Background: There is growing evidence for the prevalence of copy number variation (CNV) and its role in phenotypic variation in many eukaryotic species. Here we use array comparative genomic hybridization to explore the extent of this type of structural variation in domesticated barley cultivars and wild barleys. Results: A collection of 14 barley genotypes including eight cultivars and six wild barleys were used for comparative genomic hybridization. CNV affects 14.9% of all the sequences that were assessed. Higher levels of CNV diversity are present in the wild accessions relative to cultivated barley. CNVs are enriched near the ends of all chromosomes except 4H, which exhibits the lowest frequency of CNVs. CNV affects 9.5% of the coding sequences represented on the array and the genes affected by CNV are enriched for sequences annotated as disease-resistance proteins and protein kinases. Sequence-based comparisons of CNV between cultivars Barke and Morex provided evidence that DNA repair mechanisms of double-strand breaks via single-stranded annealing and synthesis-dependent strand annealing play an important role in the origin of CNV in barley. Conclusions: We present the first catalog of CNVs in a diploid Triticeae species, which opens the door for future genome diversity research in a tribe that comprises the economically important cereal species wheat, barley, and rye. Our findings constitute a valuable resource for the identification of CNV affecting genes of agronomic importance. We also identify potential mechanisms that can generate variation in copy number in plant genomes.

Background: Fusarium head blight (FHB) caused byFusarium graminearumSchwabe is one of the most prevalent diseases of wheat (Triticum aestivumL.) and other small grain cereals. Resistance against the fungus is quantitative and more than 100 quantitative trait loci (QTL) have been described. Two well-validated and highly reproducible QTL,Fhb1andQfhs.ifa-5Ahave been widely investigated, but to date the underlying genes have not been identified.Results: We have investigated a gene co-expression network activated in response toF. graminearumusing RNA-seq data from near-isogenic lines, harboring either the resistant or the susceptible allele forFhb1andQfhs.ifa-5A. The network identified pathogen-responsive modules, which were enriched for differentially expressed genes between genotypes or different time points after inoculation with the pathogen. Central gene analysis identified transcripts associated with either QTL within the network. Moreover, we present a detailed gene expression analysis of four gene families (glucanases, NBS-LRR, WRKY transcription factors and UDP-glycosyltransferases), which take prominent roles in the pathogen response.Conclusions: A combination of a network-driven approach and differential gene expression analysis identified genes and pathways associated withFhb1andQfhs.ifa-5A. We find G-protein coupled receptor kinases and biosynthesis genes for jasmonate and ethylene earlier induced forFhb1. Similarly, we find genes involved in the biosynthesis and metabolism of riboflavin more abundant forQfhs.ifa-5A.

S-Adenosyl-L-methionine (SAM) is a key enzyme involved in many important biological processes, such as ethylene and polyamine biosynthesis, transmethylation, and transsulfuration. Here, the SAM synthetase (SAMS) gene family was studied in ten different plants (Arabidopsis, tomato, eggplant, sunflower, Medicago truncatula, soybean, rice, barley, Triticum urartu and sorghum) with respect to its physical structure, physicochemical characteristics, and post-transcriptional and post-translational modifications. Additionally, the expression patterns of SAMS genes in tomato were analyzed based on a real-time quantitative PCR assay and an analysis of a public expression dataset. SAMS genes of monocots were more conserved according to the results of a phylogenetic analysis and the prediction of phosphorylation and glycosylation patterns. SAMS genes showed differential expression in response to abiotic stresses and exogenous hormone treatments. Solyc01g101060 was especially expressed in fruit and root tissues, while Solyc09g008280 was expressed in leaves. Additionally, our results revealed that exogenous BR and ABA treatments strongly reduced the expression of tomato SAMS genes. Our research provides new insights and clues about the role of SAMS genes. In particular, these results can inform future functional analyses aimed at revealing the molecular mechanisms underlying the functions of SAMS genes in plants.

Motivation: RNA-seq techniques generate massive amounts of expression data. Several pipelines (e.g. Tophat and Cufflinks) are broadly applied to analyse these datasets. However, accessing and handling the analytical output remain challenging for non-experts. Results: We present the RNASeqExpressionBrowser, an open-source web interface that can be used to access the output from RNA-seq expression analysis packages in different ways, as it allows browsing for genes by identifiers, annotations or sequence similarity. Gene expression information can be loaded as long as it is represented in a matrix-like format. Additionally, data can be made available by setting up the tool on a public server. For demonstration purposes, we have set up a version providing expression information from the barley genome. Availability and implementation: The source code and a show case are accessible at http://mips.helmholtz-muenchen.de/plant/RNASeqExpressionBrowser/. Contact: k.mayer@helmholtz-muenchen.de

Background: Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. Results: We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (10⁸–10³ cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 10⁶ cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera. Conclusions: MicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.

The genomic sequences of many important Triticeae crop species are hard to assemble and analyse due to their large genome sizes, (in part) polyploid genomes and high repeat content. Recently, the draft genomes of barley and bread wheat were reported thanks to cost-efficient and fast NGS technologies. The genome of barley is estimated to be 5 Gb in size whereas the genome of bread wheat accounts for 17 Gb and harbours an allo-hexaploid genome. Direct assembly of the sequence reads and access to the gene content is hampered by the repeat content. As a consequence, novel strategies and data analysis concepts had to be developed to provide much-needed whole genome sequence surveys and access to the gene repertoires. Here we describe some analytical strategies that now enable structuring of massive NGS data generated and pave the way towards structured and ordered sequence data and gene order. Specifically we report on the GenomeZipper, a synteny driven approach to order and structure NGS survey sequences of grass genomes that lack a physical map. In addition, to access and analyse the gene repertoire of allo-hexaploid bread wheat from the raw sequence reads, a reference-guided approach was developed utilizing representative genes from rice, Brachypodium distachyon, sorghum and barley. Stringent sub-assembly on the reference genes prevented collapsing of homeologous wheat genes and allowed to estimate gene retention rate and determine gene family sizes. Genomic sequences from the wheat sub-genome progenitors enabled to discriminate a large number of sub-assemblies between the wheat A, B or D sub-genome using machine learning algorithms. Many of the concepts outlined here can readily be applied to other complex plant and non-plant genomes.