Groundwater microbial communities reflect geothermal activity on volcanic island.

Studies of the effects of volcanic activity on the Hawaiian Islands are extremely relevant due to the past and current co-eruptions at both Mauna Loa and Kilauea. The Big Island of Hawai'i is one of the most seismically monitored volcanic systems in the world, and recent investigations of the Big Island suggest a widespread subsurface connectivity between volcanoes. Volcanic activity has the potential to add mineral contaminants into groundwater ecosystems, thus affecting water quality, and making inhabitants of volcanic islands particularly vulnerable due to dependence on groundwater aquifers. As part of an interdisciplinary study on groundwater aquifers in Kona, Hawai'i, over 40 groundwater wells were sampled quarterly from August 2017 through March 2019, before and after the destructive eruption of the Kilauea East Rift Zone in May 2018. Sample sites occurred at great distance (~80 km) from Kilauea, allowing us to pose questions of how volcanic groundwater aquifers might be influenced by volcanic subsurface activity. Approximately 400 water samples were analyzed and temporally split by pre-eruption and post-eruption for biogeochemical analysis. While most geochemical constituents did not differ across quarterly sampling, microbial communities varied temporally (pre- and post-eruption). When a salinity threshold amongst samples was set, the greatest microbial community differences were observed in the freshest groundwater samples. Differential analysis indicated bacterial families with sulfur (S) metabolisms (sulfate reducers, sulfide oxidation, and disproportionation of S-intermediates) were enriched post-eruption. The diversity in S-cyclers without a corresponding change in sulfate geochemistry suggests cryptic cycling may occur in groundwater aquifers as a result of distant volcanic subsurface activity. Microbial communities, including taxa that cycle S, may be superior tracers to changes in groundwater quality, especially from direct inputs of subsurface volcanic activity.

Geology and land use shape nitrogen and sulfur cycling groundwater microbial communities in Pacific Island aquifers.

Resource-constrained island populations have thrived in Hawai'i for over a millennium, but now face aggressive new challenges to fundamental resources, including the security and sustainability of water resources. Characterizing the microbial community in groundwater ecosystems is a powerful approach to infer changes from human impacts due to land management in hydrogeological complex aquifers. In this study, we investigate how geology and land management influence geochemistry, microbial diversity and metabolic functions. We sampled a total of 19 wells over 2-years across the Hualalai watershed of Kona, Hawai'i analyzing geochemistry, and microbial communities by 16S rRNA amplicon sequencing. Geochemical analysis revealed significantly higher sulfate along the northwest volcanic rift zone, and high nitrogen (N) correlated with high on-site sewage disposal systems (OSDS) density. A total of 12,973 Amplicon Sequence Variants (ASV) were identified in 220 samples, including 865 ASVs classified as putative N and sulfur (S) cyclers. The N and S cyclers were dominated by a putative S-oxidizer coupled to complete denitrification (Acinetobacter), significantly enriched up to 4-times comparatively amongst samples grouped by geochemistry. The significant presence of Acinetobacter infers the bioremediation potential of volcanic groundwater for microbial-driven coupled S-oxidation and denitrification providing an ecosystem service for island populations dependent upon groundwater aquifers.

CoCoNet: an efficient deep learning tool for viral metagenome binning.

MOTIVATION: Metagenomic approaches hold the potential to characterize microbial communities and unravel the intricate link between the microbiome and biological processes. Assembly is one of the most critical steps in metagenomics experiments. It consists of transforming overlapping DNA sequencing reads into sufficiently accurate representations of the community's genomes. This process is computationally difficult and commonly results in genomes fragmented across many contigs. Computational binning methods are used to mitigate fragmentation by partitioning contigs based on their sequence composition, abundance or chromosome organization into bins representing the community's genomes. Existing binning methods have been principally tuned for bacterial genomes and do not perform favorably on viral metagenomes. RESULTS: We propose Composition and Coverage Network (CoCoNet), a new binning method for viral metagenomes that leverages the flexibility and the effectiveness of deep learning to model the co-occurrence of contigs belonging to the same viral genome and provide a rigorous framework for binning viral contigs. Our results show that CoCoNet substantially outperforms existing binning methods on viral datasets. AVAILABILITY AND IMPLEMENTATION: CoCoNet was implemented in Python and is available for download on PyPi (https://pypi.org/). The source code is hosted on GitHub at https://github.com/Puumanamana/CoCoNet and the documentation is available at https://coconet.readthedocs.io/en/latest/index.html. CoCoNet does not require extensive resources to run. For example, binning 100k contigs took about 4 h on 10 Intel CPU Cores (2.4 GHz), with a memory peak at 27 GB (see Supplementary Fig. S9). To process a large dataset, CoCoNet may need to be run on a high RAM capacity server. Such servers are typically available in high-performance or cloud computing settings. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The amphibian microbiome exhibits poor resilience following pathogen-induced disturbance.

