Dataset Information

"Targeted Sequencing by Gene Synteny," a New Strategy for Polyploid Species: Sequencing and Physical Structure of a Complex Sugarcane Region.

ABSTRACT: Sugarcane exhibits a complex genome mainly due to its aneuploid nature and high ploidy level, and sequencing of its genome poses a great challenge. Closely related species with well-assembled and annotated genomes can be used to help assemble complex genomes. Here, a stable quantitative trait locus (QTL) related to sugar accumulation in sorghum was successfully transferred to the sugarcane genome. Gene sequences related to this QTL were identified in silico from sugarcane transcriptome data, and molecular markers based on these sequences were developed to select bacterial artificial chromosome (BAC) clones from the sugarcane variety SP80-3280. Sixty-eight BAC clones containing at least two gene sequences associated with the sorghum QTL were sequenced using Pacific Biosciences (PacBio) technology. Twenty BAC sequences were found to be related to the syntenic region, of which nine were sufficient to represent this region. The strategy we propose is called "targeted sequencing by gene synteny," which is a simpler approach to understanding the genome structure of complex genomic regions associated with traits of interest.

SUBMITTER: Mancini MC

PROVIDER: S-EPMC5882829 | biostudies-other | 2018

REPOSITORIES: biostudies-other

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Publications

"Targeted Sequencing by Gene Synteny," a New Strategy for Polyploid Species: Sequencing and Physical Structure of a Complex Sugarcane Region.

Mancini Melina C MC Cardoso-Silva Claudio B CB Sforça Danilo A DA Pereira de Souza Anete A

Frontiers in plant science 20180328

Sugarcane exhibits a complex genome mainly due to its aneuploid nature and high ploidy level, and sequencing of its genome poses a great challenge. Closely related species with well-assembled and annotated genomes can be used to help assemble complex genomes. Here, a stable quantitative trait locus (QTL) related to sugar accumulation in sorghum was successfully transferred to the sugarcane genome. Gene sequences related to this QTL were identified <i>in silico</i> from sugarcane transcriptome da ...[more]

PMID: 29643861

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Project description:BackgroundDespite the economic importance of sugarcane in sugar and bioenergy production, there is not yet a reference genome available. Most of the sugarcane transcriptomic studies have been based on Saccharum officinarum gene indices (SoGI), expressed sequence tags (ESTs) and de novo assembled transcript contigs from short-reads; hence knowledge of the sugarcane transcriptome is limited in relation to transcript length and number of transcript isoforms.ResultsThe sugarcane transcriptome was sequenced using PacBio isoform sequencing (Iso-Seq) of a pooled RNA sample derived from leaf, internode and root tissues, of different developmental stages, from 22 varieties, to explore the potential for capturing full-length transcript isoforms. A total of 107,598 unique transcript isoforms were obtained, representing about 71% of the total number of predicted sugarcane genes. The majority of this dataset (92%) matched the plant protein database, while just over 2% was novel transcripts, and over 2% was putative long non-coding RNAs. About 56% and 23% of total sequences were annotated against the gene ontology and KEGG pathway databases, respectively. Comparison with de novo contigs from Illumina RNA-Sequencing (RNA-Seq) of the internode samples from the same experiment and public databases showed that the Iso-Seq method recovered more full-length transcript isoforms, had a higher N50 and average length of largest 1,000 proteins; whereas a greater representation of the gene content and RNA diversity was captured in RNA-Seq. Only 62% of PacBio transcript isoforms matched 67% of de novo contigs, while the non-matched proportions were attributed to the inclusion of leaf/root tissues and the normalization in PacBio, and the representation of more gene content and RNA classes in the de novo assembly, respectively. About 69% of PacBio transcript isoforms and 41% of de novo contigs aligned with the sorghum genome, indicating the high conservation of orthologs in the genic regions of the two genomes.ConclusionsThe transcriptome dataset should contribute to improved sugarcane gene models and sugarcane protein predictions; and will serve as a reference database for analysis of transcript expression in sugarcane.

Project description:BackgroundThe understanding of sugarcane genetics has lagged behind that of other members of the Poaceae family such as wheat, rice, barley and sorghum mainly due to the complexity, size and polyploidization of the genome. We have used the genetic map of a sugarcane cultivar to generate a consensus genetic map to increase genome coverage for comparison to the sorghum genome. We have utilized the recently developed sugarcane DArT array to increase the marker density within the genetic map. The sequence of these DArT markers plus SNP and EST-SSR markers was then used to form a bridge to the sorghum genomic sequence by BLAST alignment to start to unravel the complex genomic architecture of sugarcane.ResultsComparative mapping revealed that certain sugarcane chromosomes show greater levels of synteny to sorghum than others. On a macrosyntenic level a good collinearity was observed between sugarcane and sorghum for 4 of the 8 homology groups (HGs). These 4 HGs were syntenic to four sorghum chromosomes with from 98% to 100% of these chromosomes covered by these linked markers. Four major chromosome rearrangements were identified between the other four sugarcane HGs and sorghum, two of which were condensations of chromosomes reducing the basic chromosome number of sugarcane from x?=?10 to x?=?8. This macro level of synteny was transferred to other members within the Poaceae family such as maize to uncover the important evolutionary relationships that exist between sugarcane and these species.ConclusionsComparative mapping of sugarcane to the sorghum genome has revealed new information on the genome structure of sugarcane which will help guide identification of important genes for use in sugarcane breeding. Furthermore of the four major chromosome rearrangements identified in this study, three were common to maize providing some evidence that chromosome reduction from a common paleo-ancestor of both maize and sugarcane was driven by the same translocation events seen in both species.

