scholarly journals PEMA: from the raw .fastq files of 16S rRNA and COI marker genes to the (M)OTU-table, a thorough metabarcoding analysis

2019 ◽  
Author(s):  
Haris Zafeiropoulos ◽  
Ha Quoc Viet ◽  
Katerina Vasileiadou ◽  
Antonis Potirakis ◽  
Christos Arvanitidis ◽  
...  
Keyword(s):  
16S Rrna ◽  
Programming Languages ◽  
High Performance ◽  
Marker Gene ◽  
Environmental Dna ◽  
Operational Taxonomic Unit ◽  
Third Party ◽  
Marker Genes ◽  
New Era ◽  
Efficient Performance

AbstractBackgroundEnvironmental DNA (eDNA) and metabarcoding, allow the identification of a mixture of individuals and launch a new era in bio- and eco-assessment. A number of steps are required to obtain taxonomically assigned (Molecular) Operational Taxonomic Unit ((M)OTU) tables from raw data. For most of these, a plethora of tools is available; each tool’s execution parameters need to be tailored to reflect each experiment’s idiosyncrasy. Adding to this complexity, for such analyses, the computation capacity of High Performance Computing (HPC) systems is frequently required.Software containerization technologies ease the sharing and running of software packages across operating systems; thus, they strongly facilitate pipeline development and usage. Likewise are programming languages specialized for big data pipelines, incorporating features like roll-back checkpoints and on-demand partial pipeline execution.FindingsPEMA is a containerized assembly of key metabarcoding analysis tools with a low effort in setting up, running and customizing to researchers’ needs. Based on third party tools, PEMA performs reads’ pre-processing, clustering to (M)OTUs and taxonomy assignment for 16S rRNA and COI marker gene data. Due to its simplified parameterisation and checkpoint support, PEMA allows users to explore alternative algorithms for specific steps of the pipeline without the need of a complete re-execution. PEMA was evaluated against previously published datasets and achieved comparable quality results.ConclusionsGiven its time-efficient performance and its quality results, it is suggested that PEMA can be used for accurate eDNA metabarcoding analysis, thus enhancing the applicability of next-generation biodiversity assessment studies.

scholarly journals PEMA v2: addressing metabarcoding bioinformatics analysis challenges

2021 ◽  
Vol 4 ◽  
Author(s):  
Haris Zafeiropoulos ◽  
Christina Pavloudi ◽  
Evangelos Pafilis
Keyword(s):  
High Performance ◽  
Bioinformatics Analysis ◽  
Marker Gene ◽  
Environmental Dna ◽  
Third Party ◽  
Reference Database ◽  
Marker Genes ◽  
Specific Reference ◽  
Taxonomic Assignment ◽  
Internal Joint

Environmental DNA (eDNA) and metabarcoding have launched a new era in bio- and eco-assessment over the last years (Ruppert et al. 2019). The simultaneous identification, at the lowest taxonomic level possible, of a mixture of taxa from a great range of samples is now feasible; thus, the number of eDNA metabarcoding studies has increased radically (Deiner and 2017). While the experimental part of eDNA metabarcoding can be rather challenging depending on the special characteristics of the different studies, computational issues are considered to be its major bottlenecks. Among the latter, the bioinformatics analysis of metabarcoding data and especially the taxonomy assignment of the sequences are fundamental challenges. Many steps are required to obtain taxonomically assigned matrices from raw data. For most of these, a plethora of tools are available. However, each tool's execution parameters need to be tailored to reflect each experiment's idiosyncrasy; thus, tuning bioinformatics analysis has proved itself fundamental (Kamenova 2020). The computation capacity of high-performance computing systems (HPC) is frequently required for such analyses. On top of that, the non perfect completeness and correctness of the reference taxonomy databases is another important issue (Loos et al. 2020). Based on third-party tools, we have developed the Pipeline for Environmental Metabarcoding Analysis (PEMA), a HPC-centered, containerized assembly of key metabarcoding analysis tools. PEMA combines state-of-the art technologies and algorithms with an easy to get-set-use framework, allowing researchers to tune thoroughly each study thanks to roll-back checkpoints and on-demand partial pipeline execution features (Zafeiropoulos 2020). Once PEMA was released, there were two main pitfalls soon to be highlighted by users. PEMA supported 4 marker genes and was bounded by specific reference databases. In this new version of PEMA the analysis of any marker gene is now available since a new feature was added, allowing classifiers to train a user-provided reference database and use it for taxonomic assignment. Fig. 1 shows the taxonomy assignment related PEMA modules; all those out of the dashed box have been developed for this new PEMA release. As shown, the RDPClassifier has been trained with Midori reference 2 and has been added as an option, classifying not only metazoans but sequences from all taxonomic groups of Eukaryotes for the case of the COI marker gene. A PEMA documentation site is now also available. PEMA.v2 containers are available via the DockerHub and SingularityHub as well as through the Elixir Greece AAI Service. It has also been selected to be part of the LifeWatch ERIC Internal Joint Initiative for the analysis of ARMS data and soon will be available through the Tesseract VRE.

