“Truth and Behold” is a point and click adventure game in which you control Rose, amechanic that was screwed over by the actions of a corrupt company. Now Rose has to sneakinto the company’s headquarters and find evidence to convict the head of the company of illegaldoings. Rose has to be careful and make sure she isn’t caught by guards that are patrolling thebuilding. To do this Rose has to make sure that she only looks in places that will help her findthe evidence she needs. She will meet others that will help her find what she needs to bring thecriminals to justice.

Individual differences in the effects of stress on causal attribution were studied in the context of a first-person-shooter video game. Participants were tasked with identifying the source of an explosion by repeatedly choosing among three possible enemy targets that were firing their weapons at random. In each trio of possible targets, the true enemy (the cause) produced these explosions at a delay ofeither 0.5, 1.0, or 2.0 seconds and with a probability of 100%, 75%, or 50%; condition varied across trios of targets. In Experiment 1, half of the participants made these choices while under stress (by being under fire by snipers in the hills surrounding the choice area) and half were not under fire. Men had higher accuracies and shorter latencies, and being under fire produced lower accuracy but had no effect on latency. In Experiment 2, a more explicit form of time pressure was used in which participants had a fixed amount of time in which to make their choice. This form of time pressure succeeded in dramatically reducing decision latency with an associated drop in accuracy. There was unreliable evidence of a higher accuracy for men. Neither experiment revealed a relationship between self-reported video game play and performance. The results suggest that causal decisions are negatively affected by time pressure, and the manipulations affected men and women similarly.

Shotgun metagenomic sequencing does not depend on gene-targeted primers or PCR amplification; thus, it is not affected by primer bias or chimeras. However, searching rRNA genes from large shotgun Illumina data sets is computationally expensive, and no approach exists for unsupervised community analysis of small-subunit (SSU) rRNA gene fragments retrieved from shotgun data. We present a pipeline, SSUsearch, to achieve the faster identification of short-subunit rRNA gene fragments and enabled unsupervised community analysis with shotgun data. It also includes classification and copy number correction, and the output can be used by traditional amplicon analysis platforms. Shotgun metagenome data using this pipeline yielded higher diversity estimates than amplicon data but retained the grouping of samples in ordination analyses. We applied this pipeline to soil samples with paired shotgun and amplicon data and confirmed bias against Verrucomicrobia in a commonly used V6-V8 primer set, as well as discovering likely bias against Actinobacteria and for Verrucomicrobia in a commonly used V4 primer set. This pipeline can utilize all variable regions in SSU rRNA and also can be applied to large-subunit (LSU) rRNA genes for confirmation of community structure. The pipeline can scale to handle large amounts of soil metagenomic data (5 Gb memory and 5 central processing unit hours to process 38 Gb [1 lane] of trimmed Illumina HiSeq2500 data) and is freely available at https://github.com/dib-lab/SSUsearch under a BSD license.

Background

Metagenomics can provide important insight into microbial communities. However, assembling metagenomic datasets has proven to be computationally challenging. Current methods often assemble only fragmented partial genes.

Results

We present a novel method for targeting assembly of specific protein-coding genes. This method combines a de Bruijn graph, as used in standard assembly approaches, and a protein profile hidden Markov model (HMM) for the gene of interest, as used in standard annotation approaches. These are used to create a novel combined weighted assembly graph. Xander performs both assembly and annotation concomitantly using information incorporated in this graph. We demonstrate the utility of this approach by assembling contigs for one phylogenetic marker gene and for two functional marker genes, first on Human Microbiome Project (HMP)-defined community Illumina data and then on 21 rhizosphere soil metagenomic datasets from three different crops totaling over 800 Gbp of unassembled data. We compared our method to a recently published bulk metagenome assembly method and a recently published gene-targeted assembler and found our method produced more, longer, and higher quality gene sequences.

Conclusion

Xander combines gene assignment with the rapid assembly of full-length or near full-length functional genes from metagenomic data without requiring bulk assembly or post-processing to find genes of interest. HMMs used for assembly can be tailored to the targeted genes, allowing flexibility to improve annotation over generic annotation pipelines. This method is implemented as open source software and is available at https://github.com/rdpstaff/Xander_assembler.

