Analysis Of Amplicon Sequencing Data Research Articles

Metagenomic profiling pipelines improve taxonomic classification for 16S amplicon sequencing data

Most experiments studying bacterial microbiomes rely on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiome sample. Several computational methods exist for analyzing 16S amplicon sequencing. However, the most-used bioinformatics tools cannot produce high quality genus-level or species-level taxonomic calls and may underestimate the potential accuracy of these calls. We used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, concentrating on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. We evaluated the tools DADA2, QIIME 2, Mothur, PathoScope 2, and Kraken 2 in conjunction with reference libraries from Greengenes, SILVA, Kraken 2, and RefSeq. Profiling tools were compared using publicly available mock community data from several sources, comprising 136 samples with varied species richness and evenness, several different amplified regions within the 16S rRNA gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole-genome metagenomics, outperformed DADA2, QIIME 2 using the DADA2 plugin, and Mothur, which are theoretically specialized for 16S analyses. Evaluations of reference libraries identified the SILVA and RefSeq/Kraken 2 Standard libraries as superior in accuracy compared to Greengenes. These findings support PathoScope and Kraken 2 as fully capable, competitive options for genus- and species-level 16S amplicon sequencing data analysis, whole genome sequencing, and metagenomics data tools.

Exploring the elevation dynamics of rumen bacterial communities in Barn feeding cattle from 900 to 3,600 meters by full-length 16S sequencing.

The diversity and abundance of rumen microorganisms serve as indicators not only of the host's digestive and metabolic capacity but also of its health status. The complex microbial communities in the rumen are influenced to varying degrees by environmental adaptability. In this study, we collected 24 rumen fluid samples from 24 healthy male cattle in three regions of Yunnan, China. Using 16S rRNA amplicon sequencing data analysis, we examined the variations in rumen microorganisms among cattle fed at altitudes of 900 m, 1800 m, and 3,600 m. Altitude-related environmental factors did not surpass phylogeny as the main driving force behind the convergent evolution of yellow cattle rumen microbiome composition. However, they did have an impact on the alpha diversity of the rumen microbiome and the coevolution of the core microbiome. The change in altitude noticeably influenced the diversity and richness of the rumen microbiota, highlighting the environmental effect of altitude. As altitude increased, there was an observed increase in the abundance of Firmicutes and Bacteroidetes, while the abundance of ruminal Proteobacteria and Kiritimatiellaeota decreased. Importantly, at the genus level, the core genus exhibited distinct dynamic changes as altitude increased. Ruminants exhibit the ability to adapt their gut type in accordance with altitude, thereby optimizing energy utilization, especially in high-altitude settings. These discoveries offer valuable insights into the coevolution of host-microbe interactions during ruminant adaptation to various altitudinal environments.

The Succession of the Cellulolytic Microbial Community from the Soil during Oat Straw Decomposition.

The process of straw decomposition is dynamic and is accompanied by the succession of the microbial decomposing community, which is driven by poorly understood interactions between microorganisms. Soil is a complex ecological niche, and the soil microbiome can serve as a source of potentially active cellulolytic microorganisms. Here, we performed an experiment on the de novo colonization of oat straw by the soil microbial community by placing nylon bags with sterilized oat straw in the pots filled with chernozem soil and incubating them for 6 months. The aim was to investigate the changes in decomposer microbiota during this process using conventional sequencing techniques. The bacterial succession during straw decomposition occurred in three phases: the early phase (first month) was characterized by high microbial activity and low diversity, the middle phase (second to third month) was characterized by low activity and low diversity, and the late phase (fourth to sixth months) was characterized by low activity and high diversity. Analysis of amplicon sequencing data revealed three groups of co-changing phylotypes corresponding to these phases. The early active phase was abundant in the cellulolytic members from Pseudomonadota, Bacteroidota, Bacillota, and Actinobacteriota for bacteria and Ascomycota for fungi, and most of the primary phylotypes were gone by the end of the phase. The second intermediate phase was marked by the set of phylotypes from the same phyla persisting in the community. In the mature community of the late phase, apart from the core phylotypes, non-cellulolytic members from Bdellovibrionota, Myxococcota, Chloroflexota, and Thermoproteota appeared. Full metagenome sequencing of the microbial community from the end of the middle phase confirmed that major bacterial and fungal members of this consortium had genes of glycoside hydrolases (GH) connected to cellulose and chitin degradation. The real-time analysis of the selection of these genes showed that their representation varied between phases, and this occurred under the influence of the host, and not the GH family factor. Our findings demonstrate that soil microbial community may act as an efficient source of cellulolytic microorganisms and that colonization of the cellulolytic substrate occurs in several phases, each characterized by its own taxonomic and functional profile.

