pattern observed in these genomes for one such gene, AT1G31390. Each color represents gene annotation from TAIR10. Multiple rectangles of the same copies denote multiple copies of the same gene.

Figure 4:
Figure 4:

A Gypsy element and a gene transpose together. (A) Methylation levels on regions containing AT1G20390 and AT1G20400 for 6 accessions, calculated in 200 bp windows after mapping reads to the TAIR10 reference genome (annotation shown in black). (B) GWAS results for the putatively heterozygous SNPs in AT1G20390 and AT1G20400. Each line represents the link between the position of the pseudo-SNP (upper side) and a GWAS hit position in the genome (lower side). The color corresponds to the location of the original heterozygous SNPs (phenotype), Blue for the gypsy element (AT1G20390) and orange for the myosin heavy chain-like gene (AT1G20400). The lower part shows the presence of the new transposable element in the 6 PacBio genomes. (C) Genotype around the AT1G20400 region for the 1001 genomes data set: Three different genotypes are shown, Homozygote reference (HOM REF), Heterozygot (HET) and Homozygote Alternative (HOM ALT). White represents a lack of coverage. I ( D) Presence of the gene and the transposon in related species. Green represents the presence of the TE, gene or both in each species.

Having located precise insertions in the new genomes, we attempted to find them using short-read data in the 1001 Genomes dataset. Except for one insertion that was shared by 60% of accessions, the rest were found in less than 20% of accessions, suggesting that this new element has no fixed insertions in the genome (Supplemental Figure 13). We looked for the element of interest in the genomes of A. lyrata (two genomes), A. suecica (Burns et al. 2021), and the outgroup Capsella rubella (Slotte et al. 2013). The gene and the Gypsy element were only found together in A. thaliana (including the A. thaliana sub-genome of the allopolyploid A. suecica). The Gypsy element alone is present in the other Arabidopis species, and the gene alone is present in A. lyrata, but only in one of two genomes. In Capsella rubella neither the transposon nor the gene could be detected (Supplemental Figure 14). The transposon and gene are specific to Arabidopsis while their co-transposition is specific to A. thaliana, suggesting that these “jumps” occurred recently evolutionary speaking since divergence of A. thaliana with the other Arabidopsis species.

Discussion

A duplication can lead to pseudo-SNPs when SNPs are identified by mapping short reads to a reference genome that does not contain the duplication. Typically pseudo-SNPs have to be identified using non-Mendelian segregation patterns in families or crosses, but in inbred lines they can be identified solely by their presence. The overwhelming majority of the 3.3 million heterozygous SNPs (44% of total) identified by our SNP-calling of the 1001 Genomes Project (1001 Genomes Consortium 2016) data are likely to be pseudo-SNPs. Assuming this, we used (pseudo-)heterozygosity as a “phenotype”, and tried to map its cause, i.e. the duplication, using a simple but powerful GWAS approach. Focusing on annotated genes, we find that over 2500 (roughly 10% of total) harbor pseudo-SNPs and show evidence of duplication. Using 6 independent long-read assemblies, we were able to confirm 60% of these duplications, using conservative criteria (see Methods). Most of the remaining duplications are located in pericentromeric regions where SNP-calling has lower quality, and which are difficult to assemble even with long-read (Supplemental Figure 15).

These numbers nearly certainly underestimate the true extent of duplication, which have been known to be common in A. thaliana for over a decade (Cao et al. 2011; Gan et al. 2011; Schneeberger et al. 2011). While unlinked trans-duplications are fairly likely to give rise to pseudo-SNPs, local cis-duplications will only do so once sufficient time has passed for substantial sequence divergence to occur, or if they arise via non-homologous recombination in a heterozygous individual (which is less likely in A. thaliana). As for the GWAS approach, it lacks statistical power to detect rare duplications, and can be misled by allelic heterogeneity (due to multiple independent duplications). Finally, duplications are just a subset of structural variants, and it is therefore not surprising that other short-read approaches to detect such variants have identified many more using the 1001 Genomes data (Zmienko et al. 2020; D.-X. Liu et al. 2021; Göktay, Fulgione, and Hancock 2020).

Pseudo-SNPs is not the only problem with relying on a reference genome. Our analysis uncovered a striking example of the potential importance of the “mobileome” in shaping genome diversity (Morgante et al. 2005): we show that an annotated gene and an annotated transposon are both part of a much large mobile element, and the insertion in the reference genome is missing from most other accessions. When short reads from another accession are mapped to this “gene” using the reference genome, you are neither mapping to a gene, nor to the position you think.

Time (and more independently assembled genomes) will tell how significant this problem is, but the potential for artifactual results is clearly substantial, and likely depends on the amount of recent transposon activity (Morgante et al. 2005). It is also important to realize that the artefactual nature of the 44% heterozygous SNPs was only apparent because we are working with inbred lines. Other researchers working on inbred lines have reached similar conclusions, and used various methods to eliminate them e.g. Zea (Chia et al. 2012; Lu et al. 2015; Bukowski et al. 2018) and Brachypodium (Stritt et al. 2021). In human genetics, SNP-calling relies heavily on family trios, but in outcrossing organisms where this is not possible, there is great cause for concern. The increasing ease and ability to sequence more and more complex genomes, such as projects associated with the 1001G+ and Tree of Life, will allow population analyses to avoid the use of a single reference genome and reveal new mechanisms of gene duplication and structural variants such as those reported here.