scholarly journals PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes

GigaScience ◽  
2020 ◽  
Vol 9 (3) ◽  
Author(s):  
Haris Zafeiropoulos ◽  
Ha Quoc Viet ◽  
Katerina Vasileiadou ◽  
Antonis Potirakis ◽  
Christos Arvanitidis ◽  
...  
Keyword(s):  
High Performance Computing ◽  
Ribosomal Rna ◽  
High Performance ◽  
Marker Gene ◽  
Environmental Dna ◽  
Marker Genes ◽  
Sequence Variant ◽  
18S Ribosomal Rna ◽  
Dna Metabarcoding ◽  
Performance Computing

Abstract Background Environmental DNA and metabarcoding allow the identification of a mixture of species and launch a new era in bio- and eco-assessment. Many steps are required to obtain taxonomically assigned matrices from raw data. For most of these, a plethora of tools are available; each tool's execution parameters need to be tailored to reflect each experiment's idiosyncrasy. Adding to this complexity, the computation capacity of high-performance computing systems is frequently required for such analyses. To address the difficulties, bioinformatic pipelines need to combine state-of-the art technologies and algorithms with an easy to get-set-use framework, allowing researchers to tune each study. Software containerization technologies ease the sharing and running of software packages across operating systems; thus, they strongly facilitate pipeline development and usage. Likewise programming languages specialized for big data pipelines incorporate features like roll-back checkpoints and on-demand partial pipeline execution. Findings PEMA is a containerized assembly of key metabarcoding analysis tools that requires low effort in setting up, running, and customizing to researchers’ needs. Based on third-party tools, PEMA performs read pre-processing, (molecular) operational taxonomic unit clustering, amplicon sequence variant inference, and taxonomy assignment for 16S and 18S ribosomal RNA, as well as ITS and COI marker gene data. Owing to its simplified parameterization and checkpoint support, PEMA allows users to explore alternative algorithms for specific steps of the pipeline without the need of a complete re-execution. PEMA was evaluated against both mock communities and previously published datasets and achieved results of comparable quality. Conclusions A high-performance computing–based approach was used to develop PEMA; however, it can be used in personal computers as well. PEMA's time-efficient performance and good results will allow it to be used for accurate environmental DNA metabarcoding analysis, thus enhancing the applicability of next-generation biodiversity assessment studies.

scholarly journals Are multiplexed metabarcoding panels comparable to individual marker gene library preparations?

2021 ◽  
Vol 4 ◽  
Author(s):  
Niamh Eastwood ◽  
Luisa Orsini
Keyword(s):  
Environmental Gradients ◽  
Marker Gene ◽  
Environmental Dna ◽  
18S Rrna Gene ◽  
Marker Genes ◽  
Rrna Gene ◽  
Rbcl Gene ◽  
Single Marker ◽  
Multiple Regions ◽  
Phylogenetic Resolution

Analysis of multiple marker genes using metabarcoding of environmental DNA (eDNA) can offer information greater than that from sequencing single marker genes, such as responses from across the phylogenetic tree to environmental gradients (Cordier et al. 2019). Furthermore, multiple regions of the same gene can be sequenced to improve phylogenetic resolution (Fuks et al. 2018). However, separate amplification reactions and library preparation steps for each marker can be costly and time consuming. Here, we have designed and optimised a multiplex panel of four marker genes (two regions of 18S rRNA gene, one region of the 16S rRNA gene and one region of the rbcL gene). By combining steps into a single reaction, the labwork required is decreased, reducing cost and time. This multiplex is compared with a widely available commercial microbial (bacterial and fungal) screening panel and individual library preparations of each marker gene.

scholarly journals The mutL Gene as a Genome-Wide Taxonomic Marker for High Resolution Discrimination of Lactiplantibacillus plantarum and Its Closely Related Taxa

2021 ◽  
Vol 9 (8) ◽  
pp. 1570
Author(s):  
Chien-Hsun Huang ◽  
Chih-Chieh Chen ◽  
Yu-Chun Lin ◽  
Chia-Hsuan Chen ◽  
Ai-Yun Lee ◽  
...  
Keyword(s):  
16S Rrna ◽  
16S Rrna Gene ◽  
Target Genes ◽  
Marker Genes ◽  
Rrna Gene ◽  
Accurate Identification ◽  
Discrimination Power ◽  
Sequence Identity ◽  
Genome Wide ◽  
A Genome