16S-FASAS: an integrated pipeline for synthetic full-length 16S rRNA gene sequencing data analysis.

BackgroundThe full-length 16S rRNA sequencing can better improve the taxonomic and phylogenetic resolution compared to the partial 16S rRNA gene sequencing. The 16S-FAS-NGS (16S rRNA full-length amplicon sequencing based on a next-generation sequencing platform) technology can generate high-quality, full-length 16S rRNA gene sequences using short-read sequencers, together with assembly procedures. However there is a lack of a data analysis suite that can help process and analyze the synthetic long read data.ResultsHerein, we developed software named 16S-FASAS (16S full-length amplicon sequencing data analysis software) for 16S-FAS-NGS data analysis, which provided high-fidelity species-level microbiome data. 16S-FASAS consists of data quality control, de novo assembly, annotation, and visualization modules. We verified the performance of 16S-FASAS on both mock and fecal samples. In mock communities, we proved that taxonomy assignment by MegaBLAST had fewer misclassifications and tended to find more low abundance species than the USEARCH-UNOISE3-based classifier, resulting in species-level classification of 85.71% (6/7), 85.71% (6/7), 72.72% (8/11), and 70% (7/10) of the target bacteria. When applied to fecal samples, we found that the 16S-FAS-NGS datasets generated contigs grouped into 60 and 56 species, from which 71.62% (43/60) and 76.79% (43/56) were shared with the Pacbio datasets.Conclusions16S-FASAS is a valuable tool that helps researchers process and interpret the results of full-length 16S rRNA gene sequencing. Depending on the full-length amplicon sequencing technology, the 16S-FASAS pipeline enables a more accurate report on the bacterial complexity of microbiome samples. 16S-FASAS is freely available for use at https://github.com/capitalbio-bioinfo/FASAS.

Ensuring That Fundamentals of Quantitative Microbiology Are Reflected in Microbial Diversity Analyses Based on Next-Generation Sequencing.

Diversity analysis of amplicon sequencing data has mainly been limited to plug-in estimates calculated using normalized data to obtain a single value of an alpha diversity metric or a single point on a beta diversity ordination plot for each sample. As recognized for count data generated using classical microbiological methods, amplicon sequence read counts obtained from a sample are random data linked to source properties (e.g., proportional composition) by a probabilistic process. Thus, diversity analysis has focused on diversity exhibited in (normalized) samples rather than probabilistic inference about source diversity. This study applies fundamentals of statistical analysis for quantitative microbiology (e.g., microscopy, plating, and most probable number methods) to sample collection and processing procedures of amplicon sequencing methods to facilitate inference reflecting the probabilistic nature of such data and evaluation of uncertainty in diversity metrics. Following description of types of random error, mechanisms such as clustering of microorganisms in the source, differential analytical recovery during sample processing, and amplification are found to invalidate a multinomial relative abundance model. The zeros often abounding in amplicon sequencing data and their implications are addressed, and Bayesian analysis is applied to estimate the source Shannon index given unnormalized data (both simulated and experimental). Inference about source diversity is found to require knowledge of the exact number of unique variants in the source, which is practically unknowable due to library size limitations and the inability to differentiate zeros corresponding to variants that are actually absent in the source from zeros corresponding to variants that were merely not detected. Given these problems with estimation of diversity in the source even when the basic multinomial model is valid, diversity analysis at the level of samples with normalized library sizes is discussed.