Shotgun metagenomics has greatly advanced our understanding of microbial communities over the last decade. Metagenomic analyses often include assembly and genome binning, computationally daunting tasks especially for big data from complex environments such as soil and sediments. In many studies, however, only a subset of genes and pathways involved in specific functions are of interest; thus, it is not necessary to attempt global assembly. In addition, methods that target genes can be computationally more efficient and produce more accurate assembly by leveraging rich databases, especially for those genes that are of broad interest such as those involved in biogeochemical cycles, biodegradation, and antibiotic resistance or used as phylogenetic markers. Here, we review six gene-targeted assemblers with unique algorithms for extracting and/or assembling targeted genes: Xander, MegaGTA, SAT-Assembler, HMM-GRASPx, GenSeed-HMM, and MEGAN. We tested these tools using two datasets with known genomes, a synthetic community of artificial reads derived from the genomes of 17 bacteria, shotgun sequence data from a mock community with 48 bacteria and 16 archaea genomes, and a large soil shotgun metagenomic dataset. We compared assemblies of a universal single copy gene (rplB) and two N cycle genes (nifH and nirK). We measured their computational efficiency, sensitivity, specificity, and chimera rate and found Xander and MegaGTA, which both use a probabilistic graph structure to model the genes, have the best overall performance with all three datasets, although MEGAN, a reference matching assembler, had better sensitivity with synthetic and mock community members chosen from its reference collection. Also, Xander and MegaGTA are the only tools that include post-assembly scripts tuned for common molecular ecology and diversity analyses. Additionally, we provide a mathematical model for estimating the probability of assembling targeted genes in a metagenome for estimating required sequencing depth.

Ribosomal Database Project (RDP; http://rdp.cme.msu.edu/) provides the research community with aligned and annotated rRNA gene sequence data, along with tools to allow researchers to analyze their own rRNA gene sequences in the RDP framework. RDP data and tools are utilized in fields as diverse as human health, microbial ecology, environmental microbiology, nucleic acid chemistry, taxonomy and phylogenetics. In addition to aligned and annotated collections of bacterial and archaeal small subunit rRNA genes, RDP now includes a collection of fungal large subunit rRNA genes. RDP tools, including Classifier and Aligner, have been updated to work with this new fungal collection. The use of high-throughput sequencing to characterize environmental microbial populations has exploded in the past several years, and as sequence technologies have improved, the sizes of environmental datasets have increased. With release 11, RDP is providing an expanded set of tools to facilitate analysis of high-throughput data, including both single-stranded and paired-end reads. In addition, most tools are now available as open source packages for download and local use by researchers with high-volume needs or who would like to develop custom analysis pipelines.

The most common practice in studying and cataloguing prokaryotic diversity involves the grouping of sequences into operational taxonomic units (OTUs) at the 97% 16S rRNA gene sequence identity level, often using partial gene sequences, such as PCR-generated amplicons. Due to the high sequence conservation of rRNA genes, organisms belonging to closely related yet distinct species may be grouped under the same OTU. However, it remains unclear how much diversity has been underestimated by this practice. To address this question, we compared the OTUs of genomes defined at the 97% or 98.5% 16S rRNA gene identity level against OTUs of the same genomes defined at the 95% whole-genome average nucleotide identity (ANI), which is a much more accurate proxy for species. Our results show that OTUs resulting from a 98.5% 16S rRNA gene identity cutoff are more accurate than 97% compared to 95% ANI (90.5% versus 89.9% accuracy) but indistinguishable from any other threshold in the 98.29 to 98.78% range. Even with the more stringent thresholds, however, the 16S rRNA gene-based approach commonly underestimates the number of OTUs by ∼12%, on average, compared to the ANI-based approach (∼14% underestimation when using the 97% identity threshold). More importantly, the degree of underestimation can become 50% or more for certain taxa, such as the genera Pseudomonas, Burkholderia, Escherichia, Campylobacter, and Citrobacter These results provide a quantitative view of the degree of underestimation of extant prokaryotic diversity by 16S rRNA gene-defined OTUs and suggest that genomic resolution is often necessary.IMPORTANCE Species diversity is one of the most fundamental pieces of information for community ecology and conservational biology. Therefore, employing accurate proxies for what a species or the unit of diversity is are cornerstones for a large set of microbial ecology and diversity studies. The most common proxies currently used rely on the clustering of 16S rRNA gene sequences at some threshold of nucleotide identity, typically 97% or 98.5%. Here, we explore how well this strategy reflects the more accurate whole-genome-based proxies and determine the frequency with which the high conservation of 16S rRNA sequences masks substantial species-level diversity.