Methods

Genome assemblies

Long-read sequencing of six A. thaliana

We sequenced six Swedish A. thaliana lines part of the 1001 genomes collection ((1001 Genomes Consortium 2016)), ecotype ids 1254, 5856, 6021, 6024, 9412 and 9470. Plants were grown in the growth chamber at 21 C in long day settings for 3 weeks and dark-treated for 24-48 hours before being collected. DNA was extracted from ~20 g of frozen whole seedling material following a high molecular weight DNA extraction protocol adapted for plant tissue (Cristina Barragan et al. 2021). All six genomes were sequenced with PacBio, 6021 with PacBio RSII technology, while the rest were sequenced with Sequel. Accession 9412 had two rounds of Sequel sequencing and 6024 was additionally sequenced with Nanopore (4.1 Gbp sequenced, 376 K reads with N50 18.7 Kb), all data was used in the assemblies.

MinION sequencing of two A. lyrata

We sequenced two North American A. lyrata accessions, 11B02 and 11B21. Both individuals come from the 11B population of A. lyrata, which is self compatible and situated in Missouri (Griffin and Willi 2014) (GPS coordinates 38° 28′ 07.1′’ N; 90° 42′ 34.3′’ W) . Plants were bulked for 1 generation in the lab and DNA was extracted from ~20g of 3 week old seedlings, grown at 21°C and dark treated for 3 days prior to tissue collection. DNA was extracted using a modified protocol for high molecular weight DNA extraction from plant tissue. DNA quality was assessed with a Qubit fluorometer and a Nanodrop analysis. We used a Spot-ON Flow Cell FLO-MIN106D R9 Version with a ligation sequencing kit SQK-LSK109. Bases were called using guppy (https://nanoporetech.com/community) (version 3.2.6). The final output of MinION sequencing for 11B02 was 13,67 Gbp in 763,800 reads and an N50 of 31,15 Kb. The final output of MinION sequencing for 11B21 was 17.55 Gb, 1.11 M reads with an N50 of 33.26 Kb.

Genome assembly, polishing and scaffolding

The six A. thaliana genomes (ecotype ids 1254, 5856, 6021, 6024, 9412 and 9470) were assembled using Canu (v 1.7.1) ((Koren et al. 2017)) with default settings, except for genomeSize. Previous estimates of flow cytometry were used for this parameter (Long et al. 2013) when available or 170m was used. The values were 170m, 178m, 135m, 170m, 170m and 170m, respectively. The assemblies were corrected with two rounds of arrow (PacBio’s SMRT Link software release 5.0.0.6792) and one of Pilon (Walker et al. 2014). For arrow, the respective long reads were used and for Pilon the 1001 genomes DNA sequencing data, plus PCR-free Illumina 150bp data that was generated for accessions 6024 and 9412; lines 5856, 6021, 9470 had available PCR-free data (250bp reads generated by David Jaffe, Broad Institute). This resulted in 125.6Mb, 124.3Mb, 124.5Mb, 124.7Mb, 127.1Mb and 128Mb assembled bases, respectively; contained in 99, 436, 178, 99, 109 and 124 contigs, respectively. The polished contigs were ordered and scaffolded with respect to the Col-0 reference genome, using RaGOO (Alonge et al. 2019).

We assembled the genome of the two A. lyrata accessions 11B02 and 11B21 using Canu (Koren et al. 2017) (v 1.8) with default settings and a genome size set to 200Mb. The genome of 11B02 is contained in 498 contigs and the genome of 11B02 in 265 contigs. The contig assemblies were polished using Racon (Vaser et al. 2017) (v 1.4) and ONT long reads were mapped using nglmr (Sedlazeck et al. 2018) (v 0.2.7). Assemblies were further polished by mapping PCR-free Illumina 150bp short reads (~100X for 11B02 and ~88X for 11B21) to the long read corrected assemblies. Short read correction of assembly errors was carried out using Pilon (Walker et al. 2014) (v1.23). Contigs were scaffolded into pseudo-chromosomes using RaGOO (Alonge et al. 2019) and by using the error corrected long reads from Canu and the A. lyrata reference genome (Hu et al. 2011) and the A. arenosa subgenome of A. suecica (Burns et al. 2021) as a guide followed by manual inspection of regions. The assembly size for 11B02 was 213Mb and 11B21 was 202Mb. Genome size was estimated using findGSE (Sun et al. 2018) with a resulting estimated genome size of ~256Mb for 11B02 and ~237Mb for 11B21.

