Dataset Information

Optimizing depth and type of high-throughput sequencing data for microsatellite discovery.

ABSTRACT: Premise:Simple sequence repeat (SSR) markers (microsatellites) are a mainstay of many labs, especially when working on a limited budget, carrying out preliminary analyses, and in teaching. Whether SSRs mined from plant genomes or transcriptomes are preferred for certain applications, and the depth of sequencing needed to allow efficient SSR discovery, has not been tested. Methods:I used genome and transcriptome high-throughput sequencing data at a range of sequencing depths to compare efficacy of SSR identification. I then tested primers from tomato for amplification, polymorphism, and transferability to related species. Results:Small assemblies (two million read pairs) identified ca. 200-2000 potential markers from the genome assemblies and ca. 600-3650 from the transcriptome assemblies. Genome-derived contigs were often short, potentially precluding primer design. Genomic SSR primers were less transferable across species but exhibited greater variation (partially explained by being composed of more repeat units) than transcriptome-derived primers. Discussion:Small high-throughput sequencing resources may be sufficient for identification of hundreds of SSRs. Genomic data may be preferable in species with low polymorphism, but transcriptome data may result in longer loci (more amenable to primer design) and primers may be more transferable to related species.

SUBMITTER: Chapman MA

PROVIDER: S-EPMC6858294 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Optimizing depth and type of high-throughput sequencing data for microsatellite discovery.

Chapman Mark A MA

Applications in plant sciences 20191103 11

<h4>Premise</h4>Simple sequence repeat (SSR) markers (microsatellites) are a mainstay of many labs, especially when working on a limited budget, carrying out preliminary analyses, and in teaching. Whether SSRs mined from plant genomes or transcriptomes are preferred for certain applications, and the depth of sequencing needed to allow efficient SSR discovery, has not been tested.<h4>Methods</h4>I used genome and transcriptome high-throughput sequencing data at a range of sequencing depths to com ...[more]

PMID: 31832281

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Project description:BackgroundWith the advance of new massively parallel genotyping technologies, quantitative trait loci (QTL) fine mapping and map-based cloning become more achievable in identifying genes for important and complex traits. Development of high-density genetic markers in the QTL regions of specific mapping populations is essential for fine-mapping and map-based cloning of economically important genes. Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variation existing between any diverse genotypes that are usually used for QTL mapping studies. The massively parallel sequencing technologies (Roche GS/454, Illumina GA/Solexa, and ABI/SOLiD), have been widely applied to identify genome-wide sequence variations. However, it is still remains unclear whether sequence data at a low sequencing depth are enough to detect the variations existing in any QTL regions of interest in a crop genome, and how to prepare sequencing samples for a complex genome such as soybean. Therefore, with the aims of identifying SNP markers in a cost effective way for fine-mapping several QTL regions, and testing the validation rate of the putative SNPs predicted with Solexa short sequence reads at a low sequencing depth, we evaluated a pooled DNA fragment reduced representation library and SNP detection methods applied to short read sequences generated by Solexa high-throughput sequencing technology.ResultsA total of 39,022 putative SNPs were identified by the Illumina/Solexa sequencing system using a reduced representation DNA library of two parental lines of a mapping population. The validation rates of these putative SNPs predicted with low and high stringency were 72% and 85%, respectively. One hundred sixty four SNP markers resulted from the validation of putative SNPs and have been selectively chosen to target a known QTL, thereby increasing the marker density of the targeted region to one marker per 42 K bp.ConclusionsWe have demonstrated how to quickly identify large numbers of SNPs for fine mapping of QTL regions by applying massively parallel sequencing combined with genome complexity reduction techniques. This SNP discovery approach is more efficient for targeting multiple QTL regions in a same genetic population, which can be applied to other crops.

Project description:BackgroundIn a high throughput setting, effective flow cytometry data analysis depends heavily on proper data preprocessing. While usual preprocessing steps of quality assessment, outlier removal, normalization, and gating have received considerable scrutiny from the community, the influence of data transformation on the output of high throughput analysis has been largely overlooked. Flow cytometry measurements can vary over several orders of magnitude, cell populations can have variances that depend on their mean fluorescence intensities, and may exhibit heavily-skewed distributions. Consequently, the choice of data transformation can influence the output of automated gating. An appropriate data transformation aids in data visualization and gating of cell populations across the range of data. Experience shows that the choice of transformation is data specific. Our goal here is to compare the performance of different transformations applied to flow cytometry data in the context of automated gating in a high throughput, fully automated setting. We examine the most common transformations used in flow cytometry, including the generalized hyperbolic arcsine, biexponential, linlog, and generalized Box-Cox, all within the BioConductor flowCore framework that is widely used in high throughput, automated flow cytometry data analysis. All of these transformations have adjustable parameters whose effects upon the data are non-intuitive for most users. By making some modelling assumptions about the transformed data, we develop maximum likelihood criteria to optimize parameter choice for these different transformations.ResultsWe compare the performance of parameter-optimized and default-parameter (in flowCore) data transformations on real and simulated data by measuring the variation in the locations of cell populations across samples, discovered via automated gating in both the scatter and fluorescence channels. We find that parameter-optimized transformations improve visualization, reduce variability in the location of discovered cell populations across samples, and decrease the misclassification (mis-gating) of individual events when compared to default-parameter counterparts.ConclusionsOur results indicate that the preferred transformation for fluorescence channels is a parameter- optimized biexponential or generalized Box-Cox, in accordance with current best practices. Interestingly, for populations in the scatter channels, we find that the optimized hyperbolic arcsine may be a better choice in a high-throughput setting than current standard practice of no transformation. However, generally speaking, the choice of transformation remains data-dependent. We have implemented our algorithm in the BioConductor package, flowTrans, which is publicly available.

Dataset Information

Optimizing depth and type of high-throughput sequencing data for microsatellite discovery.

Publications

Optimizing depth and type of high-throughput sequencing data for microsatellite discovery.

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