MAIRA: Protein-based Analysis of MinION Reads on a Laptop.

Third-generation sequencing technologies are being increasingly used in microbiome research and this has given rise to new challenges in computational microbiome analysis. Oxford Nanopore's MinION is a portable sequencer that streams data that can be basecalled on-the-fly. Here we give an introduction to the MAIRA software, which is designed to analyze MinION sequencing reads from a microbiome sample, as they are produced in real-time, on a laptop. The software processes reads in batches and updates the presented analysis after each batch. There are two analysis steps: First, protein alignments are calculated to determine which genera might be present in a sample. When strong evidence for a genus is found, then, in a second step, a more detailed analysis is performed by aligning the reads against the proteins of all species in the detected genus. The program presents a detailed analysis of species, antibiotic resistance genes, and virulence factors.

Using AnnoTree to Get More Assignments, Faster, in DIAMOND+MEGAN Microbiome Analysis.

In microbiome analysis, one main approach is to align metagenomic sequencing reads against a protein reference database, such as NCBI-nr, and then to perform taxonomic and functional binning based on the alignments. This approach is embodied, for example, in the standard DIAMOND+MEGAN analysis pipeline, which first aligns reads against NCBI-nr using DIAMOND and then performs taxonomic and functional binning using MEGAN. Here, we propose the use of the AnnoTree protein database, rather than NCBI-nr, in such alignment-based analyses to determine the prokaryotic content of metagenomic samples. We demonstrate a 2-fold speedup over the usage of the prokaryotic part of NCBI-nr and increased assignment rates, in particular assigning twice as many reads to KEGG. In addition to binning to the NCBI taxonomy, MEGAN now also bins to the GTDB taxonomy. IMPORTANCE The NCBI-nr database is not explicitly designed for the purpose of microbiome analysis, and its increasing size makes its unwieldy and computationally expensive for this purpose. The AnnoTree protein database is only one-quarter the size of the full NCBI-nr database and is explicitly designed for metagenomic analysis, so it should be supported by alignment-based pipelines.

Microbial Phylogenetic Context Using Phylogenetic Outlines.

Microbial studies typically involve the sequencing and assembly of draft genomes for individual microbes or whole microbiomes. Given a draft genome, one first task is to determine its phylogenetic context, that is, to place it relative to the set of related reference genomes. We provide a new interactive graphical tool that addresses this task using Mash sketches to compare against all bacterial and archaeal representative genomes in the Genome Taxonomy Database taxonomy, all within the framework of SplitsTree5. The phylogenetic context of the query sequences is then displayed as a phylogenetic outline, a new type of phylogenetic network that is more general than a phylogenetic tree, but significantly less complex than other types of phylogenetic networks. We propose to use such networks, rather than trees, to represent phylogenetic context, because they can express uncertainty in the placement of taxa, whereas a tree must always commit to a specific branching pattern. We illustrate the new method using a number of draft genomes of different assembly quality.

DIAMOND+MEGAN: Fast and Easy Taxonomic and Functional Analysis of Short and Long Microbiome Sequences.

One main approach to computational analysis of microbiome sequences is to first align against a reference database of annotated protein sequences (NCBI-nr) and then perform taxonomic and functional binning of the sequences based on the resulting alignments. For both short and long reads (or assembled contigs), alignment is performed using DIAMOND, whereas taxonomic and functional binning, followed by inter- active exploration and analysis, is performed using MEGAN. We provide two step-by-step descriptions of this approach: © 2021 The Authors. Basic Protocol 1: Taxonomic and functional analysis of short read microbiome sequences Support Protocol 1: Preprocessing Basic Protocol 2: taxonomic and functional analysis of assembled long read microbiome sequences Support Protocol 2: Taxonomic binning and CheckM.

MAIRA- real-time taxonomic and functional analysis of long reads on a laptop.

BACKGROUND: Advances in mobile sequencing devices and laptop performance make metagenomic sequencing and analysis in the field a technologically feasible prospect. However, metagenomic analysis pipelines are usually designed to run on servers and in the cloud. RESULTS: MAIRA is a new standalone program for interactive taxonomic and functional analysis of long read metagenomic sequencing data on a laptop, without requiring external resources. The program performs fast, online, genus-level analysis, and on-demand, detailed taxonomic and functional analysis. It uses two levels of frame-shift-aware alignment of DNA reads against protein reference sequences, and then performs detailed analysis using a protein synteny graph. CONCLUSIONS: We envision this software being used by researchers in the field, when access to servers or cloud facilities is difficult, or by individuals that do not routinely access such facilities, such as medical researchers, crop scientists, or teachers.

