Current bioinformatics workflows for PIWI-interacting RNA (piRNA) analysis focus primarily on germline-derived piRNAs and piRNA-clusters. Frequently, they suffer from outdated piRNA databases, questionable quantification methods, and lack of reproducibility. Often, pipelines specific to miRNA analysis are used for the piRNA research in silico. Furthermore, the absence of a well-established database for piRNA annotation, as for miRNA, leads to uniformity issues between studies and generates confusion for data analysts and biologists. For these reasons, we have developed WIND (Workflow for pIRNAs aNd beyonD), a bioinformatics workflow that addresses the crucial issue of piRNA annotation, thereby allowing a reliable analysis of small RNA sequencing data for the identification of piRNAs and other small non-coding RNAs (sncRNAs) that in the past have been incorrectly classified as piRNAs. WIND allows the creation of a comprehensive annotation track of sncRNAs combining information available in RNAcentral, with piRNA sequences from piRNABank, the first database dedicated to piRNA annotation. WIND was built with Docker containers for reproducibility and integrates widely used bioinformatics tools for sequence alignment and quantification. In addition, it includes Bioconductor packages for exploratory data and differential expression analysis. Moreover, WIND implements a "dual" approach for the evaluation of sncRNAs expression level quantifying the aligned reads to the annotated genome and carrying out an alignment-free transcript quantification using reads mapped to the transcriptome. Therefore, a broader range of piRNAs can be annotated, improving their quantification and easing the subsequent downstream analysis. WIND performance has been tested with several small RNA-seq datasets, demonstrating how our approach can be a useful and comprehensive resource to analyse piRNAs and other classes of sncRNAs.

Periodontitis is one of the most common oral inflammatory diseases, and results in connective tissue degradation and gradual tooth loss. It manifests with formation of periodontal pockets, in which anaerobic and Gram‑negative bacteria proliferate rapidly. Consequently, alteration of the subgingival microbiota is considered the primary etiologic agent of periodontitis. Previous studies have reported that smokers are at increased risk of periodontal disease, in both prevalence and severity, indicating that smoking is a risk factor for the onset and progression of the pathology. In the present study, 16S rRNA sequencing was employed to assess the subgingival microbiota in 6 smoker patients with chronic periodontitis, 6 non‑smoker patients with chronic periodontitis and 8 healthy controls. The results demonstrated significant alterations in the microbial structure of periodontitis patients. High relative abundance of Parvimonans, Desulfubulbus, Paludibacter, Haemophilus, and Sphaerochaeta genera characterized subgingival microbiota of periodontitis patients, both smokers and non‑smokers. Due to the high precision and sensitivity of the 16S rRNA sequencing method, analysis for low‑abundant genera (including Pedobacter, Granulicatella, Paracoccus, Atopobium, Bifidobacterium, Coprococcus, Oridobacteriu, Peptococcus, Oscillospira and Akkermansia) was feasible, and revealed novel phylotypes associated with periodontitis. Of note, a major microbial community alteration was evident in smoker patients, suggesting an association between smoking and severity of subgingival dysbiosis. The present study confirmed that chronic periodontitis is a polymicrobial disease where changes in the equilibrium of subgingival microbiota contribute to severity of pathology.

Background: CpG sites in an individual molecule may exist in a binary state (methylated or unmethylated) and each individual DNA molecule, containing a certain number of CpGs, is a combination of these states defining an epihaplotype. Classic quantification based approaches to study DNA methylation are intrinsically unable to fully represent the complexity of the underlying methylation substrate. Epihaplotype based approaches, on the other hand, allow methylation profiles of cell populations to be studied at the single molecule level. For such investigations, next-generation sequencing techniques can be used, both for quantitative and for epihaplotype analysis. Currently available tools for methylation analysis lack output formats that explicitly report CpG methylation profiles at the single molecule level and that have suited statistical tools for their interpretation. Results: Here we present ampliMethProfiler, a python-based pipeline for the extraction and statistical epihaplotype analysis of amplicons from targeted deep bisulfite sequencing of multiple DNA regions. Conclusions: ampliMethProfiler tool provides an easy and user friendly way to extract and analyze the epihaplotype composition of reads from targeted bisulfite sequencing experiments. ampliMethProfiler is written in python language and requires a local installation of BLAST and (optionally) QIIME tools. It can be run on Linux and OS X platforms. The software is open source and freely available at http://amplimethprofiler.sourceforge.net.

In recent years, epigenetics has gained a central role in the understanding of the process of natural selection. It is now clear how environmental impacts on the methylome could promote methylation variability with direct effects on disease etiology as well as phenotypic and genotypic variations in evolutionary processes. To identify possible factors influencing inter-individual methylation variability, we studied methylation values standard deviation of 166 healthy individuals searching for possible associations with genomic features and evolutionary signatures. We analyzed methylation variability values in relation to CpG cluster density and we found a strong association between them (p-value < 2.2 × 10− 16). Furthermore, we found that genes related to CpGs with high methylation variability values were enriched for immunological pathways; instead, those associated with low ones were enriched for pathways related to basic cellular functions. Finally, we found an association between methylation variability values and signals of both ancient (p-value < 2.2 × 10− 16) and recent selective pressure (p-value < 1 × 10− 4). Our results indicate the presence of an intricate interplay between genetics, epigenetic code and evolutionary constraints in humans.