Biology subjects

Turnbull, R.

Publications and source records attributed to Turnbull, R..

3 recordsLinked to original sources

bioRxiv2026

First community challenge for automated virus taxonomy

The rapid rate of virus discovery renders manual curation by taxonomy experts increasingly impractical, creating a need for reliable software that can reproducibly assign viral contigs to taxa at all fifteen ranks of the virus taxonomy. We led an open community challenge for the computational taxonomic classification of viruses and assembled a dataset of virus sequences combining expert-curated and metagenomic sequences. Seventeen teams contributed a total of thirty-four automated, fully reproducible classification pipelines. Most tools correctly assigned viruses belonging to established species, genera, or families, but viruses that are unclassified at those lower ranks remain challenging. This study provides datasets, open-source software, novel approaches, and recommendations to benchmark computational taxonomic classification of viruses, and support organizing the many viruses discovered in big omics data.

microbiology↗

bioRxiv2023

Metaphor - A workflow for streamlined assembly and binning of metagenomes

Recent advances in bioinformatics and high-throughput sequencing have enabled the large-scale recovery of genomes from metagenomes. This has the potential to bring important insights as researchers can bypass cultivation and analyse genomes sourced directly from environmental samples. There are, however, technical challenges associated with this process, most notably the complexity of computational workflows required to process metagenomic data, which include dozens of bioinformatics software tools, each with their own set of customisable parameters that affect the final output of the workflow. At the core of these workflows are the processes of assembly - combining the short input reads into longer, contiguous fragments (contigs), and binning - clustering these contigs into individual genome bins. Both processes can be done for each sample separately or by pooling together multiple samples to leverage information from a combination of samples. Here we present Metaphor, a fully-automated workflow for genome-resolved metagenomics (GRM). Metaphor differs from existing GRM workflows by offering flexible approaches for the assembly and binning of the input data, and by combining multiple binning algorithms with a bin refinement step to achieve high quality genome bins. Moreover, Metaphor generates reports to evaluate the performance of the workflow. We showcase the functionality of Metaphor on different synthetic datasets, and the impact of available assembly and binning strategies on the final results. The workflow is freely available at https://github.com/vinisalazar/metaphor. Author summaryWe present Metaphor, a user-friendly, automated workflow for the recovery of genomes from metagenomes. Our tool offers flexible options for assembling and binning metagenomic contigs, that may be adjusted according to the characteristics of the input data and available computational resources, and a combination of binning algorithms, which improves the quantity and quality of resulting genome bins. We showcase the performance of Metaphor on synthetic benchmarking datasets and discuss the implication of methodological decisions regarding the strategy for assembling and binning metagenomic contigs.

bioinformatics↗

bioRxiv2019

Yield, nutritional quality, and fatty acid content of organic winter rye (Secale cereale) and winter wheat (Triticum aestivum) forages under cattle (Bos taurus) grazing conditions

The objective of this study was to assess yield, nutritional quality, and fatty acid compositions of winter rye (Secale cereale) and winter wheat (Triticum aestivum) forages for grazing cattle (Bos taurus) in an organic system. The rye and wheat were established on two 4 ha plots in September 2015. Six groups of dairy steers rotationally grazed rye (n = 3) or wheat (n = 3) plots divided into seven paddocks (n = 14) from April to June 2016. Forage samples (n = 96) taken prior to paddock grazing were used to analyze forage characteristics. Mixed models with fixed factors of forage, date, and their interaction, a random subject factor of group nested in paddock, and a repeated effect of date were used for each outcome. The linear effect of date on fatty acids was obtained by substituting date as a continuous variable. The mean forage yield for rye was greater (P < 0.05) than wheat (mean {+/-} standard error; 2840 and 2571 {+/-} 82 kg ha-1, respectively). However, rye yielded less in the latter part of the grazing period. Wheat (19.3 {+/-} 0.30% DM) had greater (P < 0.001) crude protein than rye (17.6 {+/-} 0.30% DM). In general, crude protein, digestibility, and minerals decreased during the grazing period. Wheat (66.3 {+/-} 0.54 g 100g-1) had greater (P < 0.001) alpha-linolenic acid (18:3n-3) concentration than rye (63.3 {+/-} 0.54 g 100g-1). Although both forages decreased (P < 0.05) in alpha-linolenic acid concentration, wheat decreased 2.49 times more (P < 0.001) per d compared to rye forage. Winter rye and winter wheat forages are viable for cattle grazing. Producers should initiate early grazing to maximize protein, digestibility, and alpha-linolenic acid intake while the forages are immature.

plant biology↗