ecc_finder: A Robust and Accurate Tool for Detecting Extrachromosomal Circular DNA From Sequencing Data

Extrachromosomal circular DNA (eccDNA) has been observed in different species for decades, and more and more evidence shows that this specific type of DNA molecules may play an important role in rapid adaptation. Therefore, characterizing the full landscape of eccDNA has become critical, and there are several protocols for enriching eccDNAs and performing short-read or long-read sequencing. However, there is currently no available bioinformatic tool to identify eccDNAs from Nanopore reads. More importantly, the current tools based on Illumina short reads lack an efficient standardized pipeline notably to identify eccDNA originating from repeated loci and cannot be applied to very large genomes. Here, we introduce a comprehensive tool to solve both of these two issues.1 Applying ecc_finder to eccDNA-seq data (either mobilome-seq, Circle-Seq and CIDER-seq) from Arabidopsis, human, and wheat (with genome sizes ranging from 120Mb to 17 Gb), we document the improvement of computational time, sensitivity, and accuracy and demonstrate ecc_finder wide applicability and functionality.

Predicting PY motif-mediated protein-protein interactions in the Nedd4 family of ubiquitin ligases

The Nedd4 family contains several structurally related but functionally distinct HECT-type ubiquitin ligases. The members of the Nedd4 family are known to recognize substrates through their multiple WW domains, which recognize PY motifs (PPxY, LPxY) or phospho-threonine or phospho-serine residues. To better understand protein interactor recognition mechanisms across the Nedd4 family, we report the development and implementation of a python-based tool, PxYFinder, to identify PY motifs in the primary sequences of previously identified interactors of Nedd4 and related ligases. Using PxYFinder, we find that, on average, half of Nedd4 family interactions are likely PY-motif mediated. Further, we find that PPxY motifs are more prevalent than LPxY motifs and are more likely to occur in proline-rich regions and that PPxY regions are more disordered on average relative to LPxY-containing regions. Informed by consensus sequences for PY motifs across the Nedd4 interactome, we rationally designed a focused peptide library and employed a computational screen, revealing sequence- and biomolecular interaction-dependent determinants of WW-domain/PY-motif interactions. Cumulatively, our efforts provide a new bioinformatic tool and expand our understanding of sequence and structural factors that contribute to PY-motif mediated interactor recognition across the Nedd4 family.

COPLA, a taxonomic classifier of plasmids

Background Plasmids are mobile genetic elements, key in the dissemination of antibiotic resistance, virulence determinants and other adaptive traits in bacteria. Obtaining a robust method for plasmid classification is necessary to better understand the genetics and epidemiology of many pathogens. Until now, plasmid classification systems focused on specific traits, which limited their precision and universality. The definition of plasmid taxonomic units (PTUs), based on average nucleotide identity metrics, allows the generation of a universal plasmid classification scheme, applicable to all bacterial taxa. Here we present COPLA, a software able to assign plasmids to known and novel PTUs, based on their genomic sequence. Results We implemented an automated pipeline able to assign a given plasmid DNA sequence to its cognate PTU, and assessed its performance using a sample of 1000 unclassified plasmids. Overall, 41% of the samples could be assigned to a previously defined PTU, a number that reached 63% in well-known taxa such as the Enterobacterales order. The remaining plasmids represent novel PTUs, indicating that a large fraction of plasmid backbones is still uncharacterized. Conclusions COPLA is a bioinformatic tool for universal, species-independent, plasmid classification. Offered both as an automatable pipeline and an open web service, COPLA will help bacterial geneticists and clinical microbiologists to quickly classify plasmids.

Computational analysis of the interaction of limonene with the fat mass and obesity-associated protein

This study aimed to predict the binding affinity, orientation, and physical interaction between limonene and fat mass and obesity-associated protein. The mechanism of limonene and protein association was explored by molecular docking, a bioinformatic tool. The results of association were compared with the reported results of the anti-obesity drug such as orlistat and with the flavonoids. AutoDock Vina tools were used for the molecular docking of limonene with fat mass and obesity-associated protein. PyMol and Discovery Studio Visualizer were used to visualize the results of this docking. The binding affinity of limonene was higher (Least negative G) than the orlistat and flavonoids such as Daidzein, Exemestane, Kaempherol, Letrozole, And Rutin. It is conducted in this study that the Limonene can alleviate obesity by making an interaction with the fat mass and obesity-associated protein. This inhibitory interaction was greater as compared to other reported phytochemicals and drugs.

