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Additional file 5. A step by step description of how to use our GO functional hierarchy to build reference databases and perform classification for Kraken2 and Kaiju.

Additional file 1: Figure S1. Estimation of substitution rate of soil metagenomic samples. Scatterplot to show the classification rate (y-axis) of Kraken2 (pink datapoints) and the MEM approaches Kaiju and PRROMenade (blue datapoints) for simulated reads as substitution rate increases (x-axis). The linear trendline for the datapoints is shown in black with its respective linear equation. The red line denotes the average classification rate of the soil metagenomic samples considering both Kraken2, Kaiju and PRROMenade to estimate the substitution rate of these samples if we were to attempt to recreate them from simulated reads. Figure S2. Read alignment of 189 soil metagenomic samples to GO functional groups. Scatterplots to show the proportion of classified reads to each GO term for each of the 189 analysed soil samples after classification to the IFGP GO term sequence database using Kraken2 (read numbers normalized to a scale of 0-1 for each sample to allow comparison). The respective trendlines of closest fit for the datapoints are shown in red. Here we show classification summarized for specific GO functional groups as follows; (a) GO:0016209 for antioxidant activity, (b) GO:0045735 for nutrient reservoir activity, (c) GO:0005488 for binding, (d) GO:0098772 for molecular function regulation, (e) GO:0005198 for structural molecule activity, (f) GO:0003824 for catalytic activity, (g) GO:0005215 for transporter activity and (h) GO:00140110 for transcription regulator activity. Table S1. Classification of simulated sequencing reads (low substitution rate) by Kraken using three different sized GO term sequence databases (DNA based) and functional annotation. Table S2. Classification of 474,930,848 simulated sequencing reads (low, medium and high substitution rate) by Kraken, Kraken2 and the MEM approaches Kaiju and PRROMenade using the full IFGP GO term sequence database and functional annotation. Table S3. Classification of real soil metagenome sequencing reads by Kraken, Kraken2 and the MEM approaches Kaiju and PRROMenade using the full IFGP GO term sequence database and functional annotation.

BackgroundPolyploidy is centrally important in the evolution and domestication of plants because it leads to major genomic changes, such as altered patterns of gene expression, which are thought to underlie the emergence of new traits. Despite the common occurrence of these globally altered patterns of gene expression in polyploids, the mechanisms involved are not well understood.ResultsUsing a precisely defined framework of highly conserved syntenic genes on hexaploid wheat chromosome 3DL and its progenitor 3 L chromosome arm of diploid Aegilops tauschii, we show that 70% of these gene pairs exhibited proportionately reduced gene expression, in which expression in the hexaploid context of the 3DL genes was ∼40% of the levels observed in diploid Ae tauschii. Several genes showed elevated expression during the later stages of grain development in wheat compared with Ae tauschii. Gene sequence and methylation differences probably accounted for only a few cases of differences in gene expression. In contrast, chromosome-wide patterns of reduced chromatin accessibility of genes in the hexaploid chromosome arm compared with its diploid progenitor were correlated with both reduced gene expression and the imposition of new patterns of gene expression.ConclusionsOur pilot-scale analyses show that chromatin compaction may orchestrate reduced gene expression levels in the hexaploid chromosome arm of wheat compared to its diploid progenitor chromosome arm.

Research conducted and rationale: The root system architecture (RSA) of a crop has a profound effect on the uptake of nutrients and consequently the potential yield. However, little is known about the genetic basis of RSA and resource dependent responses in wheat (Triticum aestivum L.). Methods: A high-throughput hydroponic root phenotyping system was used to identify N-dependent root traits in a wheat mapping population. Quantitative trait locus (QTL) analysis identified potential N-dependent QTLs for root traits followed by RNA-seq to uncover underlying genes for candidate QTLs. Key results: Fifty-five QTLs were discovered for seedling root traits across two N treatments, 25 of which were N-dependent. Transcriptomic analyses of an N-dependent root angle QTL on chromosome 2D identified 17 candidate up-regulated genes. Of these genes a nitrate transporter 1/peptide transporter (NPF) family gene was up-regulated making it an interesting candidate for N signalling and response processes for root angle change. Conclusion: Using a hydroponic pouch phenotyping system, we propose genomic regions and underlying genes controlling an N-dependent root angle response in wheat. For a candidate N-dependent root angle QTL an up-regulated NPF family gene was identified likely transporting nitrate or ABA as part of an N-dependent response affecting root angle. Abbreviations ABA, Abscisic acid; BLUEs, best linear unbiased estimates; BLUPS, best linear unbiased predictions; DAG, days after germination; DH, doubled haploid; LOD, logarithm of odds; nabim, National Association of British & Irish Millers; NPF, peptide transporter family; NRT, nitrate transporter; NUE; nitrogen use efficiency; PTR, proton-dependent oligopeptide transporter; QTL, quantitative trait locus; RNA-seq, RNA sequencing technology; RSA, root system architecture; RSML, Root System Markup Language.

