High-throughput Amplicon Sequencing Data Research Articles

MHCtools 1.5: Analysis of MHC Sequencing Data in R.

The genes of the major histocompatibility complex (MHC) play a vital role in the vertebrate immune system and have attracted considerable interest in evolutionary biology. While the MHC has been characterized in detail in humans (human leukocyte antigen, HLA) and in model organisms such as the mouse, studies in non-model organisms often lack prior knowledge about structure, genetic variability, and evolutionary properties of this locus. MHC genotyping in non-model species commonly relies on PCR-based amplicon sequencing, and while several published protocols facilitate generation of MHC sequence data, there is a lack of transparent and standardized tools for downstream data analysis.Here, I present the R package MHCtools version 1.5, which contains 15 tools that (i) assist accurate MHC genotyping from high-throughput amplicon sequencing data, and provide standardized methods to analyze (ii) MHC diversity, (iii) MHC supertypes, and (iv) MHC haplotypes.I hope that MHCtools will be helpful in future studies of the MHC in non-model species and that it may help to advance our understanding of the important roles of the MHC in ecology and evolution.

Natural restoration alters soil microbial community structure, but has contrasting effects on the diversity of bacterial and fungal assemblages in salinized grasslands

Natural restoration has often been considered an effective measure for rehabilitating degraded ecosystems. However, its impact on the structure and diversity of soil microbial communities, particularly within a salinized grassland during its restoration succession, remains unclear. In this study, we examined the effects of natural restoration on the Shannon-Wiener diversity index, Operational Taxonomic Units (OTU) richness, and structure of the soil microbial community of a sodic-saline grassland in China using high-throughput amplicon sequencing data from representative successional chronosequences. Our results indicated that natural restoration resulted in a significant mitigation of the grassland salinization (pH from 9.31 to 8.32 and electrical conductivity from 393.33 to 136.67 μs·cm−1) and a significant alteration of the soil microbial community structure of the grassland (p < 0.01). However, the effects of natural recovery differed in terms of the abundance and diversity of bacteria and fungi. For example, the relative abundance of the bacterial phyla Acidobacteria increased by 116.45 % in the topsoil and 339.03 % in the subsoil, while that of the fungal phyla Ascomycota decreased by 8.86 % in the topsoil and 30.18 % in the subsoil. There was no significant effect of restoration on bacterial diversity, but fungal diversity increased by 15.02 % in the Shannon-Wiener index and 62.20 % in the OTU richness in the topsoil. Model-selection analysis further corroborated that the alteration of the soil microbial structure by natural restoration may be due to the fact that the bacteria could adapt to the alleviated salinized grassland soil and the fungi could adapt to the improved soil fertility of the grasslands. Overall, our results contribute to an in-depth understanding of the impacts of natural restoration on soil microbial diversity and community structure in salinized grasslands during the long-term successional course. This may also help to apply natural restoration as a greener practice option for managing degraded ecosystems.

Globally nitrogen addition alters soil microbial community structure, but has minor effects on soil microbial diversity and richness

Globally increasing nitrogen (N) deposition is recognized as an important regulator of soil microbial communities. However, how N enrichment affects soil microbial diversity, richness and community structure remains unclear at the global scale. Here, by focusing on high-throughput amplicon sequencing data from field experiments using N fertilizers only, we conducted a meta-analysis of a global dataset assessing the responses of microbial diversity (Shannon index), richness (Chao1, OTU richness) and community structure to N addition. Our results showed that N addition significantly reduced soil bacterial diversity (−2.3%), and such effect was significant in cropland (rather than grassland and forest) and with urea addition (rather than ammonium nitrate). However, there was no significant effect of N addition on soil fungal diversity and microbial (bacterial or fungal) richness. Moreover, N addition shifted microbial community structure likely due to the microbial adaptation to N-excess, but had no significant effect on microbial beta-diversity. Model-selection analysis further showed that the change in soil pH was the most important factor regulating the responses of soil bacterial diversity and richness to N addition. Overall, our results contribute to an in-depth understanding of the effects of N addition on soil microbial diversity and community structure in terrestrial ecosystems at the global scale.