Heterozygous SNPs calling / extraction

We downloaded short-read data for 1,057 accessions from the 1001 Genomes Project (1001 Genomes Consortium 2016). Raw paired-end reads were processed with cutadapt (v1.9) (Martin 2011) to remove 3′ adapters, and to trim 5′-ends with quality 15 and 3′-ends with quality 10 or N-endings. All reads were aligned to the A. thaliana TAIR10 reference genome (Arabidopsis Genome Initiative 2000) with BWA-MEM (v0.7.8) (H. Li 2013), and both Samtools (v0.1.18) and Sambamba (v0.6.3) were used for various file format conversions, sorting and indexing (H. Li et al. 2009; Tarasov et al. 2015), while duplicated reads where by marked by Markduplicates from Picard (v1.101; http://broadinstitute.github.io/picard/). Further steps were carried out with GATK (v3.4) functions (Van der Auwera et al. 2013; DePristo et al. 2011). Local realignment around indels were done with ‘RealignerTargetCreator’ and ‘IndelRealigner’, and base recalibration with ‘BaseRecalibrator’ by providing known indels and SNPS from The 1001 Genomes Consortium (1001 Genomes Consortium 2016). Genetic variants were called with ‘HaplotypeCaller’ in individual samples followed by joint genotyping of a single cohort with ‘GenotypeGVCFs’. An initial SNP filtering was done following the variant quality score recalibration (VQSR) protocol. Briefly, a subset of ~181,000 high quality SNPs from the RegMap panel (Horton et al. 2012) were used as the training set for VariantRecalibrator with a priori probability of 15 and four maximum Gaussian distributions. Finally, only bi-allelic SNPs within at a sensitivity tranche level of 99.5 were kept, for a total of 7,311,237 SNPs.

SNPs filtering

From the raw VCF files only SNPs positions containing heterozygous labels have been extracted using GATK VariantFiltration. From the 3.3 millions of heterozygous SNPs extracted from the 1001 genome dataset, two filtering steps have been applied. First only SNPs with a frequency of at least 5% of the population have been kept. From those, only the one inside of annotated coding regions from the TAIR10 annotation have been used. After those filtering steps a core set of 26647 SNPs have been used for further analysis. (see Supplemental Figure 2) Genes name and features containing those Pseudo-SNPs have been extracted from the TAIR10 annotation.

GWAS

The presence and absence of all pseudo-SNP from the core set (coded as 1 and 0 respectively) have been used as a phenotype to run GWAS. As a genotype the matrix published by the 1001 genome consortium containing 10 Millions SNPs has been used (1001 Genomes Consortium 2016). To run all the GWAS, the pygwas package (https://github.com/timeu/PyGWAS) with the amm (accelerated mixed model) option has been used. The raw output containing all SNPs has been filtered, removing all SNPs with a minor allele frequency below 0.05 and/ or a −log10(p-value) below 4.

For each GWAS performed, the p-value as well as the position have been used to call the peaks using the fourier transform function in R (filterFFT) combine with the peak detection function (peakDetection), from the package NucleR 3.13, to automatically retrieve the position of each peak across the genome. From each peak the Highest SNPs within a region of +/− 10kb around the peak center have been used. (Example presented in Supplemental Figure 16) Using all 26647 SNPs a summary table was generated with each pseudo-hete SNPs and each GWAS peak detected (Supplemental Data). This matrix was then used to generate Figure 2C, applying thresholds of −log10(p-value) of 20 and minor allele frequency of 0.1.

Confirmation of GWAS results

To confirm the different insertions detected a combination of blast and synteny was used on the denovo assembled genomes. Only the insertions that segregate in the 6 new genomes have been used (398). For each gene considered the corresponding sequence from the TAIR10 annotation got blast to each genomes. As genomes are not annotated, a blast of the genes around the GWAS peak have been used to find the corresponding region in each genome, see Supplemental Figure 4. The presence of a blast results within 20kb of the peak position have been then used to confirm the GWAS results.

Gene ontology

Out of the 2570 genes detected to be duplicated, 2396 have a gene ontology annotation. PLAZA.4 (Van Bel et al. 2018) has been used to perform a gene enrichment analysis using the full genome as background. Data has been then retrieved and plotted using R.

Coverage and Methylation analysis

Bisulfite reads for the accessions were taken from 1001 methylomes (Kawakatsu et al. 2016). Reads were mapped to PacBio genomes using an nf-core pipeline (https://github.com/rbpisupati/methylseq). The weighted methylation levels are then later calculated on windows of 200bp using custom python scripts (Schultz et al. 2012).

The sequencing coverage for each accession has been extracted using the function bamCoverage (windows size of 50bp) from the program DeepTools (Ramírez et al. 2016). The Bigwig files generated are then processed in R using the package rtracklayer. An overall comparison between the mean sequencing coverage and the number of pseudo-SNPs detected show that no correlation is observed. (Supplemental Figure 18)

Multiple sequence alignment

For each insertion of the AT1G20390-AT1G20400 (Transposon-GENE) fragment, a fasta file including 2kb on each side of the fragment was extracted, from each corresponding denovo assembled genomes, using the getfasta function from bedtools (Quinlan and Hall 2010). Multiple alignment was performed using KALIGN (Madeira et al. 2019). Visualisation and comparison was done using Jalview 2 (Waterhouse et al. 2009).

