Microbial source tracking (MST) analysis is essential to identifying and mitigating the fecal pollution of water resources. The signature-based MST method uses a library of sequences to identify contaminants based on operational taxonomic units (OTUs) that are unique to a certain source. However, no clear guidelines for how to incorporate OTU overlap or natural variation in the raw water bacterial community into MST analyses exist. We investigated how the inclusion of bacterial overlap between sources in the library affects source prediction accuracy. To achieve this, large-scale sampling – including feces from seven species, raw sewage, and raw water samples from water treatment plants – was followed by 16S rRNA amplicon sequencing. The MST library was defined using three settings: (i) no raw water communities represented; (ii) raw water communities selected through clustering analysis; and (iii) local water communities collected across consecutive years. The results suggest that incorporating either the local background or representative bacterial composition improves MST analyses, as the results were positively correlated to measured levels of fecal indicator bacteria and the accuracy at which OTUs were assigned to the correct contamination source increased fourfold. Using the proportion of OTUs with high source origin probability, underpinning a contaminating signal, is a solid foundation in a framework for further deciphering and comparing contaminating signals derived in signature-based MST approaches. In conclusion, incorporating background bacterial composition of water in MST can improve mitigation efforts for minimizing the spread of pathogenic and antibiotic resistant bacteria into essential freshwater resources.

Background MicroRNAs (miRNAs) are small RNAs that regulate gene expression at a post-transcriptional level and are emerging as potentially important biomarkers for various disease states, including pancreatic cancer. In silico-based functional analysis of miRNAs usually consists of miRNA target prediction and functional enrichment analysis of miRNA targets. Since miRNA target prediction methods generate a large number of false positive target genes, further validation to narrow down interesting candidate miRNA targets is needed. One commonly used method correlates miRNA and mRNA expression to assess the regulatory effect of a particular miRNA. The aim of this study was to build a bioinformatics pipeline in R for miRNA functional analysis including correlation analyses between miRNA expression levels and its targets on mRNA and protein expression levels available from the cancer genome atlas (TCGA) and the cancer proteome atlas (TCPA). TCGA-derived expression data of specific mature miRNA isoforms from pancreatic cancer tissue was used. Results Fifteen circulating miRNAs with significantly altered expression levels detected in pancreatic cancer patients were queried separately in the pipeline. The pipeline generated predicted miRNA target genes, enriched gene ontology (GO) terms and Kyoto encyclopedia of genes and genomes (KEGG) pathways. Predicted miRNA targets were evaluated by correlation analyses between each miRNA and its predicted targets. MiRNA functional analysis in combination with Kaplan-Meier survival analysis suggest that hsa-miR-885-5p could act as a tumor suppressor and should be validated as a potential prognostic biomarker in pancreatic cancer. Conclusions Our miRNA functional analysis (miRFA) pipeline can serve as a valuable tool in biomarker discovery involving mature miRNAs associated with pancreatic cancer and could be developed to cover additional cancer types. Results for all mature miRNAs in TCGA pancreatic adenocarcinoma dataset can be studied and downloaded through a shiny web application at https://emmbor.shinyapps.io/mirfa/ . Electronic supplementary material The online version of this article (10.1186/s12859-019-2974-3) contains supplementary material, which is available to authorized users.

Quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) detection of waterborne RNA viruses generally requires concentration of large water volumes due to low virus levels. A common approach is to use dead-end ultrafiltration followed by precipitation with polyethylene glycol. However, this procedure often leads to the co-concentration of PCR inhibitors that impairs the limit of detection and causes false-negative results. Here, we applied the concept of pre-PCR processing to optimize RT-qPCR detection of norovirus genogroup I (GI), genogroup II (GII), and hepatitis A virus (HAV) in challenging water matrices. The RT-qPCR assay was improved by screening for an inhibitor-tolerant master mix and modifying the primers with twisted intercalating nucleic acid molecules. Additionally, a modified protocol based on chaotropic lysis buffer and magnetic silica bead nucleic acid extraction was developed for complex water matrices. A validation of the modified extraction protocol on surface and drinking waters was performed. At least a 26-fold improvement was seen in the most complex surface water studied. The modified protocol resulted in average recoveries of 33, 13, 8, and 4% for mengovirus, norovirus GI, GII, and HAV, respectively. The modified protocol also improved the limit of detection for norovirus GI and HAV. RT-qPCR inhibition with C shifts of 1.6, 2.8, and 3.5 for norovirus GI, GII, and HAV, respectively, obtained for the standard nucleic acid extraction were completely eliminated by the modified protocol. The standard nucleic acid extraction method worked well on drinking water with no RT-qPCR inhibition observed and average recoveries of 80, 124, 89, and 32% for mengovirus, norovirus GI, GII, and HAV, respectively.

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