Project description:BackgroundSugarcane (Saccharum spp.) is one of the most important economic crops because of its high sugar production and biofuel potential. Due to the high polyploid level and complex genome of sugarcane, it has been a huge challenge to investigate genomic sequence variations, which are critical for identifying alleles contributing to important agronomic traits. In order to mine the genetic variations in sugarcane, genotyping by sequencing (GBS), was used to genotype 14 representative Saccharum complex accessions. GBS is a method to generate a large number of markers, enabled by next generation sequencing (NGS) and the genome complexity reduction using restriction enzymes.ResultsTo use GBS for high throughput genotyping highly polyploid sugarcane, the GBS analysis pipelines in 14 Saccharum complex accessions were established by evaluating different alignment methods, sequence variants callers, and sequence depth for single nucleotide polymorphism (SNP) filtering. By using the established pipeline, a total of 76,251 non-redundant SNPs, 5642 InDels, 6380 presence/absence variants (PAVs), and 826 copy number variations (CNVs) were detected among the 14 accessions. In addition, non-reference based universal network enabled analysis kit and Stacks de novo called 34,353 and 109,043 SNPs, respectively. In the 14 accessions, the percentages of single dose SNPs ranged from 38.3% to 62.3% with an average of 49.6%, much more than the portions of multiple dosage SNPs. Concordantly called SNPs were used to evaluate the phylogenetic relationship among the 14 accessions. The results showed that the divergence time between the Erianthus genus and the Saccharum genus was more than 10 million years ago (MYA). The Saccharum species separated from their common ancestors ranging from 0.19 to 1.65 MYA.ConclusionsThe GBS pipelines including the reference sequences, alignment methods, sequence variant callers, and sequence depth were recommended and discussed for the Saccharum complex and other related species. A large number of sequence variations were discovered in the Saccharum complex, including SNPs, InDels, PAVs, and CNVs. Genome-wide SNPs were further used to illustrate sequence features of polyploid species and demonstrated the divergence of different species in the Saccharum complex. The results of this study showed that GBS was an effective NGS-based method to discover genomic sequence variations in highly polyploid and heterozygous species.

Project description:Genomic prediction (GP) is the procedure whereby the genetic merits of untested candidates are predicted using genome wide marker information. Although numerous examples of GP exist in plants and animals, applications to polyploid organisms are still scarce, partly due to limited genome resources and the complexity of this system. Deep learning (DL) techniques comprise a heterogeneous collection of machine learning algorithms that have excelled at many prediction tasks. A potential advantage of DL for GP over standard linear model methods is that DL can potentially take into account all genetic interactions, including dominance and epistasis, which are expected to be of special relevance in most polyploids. In this study, we evaluated the predictive accuracy of linear and DL techniques in two important small fruits or berries: strawberry and blueberry. The two datasets contained a total of 1,358 allopolyploid strawberry (2n=8x=112) and 1,802 autopolyploid blueberry (2n=4x=48) individuals, genotyped for 9,908 and 73,045 single nucleotide polymorphism (SNP) markers, respectively, and phenotyped for five agronomic traits each. DL depends on numerous parameters that influence performance and optimizing hyperparameter values can be a critical step. Here we show that interactions between hyperparameter combinations should be expected and that the number of convolutional filters and regularization in the first layers can have an important effect on model performance. In terms of genomic prediction, we did not find an advantage of DL over linear model methods, except when the epistasis component was important. Linear Bayesian models were better than convolutional neural networks for the full additive architecture, whereas the opposite was observed under strong epistasis. However, by using a parameterization capable of taking into account these non-linear effects, Bayesian linear models can match or exceed the predictive accuracy of DL. A semiautomatic implementation of the DL pipeline is available at https://github.com/lauzingaretti/deepGP/.

Dataset Information

"Targeted Sequencing by Gene Synteny," a New Strategy for Polyploid Species: Sequencing and Physical Structure of a Complex Sugarcane Region.

Publications

"Targeted Sequencing by Gene Synteny," a New Strategy for Polyploid Species: Sequencing and Physical Structure of a Complex Sugarcane Region.

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