Wood-decay type and fungal guild dominance across a North American log transplant experiment

We incubated 196 large-diameter aspen ( Populus tremuloides ), birch ( Betula papyrifera ), and pine ( Pinus taeda ) logs on the FACE Wood Decomposition Experiment encompassing eight climatically-distinct forest sites in the United States. We sampled dead wood from these large-diameter logs after 2 to 6 y of decomposition and determined wood rot type as a continuous variable using the lignin loss/density loss ratio (L/D) and assessed wood-rotting fungal guilds using high-throughput amplicon sequencing (HTAS) of the ITS-2 marker. We found L/D values in line with a white rot dominance in all three tree species, with pine having lower L/D values than aspen and birch. Based on HTAS data, white rot fungi were the most abundant and diverse wood-rotting fungal guild, and soft rot fungi were more abundant and diverse than brown rot fungi in logs with low L/D values. For aspen and birch logs, decay type was related to the wood density at sampling. For the pine logs, decay type was associated with the balance between white and brown/soft rot fungi abundance and OTU richness. Our results demonstrate that decay type is governed by biotic and abiotic factors, which vary by tree species. • Wood-decay types and wood-rotting fungal guilds were measured in large-diameter decayed logs. • White-rot processes dominated among a wide-range of temperate forest sites. • Decay type was related to the wood decomposition rate for aspen and birch dead wood. • Decay type was associated with wood-rotting fungal guild balance in pine dead wood.

Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology

High-throughput sequencing platforms are increasingly being used for targeted amplicon sequencing because they enable cost-effective sequencing of large sample sets. For meaningful interpretation of targeted amplicon sequencing data and comparison between studies, it is critical that bioinformatic analyses do not introduce artefacts and rely on detailed protocols to ensure that all methods are properly performed and documented. The analysis of large sample sets and the use of predefined indexes create challenges, such as adjusting the sequencing depth across samples and taking sequencing errors or index hopping into account. However, the potential biases these factors introduce to high-throughput amplicon sequencing data sets and how they may be overcome have rarely been addressed. On the example of a nested metabarcoding analysis of 1920 carabid beetle regurgitates to assess plant feeding, we investigated: (i) the variation in sequencing depth of individually tagged samples and the effect of library preparation on the data output; (ii) the influence of sequencing errors within index regions and its consequences for demultiplexing; and (iii) the effect of index hopping. Our results demonstrate that despite library quantification, large variation in read counts and sequencing depth occurred among samples and that the sequencing error rate in bioinformatic software is essential for accurate adapter/primer trimming and demultiplexing. Moreover, setting an index hopping threshold to avoid incorrect assignment of samples is highly recommended.

Does diversity beget diversity in microbiomes?

Microbes are embedded in complex communities where they engage in a wide array of intra- and inter-specific interactions. The extent to which these interactions drive or impede microbiome diversity is not well understood. Historically, two contrasting hypotheses have been suggested to explain how species interactions could influence diversity. 'Ecological Controls' (EC) predicts a negative relationship, where the evolution or migration of novel types is constrained as niches become filled. In contrast, 'Diversity Begets Diversity' (DBD) predicts a positive relationship, with existing diversity promoting the accumulation of further diversity via niche construction and other interactions. Using high-throughput amplicon sequencing data from the Earth Microbiome Project, we provide evidence that DBD is strongest in low-diversity biomes, but weaker in more diverse biomes, consistent with biotic interactions initially favouring the accumulation of diversity (as predicted by DBD). However, as niches become increasingly filled, diversity hits a plateau (as predicted by EC).

Biogeographic patterns of microbial co-occurrence ecological networks in six American forests

Rather than simple accumulation of individual populations, microorganisms in natural ecosystems form complex ecological networks that are critical to maintain ecosystem functions and services. Although various studies have examined the patterns of microbial community diversity and composition across spatial gradients, whether microbial co-occurrence relationships follow similar patterns remains an open question. In this study, we determined the biogeographic patterns of microbial co-occurrence networks of bacteria, fungi and nitrogen (N) fixer via analyses of high throughput amplicon sequencing data of 16S rRNA, ITS, and nifH genes from 126 forest soil samples across six forests in America. Microbial co-occurrence networks were constructed using a Random Matrix Theory based approach. Network parameters were calculated and correlated with biogeographic parameters. Gradient patterns along with biogeographic parameters were observed for network topologies. Significantly different network topologies were observed between microbial co-occurrence networks in tropical and temperate forest ecosystems. Among various biogeographic parameters potentially related with network topology indices, temperature seemed to be the strongest one. These results suggest that biogeographic variables like temperature not only mediate microbial community diversity and composition, but also the co-occurrence ecological networks among microbial species.

SEED 2: a user-friendly platform for amplicon high-throughput sequencing data analyses.

