SEPATH: benchmarking the search for pathogens in human tissue whole genome sequence data leads to template pipelines

Microbial community response to hydrocarbon exposure in iron oxide mats: an environmental study

Hydrocarbon pollution is a widespread issue in both groundwater and surface-water systems; however, research on remediation at the interface of these two systems is limited. This interface is the oxic-anoxic boundary, where hydrocarbon pollutant from contaminated groundwaters flows into surface waters and iron mats are formed by microaerophilic iron-oxidizing bacteria. Iron mats are highly chemically adsorptive and host a diverse community of microbes. To elucidate the effect of hydrocarbon exposure on iron mat geochemistry and microbial community structure and function, we sampled iron mats both upstream and downstream from a leaking underground storage tank. Hydrocarbon-exposed iron mats had significantly higher concentrations of oxidized iron and significantly lower dissolved organic carbon and total dissolved phosphate than unexposed iron mats. A strong negative correlation between dissolved phosphate and benzene was observed in the hydrocarbon-exposed iron mats and water samples. There were positive correlations between iron and other hydrocarbons with benzene in the hydrocarbon-exposed iron mats, which was unique from water samples. The hydrocarbon-exposed iron mats represented two types, flocculent and seep, which had significantly different concentrations of iron, hydrocarbons, and phosphate, indicating that iron mat is also an important context in studies of freshwater mats. Using constrained ordination, we found the best predictors for community structure to be dissolved oxygen, pH, and benzene. Alpha diversity and evenness were significantly lower in hydrocarbon-exposed iron mats than unexposed mats. Using 16S rDNA amplicon sequences, we found evidence of three putative nitrate-reducing iron-oxidizing taxa in microaerophile-dominated iron mats (Azospira, Paracoccus, and Thermomonas). 16S rDNA amplicons also indicated the presence of taxa that are associated with hydrocarbon degradation. Benzene remediation-associated genes were found using metagenomic analysis both in exposed and unexposed iron mats. Furthermore, the results indicated that season (summer vs. spring) exacerbates the negative effect of hydrocarbon exposure on community diversity and evenness and led to the increased abundance of numerous OTUs. This study represents the first of its kind to attempt to understand how contaminant exposure, specifically hydrocarbons, influences the geochemistry and microbial community of freshwater iron mats and further develops our understanding of hydrocarbon remediation at the land-water interface.

Caution regarding the specificities of pan-cancer microbial structure

Results published in an article by Poore et al. (Nature. 2020;579:567-574) suggested that machine learning models can almost perfectly distinguish between tumour types based on their microbial composition using machine learning models. Whilst we believe that there is the potential for microbial composition to be used in this manner, we have concerns with the paper that make us question the certainty of the conclusions drawn. We believe there are issues in the areas of the contribution of contamination, handling of batch effects, false positive classifications and limitations in the machine learning approaches used. This makes it difficult to identify whether the authors have identified true biological signal and how robust these models would be in use as clinical biomarkers. We commend Poore et al. on their approach to open data and reproducibility that has enabled this analysis. We hope that this discourse assists the future development of machine learning models and hypothesis generation in microbiome research.

Benchmarking of microbiome detection tools on RNA-seq synthetic databases according to diverse conditions

Motivation

Here, we performed a benchmarking analysis of five tools for microbe sequence detection using transcriptomics data (Kraken2, MetaPhlAn2, PathSeq, DRAC and Pandora). We built a synthetic database mimicking real-world structure with tuned conditions accounting for microbe species prevalence, base calling quality and sequence length. Sensitivity and positive predictive value (PPV) parameters, as well as computational requirements, were used for tool ranking.

Results

GATK PathSeq showed the highest sensitivity on average and across all scenarios considered. However, the main drawback of this tool was its slowness. Kraken2 was the fastest tool and displayed the second-best sensitivity, though with large variance depending on the species to be classified. There was no significant difference for the other three algorithms sensitivity. The sensitivity of MetaPhlAn2 and Pandora was affected by sequence number and DRAC by sequence quality and length. Results from this study support the use of Kraken2 for routine microbiome profiling based on its competitive sensitivity and runtime performance. Nonetheless, we strongly endorse to complement it by combining with MetaPhlAn2 for thorough taxonomic analyses.

Availability and implementation

https://github.com/fjuradorueda/MIME/ and https://github.com/lola4/DRAC/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Quality Control in Metagenomics Data

Experiments involving metagenomics data are become increasingly commonplace. Processing such data requires a unique set of considerations. Quality control of metagenomics data is critical to extracting pertinent insights. In this chapter, we outline some considerations in terms of study design and other confounding factors that can often only be realized at the point of data analysis.In this chapter, we outline some basic principles of quality control in metagenomics, including overall reproducibility and some good practices to follow. The general quality control of sequencing data is then outlined, and we introduce ways to process this data by using bash scripts and developing pipelines in Snakemake (Python).A significant part of quality control in metagenomics is in analyzing the data to ensure you can spot relationships between variables and to identify when they might be confounded. This chapter provides a walkthrough of analyzing some microbiome data (in the R statistical language) and demonstrates a few days to identify overall differences and similarities in microbiome data. The chapter is concluded by discussing remarks about considering taxonomic results in the context of the study and interrogating sequence alignments using the command line.