Structural variation analysis

To control the structure of the region around duplicated genes, the sequence from 3 genes up and down from the gene of interest have been extracted. Each sequence has then been blasted to each of the pacbio and the position of each blast results have been retrieved. The NCBI BLAST (Altschul et al. 1990) have been used with a percentage of identity threshold of 70% and all other parameters as default. From each blast results fragments with at least 50% of the input sequence length have been selected and plotted using R.

Frequency of the insertions in the 1001 genomes dataset

The same sequences used for the multiple alignment have been used to confirm presence or absence of each insertion in the 1001 genomes dataset. We used each of those sequences as reference to map short reads using minimap 2 (H. Li 2018). For each insertion, only reads having both pair mapping in the regions have been selected. An insertion has been validated as present in an accession if at least 3 pairs of reads are spanning the insertion border. (see Supplemental Figure 10). For each insertion the frequency across the 1001 genome has been extracted and presented in Supplemental Figure 10.

Multiple species comparison

Similarly as for the A. thaliana genomes, we used the Capsella Rubella and A.arenosa published genomes (Slotte et al. 2013; Burns et al. 2021). In the case of A. arenosa we used the subgenome of A. suecica. We blasted the TE-GENE fragments, extracted from the TAIR10 annotation, using the NCBI program as above. For A.lyrata two newly assembled genomes were assembled using MinION sequencing. The presence of the transposon or the GENE alone, the two together (full fragment) and the number of insertions have been extracted and summed up in Figure 4D.

Singleton analysis

From the 4.4 millions pseudo-hete SNPs, 1 millions singletons and 0.3 millions doubletons pseudo-hete SNPs have been extracted. First, based on their positions we overlapped those SNPs with the list of detected duplicated genes and compared with a thousand randomly generated distribution of genes across the genome. We found that a highly significant 11% overlap with genes detected to be duplicated.

For each Singleton segregating in the 6 Pacbio accessions the reads overlapping the Pseudo-SNP have been extracted and re-mapped to the corresponding PacBio. The position, the coverage as well and the number of SNPs detected have been extracted from the bam files.

Authors’ contributions

BJ and MN developed the project. BJ performed all analyses. LMS and RB assembled the A.thaliana and A.lyrata genomes, respectively. FR generated the SNP matrix. BJ and MN wrote the manuscript, with input from all authors.

Funding

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 789037)

Availability of data and materials

All genome assemblies and raw reads were deposited under the BioProject ID: PRJNA779205. Link of the genome files for the reviewers: https://dataview.ncbi.nlm.nih.gov/object/PRJNA779205?reviewer=gduvs00c97i3bd5he06gs25oos Scripts used are available under Github link: https://github.com/benjj212/duplication-paper.git. The full GWAS matrix is available at https://doi.org/10.5281/zenodo.5702395

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare no competing interests.

Acknowledgment

We thank Rahul Pisupati for providing the methylation data, and numerous people on Twitter for providing feedback on the bioRxiv version.

