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Sequencing-based large-scale genomics approaches with small numbers of isolated maize meiocytes.

ABSTRACT: High-throughput sequencing has become the large-scale approach of choice to study global gene expression and the distribution of specific chromatin marks and features. However, the limited availability of large amounts of purified cells made it very challenging to apply sequencing-based techniques in plant meiosis research in the past. In this paper, we describe a method to isolate meiocytes from maize anthers and detailed protocols to successfully perform RNA-seq, smRNA-seq, H3K4me3-ChIP-seq, and DNA bisulfite conversion sequencing with 5000-30,000 isolated maize male meiotic cells. These methods can be adjusted for other flowering plant species as well.

SUBMITTER: Dukowic-Schulze S

PROVIDER: S-EPMC3933774 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Sequencing-based large-scale genomics approaches with small numbers of isolated maize meiocytes.

Dukowic-Schulze Stefanie S Sundararajan Anitha A Ramaraj Thiruvarangan T Mudge Joann J Chen Changbin C

Frontiers in plant science 20140225

High-throughput sequencing has become the large-scale approach of choice to study global gene expression and the distribution of specific chromatin marks and features. However, the limited availability of large amounts of purified cells made it very challenging to apply sequencing-based techniques in plant meiosis research in the past. In this paper, we describe a method to isolate meiocytes from maize anthers and detailed protocols to successfully perform RNA-seq, smRNA-seq, H3K4me3-ChIP-seq, a ...[more]

PMID: 24611068

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Project description:A major goal of maize genomic research is to identify sequence polymorphisms responsible for phenotypic variation in traits of economic importance. Large-scale detection of sequence variation is critical for linking genes, or genomic regions, to phenotypes. However, due to its size and complexity, it remains expensive to generate whole genome sequences of sufficient coverage for divergent maize lines, even with access to next generation sequencing (NGS) technology. Because methods involving reduction of genome complexity, such as genotyping-by-sequencing (GBS), assess only a limited fraction of sequence variation, targeted sequencing of selected genomic loci offers an attractive alternative. We therefore designed a sequence capture assay to target 29 Mb genomic regions and surveyed a total of 4,648 genes possibly affecting biomass production in 21 diverse inbred maize lines (7 flints, 14 dents). Captured and enriched genomic DNA was sequenced using the 454 NGS platform to 19.6-fold average depth coverage, and a broad evaluation of read alignment and variant calling methods was performed to select optimal procedures for variant discovery. Sequence alignment with the B73 reference and de novo assembly identified 383,145 putative single nucleotide polymorphisms (SNPs), of which 42,685 were non-synonymous alterations and 7,139 caused frameshifts. Presence/absence variation (PAV) of genes was also detected. We found that substantial sequence variation exists among genomic regions targeted in this study, which was particularly evident within coding regions. This diversification has the potential to broaden functional diversity and generate phenotypic variation that may lead to new adaptations and the modification of important agronomic traits. Further, annotated SNPs identified here will serve as useful genetic tools and as candidates in searches for phenotype-altering DNA variation. In summary, we demonstrated that sequencing of captured DNA is a powerful approach for variant discovery in maize genes.

Project description:BackgroundIn recent years large-scale computational models for the realistic simulation of epidemic outbreaks have been used with increased frequency. Methodologies adapt to the scale of interest and range from very detailed agent-based models to spatially-structured metapopulation models. One major issue thus concerns to what extent the geotemporal spreading pattern found by different modeling approaches may differ and depend on the different approximations and assumptions used.MethodsWe provide for the first time a side-by-side comparison of the results obtained with a stochastic agent-based model and a structured metapopulation stochastic model for the progression of a baseline pandemic event in Italy, a large and geographically heterogeneous European country. The agent-based model is based on the explicit representation of the Italian population through highly detailed data on the socio-demographic structure. The metapopulation simulations use the GLobal Epidemic and Mobility (GLEaM) model, based on high-resolution census data worldwide, and integrating airline travel flow data with short-range human mobility patterns at the global scale. The model also considers age structure data for Italy. GLEaM and the agent-based models are synchronized in their initial conditions by using the same disease parameterization, and by defining the same importation of infected cases from international travels.ResultsThe results obtained show that both models provide epidemic patterns that are in very good agreement at the granularity levels accessible by both approaches, with differences in peak timing on the order of a few days. The relative difference of the epidemic size depends on the basic reproductive ratio, R0, and on the fact that the metapopulation model consistently yields a larger incidence than the agent-based model, as expected due to the differences in the structure in the intra-population contact pattern of the approaches. The age breakdown analysis shows that similar attack rates are obtained for the younger age classes.ConclusionsThe good agreement between the two modeling approaches is very important for defining the tradeoff between data availability and the information provided by the models. The results we present define the possibility of hybrid models combining the agent-based and the metapopulation approaches according to the available data and computational resources.

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Sequencing-based large-scale genomics approaches with small numbers of isolated maize meiocytes.

Publications

Sequencing-based large-scale genomics approaches with small numbers of isolated maize meiocytes.

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