The current taxonomy of the Lactiplantibacillus plantarum group comprises of 17 closely related species that are indistinguishable from each other by using commonly used 16S rRNA gene sequencing. In this study, a whole-genome-based analysis was carried out for exploring the highly distinguished target genes whose interspecific sequence identity is significantly less than those of 16S rRNA or conventional housekeeping genes. In silico analyses of 774 core genes by the cano-wgMLST_BacCompare analytics platform indicated that csbB, morA, murI, mutL, ntpJ, rutB, trmK, ydaF, and yhhX genes were the most promising candidates. Subsequently, the mutL gene was selected, and the discrimination power was further evaluated using Sanger sequencing. Among the type strains, mutL exhibited a clearly superior sequence identity (61.6–85.6%; average: 66.6%) to the 16S rRNA gene (96.7–100%; average: 98.4%) and the conventional phylogenetic marker genes (e.g., dnaJ, dnaK, pheS, recA, and rpoA), respectively, which could be used to separat tested strains into various species clusters. Consequently, species-specific primers were developed for fast and accurate identification of L. pentosus, L. argentoratensis, L. plantarum, and L. paraplantarum. During this study, one strain (BCRC 06B0048, L. pentosus) exhibited not only relatively low mutL sequence identities (97.0%) but also a low digital DNA–DNA hybridization value (78.1%) with the type strain DSM 20314T, signifying that it exhibits potential for reclassification as a novel subspecies. Our data demonstrate that mutL can be a genome-wide target for identifying and classifying the L. plantarum group species and for differentiating novel taxa from known species.

scholarly journals Metabolic pathways inferred from a bacterial marker gene illuminate ecological changes across South Pacific frontal boundaries

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Eric J. Raes ◽  
Kristen Karsh ◽  
Swan L. S. Sow ◽  
Martin Ostrowski ◽  
Mark V. Brown ◽  
...  
Keyword(s):  
16S Rrna ◽  
Metabolic Pathways ◽  
Low Cost ◽  
Marker Gene ◽  
South Pacific ◽  
Rrna Gene ◽  
South Pacific Ocean ◽  
Bacterial Marker ◽  
Gene 16S Rrna ◽  
Gene Data

AbstractGlobal oceanographic monitoring initiatives originally measured abiotic essential ocean variables but are currently incorporating biological and metagenomic sampling programs. There is, however, a large knowledge gap on how to infer bacterial functions, the information sought by biogeochemists, ecologists, and modelers, from the bacterial taxonomic information (produced by bacterial marker gene surveys). Here, we provide a correlative understanding of how a bacterial marker gene (16S rRNA) can be used to infer latitudinal trends for metabolic pathways in global monitoring campaigns. From a transect spanning 7000 km in the South Pacific Ocean we infer ten metabolic pathways from 16S rRNA gene sequences and 11 corresponding metagenome samples, which relate to metabolic processes of primary productivity, temperature-regulated thermodynamic effects, coping strategies for nutrient limitation, energy metabolism, and organic matter degradation. This study demonstrates that low-cost, high-throughput bacterial marker gene data, can be used to infer shifts in the metabolic strategies at the community scale.

scholarly journals Antibiotic-Induced Dysbiosis of Microbiota Promotes Chicken Lipogenesis by Altering Metabolomics in the Cecum

Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 487
Author(s):  
Tao Zhang ◽  
Hao Ding ◽  
Lan Chen ◽  
Yueyue Lin ◽  
Yongshuang Gong ◽  
...  
Keyword(s):  
16S Rrna ◽  
Gut Microbiota ◽  
High Performance ◽  
Fat Deposition ◽  
16S Rrna Sequencing ◽  
Oral Antibiotics ◽  
Ms Analysis ◽  
Rrna Sequencing ◽  
Cecal Microbiota ◽  
Metabolite Network