Footnotes

References

  1. ↵
    1001 Genomes Consortium. 2016. “1,135 Genomes Reveal the Global Pattern of Polymorphism in Arabidopsis Thaliana.” Cell 166 (2): 481–91.
    OpenUrlCrossRefPubMed
  2. ↵
    Alkan, Can, Bradley P. Coe, and Evan E. Eichler. 2011. “Genome Structural Variation Discovery and Genotyping.” Nature Reviews. Genetics 12 (5): 363–76.
    OpenUrlCrossRefPubMedWeb of Science
  3. ↵
    Alonge, Michael, Sebastian Soyk, Srividya Ramakrishnan, Xingang Wang, Sara Goodwin, Fritz J. Sedlazeck, Zachary B. Lippman, and Michael C. Schatz. 2019. “RaGOO: Fast and Accurate Reference-Guided Scaffolding of Draft Genomes.” Genome Biology 20 (1): 224.
    OpenUrlCrossRefPubMed
  4. ↵
    Alonge, Michael, Xingang Wang, Matthias Benoit, Sebastian Soyk, Lara Pereira, Lei Zhang, Hamsini Suresh, et al. 2020. “Major Impacts of Widespread Structural Variation on Gene Expression and Crop Improvement in Tomato.” Cell. https://doi.org/10.1016/j.cell.2020.05.021.
  5. ↵
    Altschul, S. F., W. Gish, W. Miller, E. W. Myers, and D. J. Lipman. 1990. “Basic Local Alignment Search Tool.” Journal of Molecular Biology 215 (3): 403–10.
    OpenUrlCrossRefPubMedWeb of Science
  6. ↵
    Arabidopsis Genome Initiative. 2000. “Analysis of the Genome Sequence of the Flowering Plant Arabidopsis Thaliana.” Nature 408 (6814): 796–815.
    OpenUrlCrossRefPubMedWeb of Science
  7. ↵
    Bukowski, Robert, Xiaosen Guo, Yanli Lu, Cheng Zou, Bing He, Zhengqin Rong, Bo Wang, et al. 2018. “Construction of the Third-Generation Zea Mays Haplotype Map.” GigaScience 7 (4): 1–12.
    OpenUrlCrossRefPubMed
  8. ↵
    Burns, Robin, Terezie Mandáková, Joanna Gunis, Luz Mayela Soto-Jiménez, Chang Liu, Martin A. Lysak, Polina Yu Novikova, and Magnus Nordborg. 2021. “Gradual Evolution of Allopolyploidy in Arabidopsis Suecica.” Nature Ecology & Evolution 5 (10): 1367–81.
    OpenUrl
  9. ↵
    Cao, Jun, Korbinian Schneeberger, Stephan Ossowski, Torsten Günther, Sebastian Bender, Joffrey Fitz, Daniel Koenig, et al. 2011. “Whole-Genome Sequencing of Multiple Arabidopsis Thaliana Populations.” Nature Genetics 43 (10): 956–63.
    OpenUrlCrossRefPubMed
  10. ↵
    Carter, Nigel P. 2007. “Methods and Strategies for Analyzing Copy Number Variation Using DNA Microarrays.” Nature Genetics 39 (7 Suppl): S16–21.
    OpenUrlCrossRefPubMed
  11. ↵
    Chia, Jer-Ming, Chi Song, Peter J. Bradbury, Denise Costich, Natalia de Leon, John Doebley, Robert J. Elshire, et al. 2012. “Maize HapMap2 Identifies Extant Variation from a Genome in Flux.” Nature Genetics 44 (7): 803–7.
    OpenUrlCrossRefPubMed
  12. ↵
    Cristina Barragan, A., Maximilian Collenberg, Rebecca Schwab, Merijn Kerstens, Ilja Bezrukov, Felix Bemm, Doubravka Požárová, Filip Kolář, and Detlef Weigel. 2021. “Homozygosity at Its Limit: Inbreeding Depression in Wild Arabidopsis Arenosa Populations.” bioRxiv. https://doi.org/10.1101/2021.01.24.427284.
  13. ↵
    DePristo, Mark A., Eric Banks, Ryan Poplin, Kiran V. Garimella, Jared R. Maguire, Christopher Hartl, Anthony A. Philippakis, et al. 2011. “A Framework for Variation Discovery and Genotyping Using next-Generation DNA Sequencing Data.” Nature Genetics 43 (5): 491–98.
    OpenUrlCrossRefPubMedWeb of Science
  14. ↵
    Gan, Xiangchao, Oliver Stegle, Jonas Behr, Joshua G. Steffen, Philipp Drewe, Katie L. Hildebrand, Rune Lyngsoe, et al. 2011. “Multiple Reference Genomes and Transcriptomes for Arabidopsis Thaliana.” Nature 477 (7365): 419–23.
    OpenUrlCrossRefPubMedWeb of Science
  15. ↵
    Göktay, Mehmet, Andrea Fulgione, and Angela M. Hancock. 2020. “A New Catalogue of Structural Variants in 1301 A. Thaliana Lines from Africa, Eurasia and North America Reveals a Signature of Balancing at Defense Response Genes.” Molecular Biology and Evolution, November. https://doi.org/10.1093/molbev/msaa309.
  16. ↵
    Gonzalez, Enrique, Hemant Kulkarni, Hector Bolivar, Andrea Mangano, Racquel Sanchez, Gabriel Catano, Robert J. Nibbs, et al. 2005. “The Influence of CCL3L1 Gene-Containing Segmental Duplications on HIV-1/AIDS Susceptibility.” Science 307 (5714): 1434–40.
    OpenUrlAbstract/FREE Full Text
  17. ↵