The Isolate Caproiciproducens sp. 7D4C2 Produces n-Caproate at Mildly Acidic Conditions From Hexoses: Genome and rBOX Comparison With Related Strains and Chain-Elongating Bacteria.

Bulk production of medium-chain carboxylates (MCCs) with 6-12 carbon atoms is of great interest to biotechnology. Open cultures (e.g., reactor microbiomes) have been utilized to generate MCCs in bioreactors. When in-line MCC extraction and prevention of product inhibition is required, the bioreactors have been operated at mildly acidic pH (5.0-5.5). However, model chain-elongating bacteria grow optimally at neutral pH values. Here, we isolated a chain-elongating bacterium (strain 7D4C2) that grows at mildly acidic pH. We studied its metabolism and compared its whole genome and the reverse ß-oxidation (rBOX) genes to other bacteria. Strain 7D4C2 produces lactate, acetate, n-butyrate, n-caproate, biomass, and H2/CO2 from hexoses. With only fructose as substrate (pH 5.5), the maximum n-caproate specificity (i.e., products per other carboxylates produced) was 60.9 ± 1.5%. However, this was considerably higher at 83.1 ± 0.44% when both fructose and n-butyrate (electron acceptor) were combined as a substrate. A comparison of 7D4C2 cultures with fructose and n-butyrate with an increasing pH value from 4.5 to 9.0 showed a decreasing n-caproate specificity from ∼92% at mildly acidic pH (pH 4.5-5.0) to ∼24% at alkaline pH (pH 9.0). Moreover, when carboxylates were extracted from the broth (undissociated n-caproic acid was ∼0.3 mM), the n-caproate selectivity (i.e., product per substrate fed) was 42.6 ± 19.0% higher compared to 7D4C2 cultures without extraction. Based on the 16S rRNA gene sequence, strain 7D4C2 is most closely related to the isolates Caproicibacter fermentans (99.5%) and Caproiciproducens galactitolivorans (94.7%), which are chain-elongating bacteria that are also capable of lactate production. Whole-genome analyses indicate that strain 7D4C2, C. fermentans, and C. galactitolivorans belong to the same genus of Caproiciproducens. Their rBOX genes are conserved and located next to each other, forming a gene cluster, which is different than for other chain-elongating bacteria such as Megasphaera spp. In conclusion, Caproiciproducens spp., comprising strain 7D4C2, C. fermentans, C. galactitolivorans, and several unclassified strains, are chain-elongating bacteria that encode a highly conserved rBOX gene cluster. Caproiciproducens sp. 7D4C2 (DSM 110548) was studied here to understand n-caproate production better at mildly acidic pH within microbiomes and has the additional potential as a pure-culture production strain to convert sugars into n-caproate.

Introduction to the Analysis of Environmental Sequences: Metagenomics with MEGAN.

Metagenomics has become a part of the standard toolkit for scientists interested in studying microbes in the environment. Compared to 16S rDNA sequencing, which allows coarse taxonomic profiling of samples, shotgun metagenomic sequencing provides a more detailed analysis of the taxonomic and functional content of samples. Long read technologies, such as developed by Pacific Biosciences or Oxford Nanopore, produce much longer stretches of informative sequence, greatly simplifying the difficult and time-consuming process of metagenomic assembly. MEGAN6 provides a wide range of analysis and visualization methods for the analysis of short and long read metagenomic data. A simple and efficient analysis pipeline for metagenomic analysis consists of the DIAMOND alignment tool on short reads, or the LAST alignment tool on long reads, followed by MEGAN. This approach performs taxonomic and functional abundance analysis, supports comparative analysis of large-scale experiments, and allows one to involve experimental metadata in the analysis.

Annotated bacterial chromosomes from frame-shift-corrected long-read metagenomic data.

BACKGROUND: Short-read sequencing technologies have long been the work-horse of microbiome analysis. Continuing technological advances are making the application of long-read sequencing to metagenomic samples increasingly feasible. RESULTS: We demonstrate that whole bacterial chromosomes can be obtained from an enriched community, by application of MinION sequencing to a sample from an EBPR bioreactor, producing 6 Gb of sequence that assembles into multiple closed bacterial chromosomes. We provide a simple pipeline for processing such data, which includes a new approach to correcting erroneous frame-shifts. CONCLUSIONS: Advances in long-read sequencing technology and corresponding algorithms will allow the routine extraction of whole chromosomes from environmental samples, providing a more detailed picture of individual members of a microbiome.

MEGAN-LR: new algorithms allow accurate binning and easy interactive exploration of metagenomic long reads and contigs.

