Alexa B. R. McIntyre

A as a conserved regulatory mark across Flaviviridae genomes.

BACKGROUND: One of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited. RESULTS: In this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages. CONCLUSIONS: This study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

We evaluated the performance of the MinION DNA sequencer in-flight on the International Space Station (ISS), and benchmarked its performance off-Earth against the MinION, Illumina MiSeq, and PacBio RS II sequencing platforms in terrestrial laboratories. Samples contained equimolar mixtures of genomic DNA from lambda bacteriophage, Escherichia coli (strain K12, MG1655) and Mus musculus (female BALB/c mouse). Nine sequencing runs were performed aboard the ISS over a 6-month period, yielding a total of 276,882 reads with no apparent decrease in performance over time. From sequence data collected aboard the ISS, we constructed directed assemblies of the ~4.6 Mb E. coli genome, ~48.5 kb lambda genome, and a representative M. musculus sequence (the ~16.3 kb mitochondrial genome), at 100%, 100%, and 96.7% consensus pairwise identity, respectively; de novo assembly of the E. coli genome from raw reads yielded a single contig comprising 99.9% of the genome at 98.6% consensus pairwise identity. Simulated real-time analyses of in-flight sequence data using an automated bioinformatic pipeline and laptop-based genomic assembly demonstrated the feasibility of sequencing analysis and microbial identification aboard the ISS. These findings illustrate the potential for sequencing applications including disease diagnosis, environmental monitoring, and elucidating the molecular basis for how organisms respond to spaceflight.

Abstract Many cellular mRNAs contain the modified base m 6 A, and recent studies have suggested that various stimuli can lead to changes in m 6 A. The most common method to map m 6 A and to predict changes in m 6 A between conditions is methylated RNA immunoprecipitation sequencing (MeRIP-seq), through which methylated regions are detected as peaks in transcript coverage from immunoprecipitated RNA relative to input RNA. Here, we generated replicate controls and reanalyzed published MeRIP-seq data to estimate reproducibility across experiments. We found that m 6 A peak overlap in mRNAs varies from ~30 to 60% between studies, even in the same cell type. We then assessed statistical methods to detect changes in m 6 A peaks as distinct from changes in gene expression. However, from these published data sets, we detected few changes under most conditions and were unable to detect consistent changes across studies of similar stimuli. Overall, our work identifies limits to MeRIP-seq reproducibility in the detection both of peaks and of peak changes and proposes improved approaches for analysis of peak changes.

Significance N6 -methyladenosine (m 6 A) has recently been found to regulate numerous aspects of RNA biology. Similar to methylation of cytosine residues in DNA, eukaryotic RNA is modified by enzymatic addition of methyl groups at adenosines. m 6 A modification of RNA affects a wide variety of RNA functions, including mRNA stability, translation, and in the case of viruses, viral replication and production. Our investigation revealed that the adenosine residues present in the known m 6 A consensus motif within the 5′ epsilon stem loop of pregenomic RNA and the 3′ ends of all the hepatitis B virus (HBV) transcripts are m 6 A modified. We demonstrate here that m 6 A modification differentially modulates HBV RNA stability and reverse transcription, thereby playing two distinct regulatory roles in the HBV life cycle.