Peer Review #2 of "Evaluation of computational methods for human microbiome analysis using simulated data (v0.1)"

ABSTRACT Numerous studies relate differences in microbial communities to human health and disease; however, little is known about microbial changes that occur postmortem or the possible applications of microbiome analysis in the field of forensic science. The aim of this review was to study the microbiome and its applications in forensic sciences and to determine the main lines of investigation that are emerging, as well as its possible contributions to the forensic field. A systematic review of the human microbiome in relation to forensic science was carried out by following PRISMA guidelines. This study sheds light on the role of microbiome research in the postmortem interval during the process of decomposition, identifying death caused by drowning or sudden death, locating the geographical location of death, establishing a connection between the human microbiome and personal items, sexual contact, and the identification of individuals. Actinomycetaceae, Bacteroidaceae, Alcaligenaceae, and Bacilli play an important role in determining the postmortem interval. Aeromonas can be used to determine the cause of death, and Corynebacterium or Helicobacter pylori can be used to ascertain personal identity or geographical location. Several studies point to a promising future for microbiome analysis in the different fields of forensic science, opening up an important new area of research.

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Peer Review #1 of "Guanidine thiocyanate solution facilitates sample collection for plant rhizosphere microbiome analysis (v0.1)"

10.7287/peerj.6440v0.1/reviews/1 ◽

2019 ◽

Keyword(s):

Peer Review ◽

Guanidine Thiocyanate ◽

Sample Collection ◽

Microbiome Analysis ◽

Rhizosphere Microbiome ◽

Thiocyanate Solution

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A Bayesian zero-inflated negative binomial regression model for the integrative analysis of microbiome data

Biostatistics ◽

10.1093/biostatistics/kxz050 ◽

2019 ◽

Author(s):

Shuang Jiang ◽

Guanghua Xiao ◽

Andrew Y Koh ◽

Jiwoong Kim ◽

Qiwei Li ◽

...

Keyword(s):

Regression Model ◽

Negative Binomial ◽

Human Microbiome ◽

Simulated Data ◽

Negative Binomial Regression ◽

Bayesian Regression ◽

Negative Binomial Regression Model ◽

Disease States ◽

Binomial Regression ◽

Microbiome Data

Summary Microbiome omics approaches can reveal intriguing relationships between the human microbiome and certain disease states. Along with identification of specific bacteria taxa associated with diseases, recent scientific advancements provide mounting evidence that metabolism, genetics, and environmental factors can all modulate these microbial effects. However, the current methods for integrating microbiome data and other covariates are severely lacking. Hence, we present an integrative Bayesian zero-inflated negative binomial regression model that can both distinguish differentially abundant taxa with distinct phenotypes and quantify covariate-taxa effects. Our model demonstrates good performance using simulated data. Furthermore, we successfully integrated microbiome taxonomies and metabolomics in two real microbiome datasets to provide biologically interpretable findings. In all, we proposed a novel integrative Bayesian regression model that features bacterial differential abundance analysis and microbiome-covariate effects quantifications, which makes it suitable for general microbiome studies.

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Editorial: Computational Methods for Microbiome Analysis

Frontiers in Genetics ◽

10.3389/fgene.2020.623897 ◽

2020 ◽

Vol 11 ◽

Author(s):

João C. Setubal ◽

Jens Stoye ◽

Bas E. Dutilh

Keyword(s):

Computational Methods ◽

Microbiome Analysis

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Peer Review #1 of "Microbiome analysis of Pacific white shrimp gut and rearing water from Malaysia and Vietnam: implications for aquaculture research and management (v0.2)"

10.7287/peerj.5826v0.2/reviews/1 ◽

2018 ◽

Keyword(s):

Peer Review ◽

Pacific White Shrimp ◽

White Shrimp ◽

Microbiome Analysis

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The electronic tree of life (eTOL): a net of long probes to characterize the human microbiome from RNA-seq data

10.21203/rs.3.rs-1208849/v1 ◽

2021 ◽

Author(s):

Xinyue Hu ◽

Jürgen Haas ◽

Richard Lathe

Keyword(s):

Human Tissue ◽

Human Microbiome ◽

Small Subunit ◽

Tree Of Life ◽

Alternative Methods ◽

Target Tissue ◽

Rna Seq ◽

Small Subunit Rrna ◽

Microbiome Analysis ◽

Rrna Sequences

Abstract Background Microbiome analysis generally requires PCR-based or metagenomic shotgun sequencing, sophisticated programs, and large volumes of data. Alternative approaches based on widely available RNA-seq data are constrained because of sequence similarities between the transcriptomes of microbes/viruses and those of the host, compounded by the extreme abundance of host sequences in such libraries. Current approaches are also limited to specific microbial groups. There is a need for alternative methods of microbiome analysis that encompass the entire tree of life. Results We report a method to specifically retrieve non-human sequences in human tissue RNA-seq data. For cellular microbes we used a bioinformatic 'net', based on filtered 64-mer small subunit rRNA sequences across the Tree of Life (the 'electronic tree of life', eTOL), to comprehensively (98%) entrap all non-human rRNA sequences present in the target tissue. Using brain as a model, retrieval of matching reads, re-exclusion of human-related sequences, followed by contig building and species identification, is followed by reconfirmation of the abundance and identity of the corresponding species groups. We provide methods to automate this analysis. A variant approach is necessary for viruses. Again, because of significant matches between viral and human sequences, a 'stripping' approach is essential. In addition, contamination during workup is a potential problem, and we discuss strategies to circumvent this issue. To illustrate the versatility of the method, we report the use of the eTOL methodology to unambiguously identify exogenous microbial and viral sequences in human tissue RNA-seq data across the entire tree of life including Archaea, Bacteria, Chloroplastida, basal Eukaryota, Fungi, and Holozoa/Metazoa, and discuss the technical and bioinformatic challenges involved. Conclusions This generic methodology may find wider application in microbiome analysis including diagnostics.

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