BACKGROUND: There are numerous computational tools for taxonomic or functional analysis of microbiome samples, optimized to run on hundreds of millions of short, high quality sequencing reads. Programs such as MEGAN allow the user to interactively navigate these large datasets. Long read sequencing technologies continue to improve and produce increasing numbers of longer reads (of varying lengths in the range of 10k-1M bps, say), but of low quality. There is an increasing interest in using long reads in microbiome sequencing, and there is a need to adapt short read tools to long read datasets. METHODS: We describe a new LCA-based algorithm for taxonomic binning, and an interval-tree based algorithm for functional binning, that are explicitly designed for long reads and assembled contigs. We provide a new interactive tool for investigating the alignment of long reads against reference sequences. For taxonomic and functional binning, we propose to use LAST to compare long reads against the NCBI-nr protein reference database so as to obtain frame-shift aware alignments, and then to process the results using our new methods. RESULTS: All presented methods are implemented in the open source edition of MEGAN, and we refer to this new extension as MEGAN-LR (MEGAN long read). We evaluate the LAST+MEGAN-LR approach in a simulation study, and on a number of mock community datasets consisting of Nanopore reads, PacBio reads and assembled PacBio reads. We also illustrate the practical application on a Nanopore dataset that we sequenced from an anammox bio-rector community. REVIEWERS: This article was reviewed by Nicola Segata together with Moreno Zolfo, Pete James Lockhart and Serghei Mangul. CONCLUSION: This work extends the applicability of the widely-used metagenomic analysis software MEGAN to long reads. Our study suggests that the presented LAST+MEGAN-LR pipeline is sufficiently fast and accurate.

One Step Forward, Two Steps Back; Xeno-MicroRNAs Reported in Breast Milk Are Artifacts.

BACKGROUND: MicroRNAs (miRNAs) are short RNA sequences that guide post-transcriptional regulation of gene expression via complementarity to their target mRNAs. Discovered only recently, miRNAs have drawn a lot of attention. Multiple protein complexes interact to first cleave a hairpin from nascent RNA, export it into the cytosol, trim its loop, and incorporate it into the RISC complex which is important for binding its target mRNA. This process works within one cell, but circulating miRNAs have been described suggesting a role in cell-cell communication. MOTIVATION: Viruses and intracellular parasites like Toxoplasma gondii use miRNAs to manipulate host gene expression from within the cellular environment. However, recent research has claimed that a rice miRNA may regulate human gene expression. Despite ongoing debates about these findings and general reluctance to accept them, a recent report claimed that foodborne plant miRNAs pass through the digestive tract, travel through blood to be incorporated by alveolar cells excreting milk. The miRNAs are then said to have some immune-related function in the newborn. PRINCIPAL FINDINGS: We acquired the data that supports their claim and performed further analyses. In addition to the reported miRNAs, we were able to detect almost complete mRNAs and found that the foreign RNA expression profiles among samples are exceedingly similar. Inspecting the source of the data helped understand how RNAs could contaminate the samples. CONCLUSION: Viewing these findings in context with the difficulties foreign RNAs face on their route into breast milk and the fact that many identified foodborne miRNAs are not from actual food sources, we can conclude beyond reasonable doubt that the original claims and evidence presented may be due to artifacts. We report that the study claiming their existence is more likely to have detected RNA contamination than miRNAs.

Computational prediction of microRNAs from Toxoplasma gondii potentially regulating the hosts' gene expression.

MicroRNAs (miRNAs) were discovered two decades ago, yet there is still a great need for further studies elucidating their genesis and targeting in different phyla. Since experimental discovery and validation of miRNAs is difficult, computational predictions are indispensable and today most computational approaches employ machine learning. Toxoplasma gondii, a parasite residing within the cells of its hosts like human, uses miRNAs for its post-transcriptional gene regulation. It may also regulate its hosts' gene expression, which has been shown in brain cancer. Since previous studies have shown that overexpressed miRNAs within the host are causal for disease onset, we hypothesized that T. gondii could export miRNAs into its host cell. We computationally predicted all hairpins from the genome of T. gondii and used mouse and human models to filter possible candidates. These were then further compared to known miRNAs in human and rodents and their expression was examined for T. gondii grown in mouse and human hosts, respectively. We found that among the millions of potential hairpins in T. gondii, only a few thousand pass filtering using a human or mouse model and that even fewer of those are expressed. Since they are expressed and differentially expressed in rodents and human, we suggest that there is a chance that T. gondii may export miRNAs into its hosts for direct regulation.