    Griffin, P. C., and Y. Willi. 2014. “Evolutionary Shifts to Self-Fertilisation Restricted to Geographic Range Margins in North American Arabidopsis Lyrata.” Ecology Letters 17 (4): 484–90.
    OpenUrlCrossRef
  18. ↵
    Handsaker, Robert E., Joshua M. Korn, James Nemesh, and Steven A. McCarroll. 2011. “Discovery and Genotyping of Genome Structural Polymorphism by Sequencing on a Population Scale.” Nature Genetics 43 (3): 269–76.
    OpenUrlCrossRefPubMedWeb of Science
  19. ↵
    Horton, Matthew W., Angela M. Hancock, Yu S. Huang, Christopher Toomajian, Susanna Atwell, Adam Auton, N. Wayan Muliyati, et al. 2012. “Genome-Wide Patterns of Genetic Variation in Worldwide Arabidopsis Thaliana Accessions from the RegMap Panel.” Nature Genetics 44 (2): 212–16.
    OpenUrlCrossRefPubMed
  20. ↵
    Hufford, Matthew B., Arun S. Seetharam, Margaret R. Woodhouse, Kapeel M. Chougule, Shujun Ou, Jianing Liu, William A. Ricci, et al. 2021. “De Novo Assembly, Annotation, and Comparative Analysis of 26 Diverse Maize Genomes.” Cold Spring Harbor Laboratory. https://doi.org/10.1101/2021.01.14.426684.
  21. ↵
    Hurles, Matthew. 2002. “Are 100,000 ‘SNPs’ Useless?” Science.
  22. ↵
    Hu, Tina T., Pedro Pattyn, Erica G. Bakker, Jun Cao, Jan-Fang Cheng, Richard M. Clark, Noah Fahlgren, et al. 2011. “The Arabidopsis Lyrata Genome Sequence and the Basis of Rapid Genome Size Change.” Nature Genetics 43 (5): 476–81.
    OpenUrlCrossRefPubMedWeb of Science
  23. ↵
    Jiao, Wen-Biao, and Korbinian Schneeberger. 2019. “Chromosome-Level Assemblies of Multiple Arabidopsis Thaliana Accessions Reveal Hotspots of Genomic Rearrangements.” bioRxiv. https://doi.org/10.1101/738880.
  24. ↵
    Kawakatsu, Taiji, Shao-Shan Carol Huang, Florian Jupe, Eriko Sasaki, Robert J. Schmitz, Mark A. Urich, Rosa Castanon, et al. 2016. “Epigenomic Diversity in a Global Collection of Arabidopsis Thaliana Accessions.” Cell 166 (2): 492–505.
    OpenUrlCrossRefPubMed
  25. ↵
    Koren, Sergey, Brian P. Walenz, Konstantin Berlin, Jason R. Miller, Nicholas H. Bergman, and Adam M. Phillippy. 2017. “Canu: Scalable and Accurate Long-Read Assembly via Adaptive K-Mer Weighting and Repeat Separation.” Genome Research 27 (5): 722–36.
    OpenUrlAbstract/FREE Full Text
  26. ↵
    Li, Changsheng, Xiaoli Xiang, Yongcai Huang, Yong Zhou, Dong An, Jiaqiang Dong, Chenxi Zhao, et al. 2020. “Long-Read Sequencing Reveals Genomic Structural Variations That Underlie Creation of Quality Protein Maize.” Nature Communications 11 (1): 17.
    OpenUrl
  27. ↵
    Li, Heng. 2013. “Aligning Sequence Reads, Clone Sequences and Assembly Contigs with BWA-MEM.” arXiv [q-bio.GN]. arXiv. http://arxiv.org/abs/1303.3997.
  28. ↵
    Li, Heng. 2018. “Minimap2: Pairwise Alignment for Nucleotide Sequences.” Bioinformatics 34 (18): 3094–3100.
    OpenUrlCrossRefPubMed
  29. ↵
    Li, Heng, Bob Handsaker, Alec Wysoker, Tim Fennell, Jue Ruan, Nils Homer, Gabor Marth, Goncalo Abecasis, Richard Durbin, and 1000 Genome Project Data Processing Subgroup. 2009. “The Sequence Alignment/Map Format and SAMtools.” Bioinformatics 25 (16): 2078–79.
    OpenUrlCrossRefPubMedWeb of Science
  30. ↵
    Lin, Ke, Ningwen Zhang, Edouard I. Severing, Harm Nijveen, Feng Cheng, Richard G. F. Visser, Xiaowu Wang, Dick de Ridder, and Guusje Bonnema. 2014. “Beyond Genomic Variation - Comparison and Functional Annotation of Three Brassica Rapagenomes: A Turnip, a Rapid Cycling and a Chinese Cabbage.” BMC Genomics 15 (1): 250.
    OpenUrlCrossRef
  31. ↵
    Liu, Dong-Xu, Ramesh Rajaby, Lu-Lu Wei, Lei Zhang, Zhi-Quan Yang, Qing-Yong Yang, and Wing-Kin Sung. 2021. “Calling Large Indels in 1047 Arabidopsis with IndelEnsembler.” Nucleic Acids Research, October. https://doi.org/10.1093/nar/gkab904.
  32. ↵
    Liu, Yucheng, Huilong Du, Pengcheng Li, Yanting Shen, Hua Peng, Shulin Liu, Guo-An Zhou, et al. 2020. “Pan-Genome of Wild and Cultivated Soybeans.” Cell, June. https://doi.org/10.1016/j.cell.2020.05.023.
  33. ↵
    Long, Quan, Fernando A. Rabanal, Dazhe Meng, Christian D. Huber, Ashley Farlow, Alexander Platzer, Qingrun Zhang, et al. 2013. “Massive Genomic Variation and Strong Selection in Arabidopsis Thaliana Lines from Sweden.” Nature Genetics 45 (8): 884–90.
