Assessing the Gene Content of the Megagenome: Sugar Pine (<i>Pinus lambertiana</i>)

González-Ibeas, Daniel; Martínez-García, Pedro Manuel; Famula, Randi; Delfino-Mix, Annette; Stevens, Kristian; Loopstra, Carol A.; Langley, Charles H.; Neale, David B.; Wegrzyn, Jill L.

doi:10.1534/g3.116.032805

Cited by 49 publications

(48 citation statements)

References 78 publications

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“…Additional scaffolding steps used a set of transcript sequences assembled from Pacific Biosciences (PacBio) and Illumina RNA-seq data (Table S7) from Gonzalez-Ibeas et al (2016). We aligned transcript sequences to the whole genome shotgun (WGS) scaffolds using both nucmer (-maxmatch -nosimplify -l 45 -c 4) (Kurtz et al 2004) and bwa-mem (-k 45 -O 60 -E 10) (Li 2013).…”

Section: Transcriptome Scaffoldingmentioning

confidence: 99%

Sequence of the Sugar Pine Megagenome

et al. 2016

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Until very recently, complete characterization of the megagenomes of conifers has remained elusive. The diploid genome of sugar pine (Pinus lambertiana Dougl.) has a highly repetitive, 31 billion bp genome. It is the largest genome sequenced and assembled to date, and the first from the subgenus Strobus, or white pines, a group that is notable for having the largest genomes among the pines. The genome represents a unique opportunity to investigate genome "obesity" in conifers and white pines. Comparative analysis of P. lambertiana and P. taeda L. reveals new insights on the conservation, age, and diversity of the highly abundant transposable elements, the primary factor determining genome size. Like most North American white pines, the principal pathogen of P. lambertiana is white pine blister rust (Cronartium ribicola J.C. Fischer ex Raben.). Identification of candidate genes for resistance to this pathogen is of great ecological importance. The genome sequence afforded us the opportunity to make substantial progress on locating the major dominant gene for simple resistance hypersensitive response, Cr1. We describe new markers and gene annotation that are both tightly linked to Cr1 in a mapping population, and associated with Cr1 in unrelated sugar pine individuals sampled throughout the species' range, creating a solid foundation for future mapping. This genomic variation and annotated candidate genes characterized in our study of the Cr1 region are resources for future marker-assisted breeding efforts as well as for investigations of fundamental mechanisms of invasive disease and evolutionary response.KEYWORDS conifer genome; transposable elements; white pine blister rust T HE gymnosperm genus Pinus is diverse and ubiquitous in temperate zones (Critchfield and Little 1966;Farjon and Filer 2013). Pines are often the keystone trees of terrestrial ecosystems (Richardson and Rundel 1998;Keane et al. 2012, and citations therein). Typical of conifers, pines have megagenomes that vary greatly in size among species, yet their karyotype is highly conserved. Pinus is divided into two large, ancient monophyletic subgenera, Strobus and Pinus, "white pines" and "yellow pines," respectively (Critchfield and Little 1966;Gernandt et al. 2005). The first Pinus genome sequence (22 Gbp) was recently reported for Pinus taeda L. ), a yellow pine commonly known as loblolly pine. The genomes of white pines are larger and more variable in size (Tomback 1982). Fossils allied with Strobus are known from the early Tertiary and late Cretaceous (Millar 1998) et al. 2006), the discovery of the underlying genes, and of markers serviceable for genetic improvement in reforestation, may be greatly accelerated by the genome sequence itself. P. lambertiana, commonly known as sugar pine, is a white pine native to western North America that is distributed from northern Oregon to Baja California at a wide span of altitudes. It is currently the tallest pine species, with heights reaching 76 m. The female cones of sugar pine are also gigan...

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Section: Transcriptome Scaffoldingmentioning

confidence: 99%

Sequence of the Sugar Pine Megagenome

et al. 2016

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“…The family Pinaceae, with their haploid genome sizes ranging between~10 and~36 Gb (De La Torre et al, 2014), is one of such groups. Owing to a combination of sequencing techniques and, more importantly, to a series of novel bioinformatic strategies and tools, the enormous genomes of several Pinaceae have been recently sequenced and assembled (Birol et al, 2013;Nystedt et al, 2013;Neale et al, 2014Neale et al, , 2017Wegrzyn et al, 2014;Zimin et al, 2014;Warren et al, 2015;Gonzalez-Ibeas et al, 2016;Stevens et al, 2016). Given the size and repetitiveness of these genomes, the resulting assemblies tend to be highly fragmented, with a negative effect on the accuracy of gene annotation.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the high number of transposable elements (TEs) found in conifer genomes may have been erroneously annotated as 'host' genes. However, predicted Pinaceae genes with typical TE domains have been removed from the final gene sets in the analyzed species (Nystedt et al, 2013;Wegrzyn et al, 2014;Gonzalez-Ibeas et al, 2016;Neale et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Pinaceae show elevated rates of gene turnover that are robust to incomplete gene annotation

