De-MetaST-BLAST: A Tool for the Validation of Degenerate Primer Sets and Data Mining of Publicly Available Metagenomes

The scope for ecological studies of eukaryotic algal viruses has greatly improved with the development of molecular and bioinformatic approaches that do not require algal cultures. Here, we review the history and perceived future opportunities for research on eukaryotic algal viruses. We begin with a summary of the 65 eukaryotic algal viruses that are presently in culture collections, with emphasis on shared evolutionary traits (e.g., conserved core genes) of each known viral type. We then describe how core genes have been used to enable molecular detection of viruses in the environment, ranging from PCR-based amplification to community scale “-omics” approaches. Special attention is given to recent studies that have employed network-analyses of -omics data to predict virus-host relationships, from which a general bioinformatics pipeline is described for this type of approach. Finally, we conclude with acknowledgement of how the field of aquatic virology is adapting to these advances, and highlight the need to properly characterize new virus-host systems that may be isolated using preliminary molecular surveys. Researchers can approach this work using lessons learned from the Chlorella virus system, which is not only the best characterized algal-virus system, but is also responsible for much of the foundation in the field of aquatic virology.

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“…virus [ 11 ]. PCR amplification predictions were done using motif searches in CLC Genomics and the software De-MetaST-BLAST [ 127 ].…”

Section: Figurementioning

confidence: 99%

Viruses of Eukaryotic Algae: Diversity, Methods for Detection, and Future Directions

Coy

Gann

Pound

et al. 2018

Viruses

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“…Degenerate primers were further modified and validated against nirK in the 1 st , 2 nd , 3 rd and 4 th databases using De-MetaST for an in silico amplification (Gulvik et al, 2012). Four primer pairs were able to in silico amplify diverse fungal nirK, but were unlikely for nirK of bacteria, algae and protozoa, if allowing maximal 3-bp mismatches (Table 1).…”

Section: Primer Designmentioning

confidence: 99%

Detection of N2O-producing fungi in environment using nitrite reductase gene (nirK)-targeting primers

2016

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Fungal denitrification has been increasingly investigated, but its community ecology is poorly understood due to the lack of culture-independent tools. In this work, four pairs of nirK-targeting primers were designed and evaluated for primer specificity and efficiency using thirty NO-producing fungal cultures and an agricultural soil. All primers amplified nirK from fungi and soil, but their efficiency and specificity were different. A primer set, FnirK_F3/R2 amplified ∼80 % of tested fungi, including Aspergillus, Fusarium, Penicillium, and Trichoderma, as compared to ∼40-70 % for other three primers. The nirK fragments of fungal and soil DNA amplified by FnirK_F3/R2 were phylogenetically related to denitrifying fungi in the orders Eurotiales, Hypocreales, and Sordariales; and clone sequences were also distributed in the clusters of Chaetomium, Metarhizium, and Myceliophthora that were uncultured from soil in our previous work. This proved the wide-range capability of primers for amplifying diverse denitrifying fungi from environment. However, our primers and recently-developed other primers amplified bacterial nirK from soil and this co-amplification of fungal and bacterial nirK was theoretically discussed. The FnirK_F3/R2 was further compared with published primers; results from clone libraries demonstrated that FnirK_F3/R2 was more specifically targeted on fungi and had broader taxonomical coverage than some others.

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“…MIPE avoids this problem through the use of a global alignment and the anchoring of primer binding sites. Because the results obtained by PrimerProspector included incorrect information for uncovered sections and cannot be used for primer evaluations and metagenome modifications, just like De-MetaST [27], we can only compare the match types from these two scripts case by case (Sheet: F515_annotation_of_difference and F806_annotation_of_difference in S3 Table). …”

Section: Resultsmentioning

confidence: 99%

“…Some programs, such as PrimerProspector [25] and DegePrime [26], evaluate and develop primers for taxonomic classification, but these programs cannot be used for metagenome sequence datasets and do not include rRNA extraction and global alignment processes, which are necessary for rejecting incorrect primer binding sites in shotgun reads. The tool De-MetaST [27] is available for metagenome datasets but was designed to provide in silico amplicons generated by user-defined degenerate primers found within a user-defined nucleotide database. Therefore, this software only provides information for the covered but not the uncovered parts and cannot be used for the evaluation of primer coverage or for primer modification.…”

Section: Introductionmentioning

confidence: 99%

MIPE: A metagenome-based community structure explorer and SSU primer evaluation tool

Zou

Zhou

et al. 2017

PLoS ONE

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An understanding of microbial community structure is an important issue in the field of molecular ecology. The traditional molecular method involves amplification of small subunit ribosomal RNA (SSU rRNA) genes by polymerase chain reaction (PCR). However, PCR-based amplicon approaches are affected by primer bias and chimeras. With the development of high-throughput sequencing technology, unbiased SSU rRNA gene sequences can be mined from shotgun sequencing-based metagenomic or metatranscriptomic datasets to obtain a reflection of the microbial community structure in specific types of environment and to evaluate SSU primers. However, the use of short reads obtained through next-generation sequencing for primer evaluation has not been well resolved. The software MIPE (MIcrobiota metagenome Primer Explorer) was developed to adapt numerous short reads from metagenomes and metatranscriptomes. Using metagenomic or metatranscriptomic datasets as input, MIPE extracts and aligns rRNA to reveal detailed information on microbial composition and evaluate SSU rRNA primers. A mock dataset, a real Metagenomics Rapid Annotation using Subsystem Technology (MG-RAST) test dataset, two PrimerProspector test datasets and a real metatranscriptomic dataset were used to validate MIPE. The software calls Mothur (v1.33.3) and the SILVA database (v119) for the alignment and classification of rRNA genes from a metagenome or metatranscriptome. MIPE can effectively extract shotgun rRNA reads from a metagenome or metatranscriptome and is capable of classifying these sequences and exhibiting sensitivity to different SSU rRNA PCR primers. Therefore, MIPE can be used to guide primer design for specific environmental samples.

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De-MetaST-BLAST: A Tool for the Validation of Degenerate Primer Sets and Data Mining of Publicly Available Metagenomes

Cited by 13 publications

References 44 publications

Viruses of Eukaryotic Algae: Diversity, Methods for Detection, and Future Directions

Viruses of Eukaryotic Algae: Diversity, Methods for Detection, and Future Directions

Detection of N2O-producing fungi in environment using nitrite reductase gene (nirK)-targeting primers

MIPE: A metagenome-based community structure explorer and SSU primer evaluation tool

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