There is an increasing interest in the clustered regularly interspaced short palindromic repeats CRISPR-associated protein (CRISPR-Cas) system to reveal potential virus-host dynamics. The universal and most conserved Cas protein, cas1 is an ideal marker to elucidate CRISPR-Cas ecology. We constructed eight Hidden Markov Models (HMMs) and assembled cas1 directly from metagenomes by a targeted-gene assembler, Xander, to improve detection capacity and resolve the diverse CRISPR-Cas systems. The eight HMMs were first validated by recovering all 17 cas1 subtypes from the simulated metagenome generated from 91 prokaryotic genomes across 11 phyla. We challenged the targeted method with 48 metagenomes from a tallgrass prairie in Central Oklahoma recovering 3394 cas1. Among those, 88 were near full length, 5 times more than in de-novo assemblies from the Oklahoma metagenomes. To validate the host assignment by cas1, the targeted-assembled cas1 was mapped to the de-novo assembled contigs. All the phylum assignments of those mapped contigs were assigned independent of CRISPR-Cas genes on the same contigs and consistent with the host taxonomies predicted by the mapped cas1. We then investigated whether 8 years of soil warming altered cas1 prevalence within the communities. A shift in microbial abundances was observed during the year with the biggest temperature differential (mean 4.16 °C above ambient). cas1 prevalence increased and even in the phyla with decreased microbial abundances over the next 3 years, suggesting increasing virus-host interactions in response to soil warming. This targeted method provides an alternative means to effectively mine cas1 from metagenomes and uncover the host communities.

We analyzed the microbial community that developed after 4 years of testing different soil-crop management systems in the savannah–forest transition zone of Eastern Ghana where management systems can rapidly alter stored soil carbon as well as soil fertility. The agricultural managements were: (i) the local practice of fallow regrowth of native elephant grass (Pennisetum purpureum) followed by biomass burning before planting maize in the spring, (ii) the same practice but without burning and the maize receiving mineral nitrogen fertilizer, (iii) a winter crop of a legume, pigeon pea (Cajanus cajan), followed by maize, (iv) vegetation free winter period (bare fallow) followed by maize, and (v) unmanaged elephant grass-shrub vegetation. The mean soil organic carbon (SOC) contents of the soils after 4 years were: 1.29, 1.67, 1.54, 0.80 and 1.34%, respectively, differences that should affect resources for the microbial community.From about 290,000 sequences obtained by pyrosequencing the SSU rRNA gene, canonical correspondence analysis showed that SOC was the most important factor that explained differences in microbial community structure among treatments. This analysis as well as phylogenetic ecological network construction indicated that members of the Acidobacteria GP4 and GP6 were more abundant in soils with relatively high SOC whereas Acidobacteria GP1, GP7, and Actinobacteria were more prevalent in soil with lower SOC. Burning of winter fallow vegetation led to an increase in Bacillales, especially those belonging to spore-forming genera. Of the managements, pigeon-pea cultivation during the winter period promoted a higher microbial diversity and also sequestered more SOC, presumably improving soil structure, fertility, and resiliency.

The susceptibility of soil organic carbon (SOC) in tundra to microbial decomposition under warmer climate scenarios potentially threatens a massive positive feedback to climate change, but the underlying mechanisms of stable SOC decomposition remain elusive. Herein, Alaskan tundra soils from three depths (a fibric O horizon with litter and course roots, an O horizon with decomposing litter and roots, and a mineral-organic mix, laying just above the permafrost) were incubated. Resulting respiration data were assimilated into a 3-pool model to derive decomposition kinetic parameters for fast, slow, and passive SOC pools. Bacterial, archaeal, and fungal taxa and microbial functional genes were profiled throughout the 3-year incubation. Correlation analyses and a Random Forest approach revealed associations between model parameters and microbial community profiles, taxa, and traits. There were more associations between the microbial community data and the SOC decomposition parameters of slow and passive SOC pools than those of the fast SOC pool. Also, microbial community profiles were better predictors of model parameters in deeper soils, which had higher mineral contents and relatively greater quantities of old SOC than in surface soils. Overall, our analyses revealed the functional potential of microbial communities to decompose tundra SOC through a suite of specialized genes and taxa. These results portray divergent strategies by which microbial communities access SOC pools across varying depths, lending mechanistic insights into the vulnerability of what is considered stable SOC in tundra regions.