Streamlined processing and analysis of 16S rRNA amplicon sequencing data with OCMS_16S and OCMSlooksy

16S rRNA gene sequencing is a cost-effective method for profiling the bacterial component of a microbiome. Nevertheless, processing and analysis of the resulting sequencing data is often constrained by the availability of dedicated bioinformaticians - creating a bottleneck for biological interpretation. Multiple visualisation and analysis tools now exist for downstream analysis of 16S rRNA data. These tools are designed with biological scientists in mind and therefore consist of a graphical user interface that interacts with taxonomic counts tables to perform tasks such as alpha- and beta-diversity analysis and differential abundance. However, generating the input to these applications still relies on bioinformatics experience, creating a disconnect between data processing and data analysis. We aimed to bridge the gap between data processing and data analysis. To do this we have created two tools - OCMS_16S and OCMSlooksy - that perform data processing and data visualisation/analysis, respectively. OCMS_16S is a cgat-core based pipeline that wraps DADA2 functionality in order to facilitate processing of raw sequence reads into tables of amplicon sequence variant (ASV) counts using a simple command line interface. OCMSlooksy is an RShiny application that takes an OCMS_16S-generated SQLite database as input to facilitate data exploration and analysis. Combining these tools provides a simple, user-friendly workflow to facilitate 16S rRNA gene amplicon sequencing data analysis from raw reads to results.

Microeco: an R package for data mining in microbial community ecology.

A large amount of sequencing data is produced in microbial community ecology studies using the high-throughput sequencing technique, especially amplicon-sequencing-based community data. After conducting the initial bioinformatic analysis of amplicon sequencing data, performing the subsequent statistics and data mining based on the operational taxonomic unit and taxonomic assignment tables is still complicated and time-consuming. To address this problem, we present an integrated R package-'microeco' as an analysis pipeline for treating microbial community and environmental data. This package was developed based on the R6 class system and combines a series of commonly used and advanced approaches in microbial community ecology research. The package includes classes for data preprocessing, taxa abundance plotting, venn diagram, alpha diversity analysis, beta diversity analysis, differential abundance test and indicator taxon analysis, environmental data analysis, null model analysis, network analysis and functional analysis. Each class is designed to provide a set of approaches that can be easily accessible to users. Compared with other R packages in the microbial ecology field, the microeco package is fast, flexible and modularized to use and provides powerful and convenient tools for researchers. The microeco package can be installed from CRAN (The Comprehensive R Archive Network) or github (https://github.com/ChiLiubio/microeco).

CoMA - an intuitive and user-friendly pipeline for amplicon-sequencing data analysis.

In recent years, there has been a veritable boost in next-generation sequencing (NGS) of gene amplicons in biological and medical studies. Huge amounts of data are produced and need to be analyzed adequately. Various online and offline analysis tools are available; however, most of them require extensive expertise in computer science or bioinformatics, and often a Linux-based operating system. Here, we introduce "CoMA-Comparative Microbiome Analysis" as a free and intuitive analysis pipeline for amplicon-sequencing data, compatible with any common operating system. Moreover, the tool offers various useful services including data pre-processing, quality checking, clustering to operational taxonomic units (OTUs), taxonomic assignment, data post-processing, data visualization, and statistical appraisal. The workflow results in highly esthetic and publication-ready graphics, as well as output files in standardized formats (e.g. tab-delimited OTU-table, BIOM, NEWICK tree) that can be used for more sophisticated analyses. The CoMA output was validated by a benchmark test, using three mock communities with different sample characteristics (primer set, amplicon length, diversity). The performance was compared with that of Mothur, QIIME and QIIME2-DADA2, popular packages for NGS data analysis. Furthermore, the functionality of CoMA is demonstrated on a practical example, investigating microbial communities from three different soils (grassland, forest, swamp). All tools performed well in the benchmark test and were able to reveal the majority of all genera in the mock communities. Also for the soil samples, the results of CoMA were congruent to those of the other pipelines, in particular when looking at the key microbial players.

Cascabel: A Scalable and Versatile Amplicon Sequence Data Analysis Pipeline Delivering Reproducible and Documented Results.

