CoMA - an intuitive and user-friendly pipeline for amplicon-sequencing data analysis

A pile of pipelines: An overview of the bioinformatics software for metabarcoding data analyses

Environmental DNA (eDNA) metabarcoding has gained growing attention as a strategy for monitoring biodiversity in ecology. However, taxa identifications produced through metabarcoding require sophisticated processing of high-throughput sequencing data from taxonomically informative DNA barcodes. Various sets of universal and taxon-specific primers have been developed, extending the usability of metabarcoding across archaea, bacteria and eukaryotes. Accordingly, a multitude of metabarcoding data analysis tools and pipelines have also been developed. Often, several developed workflows are designed to process the same amplicon sequencing data, making it somewhat puzzling to choose one among the plethora of existing pipelines. However, each pipeline has its own specific philosophy, strengths and limitations, which should be considered depending on the aims of any specific study, as well as the bioinformatics expertise of the user. In this review, we outline the input data requirements, supported operating systems and particular attributes of thirty-two amplicon processing pipelines with the goal of helping users to select a pipeline for their metabarcoding projects.

Omics approaches in understanding the benefits of plant-microbe interactions

Plant-microbe interactions are pivotal for ecosystem dynamics and sustainable agriculture, and are influenced by various factors, such as host characteristics, environmental conditions, and human activities. Omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have revolutionized our understanding of these interactions. Genomics elucidates key genes, transcriptomics reveals gene expression dynamics, proteomics identifies essential proteins, and metabolomics profiles small molecules, thereby offering a holistic perspective. This review synthesizes diverse microbial-plant interactions, showcasing the application of omics in understanding mechanisms, such as nitrogen fixation, systemic resistance induction, mycorrhizal association, and pathogen-host interactions. Despite the challenges of data integration and ethical considerations, omics approaches promise advancements in precision intervention and resilient agricultural practices. Future research should address data integration challenges, enhance omics technology resolution, explore epigenomics, and understand plant-microbe dynamics under diverse conditions. In conclusion, omics technologies hold immense promise for optimizing agricultural strategies and fortifying resilient plant-microbe alliances, paving the way for sustainable agriculture and environmental stewardship.

Decentralised system for demand-oriented collection of food waste - Assessment of biomethane potential, pathogen development and microbial community structure

Enormous amounts of food waste (FW) are produced worldwide, requiring efficient disposal strategies, both economically and ecologically. Anaerobic digestion to produce biomethane is among the most promising strategies, but requires proper solutions for storage and delivery of the waste material. Here, a decentralized system for demand-oriented FW storage and its practical usability was assessed. FW was stored under batch and fed-batch strategies at 5 °C, 20 °C and 30 °C for 28 days. The results showed that FW can be stored without cooling since bacterially produced lactic acid rapidly stabilized the material and inactivated pathogens. While FW storage worked well under all storage conditions and strategies, 16S analysis revealed a distinct microbiota, which was highly characteristic for each storage temperature. Moreover, FW storage had no negative impact on methane yield and stored FW contained readily degradable substances for demand-oriented biogas production.

LotuS2: an ultrafast and highly accurate tool for amplicon sequencing analysis

BACKGROUND

Amplicon sequencing is an established and cost-efficient method for profiling microbiomes. However, many available tools to process this data require both bioinformatics skills and high computational power to process big datasets. Furthermore, there are only few tools that allow for long read amplicon data analysis. To bridge this gap, we developed the LotuS2 (less OTU scripts 2) pipeline, enabling user-friendly, resource friendly, and versatile analysis of raw amplicon sequences.

RESULTS

In LotuS2, six different sequence clustering algorithms as well as extensive pre- and post-processing options allow for flexible data analysis by both experts, where parameters can be fully adjusted, and novices, where defaults are provided for different scenarios. We benchmarked three independent gut and soil datasets, where LotuS2 was on average 29 times faster compared to other pipelines, yet could better reproduce the alpha- and beta-diversity of technical replicate samples. Further benchmarking a mock community with known taxon composition showed that, compared to the other pipelines, LotuS2 recovered a higher fraction of correctly identified taxa and a higher fraction of reads assigned to true taxa (48% and 57% at species; 83% and 98% at genus level, respectively). At ASV/OTU level, precision and F-score were highest for LotuS2, as was the fraction of correctly reported 16S sequences.

CONCLUSION

LotuS2 is a lightweight and user-friendly pipeline that is fast, precise, and streamlined, using extensive pre- and post-ASV/OTU clustering steps to further increase data quality. High data usage rates and reliability enable high-throughput microbiome analysis in minutes.

AVAILABILITY

LotuS2 is available from GitHub, conda, or via a Galaxy web interface, documented at http://lotus2.earlham.ac.uk/ . Video Abstract.

