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 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

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 aRNApipe: A balanced, efficient and distributed pipeline for processing RNA-seq data in high performance computing environments

2016 ◽  
Author(s):  
Arnald Alonso ◽  
Brittany N. Lasseigne ◽  
Kelly Williams ◽  
Josh Nielsen ◽  
Ryne C. Ramaker ◽  
...  
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Variant Calling ◽  
Supplementary Information ◽  
Sequence Variant ◽  
Rna Seq ◽  
Wide Range ◽  
Additional Processing ◽  
Computational Resources ◽  
Performance Computing

AbstractSummaryThe wide range of RNA-seq applications and their high computational needs require the development of pipelines orchestrating the entire workflow and optimizing usage of available computational resources. We present aRNApipe, a project-oriented pipeline for processing of RNA-seq data in high performance cluster environments. aRNApipe is highly modular and can be easily migrated to any high performance computing (HPC) environment. The current applications included in aRNApipe combine the essential RNA-seq primary analyses, including quality control metrics, transcript alignment, count generation, transcript fusion identification, alternative splicing, and sequence variant calling. aRNApipe is project-oriented and dynamic so users can easily update analyses to include or exclude samples or enable additional processing modules. Workflow parameters are easily set using a single configuration file that provides centralized tracking of all analytical processes. Finally, aRNApipe incorporates interactive web reports for sample tracking and a tool for managing the genome assemblies available to perform an analysis.Availability and documentationhttps://github.com/HudsonAlpha/aRNAPipe; DOI:10.5281/[email protected] informationSupplementary data are available at Bioinformatics online.

The Recent Revolution in High Performance Computing

MRS Bulletin ◽  
1997 ◽  
Vol 22 (10) ◽  
pp. 5-6
Author(s):  
Horst D. Simon
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
New Technologies ◽  
New Technology ◽  
Parallel Architecture ◽  
Time Frame ◽  
Good News ◽  
Computing Industry ◽  
Time And Energy ◽  
Performance Computing

Recent events in the high-performance computing industry have concerned scientists and the general public regarding a crisis or a lack of leadership in the field. That concern is understandable considering the industry's history from 1993 to 1996. Cray Research, the historic leader in supercomputing technology, was unable to survive financially as an independent company and was acquired by Silicon Graphics. Two ambitious new companies that introduced new technologies in the late 1980s and early 1990s—Thinking Machines and Kendall Square Research—were commercial failures and went out of business. And Intel, which introduced its Paragon supercomputer in 1994, discontinued production only two years later.During the same time frame, scientists who had finished the laborious task of writing scientific codes to run on vector parallel supercomputers learned that those codes would have to be rewritten if they were to run on the next-generation, highly parallel architecture. Scientists who are not yet involved in high-performance computing are understandably hesitant about committing their time and energy to such an apparently unstable enterprise.However, beneath the commercial chaos of the last several years, a technological revolution has been occurring. The good news is that the revolution is over, leading to five to ten years of predictable stability, steady improvements in system performance, and increased productivity for scientific applications. It is time for scientists who were sitting on the fence to jump in and reap the benefits of the new technology.

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