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Greenfield, John C.; Loux, Sheryl J.; Sood, Virendra K.; Jenkins, K; Davio, Stephen R.

doi:10.1023/a:1015847027254

Cited by 6 publications

(3 citation statements)

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“…As part of the testing of Kelpie, an EBI Illumina WGS submission (MGYS00000465/ERP008951) was chosen at random. The 16S rRNA HMM-filtered reads were downloaded from EBI, assembled with Kelpie using the above v4 primers, and run through the GHAP amplicon pipeline (Greenfield, 2017). This test both demonstrated that Kelpie was compatible with 16S rRNA reads selected using conventional HMM-based tools, and also allowed a comparison between the profiles produced from the Kelpie-assembled reads and the results generated by the EBI pipeline.…”

Section: Testing and Evaluationmentioning

confidence: 99%

“…The DNA extracted from each of these three samples was split, with one part being sent off for WGS sequencing (paired-end 100 bp Illumina HiSeq), and the other amplified using the EMP V4 PCR primers and then sequenced (paired-end 300 bp Illumina MiSeq). The amplicon and WGS datasets for all three samples are available for download at Coal Seam Formation Water Community Profiles (Greenfield, 2018c). These three samples are also included in the Coal Seam Microbiome reference set (Vick et al, 2018) as Surat 3 (W1), Bowen 3 (W2) and Surat 1 (W3).…”

Section: Testing and Evaluationmentioning

confidence: 99%

“…The Kelpie-generated and the real amplicon datasets were then run through a conventional amplicon pipeline (Greenfield, 2017) based on USearch and RDP (Wang et al, 2007) tools, both separately and together. The results were assessed to see if similar community profiles were generated from both sets of reads, and if the actual sequences generated by PCR and Kelpie were identical.…”

Section: Testing and Evaluationmentioning

confidence: 99%

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Kelpie: generating full-length ‘amplicons’ from whole-metagenome datasets

Greenfield

Tran‐Dinh

Midgley

2019

PeerJ

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IntroductionWhole-metagenome sequencing can be a rich source of information about the structure and function of entire metagenomic communities, but getting accurate and reliable results from these datasets can be challenging. Analysis of these datasets is founded on the mapping of sequencing reads onto known genomic regions from known organisms, but short reads will often map equally well to multiple regions, and to multiple reference organisms. Assembling metagenomic datasets prior to mapping can generate much longer and more precisely mappable sequences but the presence of closely related organisms and highly conserved regions makes metagenomic assembly challenging, and some regions of particular interest can assemble poorly. One solution to these problems is to use specialised tools, such as Kelpie, that can accurately extract and assemble full-length sequences for defined genomic regions from whole-metagenome datasets.MethodsKelpie is a kMer-based tool that generates full-length amplicon-like sequences from whole-metagenome datasets. It takes a pair of primer sequences and a set of metagenomic reads, and uses a combination of kMer filtering, error correction and assembly techniques to construct sets of full-length inter-primer sequences.ResultsThe effectiveness of Kelpie is demonstrated here through the extraction and assembly of full-length ribosomal marker gene regions, as this allows comparisons with conventional amplicon sequencing and published metagenomic benchmarks. The results show that the Kelpie-generated sequences and community profiles closely match those produced by amplicon sequencing, down to low abundance levels, and running Kelpie on the synthetic CAMI metagenomic benchmarking datasets shows similar high levels of both precision and recall.ConclusionsKelpie can be thought of as being somewhat like an in-silico PCR tool, taking a primer pair and producing the resulting ‘amplicons’ from a whole-metagenome dataset. Marker regions from the 16S rRNA gene were used here as an example because this allowed the overall accuracy of Kelpie to be evaluated through comparisons with other datasets, approaches and benchmarks. Kelpie is not limited to this application though, and can be used to extract and assemble any genomic region present in a whole metagenome dataset, as long as it is bound by a pairs of highly conserved primer sequences.

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Section: Testing and Evaluationmentioning

confidence: 99%

Section: Testing and Evaluationmentioning

confidence: 99%