Scientific challenges of convective-scale numerical weather prediction

Numerical weather prediction (NWP) models are increasing in resolution and becoming capable of explicitly representing individual convective storms. Is this increase in resolution leading to better forecasts? Unfortunately, we do not have sufficient theoretical understanding about this weather regime to make full use of these NWPs. After extensive efforts over the course of a decade, convective–scale weather forecasts with horizontal grid spacings of 1–5 km are now operational at national weather services around the world, accompanied by ensemble prediction systems (EPSs). However, though already operational, the capacity of forecasts for this scale is still to be fully exploited by overcoming the fundamental difficulty in prediction: the fully three–dimensional and turbulent nature of the atmosphere. The prediction of this scale is totally different from that of the synoptic scale (103 km) with slowly–evolving semi–geostrophic dynamics and relatively long predictability on the order of a few days. Even theoretically, very little is understood about the convective scale compared to our extensive knowledge of the synoptic-scale weather regime as a partial–differential equation system, as well as in terms of the fluid mechanics, predictability, uncertainties, and stochasticity. Furthermore, there is a requirement for a drastic modification of data assimilation methodologies, physics (e.g., microphysics), parameterizations, as well as the numerics for use at the convective scale. We need to focus on more fundamental theoretical issues: the Liouville principle and Bayesian probability for probabilistic forecasts; and more fundamental turbulence research to provide robust numerics for the full variety of turbulent flows. The present essay reviews those basic theoretical challenges as comprehensibly as possible. The breadth of the problems that we face is a challenge in itself: an attempt to reduce these into a single critical agenda should be avoided

Discovery of Single Nucleotide Polymorphisms in Complex Genomes Using SGSautoSNP

Single nucleotide polymorphisms (SNPs) are becoming the dominant form of molecular marker for genetic and genomic analysis. The advances in second generation DNA sequencing provide opportunities to identify very large numbers of SNPs in a range of species. However, SNP identification remains a challenge for large and polyploid genomes due to their size and complexity. We have developed a pipeline for the robust identification of SNPs in large and complex genomes using Illumina second generation DNA sequence data and demonstrated this by the discovery of SNPs in the hexaploid wheat genome. We have developed a SNP discovery pipeline called SGSautoSNP (Second-Generation Sequencing AutoSNP) and applied this to discover more than 800,000 SNPs between four hexaploid wheat cultivars across chromosomes 7A, 7B and 7D. All SNPs are presented for download and viewing within a public GBrowse database. Validation suggests an accuracy of greater than 93% of SNPs represent polymorphisms between wheat cultivars and hence are valuable for detailed diversity analysis, marker assisted selection and genotyping by sequencing. The pipeline produces output in GFF3, VCF, Flapjack or Illumina Infinium design format for further genotyping diverse populations. As well as providing an unprecedented resource for wheat diversity analysis, the method establishes a foundation for high resolution SNP discovery in other large and complex genomes

Genomic databases for crop improvement

The majority of DNA sequence and expressed gene sequence data generated today comes from the next-or second-generation sequencing (NGS/2GS) technologies. NGS technologies produce vast quantities of short data rather than Sanger sequencing at a relatively low cost and short time. Genomics is undergoing a revolution, driven by advances in DNA sequencing technology, and this data flood is having a major impact on approaches and strategies for crop improvement. NGS technologies have been applied for sequenced genomes of a number of cereal crop species including rice, Sorghum and maize. A quality sequence of rice that covers 95% of the 389 Mb genome has been produced [1]. The Sorghum bicolor (L.) Moench genome has been assembled in size of 730-megabase, placing ~98% of genes in their chromosomal context [2]. The draft nucleotide sequence of the 2.3-gigabase genome of maize has also been improved [3]. One of the challenges encountered by researchers is to translate this abundance of data into improved crops in the field. There remains a gap between genome data production and next-generation crop improvement strategies, but this is being rapidly closed by far sighted companies and individuals with the ability to combine the ability to mine the genomic data with practical crop-improvement skills. Bioinformatics can be defined as the structuring of biological information to enable logical interrogation, and databases are a key part of the bioinformatics toolbox. Numerous databases have been developed for genomic data, on a range of platforms and to suite a variety of different purposes (see Table 1 for examples). These range from generic DNA sequence or molecular marker databases, to those hosting a variety of data for specific species

Genomic databases for crop improvement

The majority of DNA sequence and expressed gene sequence data generated today comes from the next-or second-generation sequencing (NGS/2GS) technologies. NGS technologies produce vast quantities of short data rather than Sanger sequencing at a relatively low cost and short time. Genomics is undergoing a revolution, driven by advances in DNA sequencing technology, and this data flood is having a major impact on approaches and strategies for crop improvement. NGS technologies have been applied for sequenced genomes of a number of cereal crop species including rice, Sorghum and maize. A quality sequence of rice that covers 95% of the 389 Mb genome has been produced [1]. The Sorghum bicolor (L.) Moench genome has been assembled in size of 730-megabase, placing ~98% of genes in their chromosomal context [2]. The draft nucleotide sequence of the 2.3-gigabase genome of maize has also been improved [3]. One of the challenges encountered by researchers is to translate this abundance of data into improved crops in the field. There remains a gap between genome data production and next-generation crop improvement strategies, but this is being rapidly closed by far sighted companies and individuals with the ability to combine the ability to mine the genomic data with practical crop-improvement skills. Bioinformatics can be defined as the structuring of biological information to enable logical interrogation, and databases are a key part of the bioinformatics toolbox. Numerous databases have been developed for genomic data, on a range of platforms and to suite a variety of different purposes (see Table 1 for examples). These range from generic DNA sequence or molecular marker databases, to those hosting a variety of data for specific species.</p

The widely used Nicotiana benthamiana 16c line has an unusual T-DNA integration pattern including a transposon sequence.