Marker gene sequencing of the rRNA operon (16S, 18S, ITS) or cytochrome c oxidase I (CO1) is a popular means to assess microbial communities of the environment, microbiomes associated with plants and animals, as well as communities of multicellular organisms via environmental DNA sequencing. Since this technique is based on sequencing a single gene, or even only parts of a single gene rather than the entire genome, the number of reads needed per sample to assess the microbial community structure is lower than that required for metagenome sequencing. This makes marker gene sequencing affordable to nearly any laboratory. Despite the relative ease and cost-efficiency of data generation, analyzing the resulting sequence data requires computational skills that may go beyond the standard repertoire of a current molecular biologist/ecologist. We have developed Cascabel, a scalable, flexible, and easy-to-use amplicon sequence data analysis pipeline, which uses Snakemake and a combination of existing and newly developed solutions for its computational steps. Cascabel takes the raw data as input and delivers a table of operational taxonomic units (OTUs) or Amplicon Sequence Variants (ASVs) in BIOM and text format and representative sequences. Cascabel is a highly versatile software that allows users to customize several steps of the pipeline, such as selecting from a set of OTU clustering methods or performing ASV analysis. In addition, we designed Cascabel to run in any linux/unix computing environment from desktop computers to computing servers making use of parallel processing if possible. The analyses and results are fully reproducible and documented in an HTML and optional pdf report. Cascabel is freely available at Github: https://github.com/AlejandroAb/CASCABEL.

Systematic processing of ribosomal RNA gene amplicon sequencing data.

BackgroundWith the advent of high-throughput sequencing, microbiology is becoming increasingly data-intensive. Because of its low cost, robust databases, and established bioinformatic workflows, sequencing of 16S/18S/ITS ribosomal RNA (rRNA) gene amplicons, which provides a marker of choice for phylogenetic studies, has become ubiquitous. Many established end-to-end bioinformatic pipelines are available to perform short amplicon sequence data analysis. These pipelines suit a general audience, but few options exist for more specialized users who are experienced in code scripting, Linux-based systems, and high-performance computing (HPC) environments. For such an audience, existing pipelines can be limiting to fully leverage modern HPC capabilities and perform tweaking and optimization operations. Moreover, a wealth of stand-alone software packages that perform specific targeted bioinformatic tasks are increasingly accessible, and finding a way to easily integrate these applications in a pipeline is critical to the evolution of bioinformatic methodologies.ResultsHere we describe AmpliconTagger, a short rRNA marker gene amplicon pipeline coded in a Python framework that enables fine tuning and integration of virtually any potential rRNA gene amplicon bioinformatic procedure. It is designed to work within an HPC environment, supporting a complex network of job dependencies with a smart-restart mechanism in case of job failure or parameter modifications. As proof of concept, we present end results obtained with AmpliconTagger using 16S, 18S, ITS rRNA short gene amplicons and Pacific Biosciences long-read amplicon data types as input.ConclusionsUsing a selection of published algorithms for generating operational taxonomic units and amplicon sequence variants and for computing downstream taxonomic summaries and diversity metrics, we demonstrate the performance and versatility of our pipeline for systematic analyses of amplicon sequence data.

Cascabel: a flexible, scalable and easy to use amplicon sequence data analysis pipeline

Cascabel: a flexible, scalable and easy to use amplicon sequence data analysis pipeline

Improved Efficiency and Reliability of NGS Amplicon Sequencing Data Analysis for Genetic Diagnostic Procedures Using AGSA Software.

Screening for BRCA mutations in women with familial risk of breast or ovarian cancer is an ideal situation for high-throughput sequencing, providing large amounts of low cost data. However, 454, Roche, and Ion Torrent, Thermo Fisher, technologies produce homopolymer-associated indel errors, complicating their use in routine diagnostics. We developed software, named AGSA, which helps to detect false positive mutations in homopolymeric sequences. Seventy-two familial breast cancer cases were analysed in parallel by amplicon 454 pyrosequencing and Sanger dideoxy sequencing for genetic variations of the BRCA genes. All 565 variants detected by dideoxy sequencing were also detected by pyrosequencing. Furthermore, pyrosequencing detected 42 variants that were missed with Sanger technique. Six amplicons contained homopolymer tracts in the coding sequence that were systematically misread by the software supplied by Roche. Read data plotted as histograms by AGSA software aided the analysis considerably and allowed validation of the majority of homopolymers. As an optimisation, additional 250 patients were analysed using microfluidic amplification of regions of interest (Access Array Fluidigm) of the BRCA genes, followed by 454 sequencing and AGSA analysis. AGSA complements a complete line of high-throughput diagnostic sequence analysis, reducing time and costs while increasing reliability, notably for homopolymer tracts.

