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Penington, J. S.

Publications and source records attributed to Penington, J. S..

2 recordsLinked to original sources

Influence of fecal collection conditions and 16S rRNA gene sequencing protocols at two centers on human gut microbiota analysis

BackgroundTo optimise fecal sampling and analysis yielding reproducible microbiome data, and gain further insight into sources of its variation, we compared different collection conditions and 16S rRNA gene sequencing protocols in two centers. Fecal samples were collected on three sequential days from six healthy adults and placed in commercial collection tubes (OMNIgeneGut OMR-200) at room temperature or in sterile 5 ml screw-top tubes in a home fridge or home freezer for 6-24 h, before transfer at 4{degrees}C to the laboratory and storage at - 80{degrees}C within 24 hours. Replicate samples were shipped on dry ice to centers in Australia and the USA for DNA extraction and sequencing of the V4 region of the 16S rRNA gene, using different PCR protocols. Sequences were analysed with the QIIME pipeline and Greengenes database at the Australian center and with an in-house pipeline and SILVA database at the USA center.\n\nResultsVariation in gut microbiome composition and diversity was dominated by differences between individuals. Minor differences in the abundance of taxa were found between collection-processing methods and day of collection. Larger differences were evident between the two centers, including in the relative abundances of genus Akkermansia, in phylum Verrucomicrobiales, and Bifidobacteria in Actinobacteria.\n\nConclusionsCollection with storage and transport at 4{degrees}C within 24 h is adequate for 16S rRNA analysis of the gut microbiome. However, variation between sequencing centers suggests that cohort samples should be sequenced by the same method in one center. Differences in handling, shipping and methods of PCR gene amplification and sequence analysis in different centers introduce variation in ways that are not fully understood. These findings are particularly relevant as microbiome studies shift towards larger population-based and multicenter studies.

bioinformatics

GRIDSS: sensitive and specific genomic rearrangement detection using positional de Bruijn graph assembly

The identification of genomic rearrangements, particularly in cancers, with high sensitivity and specificity using massively parallel sequencing remains a major challenge. Here, we describe the Genome Rearrangement IDentification Software Suite (GRIDSS), a high-speed structural variant (SV) caller that performs efficient genome-wide break-end assembly prior to variant calling using a novel positional de Bruijn graph assembler. By combining assembly, split read and read pair evidence using a probabilistic scoring, GRIDSS achieves high sensitivity and specificity on simulated, cell line and patient tumour data, recently winning SV sub-challenge #5 of the ICGC-TCGA DREAM Somatic Mutation Calling Challenge. On human cell line data, GRIDSS halves the false discovery rate compared to other recent methods. GRIDSS identifies non-template sequence insertions, micro-homologies and large imperfect homologies, and supports multi-sample analysis. GRIDSS is freely available at https://github.com/PapenfussLab/gridss.

bioinformatics