Nicotiana benthamiana is employed around the world for many types of research and one transgenic line has been used more extensively than any other. This line, 16c, expresses the Aequorea victoria green fluorescent protein (GFP), highly and constitutively, and has been a major resource for visualising the mobility and actions of small RNAs. Insights into the mechanisms studied at a molecular level in N. benthamiana 16c are likely to be deeper and more accurate with a greater knowledge of the GFP gene integration site. Therefore, using next generation sequencing, genome mapping and local alignment, we identified the location and characteristics of the integrated T-DNA. As suggested from previous molecular hybridisation and inheritance data, the transgenic line contains a single GFP-expressing locus. However, the GFP coding sequence differs from that originally reported. Furthermore, a 3.2 kb portion of a transposon, appears to have co-integrated with the T-DNA. The location of the integration mapped to a region of the genome represented by Nbv0.5scaffold4905 in the www.benthgenome.com assembly, and with less integrity to Niben101Scf03641 in the www.solgenomics.net assembly. The transposon is not endogenous to laboratory strains of N. benthamiana or Agrobacterium tumefaciens strain GV3101 (MP90), which was reportedly used in the generation of line 16c. However, it is present in the popular LBA4404 strain. The integrated transposon sequence includes its 5' terminal repeat and a transposase gene, and is immediately adjacent to the GFP gene. This unexpected genetic arrangement may contribute to the characteristics that have made the 16c line such a popular research tool and alerts researchers, taking transgenic plants to commercial release, to be aware of this genomic hitchhiker

Chromatin immunoprecipitation (ChIP) method for non-model fruit flies (Diptera: Tephritidae) and evidence of histone modifications

<div><p>Interactions between DNA and proteins located in the cell nucleus play an important role in controlling physiological processes by specifying, augmenting and regulating context-specific transcription events. Chromatin immunoprecipitation (ChIP) is a widely used methodology to study DNA-protein interactions and has been successfully used in various cell types for over three decades. More recently, by combining ChIP with genomic screening technologies and Next Generation Sequencing (e.g. ChIP-seq), it has become possible to profile DNA-protein interactions (including covalent histone modifications) across entire genomes. However, the applicability of ChIP-chip and ChIP-seq has rarely been extended to non-model species because of a number of technical challenges. Here we report a method that can be used to identify genome wide covalent histone modifications in a group of non-model fruit fly species (Diptera: Tephritidae). The method was developed by testing and refining protocols that have been used in model organisms, including <i>Drosophila melanogaster</i>. We demonstrate that this method is suitable for a group of economically important pest fruit fly species, viz., <i>Bactrocera dorsalis</i>, <i>Ceratitis capitata</i>, <i>Zeugodacus cucurbitae</i> and <i>Bactrocera tryoni</i>. We also report an example ChIP-seq dataset for <i>B</i>. <i>tryoni</i>, providing evidence for histone modifications in the genome of a tephritid fruit fly for the first time. Since tephritids are major agricultural pests globally, this methodology will be a valuable resource to study taxa-specific evolutionary questions and to assist with pest management. It also provides a basis for researchers working with other non-model species to undertake genome wide DNA-protein interaction studies.</p></div

Output DNA of 200–300 bp size (enriched product) obtained from a starting material of 10 ng of antibody conjugated DNA from <i>Bactrocera tryoni</i> head tissues using Illumina Truseq ChIP sample preparation kit.

<p>Output DNA of 200–300 bp size (enriched product) obtained from a starting material of 10 ng of antibody conjugated DNA from <i>Bactrocera tryoni</i> head tissues using Illumina Truseq ChIP sample preparation kit.</p

Summary sequence data from a single Illumina run (several runs were performed for different antibodies).

<p>Summary sequence data from a single Illumina run (several runs were performed for different antibodies).</p

Buffers used for cell lysis and immunoprecipitation for ChIP sequencing of tephritid fruit flies.

<p>Buffers used for cell lysis and immunoprecipitation for ChIP sequencing of tephritid fruit flies.</p

An example of genomic regions displaying enrichment for H3K4me3, H3K27Ac, H3K36me1 and H3K36me3 histone proteins in <i>Bactrocera tryoni</i> when visualised using the ‘Sushi.R’ R/Bioconductor package (SPMR—Sequences Per Million Reads).

<p>‘Input’ samples that were not conjugated with any antibodies served as ‘control’ to compare histone modifications in antibody conjugated samples. Differential peak height and width represents enrichment of histone proteins at various genomic regions.</p