Infectious pathogens can disrupt the microbiome in addition to directly affecting the host. Impacts of disease may be dependent on the ability of the microbiome to recover from such disturbance, yet remarkably little is known about microbiome recovery after disease, particularly in nonhuman animals. We assessed the resilience of the amphibian skin microbial community after disturbance by the pathogen, Batrachochytrium dendrobatidis (Bd). Skin microbial communities of laboratory-reared mountain yellow-legged frogs were tracked through three experimental phases: prior to Bd infection, after Bd infection (disturbance), and after clearing Bd infection (recovery period). Bd infection disturbed microbiome composition and altered the relative abundances of several dominant bacterial taxa. After Bd infection, frogs were treated with an antifungal drug that cleared Bd infection, but this did not lead to recovery of microbiome composition (measured as Unifrac distance) or relative abundances of dominant bacterial groups. These results indicate that Bd infection can lead to an alternate stable state in the microbiome of sensitive amphibians, or that microbiome recovery is extremely slow-in either case resilience is low. Furthermore, antifungal treatment and clearance of Bd infection had the additional effect of reducing microbial community variability, which we hypothesize results from similarity across frogs in the taxa that colonize community vacancies resulting from the removal of Bd. Our results indicate that the skin microbiota of mountain yellow-legged frogs has low resilience following Bd-induced disturbance and is further altered by the process of clearing Bd infection, which may have implications for the conservation of this endangered amphibian.

GranatumX: A Community-engaging, Modularized, and Flexible Webtool for Single-cell Data Analysis.

We present GranatumX, a next-generation software environment for single-cell RNA sequencing (scRNA-seq) data analysis. GranatumX is inspired by the interactive webtool Granatum. GranatumX enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical environment. It also offers software developers the opportunity to rapidly promote their own tools with others in customizable pipelines. The architecture of GranatumX allows for easy inclusion of plugin modules, named Gboxes, which wrap around bioinformatics tools written in various programming languages and on various platforms. GranatumX can be run on the cloud or private servers and generate reproducible results. It is a community-engaging, flexible, and evolving software ecosystem for scRNA-seq analysis, connecting developers with bench scientists. GranatumX is freely accessible at http://garmiregroup.org/granatumx/app.

Evaluation of STAR and Kallisto on Single Cell RNA-Seq Data Alignment.

Alignment of scRNA-Seq data are the first and one of the most critical steps of the scRNA-Seq analysis workflow, and thus the choice of proper aligners is of paramount importance. Recently, STAR an alignment method and Kallisto a pseudoalignment method have both gained a vast amount of popularity in the single cell sequencing field. However, an unbiased third-party comparison of these two methods in scRNA-Seq is lacking. Here we conduct a systematic comparison of them on a variety of Drop-seq, Fluidigm and 10x genomics data, from the aspects of gene abundance, alignment accuracy, as well as computational speed and memory use. We observe that STAR globally produces more genes and higher gene-expression values, compared to Kallisto, as well as Bowtie2, another popular alignment method for bulk RNA-Seq. STAR also yields higher correlations of the Gini index for the genes with RNA-FISH validation results. Using 10x genomics PBMC 3K scRNA-Seq and mouse cortex single nuclei RNA-Seq data, STAR shows similar or better cell-type annotation results, by detecting a larger subset of known gene markers. However, the gain of accuracy and gene abundance of STAR alignment comes with the price of significantly slower computation time (4 folds) and more memory (7.7 folds), compared to Kallisto.

DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data.

Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson's correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data, and is freely available at https://github.com/lanagarmire/DeepImpute .

Granatum: a graphical single-cell RNA-Seq analysis pipeline for genomics scientists.

BACKGROUND: Single-cell RNA sequencing (scRNA-Seq) is an increasingly popular platform to study heterogeneity at the single-cell level. Computational methods to process scRNA-Seq data are not very accessible to bench scientists as they require a significant amount of bioinformatic skills. RESULTS: We have developed Granatum, a web-based scRNA-Seq analysis pipeline to make analysis more broadly accessible to researchers. Without a single line of programming code, users can click through the pipeline, setting parameters and visualizing results via the interactive graphical interface. Granatum conveniently walks users through various steps of scRNA-Seq analysis. It has a comprehensive list of modules, including plate merging and batch-effect removal, outlier-sample removal, gene-expression normalization, imputation, gene filtering, cell clustering, differential gene expression analysis, pathway/ontology enrichment analysis, protein network interaction visualization, and pseudo-time cell series construction. CONCLUSIONS: Granatum enables broad adoption of scRNA-Seq technology by empowering bench scientists with an easy-to-use graphical interface for scRNA-Seq data analysis. The package is freely available for research use at http://garmiregroup.org/granatum/app.