Monitoring the microbiome for food safety and quality using deep shotgun sequencing

AbstractIn this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced the total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species’ viability from total RNA sequencing.

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Monitoring the microbiome for food safety and quality using deep shotgun sequencing

10.1101/2020.05.18.102574 ◽

2020 ◽

Author(s):

Kristen L. Beck ◽

Niina Haiminen ◽

David Chambliss ◽

Stefan Edlund ◽

Mark Kunitomi ◽

...

Keyword(s):

Rna Sequencing ◽

Environmental Changes ◽

Pet Food ◽

Analysis Pipeline ◽

Multiple Sequence ◽

Food Ingredients ◽

Total Rna ◽

Specific Species ◽

Ingredient Composition ◽

Food Safety And Quality

ABSTRACTIn this work, we hypothesized that shifts in the food microbiome can be used as an indicator of unexpected contaminants or environmental changes. To test this hypothesis, we sequenced total RNA of 31 high protein powder (HPP) samples of poultry meal pet food ingredients. We developed a microbiome analysis pipeline employing a key eukaryotic matrix filtering step that improved microbe detection specificity to >99.96% during in silico validation. The pipeline identified 119 microbial genera per HPP sample on average with 65 genera present in all samples. The most abundant of these were Bacteroides, Clostridium, Lactococcus, Aeromonas, and Citrobacter. We also observed shifts in the microbial community corresponding to ingredient composition differences. When comparing culture-based results for Salmonella with total RNA sequencing, we found that Salmonella growth did not correlate with multiple sequence analyses. We conclude that microbiome sequencing is useful to characterize complex food microbial communities, while additional work is required for predicting specific species’ viability from total RNA sequencing.

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Faculty Opinions recommendation of Total RNA sequencing reveals nascent transcription and widespread co-transcriptional splicing in the human brain.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717991048.793473278 ◽

2013 ◽

Author(s):

Karla Neugebauer

Keyword(s):

Human Brain ◽

Rna Sequencing ◽

Total Rna

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Statistical inference of the rate of RNA polymerase II elongation by total RNA sequencing

Bioinformatics ◽

10.1093/bioinformatics/bty886 ◽

2018 ◽

Vol 35 (11) ◽

pp. 1877-1884

Author(s):

Yumi Kawamura ◽

Shinsuke Koyama ◽

Ryo Yoshida

Keyword(s):

Rna Polymerase ◽

Rna Polymerase Ii ◽

Statistical Inference ◽

Rna Sequencing ◽

Total Rna

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TagSeqTools: a flexible and comprehensive analysis pipeline for NAD tagSeq data

10.1101/2020.03.09.982934 ◽

2020 ◽

Cited By ~ 1

Author(s):

Huan Zhong ◽

Zongwei Cai ◽

Zhu Yang ◽

Yiji Xia

Keyword(s):

Rna Sequencing ◽

Comprehensive Analysis ◽

Enzymatic Reactions ◽

Computational Tool ◽

Sequencing Data ◽

Analysis Pipeline ◽

Oxford Nanopore ◽

Long Read ◽

Identification And Characterization

AbstractNAD tagSeq has recently been developed for the identification and characterization of NAD+-capped RNAs (NAD-RNAs). This method adopts a strategy of chemo-enzymatic reactions to label the NAD-RNAs with a synthetic RNA tag before subjecting to the Oxford Nanopore direct RNA sequencing. A computational tool designed for analyzing the sequencing data of tagged RNA will facilitate the broader application of this method. Hence, we introduce TagSeqTools as a flexible, general pipeline for the identification and quantification of tagged RNAs (i.e., NAD+-capped RNAs) using long-read transcriptome sequencing data generated by NAD tagSeq method. TagSeqTools comprises two major modules, TagSeek for differentiating tagged and untagged reads, and TagSeqQuant for the quantitative and further characterization analysis of genes and isoforms. Besides, the pipeline also integrates some advanced functions to identify antisense or splicing, and supports the data reformation for visualization. Therefore, TagSeqTools provides a convenient and comprehensive workflow for researchers to analyze the data produced by the NAD tagSeq method or other tagging-based experiments using Oxford nanopore direct RNA sequencing. The pipeline is available at https://github.com/dorothyzh/TagSeqTools, under Apache License 2.0.

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scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data

10.1101/2021.08.16.456499 ◽

2021 ◽

Author(s):

Combiz Khozoie ◽

Nurun Fancy ◽

Mahdi Moradi Marjaneh ◽

Alan E. Murphy ◽

Paul M. Matthews ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Sparse Matrix ◽

Data Generation ◽

Sequencing Data ◽

Alternative Analysis ◽

Analysis Pipeline ◽

Matrix Quality ◽

Single Cell Rna Sequencing ◽

Public Datasets

Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a million cells are becoming commonplace. The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods, led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data. The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities. Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users. We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.

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Single-cell full-length total RNA sequencing uncovers dynamics of recursive splicing and enhancer RNAs

Nature Communications ◽

10.1038/s41467-018-02866-0 ◽

2018 ◽

Vol 9 (1) ◽

Cited By ~ 70

Author(s):

Tetsutaro Hayashi ◽

Haruka Ozaki ◽

Yohei Sasagawa ◽

Mana Umeda ◽

Hiroki Danno ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Full Length ◽

Total Rna ◽

Recursive Splicing

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Advances in fingerprint and rapid methods for improved traceability in agri-food supply chains

10.19103/as.2021.0097.21 ◽

2021 ◽

pp. 29-42

Author(s):

Daniel Cozzolino ◽

◽

Heather E. Smyth ◽

Yasmina Sultanbawa ◽

◽

...

Keyword(s):

Value Chain ◽

Food Supply ◽

Data Analytics ◽

Food Industry ◽

Food Authenticity ◽

Food Ingredients ◽

Food Supply Chains ◽

Food Standards ◽

Regulatory Bodies ◽

Food Safety And Quality

Agri-food supply and value chain markets have become increasingly complex due to the changes in consumers demands, the development of complex food standards associated with food safety and quality, advances in technology (e.g. big data, machine learning), and changes in the food industry structure. However, recent issues related to food authenticity, adulteration, fraud, mislabelling, traceability and provenance have added a new dimension to consumers’ concerns, and food industry and regulatory bodies worldwide. The incorporation of sensing technologies combined with data analytics, are determining a paradigm shift in the way food ingredients and foods are both evaluated and monitored. This chapter discusses the utilisation of data analytics and sensing technologies to address issues related with food authenticity, adulteration, fraud, traceability and provenance in the food supply and value chains. In particular, this chapter will focus on the use of rapid analytical methods based in vibrational spectroscopy in combination with data analytics.

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