MotivationModern molecular methods have increased our ability to describe microbial communities. Along with the advances brought by new sequencing technologies, we now require intensive computational resources to make sense of the large numbers of sequences continuously produced. The software developed by the scientific community to address this demand, although very useful, require experience of the command-line environment, extensive training and have steep learning curves, limiting their use. We created SEED 2, a graphical user interface for handling high-throughput amplicon-sequencing data under Windows operating systems.ResultsSEED 2 is the only sequence visualizer that empowers users with tools to handle amplicon-sequencing data of microbial community markers. It is suitable for any marker genes sequences obtained through Illumina, IonTorrent or Sanger sequencing. SEED 2 allows the user to process raw sequencing data, identify specific taxa, produce of OTU-tables, create sequence alignments and construct phylogenetic trees. Standard dual core laptops with 8 GB of RAM can handle ca. 8 million of Illumina PE 300 bp sequences, ca. 4 GB of data.Availability and implementationSEED 2 was implemented in Object Pascal and uses internal functions and external software for amplicon data processing. SEED 2 is a freeware software, available at http://www.biomed.cas.cz/mbu/lbwrf/seed/ as a self-contained file, including all the dependencies, and does not require installation. Supplementary data contain a comprehensive list of supported functions.Supplementary information Supplementary data are available at Bioinformatics online.

PipeCraft: Flexible open-source toolkit for bioinformatics analysis of custom high-throughput amplicon sequencing data.

High-throughput sequencing methods have become a routine analysis tool in environmental sciences as well as in public and private sector. These methods provide vast amount of data, which need to be analysed in several steps. Although the bioinformatics may be applied using several public tools, many analytical pipelines allow too few options for the optimal analysis for more complicated or customized designs. Here, we introduce PipeCraft, a flexible and handy bioinformatics pipeline with a user-friendly graphical interface that links several public tools for analysing amplicon sequencing data. Users are able to customize the pipeline by selecting the most suitable tools and options to process raw sequences from Illumina, Pacific Biosciences, Ion Torrent and Roche 454 sequencing platforms. We described the design and options of PipeCraft and evaluated its performance by analysing the data sets from three different sequencing platforms. We demonstrated that PipeCraft is able to process large data sets within 24hr. The graphical user interface and the automated links between various bioinformatics tools enable easy customization of the workflow. All analytical steps and options are recorded in log files and are easily traceable.

CASPER: context-aware scheme for paired-end reads from high-throughput amplicon sequencing.

Merging the forward and reverse reads from paired-end sequencing is a critical task that can significantly improve the performance of downstream tasks, such as genome assembly and mapping, by providing them with virtually elongated reads. However, due to the inherent limitations of most paired-end sequencers, the chance of observing erroneous bases grows rapidly as the end of a read is approached, which becomes a critical hurdle for accurately merging paired-end reads. Although there exist several sophisticated approaches to this problem, their performance in terms of quality of merging often remains unsatisfactory. To address this issue, here we present a context-aware scheme for paired-end reads (CASPER): a computational method to rapidly and robustly merge overlapping paired-end reads. Being particularly well suited to amplicon sequencing applications, CASPER is thoroughly tested with both simulated and real high-throughput amplicon sequencing data. According to our experimental results, CASPER significantly outperforms existing state-of-the art paired-end merging tools in terms of accuracy and robustness. CASPER also exploits the parallelism in the task of paired-end merging and effectively speeds up by multithreading. CASPER is freely available for academic use at http://best.snu.ac.kr/casper.

The bias associated with amplicon sequencing does not affect the quantitative assessment of bacterial community dynamics.

The performance of two sets of primers targeting variable regions of the 16S rRNA gene V1–V3 and V4 was compared in their ability to describe changes of bacterial diversity and temporal turnover in full-scale activated sludge. Duplicate sets of high-throughput amplicon sequencing data of the two 16S rRNA regions shared a collection of core taxa that were observed across a series of twelve monthly samples, although the relative abundance of each taxon was substantially different between regions. A case in point was the changes in the relative abundance of filamentous bacteria Thiothrix, which caused a large effect on diversity indices, but only in the V1–V3 data set. Yet the relative abundance of Thiothrix in the amplicon sequencing data from both regions correlated with the estimation of its abundance determined using fluorescence in situ hybridization. In nonmetric multidimensional analysis samples were distributed along the first ordination axis according to the sequenced region rather than according to sample identities. The dynamics of microbial communities indicated that V1–V3 and the V4 regions of the 16S rRNA gene yielded comparable patterns of: 1) the changes occurring within the communities along fixed time intervals, 2) the slow turnover of activated sludge communities and 3) the rate of species replacement calculated from the taxa–time relationships. The temperature was the only operational variable that showed significant correlation with the composition of bacterial communities over time for the sets of data obtained with both pairs of primers. In conclusion, we show that despite the bias introduced by amplicon sequencing, the variable regions V1–V3 and V4 can be confidently used for the quantitative assessment of bacterial community dynamics, and provide a proper qualitative account of general taxa in the community, especially when the data are obtained over a convenient time window rather than at a single time point.