CGtag: Complete genomics toolkit and annotation in a cloud-based Galaxy

Background: Complete Genomics provides an open-source suite of command-line tools for the analysis of their CG-formatted mapped sequencing files. Determination of; for example, the functional impact of detected variants, requires annotation with various databases that often require command-line and/or programming experience; thus, limiting their use to the average research scientist. We have therefore implemented this CG toolkit, together with a number of annotation, visualisation and file manipulation tools in Galaxy called CGtag (Complete Genomics Toolkit and Annotation in a Cloud-based Galaxy).Findings: In order to provide research scientists with web-based, simple and accurate analytical and visualisation applications for the selection of candidate mutations from Complete Genomics data, we have implemented the open-source Complete Genomics tool set, CGATools, in Galaxy. In addition we implemented some of the most popular command-line annotation and visualisation tools to allow research scientists to select candidate pathological mutations (SNV, and indels). Furthermore, we have developed a cloud-based public Galaxy instance to host the CGtag toolkit and other associated modules.Conclusions: CGtag provides a user-friendly interface to all research scientists wishing to select candidate variants from CG or other next-generation sequencing platforms' data. By using a cloud-based infrastructure, we can also assure sufficient and on-demand computation and storage resources to handle the analysis tasks. The tools are freely available for use from an NBIC/CTMM-TraIT (The Netherlands Bioinformatics Center/Center for Translational Molecular Medicine) cloud-based Galaxy instance, or can be installed to a local (production) Galaxy via the NBIC Galaxy tool shed

Structural and functional variation in soil fungal communities associated with litter bags containing maize leaf

Soil fungi are key players in the degradation of recalcitrant organic matter in terrestrial ecosystems. To examine the organisms and genes responsible for complex organic matter degradation in soil, we tracked changes in fungal community composition and expressed genes in soil adjacent to mesh bags containing maize leaves undergoing decomposition. Using high-throughput sequencing approaches, changes in fungal community composition were determined by targeting 18S rRNA gene sequences, whereas community gene expression was examined via a metatranscriptomic approach. The majority of the 93 000 partial 18S rRNA gene sequences generated, were affiliated with the Ascomycota and Basidiomycota. Fungal diversity was at least 224 operational taxonomic units at the 97% similarity cutoff level. During litter degradation, the relative proportion of Basidiomycota increased, with a decrease in Ascomycota : Basidiomycota ratios over time. The most commonly detected decomposition-associated fungi included Agaricomycetes and Tremellales as well as unclassified Mucoromycotina. The majority of protein families found in the metatranscriptomic data were affiliated to fungal groups described to degrade plant-derived cellulose, such as Mucoraceae, Chaetomiaceae, Sordariaceae, Sebacinaceae, Tremellaceae, Psathyrellaceae and Schizophyllaceae. The combination of high-throughput rRNA gene-based and metatranscriptomic approaches provided perspectives into the organisms and genes involved in complex organic matter in soi

Can subtle changes in gene expression be consistently detected with different microarray platforms?

Background: The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle. Results: Gene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms. Quantitative RT-PCR analysis confirmed 20 out of 28 of the genes detected by two or more platforms and 8 out of 15 of the genes detected by Agilent only. We observed improved correlations between platforms when ranking the genes based on the significance level than with a fixed statistical cut-off. We demonstrate significant overlap in the affected gene sets identified by the different platforms, although biological processes were represented by only partially overlapping sets of genes. Aberrances in GABA-ergic signalling in the transgenic mice were consistently found by all platforms. Conclusion: The different microarray platforms give partially complementary views on biological processes affected. Our data indicate that when analyzing samples with only subtle differences in gene expression the use of two different platforms might be more attractive than increasing the number of replicates. Commercial two-color platforms seem to have higher power for finding differentially expressed genes between groups with small differences in expression

Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome

Microorganisms living inside plants can promote plant growth and health, but their genomic and functional diversity remain largely elusive. Here, metagenomics and network inference show that fungal infection of plant roots enriched for Chitinophagaceae and Flavobacteriaceae in the root endosphere and for chitinase genes and various unknown biosynthetic gene clusters encoding the production of nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs). After strain-level genome reconstruction, a consortium of Chitinophaga and Flavobacterium was designed that consistently suppressed fungal root disease. Site-directed mutagenesis then revealed that a previously unidentified NRPS-PKS gene cluster from Flavobacterium was essential for disease suppression by the endophytic consortium. Our results highlight that endophytic root microbiomes harbor a wealth of as yet unknown functional traits that, in concert, can protect the plant inside out.</p

Soil networks become more connected and take up more carbon as nature restoration progresses

Soil organisms have an important role in aboveground community dynamics and ecosystem functioning in terrestrial ecosystems. However, most studies have considered soil biota as a black box or focussed on specific groups, whereas little is known about entire soil networks. Here we show that during the course of nature restoration on abandoned arable land a compositional shift in soil biota, preceded by tightening of the belowground networks, corresponds with enhanced efficiency of carbon uptake. In mid- and long-term abandoned field soil, carbon uptake by fungi increases without an increase in fungal biomass or shift in bacterial-to-fungal ratio. The implication of our findings is that during nature restoration the efficiency of nutrient cycling and carbon uptake can increase by a shift in fungal composition and/or fungal activity. Therefore, we propose that relationships between soil food web structure and carbon cycling in soils need to be reconsidered

A Comparison of rpoB and 16S rRNA as Markers in Pyrosequencing Studies of Bacterial Diversity

Background: The 16S rRNA gene is the gold standard in molecular surveys of bacterial and archaeal diversity, but it has the disadvantages that it is often multiple-copy, has little resolution below the species level and cannot be readily interpreted in an evolutionary framework. We compared the 16S rRNA marker with the single-copy, protein-coding rpoB marker by amplifying and sequencing both from a single soil sample. Because the higher genetic resolution of the rpoB gene prohibits its use as a universal marker, we employed consensus-degenerate primers targeting the Proteobacteria. &lt;p/&gt;Methodology/Principal Findings: Pyrosequencing can be problematic because of the poor resolution of homopolymer runs. As these erroneous runs disrupt the reading frame of protein-coding sequences, removal of sequences containing nonsense mutations was found to be a valuable filter in addition to flowgram-based denoising. Although both markers gave similar estimates of total diversity, the rpoB marker revealed more species, requiring an order of magnitude fewer reads to obtain 90% of the true diversity. The application of population genetic methods was demonstrated on a particularly abundant sequence cluster. &lt;p/&gt;Conclusions/Significance: The rpoB marker can be a complement to the 16S rRNA marker for high throughput microbial diversity studies focusing on specific taxonomic groups. Additional error filtering is possible and tests for recombination or selection can be employed

Tracking fungal community responses to maize plants by DNA- and RNA-based pyrosequencing.

We assessed soil fungal diversity and community structure at two sampling times (t1 = 47 days and t2 = 104 days of plant age) in pots associated with four maize cultivars, including two genetically modified (GM) cultivars by high-throughput pyrosequencing of the 18S rRNA gene using DNA and RNA templates. We detected no significant differences in soil fungal diversity and community structure associated with different plant cultivars. However, DNA-based analyses yielded lower fungal OTU richness as compared to RNA-based analyses. Clear differences in fungal community structure were also observed in relation to sampling time and the nucleic acid pool targeted (DNA versus RNA). The most abundant soil fungi, as recovered by DNA-based methods, did not necessary represent the most "active" fungi (as recovered via RNA). Interestingly, RNA-derived community compositions at t1 were highly similar to DNA-derived communities at t2, based on presence/absence measures of OTUs. We recovered large proportions of fungal sequences belonging to arbuscular mycorrhizal fungi and Basidiomycota, especially at the RNA level, suggesting that these important and potentially beneficial fungi are not affected by the plant cultivars nor by GM traits (Bt toxin production). Our results suggest that even though DNA- and RNA-derived soil fungal communities can be very different at a given time, RNA composition may have a predictive power of fungal community development through time