Tourmaline: A containerized workflow for rapid and iterable amplicon sequence analysis using QIIME 2 and Snakemake

Background

Amplicon sequencing (metabarcoding) is a common method to survey diversity of environmental communities whereby a single genetic locus is amplified and sequenced from the DNA of whole or partial organisms, organismal traces (e.g., skin, mucus, feces), or microbes in an environmental sample. Several software packages exist for analyzing amplicon data, among which QIIME 2 has emerged as a popular option because of its broad functionality, plugin architecture, provenance tracking, and interactive visualizations. However, each new analysis requires the user to keep track of input and output file names, parameters, and commands; this lack of automation and standardization is inefficient and creates barriers to meta-analysis and sharing of results.

Findings

We developed Tourmaline, a Python-based workflow that implements QIIME 2 and is built using the Snakemake workflow management system. Starting from a configuration file that defines parameters and input files—a reference database, a sample metadata file, and a manifest or archive of FASTQ sequences—it uses QIIME 2 to run either the DADA2 or Deblur denoising algorithm; assigns taxonomy to the resulting representative sequences; performs analyses of taxonomic, alpha, and beta diversity; and generates an HTML report summarizing and linking to the output files. Features include support for multiple cores, automatic determination of trimming parameters using quality scores, representative sequence filtering (taxonomy, length, abundance, prevalence, or ID), support for multiple taxonomic classification and sequence alignment methods, outlier detection, and automated initialization of a new analysis using previous settings. The workflow runs natively on Linux and macOS or via a Docker container. We ran Tourmaline on a 16S ribosomal RNA amplicon data set from Lake Erie surface water, showing its utility for parameter optimization and the ability to easily view interactive visualizations through the HTML report, QIIME 2 viewer, and R- and Python-based Jupyter notebooks.

Conclusion

Automated workflows like Tourmaline enable rapid analysis of environmental amplicon data, decreasing the time from data generation to actionable results. Tourmaline is available for download at github.com/aomlomics/tourmaline.

Protein remote homology recognition using local and global structural sequence alignment

Protein Remote Homology and fold Recognition (PRHR) is the most crucial task to predict the protein patterns. To achieve this task, Sequence-Order Frequency Matrix-Sampling and Deep learning with Smith-Waterman (SOFM-SDSW) were designed using large-scale Protein Sequences (PSs), which take more time to determine the high-dimensional attributes. Also, it was ineffective since the SW was only applied for local alignment, which cannot find the most matches between the PSs. Hence, in this manuscript, a rapid semi-global alignment algorithm called SOFM-SD-GlobalSW (SOFM-SDGSW) is proposed that facilitates the affine-gap scoring and uses sequence similarity to align the PSs. The major aim of this paper is to enhance the alignment of SW algorithm in both locally and globally for PRHR. In this algorithm, the Maximal Exact Matches (MEMs) are initially obtained by the bit-level parallelism rather than to align the individual characters. After that, a subgroup of MEMs is obtained to determine the global Alignment Score (AS) using the new adaptive programming scheme. Also, the SW local alignment scheme is used to determine the local AS. Then, both local and global ASs are combined to produce a final AS. Further, this resultant AS is considered to train the Support Vector Machine (SVM) classifier to recognize the PRH and folds. Finally, the test results reveal the SOFM-SDGSW algorithm on SCOP 1.53, SCOP 1.67 and Superfamily databases attains an ROC of 0.97, 0.941 and 0.938, respectively, as well as, an ROC50 of 0.819, 0.846 and 0.86, respectively compared to the conventional PRHR algorithms.

Microbial response on the first full-scale DEMON® biomass transfer for mainstream deammonification

Sidestream partial nitritation and deammonification (pN/A) of high-strength ammonia wastewater is a well-established technology. Its expansion to the mainstream is, however mainly impeded by poor retention of anaerobic ammonia oxidizing bacteria (AnAOB), insufficient repression of nitrite oxidizing bacteria (NOB) and difficult control of soluble chemical oxygen demand and nitrite levels. At the municipal wastewater treatment plant in Strass (Austria) the microbial consortium was exhaustively monitored at full-scale over one and a half year with regular transfer of sidestream DEMON® biomass and further retention and enrichment of granular anammox biomass via hydrocyclone operation. Routine process parameters were surveyed and the response and evolution of the microbiota was followed by molecular tools, ex-situ activity tests and further, AnAOB quantification through particle tracking and heme measurement. After eight months of operation, the first anaerobic, simultaneous depletion of ammonia and nitrite was observed ex-situ, together with a direction to higher nitrite generation (68% of total NO_x-N) as compared to nitrate under aerobic conditions. Our dissolved oxygen (DO) scheme allowed for transient anoxic conditions and had a strong influence on nitrite levels and the NOB community, where Nitrobacter eventually dominated Nitrospira. The establishment of a minor but stable AnAOB biomass was accompanied by the rise of Chloroflexi and distinct emergence of Chlorobi, a trend not seen in the sidestream system. Interestingly, the most pronounced switch in the microbial community and noticeable NOB repression occurred during unfavorable conditions, i.e. the cold winter season and high organic load. Further abatement of NOB was achieved through bioaugmentation of aerobic ammonia oxidizing bacteria (AerAOB) from the sidestream-DEMON® tank. Performance of the sidestream pN/A was not impaired by this operational scheme and the average volumetric nitrogen removal rate of the mainstream even doubled in the second half of the monitoring campaign. We conclude that a combination of both, regular sidestream-DEMON® biomass transfer and granular SRT increase via hydrocyclone operation was crucial for AnAOB establishment within the mainstream.