FastViFi: Fast and accurate detection of (Hybrid) Viral DNA and RNA

DNA viruses are important infectious agents known to mediate a large number of human diseases, including cancer. Viral integration into the host genome and the formation of hybrid transcripts are also associated with increased pathogenicity. The high variability of viral genomes, however requires the use of sensitive ensemble hidden Markov models that add to the computational complexity, often requiring > 40 CPU-hours per sample. Here, we describe FastViFi, a fast 2-stage filtering method that reduces the computational burden. On simulated and cancer genomic data, FastViFi improved the running time by 2 orders of magnitude with comparable accuracy on challenging data sets. Recently published methods have focused on identification of location of viral integration into the human host genome using local assembly, but do not extend to RNA. To identify human viral hybrid transcripts, we additionally developed ensemble Hidden Markov Models for the Epstein Barr virus (EBV) to add to the models for Hepatitis B (HBV), Hepatitis C (HCV) viruses and the Human Papillomavirus (HPV), and used FastViFi to query RNA-seq data from Gastric cancer (EBV) and liver cancer (HBV/HCV). FastViFi ran in <10 minutes per sample and identified multiple hybrids that fuse viral and human genes suggesting new mechanisms for oncoviral pathogenicity. FastViFi is available at https://github.com/sara-javadzadeh/FastViFi.

Microbiomes of Urine and the Prostate Are Linked to Human Prostate Cancer Risk Groups

BACKGROUND

Bacteria play a suspected role in the development of several cancer types, and associations between the presence of particular bacteria and prostate cancer have been reported.

OBJECTIVE

To provide improved characterisation of the prostate and urine microbiome and to investigate the prognostic potential of the bacteria present.

DESIGN, SETTING, AND PARTICIPANTS

Microbiome profiles were interrogated in sample collections of patient urine (sediment microscopy: n = 318, 16S ribosomal amplicon sequencing: n = 46; and extracellular vesicle RNA-seq: n = 40) and cancer tissue (n = 204).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Microbiomes were assessed using anaerobic culture, population-level 16S analysis, RNA-seq, and whole genome DNA sequencing.

RESULTS AND LIMITATIONS

We demonstrate an association between the presence of bacteria in urine sediments and higher D'Amico risk prostate cancer (discovery, n = 215 patients, p < 0.001; validation, n = 103, p < 0.001, χ² test for trend). Characterisation of the bacterial community led to the (1) identification of four novel bacteria (Porphyromonas sp. nov., Varibaculum sp. nov., Peptoniphilus sp. nov., and Fenollaria sp. nov.) that were frequently found in patient urine, and (2) definition of a patient subgroup associated with metastasis development (p = 0.015, log-rank test). The presence of five specific anaerobic genera, which includes three of the novel isolates, was associated with cancer risk group, in urine sediment (p = 0.045, log-rank test), urine extracellular vesicles (p = 0.039), and cancer tissue (p = 0.035), with a meta-analysis hazard ratio for disease progression of 2.60 (95% confidence interval: 1.39-4.85; p = 0.003; Cox regression). A limitation is that functional links to cancer development are not yet established.

CONCLUSIONS

This study characterises prostate and urine microbiomes, and indicates that specific anaerobic bacteria genera have prognostic potential.

PATIENT SUMMARY

In this study, we investigated the presence of bacteria in patient urine and the prostate. We identified four novel bacteria and suggest a potential prognostic utility for the microbiome in prostate cancer.

Application of a bioinformatic pipeline to RNA-seq data identifies novel viruslike sequence in human blood

Numerous reports have suggested that infectious agents could play a role in neurodegenerative diseases, but specific etiological agents have not been convincingly demonstrated. To search for candidate agents in an unbiased fashion, we have developed a bioinformatic pipeline that identifies microbial sequences in mammalian RNA-seq data, including sequences with no significant nucleotide similarity hits in GenBank. Effectiveness of the pipeline was tested using publicly available RNA-seq data and in a reconstruction experiment using synthetic data. We then applied this pipeline to a novel RNA-seq dataset generated from a cohort of 120 samples from amyotrophic lateral sclerosis patients and controls, and identified sequences corresponding to known bacteria and viruses, as well as novel virus-like sequences. The presence of these novel virus-like sequences, which were identified in subsets of both patients and controls, were confirmed by quantitative RT-PCR. We believe this pipeline will be a useful tool for the identification of potential etiological agents in the many RNA-seq datasets currently being generated.

Contaminant DNA in bacterial sequencing experiments is a major source of false genetic variability

Background

Contaminant DNA is a well-known confounding factor in molecular biology and in genomic repositories. Strikingly, analysis workflows for whole-genome sequencing (WGS) data commonly do not account for errors potentially introduced by contamination, which could lead to the wrong assessment of allele frequency both in basic and clinical research.

Results

We used a taxonomic filter to remove contaminant reads from more than 4000 bacterial samples from 20 different studies and performed a comprehensive evaluation of the extent and impact of contaminant DNA in WGS. We found that contamination is pervasive and can introduce large biases in variant analysis. We showed that these biases can result in hundreds of false positive and negative SNPs, even for samples with slight contamination. Studies investigating complex biological traits from sequencing data can be completely biased if contamination is neglected during the bioinformatic analysis, and we demonstrate that removing contaminant reads with a taxonomic classifier permits more accurate variant calling. We used both real and simulated data to evaluate and implement reliable, contamination-aware analysis pipelines.

Conclusion

As sequencing technologies consolidate as precision tools that are increasingly adopted in the research and clinical context, our results urge for the implementation of contamination-aware analysis pipelines. Taxonomic classifiers are a powerful tool to implement such pipelines.