    OpenUrlCrossRefPubMed
  34. ↵
    Lu, Fei, Maria C. Romay, Jeffrey C. Glaubitz, Peter J. Bradbury, Robert J. Elshire, Tianyu Wang, Yu Li, et al. 2015. “High-Resolution Genetic Mapping of Maize Pan-Genome Sequence Anchors.” Nature Communications 6 (April): 6914.
    OpenUrl
  35. ↵
    Madeira, Fábio, Young Mi Park, Joon Lee, Nicola Buso, Tamer Gur, Nandana Madhusoodanan, Prasad Basutkar, et al. 2019. “The EMBL-EBI Search and Sequence Analysis Tools APIs in 2019.” Nucleic Acids Research 47 (W1): W636–41.
    OpenUrl
  36. ↵
    Martin, Marcel. 2011. “Cutadapt Removes Adapter Sequences from High-Throughput Sequencing Reads.” EMBnet.journal 17 (1): 10–12.
    OpenUrl
  37. ↵
    Miyahara, E., J. Pokorny, V. C. Smith, R. Baron, and E. Baron. 1998. “Color Vision in Two Observers with Highly Biased LWS/MWS Cone Ratios.” Vision Research 38 (4): 601–12.
    OpenUrlCrossRefPubMedWeb of Science
  38. ↵
    Morgante, Michele, Stephan Brunner, Giorgio Pea, Kevin Fengler, Andrea Zuccolo, and Antoni Rafalski. 2005. “Gene Duplication and Exon Shuffling by Helitron-like Transposons Generate Intraspecies Diversity in Maize.” Nature Genetics 37 (9): 997–1002.
    OpenUrlCrossRefPubMedWeb of Science
  39. ↵
    Perry, George H., Nathaniel J. Dominy, Katrina G. Claw, Arthur S. Lee, Heike Fiegler, Richard Redon, John Werner, et al. 2007. “Diet and the Evolution of Human Amylase Gene Copy Number Variation.” Nature Genetics 39 (10): 1256–60.
    OpenUrlCrossRefPubMedWeb of Science
  40. ↵
    Pinosio, Sara, Stefania Giacomello, Patricia Faivre-Rampant, Gail Taylor, Veronique Jorge, Marie Christine Le Paslier, Giusi Zaina, et al. 2016. “Characterization of the Poplar Pan-Genome by Genome-Wide Identification of Structural Variation.” Molecular Biology and Evolution 33 (10): 2706–19.
    OpenUrlCrossRefPubMed
  41. ↵
    Quadrana, Leandro, Amanda Bortolini Silveira, George F. Mayhew, Chantal LeBlanc, Robert A. Martienssen, Jeffrey A. Jeddeloh, Vincent Colot, and Daniel Zilberman. 2016. “The Arabidopsis Thaliana Mobilome and Its Impact at the Species Level.” eLife 5 (June): e15716.
    OpenUrlCrossRefPubMed
  42. ↵
    Quinlan, Aaron R., and Ira M. Hall. 2010. “BEDTools: A Flexible Suite of Utilities for Comparing Genomic Features.” Bioinformatics 26 (6): 841–42.
    OpenUrlCrossRefPubMedWeb of Science
  43. ↵
    Ramírez, Fidel, Devon P. Ryan, Björn Grüning, Vivek Bhardwaj, Fabian Kilpert, Andreas S. Richter, Steffen Heyne, Friederike Dündar, and Thomas Manke. 2016. “deepTools2: A next Generation Web Server for Deep-Sequencing Data Analysis.” Nucleic Acids Research 44 (W1): W160–65.
    OpenUrlCrossRefPubMed
  44. ↵
    Ranade, K., M. S. Chang, C. T. Ting, D. Pei, C. F. Hsiao, M. Olivier, R. Pesich, et al. 2001. “High-Throughput Genotyping with Single Nucleotide Polymorphisms.” Genome Research 11 (7): 1262–68.
    OpenUrlAbstract/FREE Full Text
  45. ↵
    Schneeberger, Korbinian, Stephan Ossowski, Felix Ott, Juliane D. Klein, Xi Wang, Christa Lanz, Lisa M. Smith, et al. 2011. “Reference-Guided Assembly of Four Diverse Arabidopsis Thaliana Genomes.” Proceedings of the National Academy of Sciences of the United States of America 108 (25): 10249–54.
    OpenUrlAbstract/FREE Full Text
  46. ↵
    Sedlazeck, Fritz J., Philipp Rescheneder, Moritz Smolka, Han Fang, Maria Nattestad, Arndt von Haeseler, and Michael C. Schatz. 2018. “Accurate Detection of Complex Structural Variations Using Single-Molecule Sequencing.” Nature Methods 15 (6): 461–68.
    OpenUrl
  47. ↵
    Shendure, Jay, and Hanlee Ji. 2008. “Next-Generation DNA Sequencing.” Nature Biotechnology 26 (10): 1135–45.
    OpenUrlCrossRefPubMedWeb of Science
  48. ↵
    Slotte, Tanja, Khaled M. Hazzouri, J. Arvid Ågren, Daniel Koenig, Florian Maumus, Ya-Long Guo, Kim Steige, et al. 2013. “The Capsella Rubella Genome and the Genomic Consequences of Rapid Mating System Evolution.” Nature Genetics 45 (7): 831–35.
    OpenUrlCrossRefPubMed
  49. ↵
    Snijders, A. M., N. Nowak, R. Segraves, S. Blackwood, N. Brown, J. Conroy, G. Hamilton, et al. 2001. “Assembly of Microarrays for Genome-Wide Measurement of DNA Copy Number.” Nature Genetics 29 (3): 263–64.
