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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.

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.