Residual municipal solid waste as co-substrate at wastewater treatment plants: An assessment of methane yield, dewatering potential and microbial diversity

Separately collected organic fraction of municipal solid waste, also known as biowaste, is typically used to fill the available capacity of digesters at wastewater treatment plants. However, this approach might impair the use of the ensuing digestate for fertilizer production due to the presence of sewage sludge, a contaminated substrate. Worldwide, unsorted municipal solid household waste, i.e. residual waste, is still typically disposed of in landfills or incinerated, despite its high content of biodegradables and recyclables. Once efficiently separated from residual waste by mechanical processes, the biodegradables might be appropriate to substitute biowaste at wastewater treatment plants. Thus, the biowaste would be available for fertilizer production and contribute to a reduction in the demand on non-renewable fertilizers. This study aimed at determining the technical feasibility of co-digesting the mechanically separated organic fraction of residual waste with sewage sludge. Further, key parameters for the implementation of co-digestion at wastewater treatment plants were determined, namely, degradation of the solids and organics, specific methane production, flocculant demand, and dewatered sludge production. The microbial community and diversity in both mono- and co-digestion was also investigated. Semi-continuous laboratory scale experiments showed that the co-substrate derived from the residual waste provided a stable anaerobic co-digestion process, producing 206 to 245 L of methane per kg of volatiles solids added to the digester. The dewaterability of the digestate increased by 4.8 percentage points when the co-substrate was added; however, there was also an increase in the flocculant demand. The specific dewatered sludge production was 955 kg per ton of total solids of co-substrate added to the digester. Amplicon sequencing analysis provided a detailed insight into the microbial communities, which were primarily affected by the addition of co-substrate. The microbiota was fully functional and no inhibition or problems in the anaerobic digestion process were observed after co-substrate addition.

Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification

Bioinformatic analysis of eDNA metabarcoding data is a crucial step toward rigorously assessing biodiversity. Many programs are now available for each step of the required analyses, but their relative abilities at providing fast and accurate species lists have seldom been evaluated. We used simulated mock communities and real fish eDNA metabarcoding data to evaluate the performance of 13 bioinformatic programs and pipelines to retrieve fish occurrence and read abundance using the 12S mt rRNA gene marker. We used four indices to compare the outputs of each program with the simulated samples: sensitivity, F-measure, root-mean-square error (RMSE) on read relative abundances, and execution time. We found marked differences among programs only for the taxonomic assignment step, both in terms of sensitivity, F-measure and RMSE. Running time was highly different between programs for each step. The fastest programs with best indices for each step were assembled into a pipeline. We compared this pipeline to pipelines constructed from existing toolboxes (OBITools, Barque, and QIIME 2). Our pipeline and Barque obtained the best performance for all indices and appear to be better alternatives to highly used pipelines for analysing fish eDNA metabarcoding data when a complete reference database is available. Analysis on real eDNA metabarcoding data also indicated differences for taxonomic assignment and execution time only. This study reveals major differences between programs during the taxonomic assignment step. The choice of algorithm for the taxonomic assignment can have a significant impact on diversity estimates and should be made according to the objectives of the study.

The Effect of a High-Grain Diet on the Rumen Microbiome of Goats with a Special Focus on Anaerobic Fungi

This work investigated the changes of the rumen microbiome of goats switched from a forage to a concentrate diet with special attention to anaerobic fungi (AF). Female goats were fed an alfalfa hay (AH) diet (0% grain; n = 4) for 20 days and were then abruptly shifted to a high-grain (HG) diet (40% corn grain, 60% AH; n = 4) and treated for another 10 days. Rumen content samples were collected from the cannulated animals at the end of each diet period (day 20 and 30). The microbiome structure was studied using high-throughput sequencing for bacteria, archaea (16S rRNA gene) and fungi (ITS2), accompanied by qPCR for each group. To further elucidate unclassified AF, clone library analyses were performed on the ITS1 spacer region. Rumen pH was significantly lower in HG diet fed goats, but did not induce subacute ruminal acidosis. HG diet altered prokaryotic communities, with a significant increase of Bacteroidetes and a decrease of Firmicutes. On the genus level Prevotella 1 was significantly boosted. Methanobrevibacter and Methanosphaera were the most abundant archaea regardless of the diet and HG induced a significant augmentation of unclassified Thermoplasmatales. For anaerobic fungi, HG triggered a considerable rise in Feramyces observed with both ITS markers, while a decline of Tahromyces was detected by ITS2 and decrease of Joblinomyces by ITS1 only. The uncultured BlackRhino group revealed by ITS1 and further elucidated in one sample by LSU analysis, formed a considerable part of the AF community of goats fed both diets. Results strongly indicate that the rumen ecosystem still acts as a source for novel microorganisms and unexplored microbial interactions and that initial rumen microbiota of the host animal considerably influences the reaction pattern upon diet change.