Amplicon sequencing of colorectal cancer: variant calling in frozen and formalin-fixed samples.

Next generation sequencing (NGS) is an emerging technology becoming relevant for genotyping of clinical samples. Here, we assessed the stability of amplicon sequencing from formalin-fixed paraffin-embedded (FFPE) and paired frozen samples from colorectal cancer metastases with different analysis pipelines. 212 amplicon regions in 48 cancer related genes were sequenced with Illumina MiSeq using DNA isolated from resection specimens from 17 patients with colorectal cancer liver metastases. From ten of these patients, paired fresh frozen and routinely processed FFPE tissue was available for comparative study. Sample quality of FFPE tissues was determined by the amount of amplifiable DNA using qPCR, sequencing libraries were evaluated using Bioanalyzer. Three bioinformatic pipelines were compared for analysis of amplicon sequencing data. Selected hot spot mutations were reviewed using Sanger sequencing. In the sequenced samples from 16 patients, 29 non-synonymous coding mutations were identified in eleven genes. Most frequent were mutations in TP53 (10), APC (7), PIK3CA (3) and KRAS (2). A high concordance of FFPE and paired frozen tissue samples was observed in ten matched samples, revealing 21 identical mutation calls and only two mutations differing. Comparison of these results with two other commonly used variant calling tools, however, showed high discrepancies. Hence, amplicon sequencing can potentially be used to identify hot spot mutations in colorectal cancer metastases in frozen and FFPE tissue. However, remarkable differences exist among results of different variant calling tools, which are not only related to DNA sample quality. Our study highlights the need for standardization and benchmarking of variant calling pipelines, which will be required for translational and clinical applications.

Inconsistent Denoising and Clustering Algorithms for Amplicon Sequence Data.

Natural microbial communities have been studied for decades using the 16S rRNA gene as a marker. In recent years, the application of second-generation sequencing technologies has revolutionized our understanding of the structure and function of microbial communities in complex environments. Using these highly parallel techniques, a detailed description of community characteristics are constructed, and even the rare biosphere can be detected. The new approaches carry numerous advantages and lack many features that skewed the results using traditional techniques, but we are still facing serious bias, and the lack of reliable comparability of produced results. Here, we contrasted publicly available amplicon sequence data analysis algorithms by using two different data sets, one with defined clone-based structure, and one with food spoilage community with well-studied communities. We aimed to assess which software and parameters produce results that resemble the benchmark community best, how large differences can be detected between methods, and whether these differences are statistically significant. The results suggest that commonly accepted denoising and clustering methods used in different combinations produce significantly different outcome: clustering method impacts greatly on the number of operational taxonomic units (OTUs) and denoising algorithm influences more on taxonomic affiliations. The magnitude of the OTU number difference was up to 40-fold and the disparity between results seemed highly dependent on the community structure and diversity. Statistically significant differences in taxonomies between methods were seen even at phylum level. However, the application of effective denoising method seemed to even out the differences produced by clustering.

Edge effects in calling variants from targeted amplicon sequencing.

BackgroundAnalysis of targeted amplicon sequencing data presents some unique challenges in comparison to the analysis of random fragment sequencing data. Whereas reads from randomly fragmented DNA have arbitrary start positions, the reads from amplicon sequencing have fixed start positions that coincide with the amplicon boundaries. As a result, any variants near the amplicon boundaries can cause misalignments of multiple reads that can ultimately lead to false-positive or false-negative variant calls.ResultsWe show that amplicon boundaries are variant calling blind spots where the variant calls are highly inaccurate. We propose that an effective strategy to avoid these blind spots is to incorporate the primer bases in obtaining read alignments and post-processing of the alignments, thereby effectively moving these blind spots into the primer binding regions (which are not used for variant calling). Targeted sequencing data analysis pipelines can provide better variant calling accuracy when primer bases are retained and sequenced.ConclusionsRead bases beyond the variant site are necessary for analysis of amplicon sequencing data. Enzymatic primer digestion, if used in the target enrichment process, should leave at least a few primer bases to ensure that these bases are available during data analysis. The primer bases should only be removed immediately before the variant calling step to ensure that the variants can be called irrespective of where they occur within the amplicon insert region.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1073) contains supplementary material, which is available to authorized users.