Previously we extended the utility of mapping-by-sequencing by combining it with sequence capture and mapping sequence data to pseudo-chromosomes that were organized using wheat-Brachypodium synteny. This, with a bespoke haplotyping algorithm, enabled us to map the flowering time locus in the diploid wheat Triticum monococcum L identifying a set of deleted genes (Gardiner et al., 2014).\ud \ud \ud Here, we develop this combination of gene enrichment and sliding window mapping-by-synteny analysis to map the Yr6 locus for yellow stripe rust resistance in hexaploid wheat. A 110MB NimbleGen capture probe set was used to enrich and sequence a doubled-haploid mapping population of hexaploid wheat derived from an Avalon and Cadenza cross. The Yr6 locus was identified by mapping to the POPSEQ chromosomal pseudomolecules using a bespoke pipeline and algorithm (Chapman et al., 2015). Furthermore the same locus was identified using newly developed pseudo-chromosome sequences as a mapping reference that are based on the genic sequence used for sequence enrichment. The pseudo-chromosomes allow us to demonstrate the application of mapping-by-sequencing to even poorly defined polyploidy genomes where chromosomes are incomplete and sub-genome assemblies are collapsed. This analysis uniquely enabled us to: compare wheat genome annotations; identify the Yr6 locus - defining a smaller genic region than was previously possible; associate the interval with one wheat sub-genome and increase the density of SNP markers associated. Finally, we built the pipeline in iPlant, making it a user-friendly community resource for phenotype mapping.

High throughput sequencing can generate hundreds of millions of reads in a single day and is revolutionizing modern genetics. This project aimed to utilize next generation genetic approaches to analyze non-model but important agronomical plant species. A key feature of these species is their complexity. Mapping and SNP calling of these sequencing datasets is fundamental to many downstream analyses that have been implemented here including; mutant identification, comparative analyses between related organisms and epigenetic studies. The first objective in this project involved developing accelerated mutant identification techniques using mapping-by-sequencing analyses that combine whole genome sequencing with genetic mapping. Such methods have largely required a complete reference sequence and are typically implemented on a mapping population with a common mutant phenotype of interest. Here mutant identification was demonstrated on the model diploid plant Arabidopsis thaliana as a proof of principle of the methodology. It was also demonstrated on a simulated hexaploid mutant that was developed using the Arabidopsis reference genome. In species such as wheat, no finished genome reference sequence is available and, due to its large genome size (17 Gb), re-sequencing at sufficient depth of coverage is not practical. Therefore a genomic target enrichment approach was validated and used here to capture the gene rich regions of hexaploid bread wheat, reducing the sequencing cost while still allowing analysis of the majority of wheat’s genic sequence. A pseudo-chromosome based reference sequence was developed from this genic sequence with a long-range order of genes based on synteny of wheat with Brachypodium distachyon. Using the capture probe set for target enrichment followed by next generation sequencing; an early flowering locus was mapped in the diploid wheat Triticum monococcum and in hexaploid bread wheat Triticum aestivum, the stripe rust resistance gene was located. A bespoke pipeline and algorithm was developed for mutant loci identification and the pseudo-chromosome reference was implemented. This novel method will allow widespread application of sliding window mapping-by-sequencing analyses to datasets that are; enriched, lacking a finished reference genome or polyploid. The second main objective of this project involved a study of methylation patterns in wheat utilizing sodium bisulfite treatment, combined with target enrichment. An enrichment system was specifically designed, developed, validated and implemented here to perform one of the first studies of methylation patterns in hexaploid bread wheat across the 3 genomes that used a genome-wide subset of genes and can thus be used to infer genome-wide methylation patterns and observations. This investigation confirmed that differential methylation exists between the A, B and D genomes of wheat and that temperature is capable of altering methylation states.

Mapping‐by‐sequencing analyses have largely required a complete reference sequence and employed whole genome re‐sequencing. In species such as wheat, no finished genome reference sequence is available. Additionally, because of its large genome size (17 Gb), re‐sequencing at sufficient depth of coverage is not practical. Here, we extend the utility of mapping by sequencing, developing a bespoke pipeline and algorithm to map an early‐flowering locus in einkorn wheat (Triticum monococcum L.) that is closely related to the bread wheat genome A progenitor. We have developed a genomic enrichment approach using the gene‐rich regions of hexaploid bread wheat to design a 110‐Mbp NimbleGen SeqCap EZ in solution capture probe set, representing the majority of genes in wheat. Here, we use the capture probe set to enrich and sequence an F2 mapping population of the mutant. The mutant locus was identified in T. monococcum, which lacks a complete genome reference sequence, by mapping the enriched data set onto pseudo‐chromosomes derived from the capture probe target sequence, with a long‐range order of genes based on synteny of wheat with Brachypodium distachyon. Using this approach we are able to map the region and identify a set of deleted genes within the interval.