    OpenUrlCrossRefPubMedWeb of Science
  50. ↵
    Stritt, Christoph, Elena L. Gimmi, Michele Wyler, Abdelmonaim H. Bakali, Aleksandra Skalska, Robert Hasterok, Luis A. J. Murs, Nicola Pecchioni, and Anne C. Roulin. 2021. “Migration without Interbreeding: Evolutionary History of a Highly Selfing Mediterranean Grass Inferred from Whole Genomes.” Molecular Ecology, October. https://doi.org/10.1111/mec.16207.
  51. ↵
    Sun, Hequan, Jia Ding, Mathieu Piednoël, and Korbinian Schneeberger. 2018. “findGSE: Estimating Genome Size Variation within Human and Arabidopsis Using K-Mer Frequencies.” Bioinformatics 34 (4): 550–57.
    OpenUrlCrossRef
  52. ↵
    Tarasov, Artem, Albert J. Vilella, Edwin Cuppen, Isaac J. Nijman, and Pjotr Prins. 2015. “Sambamba: Fast Processing of NGS Alignment Formats.” Bioinformatics 31 (12): 2032–34.
    OpenUrlCrossRefPubMed
  53. ↵
    Van Bel, Michiel, Tim Diels, Emmelien Vancaester, Lukasz Kreft, Alexander Botzki, Yves Van de Peer, Frederik Coppens, and Klaas Vandepoele. 2018. “PLAZA 4.0: An Integrative Resource for Functional, Evolutionary and Comparative Plant Genomics.” Nucleic Acids Research 46 (D1): D1190–96.
    OpenUrlCrossRef
  54. ↵
    Van der Auwera, Geraldine A., Mauricio O. Carneiro, Chris Hartl, Ryan Poplin, Guillermo Del Angel, Ami Levy-Moonshine, Tadeusz Jordan, et al. 2013. “From FastQ Data to High Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline.” Current Protocols in Bioinformatics / Editoral Board, Andreas D. Baxevanis … [et al.] 11 (1110): 11.10.1–11.10.33.
    OpenUrl
  55. ↵
    Vaser, Robert, Ivan Sović, Niranjan Nagarajan, and Mile Šikić. 2017. “Fast and Accurate de Novo Genome Assembly from Long Uncorrected Reads.” Genome Research 27 (5): 737–46.
    OpenUrlAbstract/FREE Full Text
  56. ↵
    Walker, Bruce J., Thomas Abeel, Terrance Shea, Margaret Priest, Amr Abouelliel, Sharadha Sakthikumar, Christina A. Cuomo, et al. 2014. “Pilon: An Integrated Tool for Comprehensive Microbial Variant Detection and Genome Assembly Improvement.” PloS One 9 (11): e112963.
    OpenUrlCrossRefPubMed
  57. ↵
    Waterhouse, Andrew M., James B. Procter, David M. A. Martin, Michèle Clamp, and Geoffrey J. Barton. 2009. “Jalview Version 2--a Multiple Sequence Alignment Editor and Analysis Workbench.” Bioinformatics 25 (9): 1189–91.
    OpenUrlCrossRefPubMedWeb of Science
  58. ↵
    Woodhouse, Margaret R., Brent Pedersen, and Michael Freeling. 2010. “Transposed Genes in Arabidopsis Are Often Associated with Flanking Repeats.” PLoS Genetics 6 (5): e1000949.
    OpenUrl
  59. ↵
    Yao, Wen, Guangwei Li, Hu Zhao, Gongwei Wang, Xingming Lian, and Weibo Xie. 2015. “Exploring the Rice Dispensable Genome Using a Metagenome-like Assembly Strategy.” Genome Biology 16 (September): 187.
    OpenUrlCrossRefPubMed
  60. ↵
    Zhao, Min, Qingguo Wang, Quan Wang, Peilin Jia, and Zhongming Zhao. 2013. “Computational Tools for Copy Number Variation (CNV) Detection Using next-Generation Sequencing Data: Features and Perspectives.” BMC Bioinformatics 14 Suppl 11 (September): S1.
    OpenUrlCrossRef
  61. ↵
    Zhou, Yong, Dmytro Chebotarov, Dave Kudrna, Victor Llaca, Seunghee Lee, Shanmugam Rajasekar, Nahed Mohammed, et al. 2020. “A Platinum Standard Pan-Genome Resource That Represents the Population Structure of Asian Rice.” Scientific Data 7 (1): 113.
    OpenUrl
  62. ↵
    Zmienko, Agnieszka, Malgorzata Marszalek-Zenczak, Pawel Wojciechowski, Anna Samelak-Czajka, Magdalena Luczak, Piotr Kozlowski, Wojciech M. Karlowski, and Marek Figlerowicz. 2020. “AthCNV: A Map of DNA Copy Number Variations in the Arabidopsis Genome.” The Plant Cell 32 (6): 1797–1819.
    OpenUrlAbstract/FREE Full Text
Back to top
Posted November 22, 2021.
Download PDF

Supplementary Material

Data/Code
Email
Extensive gene duplication in Arabidopsis revealed by pseudo-heterozygosity
Benjamin Jaegle, Luz Mayela Soto-Jiménez, Robin Burns, Fernando A. Rabanal, Magnus Nordborg
bioRxiv 2021.11.15.468652; doi: https://doi.org/10.1101/2021.11.15.468652
Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
Citation Tools

Subject Area