Elucidation of the mechanism of lipogenesis and fat deposition is essential for controlling excessive fat deposition in chicken. Studies have shown that gut microbiota plays an important role in regulating host lipogenesis and lipid metabolism. However, the function of gut microbiota in the lipogenesis of chicken and their relevant mechanisms are poorly understood. In the present study, the gut microbiota of chicken was depleted by oral antibiotics. Changes in cecal microbiota and metabolomics were detected by 16S rRNA sequencing and ultra-high performance liquid chromatography coupled with MS/MS (UHPLC–MS/MS) analysis. The correlation between antibiotic-induced dysbiosis of gut microbiota and metabolites and lipogenesis were analysed. We found that oral antibiotics significantly promoted the lipogenesis of chicken. 16S rRNA sequencing indicated that oral antibiotics significantly reduced the diversity and richness and caused dysbiosis of gut microbiota. Specifically, the abundance of Proteobacteria was increased considerably while the abundances of Bacteroidetes and Firmicutes were significantly decreased. At the genus level, the abundances of genera Escherichia-Shigella and Klebsiella were significantly increased while the abundances of 12 genera were significantly decreased, including Bacteroides. UHPLC-MS/MS analysis showed that antibiotic-induced dysbiosis of gut microbiota significantly altered cecal metabolomics and caused declines in abundance of 799 metabolites and increases in abundance of 945 metabolites. Microbiota-metabolite network revealed significant correlations between 4 differential phyla and 244 differential metabolites as well as 15 differential genera and 304 differential metabolites. Three metabolites of l-glutamic acid, pantothenate acid and N-acetyl-l-aspartic acid were identified as potential metabolites that link gut microbiota and lipogenesis in chicken. In conclusion, our results showed that antibiotic-induced dysbiosis of gut microbiota promotes lipogenesis of chicken by altering relevant metabolomics. The efforts in this study laid a basis for further study of the mechanisms that gut microbiota regulates lipogenesis and fat deposition of chicken.

scholarly journals Multi-view feature selection for identifying gene markers: a diversified biological data driven approach

2020 ◽  
Vol 21 (S18) ◽  
Author(s):  
Sudipta Acharya ◽  
Laizhong Cui ◽  
Yi Pan
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Selection ◽  
Marker Gene ◽  
Biological Data ◽  
Protein Interaction Data ◽  
Marker Genes ◽  
Data Sets ◽  
Gene Markers ◽  
Multi Objective

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

scholarly journals Corrigendum to: PEMA: a flexible pipeline for environmental DNA metabarcoding analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Haris Zafeiropoulos ◽  
Ha Quoc Viet ◽  
Katerina Vasileiadou ◽  
Antonis Potirakis ◽  
Christos Arvanitidis ◽  
...  
Keyword(s):  
Ribosomal Rna ◽  
Environmental Dna ◽  
Marker Genes ◽  
18S Ribosomal Rna ◽  
Dna Metabarcoding

PPIT: an R package for inferring microbial taxonomy from nifH sequences

2021 ◽  
Author(s):  
Bennett J Kapili ◽  
Anne E Dekas
Keyword(s):  
Gene Transfer ◽  
Horizontal Gene Transfer ◽  
Query Sequence ◽  
Marker Gene ◽  
R Package ◽  
Supplementary Information ◽  
Marker Genes ◽  
Pairwise Identity ◽  
Metabolic Marker ◽  
Microbial Taxonomy

Abstract Motivation Linking microbial community members to their ecological functions is a central goal of environmental microbiology. When assigned taxonomy, amplicon sequences of metabolic marker genes can suggest such links, thereby offering an overview of the phylogenetic structure underpinning particular ecosystem functions. However, inferring microbial taxonomy from metabolic marker gene sequences remains a challenge, particularly for the frequently sequenced nitrogen fixation marker gene, nitrogenase reductase (nifH). Horizontal gene transfer in recent nifH evolutionary history can confound taxonomic inferences drawn from the pairwise identity methods used in existing software. Other methods for inferring taxonomy are not standardized and require manual inspection that is difficult to scale. Results We present Phylogenetic Placement for Inferring Taxonomy (PPIT), an R package that infers microbial taxonomy from nifH amplicons using both phylogenetic and sequence identity approaches. After users place query sequences on a reference nifH gene tree provided by PPIT (n = 6317 full-length nifH sequences), PPIT searches the phylogenetic neighborhood of each query sequence and attempts to infer microbial taxonomy. An inference is drawn only if references in the phylogenetic neighborhood are: (1) taxonomically consistent and (2) share sufficient pairwise identity with the query, thereby avoiding erroneous inferences due to known horizontal gene transfer events. We find that PPIT returns a higher proportion of correct taxonomic inferences than BLAST-based approaches at the cost of fewer total inferences. We demonstrate PPIT on deep-sea sediment and find that Deltaproteobacteria are the most abundant potential diazotrophs. Using this dataset we show that emending PPIT inferences based on visual inspection of query sequence placement can achieve taxonomic inferences for nearly all sequences in a query set. We additionally discuss how users can apply PPIT to the analysis of other marker genes. Availability PPIT is freely available to non-commercial users at https://github.com/bkapili/ppit. Installation includes a vignette that demonstrates package use and reproduces the nifH amplicon analysis discussed here. The raw nifH amplicon sequence data have been deposited in the GenBank, EMBL, and DDBJ databases under BioProject number PRJEB37167. Supplementary information Supplementary data are available at Bioinformatics online.

Sign in / Sign up

Export Citation Format

Share Document