Casola

Koralewski

2018

The Plant Journal

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Gene duplications and gene losses are major determinants of genome evolution and phenotypic diversity. The frequency of gene turnover (gene gains and gene losses combined) is known to vary between organisms. Comparative genomic analyses of gene families can highlight such variation; however, estimates of gene turnover may be biased when using highly fragmented genome assemblies resulting in poor gene annotations. Here, we address potential biases introduced by gene annotation errors in estimates of gene turnover frequencies in a dataset including both well-annotated angiosperm genomes and the incomplete gene sets of four Pinaceae, including two pine species, Norway spruce and Douglas-fir. We show that Pinaceae experienced higher gene turnover rates than angiosperm lineages lacking recent whole-genome duplications. This finding is robust to both known major issues in Pinaceae gene sets: missing gene models and erroneous annotation of pseudogenes. A separate analysis limited to the four Pinaceae gene sets pointed to an accelerated gene turnover rate in pines compared with Norway spruce and Douglas-fir. Our results indicate that gene turnover significantly contributes to genome variation and possibly to speciation in Pinaceae, particularly in pines. Moreover, these findings indicate that reliable estimates of gene turnover frequencies can be discerned in incomplete and potentially inaccurate gene sets. Because gymnosperms are known to exhibit low overall substitution rates compared with angiosperms, our results suggest that the rate of single-base pair mutations is uncoupled from the rate of large DNA duplications and deletions associated with gene turnover in Pinaceae.

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“…Considering the large and complex genomes of conifers, reference transcriptomes are increasingly being used as a reference resource in a variety of applications (Müller et al, 2015;Suren et al, 2016;Wachowiak et al, 2015). Particularly in Pinaceae, where the vast majority of transcriptomes have been generated in gymnosperms (López de Heredia & Vázquez-Poletti, 2016), the number of assembled contigs (transcripts) is always larger (Table S6) than the actual number of genes estimated based on their genome sequence (Gonzales-Ibeas et al, 2016;Neale et al, 2014;Nystedt et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…This combined set of P. sylvestris contigs was used for secondary assembly using OGA (Ruttink et al, 2013) with previously published proteomes from Pinus taeda and Pinus lambertiana (Figure 1) to guide the assembly. We either used 1) all annotated proteins (ALL) from P. taeda v1.0; or 2) all annotated proteins (ALL) from P. taeda v2.01 (Neale et al, 2014); or 3) all annotated proteins from P. lambertiana v1.0 (Gonzales-Ibeas, Martinez-Garcia, Famula, & Delfino-Mix, 2016;Stevens et al, 2016); or 4) only the high quality curated proteins (HQ) from P. taeda; or 5) from P. lambertiana (Table 1). Briefly, OGA first uses sequence similarity (tBLASTn) to the proteomes of the reference species to select allelic and fragmented contigs from all genotypes (assembled individually) per reference protein, then applies CAP3 clustering on a gene-by-gene basis (Ruttink et al, 2013), and finally selects the most likely orthologous CAP3 contigs per protein of the reference species.…”

Section: Secondary Clustering: the Orthology Guided Assembly (Oga) Apmentioning

confidence: 99%

Utilization of tissue ploidy level variation inde novotranscriptome assembly ofPinus sylvestris

Ojeda

Mattila

Ruttink

et al. 2018

Preprint

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Compared to angiosperms, gymnosperms lag behind in the availability of assembled and annotated genomes. Most genomic analyses in gymnosperms, especially conifer tree species, rely on the use of de novo assembled transcriptomes. However, the level of allelic redundancy and transcript fragmentation in these assembled transcriptomes, and their effect on downstream applications have not been fully investigated. Here, we assessed three assembly strategies, including the utility of haploid (megagametophyte) tissue during de novo assembly as single-allele guides, for six individuals and five different tissues in Pinus sylvestris. We then contrasted haploid and diploid tissue genotype calls obtained from the assembled transcriptomes to evaluate the extent of paralog mapping. The use of the haploid tissue during assembly increased its completeness without reducing the number of assembled transcripts. Our results suggest that current strategies that rely on available genomic resources as guidance to minimize allelic redundancy are less effective than the application of strategies that cluster redundant assembled transcripts. The strategy yielding the lowest levels of allelic redundancy among the assembled transcriptomes assessed here was the generation of SuperTranscripts with Lace followed by CD-HIT clustering. However, we still observed some levels of heterozygosity (multiple gene fragments per transcript reflecting allelic redundancy) in this assembled transcriptome on the haploid tissue, indicating that further filtering is required before using these assemblies for downstream applications. We discuss the influence of allelic redundancy when these reference transcriptomes are used to select regions for probe design of exome capture baits and for estimation of population genetic diversity.

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Assessing the Gene Content of the Megagenome: Sugar Pine (Pinus lambertiana)

Cited by 49 publications

References 78 publications

Sequence of the Sugar Pine Megagenome

Sequence of the Sugar Pine Megagenome

Pinaceae show elevated rates of gene turnover that are robust to incomplete gene annotation

Utilization of tissue ploidy level variation inde novotranscriptome assembly ofPinus sylvestris

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