M&Ms: A software for building realistic Microbial Mock communities

Motivation: Advances in sequencing technologies have triggered the development of many bioinformatic tools aimed to analyze these data. As these tools need to be tested, it is important to simulate datasets that resemble realistic conditions. Although there is a large amount of software dedicated to produce reads from in silico microbial communities, often the simulated data diverge widely from real situations. Results: Here, we introduce M&Ms, a user-friendly open-source bioinformatic tool to produce realistic amplicon datasets from reference sequences, based on pragmatic ecological parameters. This tool creates sequence libraries for in silico microbial communities with user-controlled richness, evenness, microdiversity, and source environment. M&Ms allows the user to generate simple to complex read datasets based on real parameters that can be used in developing bioinformatic software or in benchmarking current tools. M&Ms also provides additional figures and files with extensive details on how each synthetic community is composed, so that users can make informed choices when designing their benchmarking pipelines. Availability: The source code of M&Ms is freely available from https://github.com/ggnatalia/MMs

G2S: A New Deep Learning Tool for Predicting Stool Microbiome Structure From Oral Microbiome Data

Deep learning methodologies have revolutionized prediction in many fields and show the potential to do the same in microbial metagenomics. However, deep learning is still unexplored in the field of microbiology, with only a few software designed to work with microbiome data. Within the meta-community theory, we foresee new perspectives for the development and application of deep learning algorithms in the field of the human microbiome. In this context, we developed G2S, a bioinformatic tool for taxonomic prediction of the human fecal microbiome directly from the oral microbiome data of the same individual. The tool uses a deep convolutional neural network trained on paired oral and fecal samples from populations across the globe, which allows inferring the stool microbiome at the family level more accurately than other available approaches. The tool can be used in retrospective studies, where fecal sampling was not performed, and especially in the field of paleomicrobiology, as a unique opportunity to recover data related to ancient gut microbiome configurations. G2S was validated on already characterized oral and fecal sample pairs, and then applied to ancient microbiome data from dental calculi, to derive putative intestinal components in medieval subjects.

Biosynthesis and Heterologous Expression of Cacaoidin, the First Member of the Lanthidin Family of RiPPs

Cacaoidin is produced by the strain Streptomyces cacaoi CA-170360 and represents the first member of the new lanthidin (class V lanthipeptides) RiPP family. In this work, we describe the complete identification, cloning and heterologous expression of the cacaoidin biosynthetic gene cluster, which shows unique RiPP genes whose functions were not predicted by any bioinformatic tool. We also describe that the cacaoidin pathway is restricted to strains of the subspecies Streptomyces cacaoi subsp. cacaoi found in public genome databases, where we have also identified the presence of other putative class V lanthipeptide pathways. This is the first report on the heterologous production of a class V lanthipeptide.

Comprehensive Identification and Alternative Splicing of Microexons in Drosophila

Interrupted exons in the pre-mRNA transcripts are ligated together through RNA splicing, which plays a critical role in the regulation of gene expression. Exons with a length ≤ 30 nt are defined as microexons that are unique in identification. However, microexons, especially those shorter than 8 nt, have not been well studied in many organisms due to difficulties in mapping short segments from sequencing reads. Here, we analyzed mRNA-seq data from a variety of Drosophila samples with a newly developed bioinformatic tool, ce-TopHat. In addition to the Flybase annotated, 465 new microexons were identified. Differentially alternatively spliced (AS) microexons were investigated between the Drosophila tissues (head, body, and gonad) and genders. Most of the AS microexons were found in the head and two AS microexons were identified in the sex-determination pathway gene fruitless.

Hybridisation capture allows DNA damage analysis of ancient marine eukaryotes

Marine sedimentary ancient DNA (sedaDNA) is increasingly used to study past ocean ecosystems, however, studies have been severely limited by the very low amounts of DNA preserved in the subseafloor, and the lack of bioinformatic tools to authenticate sedaDNA in metagenomic data. We applied a hybridisation capture ‘baits’ technique to target marine eukaryote sedaDNA (specifically, phyto- and zooplankton, ‘Planktonbaits1’; and harmful algal bloom taxa, ‘HABbaits1’), which resulted in up to 4- and 9-fold increases, respectively, in the relative abundance of eukaryotes compared to shotgun sequencing. We further used the bioinformatic tool ‘HOPS’ to authenticate the sedaDNA component, establishing a new proxy to assess sedaDNA authenticity, “% eukaryote sedaDNA damage”, that is positively correlated with subseafloor depth. We used this proxy to report the first-ever DNA damage profiles from a marine phytoplankton species, the ubiquitous coccolithophore Emiliania huxleyi. Our approach opens new avenues for the detailed investigation of long-term change and evolution of marine eukaryotes over geological timescales.