proovframe: frameshift-correction for long-read (meta)genomics

Long-read sequencing technologies hold big promises for the genomic analysis of complex samples such as microbial communities. Yet, despite improving accuracy, basic gene prediction on long-read data is still often impaired by frameshifts resulting from small indels. Consensus polishing using either complementary short reads or to a lesser extent the long reads themselves can mitigate this effect but requires universally high sequencing depth, which is difficult to achieve in complex samples where the majority of community members are rare. Here we present proovframe, a software implementing an alternative approach to overcome frameshift errors in long-read assemblies and raw long reads. We utilize protein-to-nucleotide alignments against reference databases to pinpoint indels in contigs or reads and correct them by deleting or inserting 1-2 bases, thereby conservatively restoring reading-frame fidelity in aligned regions. Using simulated and real-world benchmark data we show that proovframe performs comparably to short-read-based polishing on assembled data, works well with remote protein homologs, and can even be applied to raw reads directly. Together, our results demonstrate that protein-guided frameshift correction significantly improves the analyzability of long-read data both in combination with and as an alternative to common polishing strategies. Proovframe is available from https://github.com/thackl/proovframe

Influence of dissolved oxygen content on the bacteria‐induced ennoblement of stainless steels in seawater and its consequence on the localized corrosion risk

The ennoblement of stainless steel (e.g., the increase of open circuit potential [OCP]) is associated with bacterial colonization. This increases the risk of localized corrosion as the critical pitting/crevice potential can be overcome, especially for lower grade stainless steel. In this study, we assessed the influence of dissolved oxygen content (DOC) on the crevice corrosion of duplex and super duplex stainless steels. In addition, we used DNA amplicon sequencing to identify the bacteria most likely associated with the ennoblement. Above approximately 100 parts per billion (ppb) of dissolved oxygen, the ennoblement of OCP was observed leading to an increased risk of localized corrosion. Below approximately 100 ppb of dissolved oxygen, no ennoblement occurred and the risk of localized corrosion was reduced. We identified certain hydrocarbon‐degrading bacteria whose presence correlated with the ennoblement of super duplex stainless steel at saturated DOC. The role of these bacteria is not clear yet, but their distribution indicates a possible involvement in stainless steel ennoblement in seawater

Microbial-enrichment method enables high-throughput metagenomic characterization from host-rich samples

Host–microbe interactions have been linked to health and disease states through the use of microbial taxonomic profiling, mostly via 16S ribosomal RNA gene sequencing. However, many mechanistic insights remain elusive, in part because studying the genomes of microbes associated with mammalian tissue is difficult due to the high ratio of host to microbial DNA in such samples. Here we describe a microbial-enrichment method (MEM), which we demonstrate on a wide range of sample types, including saliva, stool, intestinal scrapings, and intestinal mucosal biopsies. MEM enabled high-throughput characterization of microbial metagenomes from human intestinal biopsies by reducing host DNA more than 1,000-fold with minimal microbial community changes (roughly 90% of taxa had no significant differences between MEM-treated and untreated control groups). Shotgun sequencing of MEM-treated human intestinal biopsies enabled characterization of both high- and low-abundance microbial taxa, pathways and genes longitudinally along the gastrointestinal tract. We report the construction of metagenome-assembled genomes directly from human intestinal biopsies for bacteria and archaea at relative abundances as low as 1%. Analysis of metagenome-assembled genomes reveals distinct subpopulation structures between the small and large intestine for some taxa. MEM opens a path for the microbiome field to acquire deeper insights into host–microbe interactions by enabling in-depth characterization of host-tissue-associated microbial communities