Epigenetic changes by EZH2 inhibition increase translocations in B cells with high AID activity or DNA repair deficiency.

The EZH2 histone methyltransferase inhibitors tazemetostat and valemetostat have recently received approval for clinical use in follicular lymphoma and adult T-cell leukemia/lymphoma, respectively. In follicular lymphoma, the expression of AID is responsible for increased mutational signatures and genomic instability. Because EZH2 inhibitors induce epigenetic and transcriptional changes in normal and lymphoma B cells, we studied whether these inhibitors could alter the pattern of AID-mediated chromosomal translocations.

CREDO: a friendly Customizable, REproducible, DOcker file generator for bioinformatics applications.

Background: The analysis of large and complex biological datasets in bioinformatics poses a significant challenge to achieving reproducible research outcomes due to inconsistencies and the lack of standardization in the analysis process. These issues can lead to discrepancies in results, undermining the credibility and impact of bioinformatics research and creating mistrust in the scientific process. To address these challenges, open science practices such as sharing data, code, and methods have been encouraged.

Microparticles from dental calculus disclose paleoenvironmental and palaeoecological records.

Plants have always represented a key element in landscape delineation. Indeed, plant diversity, whose distribution is influenced by geographic/climatic variability, has affected both environmental and human ecology. The present contribution represents a multi-proxy study focused on the detection of starch, pollen and non-pollen palynomorphs in ancient dental calculus collected from pre-historical individuals buried at La Sassa and Pila archaeological sites (Central Italy).

A single cell RNAseq benchmark experiment embedding "controlled" cancer heterogeneity.

Single-cell RNA sequencing (scRNA-seq) has emerged as a vital tool in tumour research, enabling the exploration of molecular complexities at the individual cell level. It offers new technical possibilities for advancing tumour research with the potential to yield significant breakthroughs. However, deciphering meaningful insights from scRNA-seq data poses challenges, particularly in cell annotation and tumour subpopulation identification.

La Sassa cave: Isotopic evidence for Copper Age and Bronze Age population dynamics in Central Italy.

This study focuses on the changes in diet and mobility of people buried in the La Sassa cave (Latium, Central Italy) during the Copper and Bronze Ages to contribute to the understanding of the complex contemporary population dynamics in Central Italy. To that purpose, carbon and nitrogen stable isotope analyses, strontium isotope analyses, and FT-IR evaluations were performed on human and faunal remains from this cave. The stable isotope analyses evidence a slight shift in diet between Copper and Bronze Age individuals, which becomes prominent in an individual, dating from a late phase, when the cave was mainly used as a cultic shelter.

CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system.

Motivation: The transition from evaluating a single time point to examining the entire dynamic evolution of a system is possible only in the presence of the proper framework. The strong variability of dynamic evolution makes the definition of an explanatory procedure for data fitting and clustering challenging.

Results: We developed CONNECTOR, a data-driven framework able to analyze and inspect longitudinal data in a straightforward and revealing way.

Bringing Cell Subpopulation Discovery on a Cloud-HPC Using rCASC and StreamFlow.

The idea behind novel single-cell RNA sequencing (scRNA-seq) pipelines is to isolate single cells through microfluidic approaches and generate sequencing libraries in which the transcripts are tagged to track their cell of origin. Modern scRNA-seq platforms are capable of analyzing up to many thousands of cells in each run. Then, combined with massive high-throughput sequencing producing billions of reads, scRNA-seq allows the assessment of fundamental biological properties of cell populations and biological systems at unprecedented resolution.

Using "Galaxy-rCASC": A Public Galaxy Instance for Single-Cell RNA-Seq Data Analysis.

rCASC is a modular workflow providing an integrated environment for single-cell RNA-seq (scRNA-Seq) data analysis exploiting Docker containers to achieve functional and computational reproducibility. It was initially developed as an R package usable also through a Java GUI. However, the Java frontend cannot be employed when running rCASC on a remote server, a typical setup due to the significant computational resources commonly needed to analyze scRNA-Seq data.

Identifying Gene Markers Associated with Cell Subpopulations.

An important point of the analysis of a single-cell RNA experiment is the identification of the key elements, i.e., genes, characterizing each cell subpopulation cluster.

Functional-Feature-Based Data Reduction Using Sparsely Connected Autoencoders.

Single-cell RNA sequencing (scRNA-seq) allows for the creation of large collections of individual cells transcriptome. Unsupervised clustering is an essential element for the analysis of these data, and it represents the initial step for the identification of different cell types to investigate the cell subpopulation structure of a biological sample. However, it is possible that the clustering aggregation features do not perfectly match the underlying biology since scRNA-seq data are characterized by high noise.

Stardust: improving spatial transcriptomics data analysis through space-aware modularity optimization-based clustering.

Background: Spatial transcriptomics (ST) combines stained tissue images with spatially resolved high-throughput RNA sequencing. The spatial transcriptomic analysis includes challenging tasks like clustering, where a partition among data points (spots) is defined by means of a similarity measure. Improving clustering results is a key factor as clustering affects subsequent downstream analysis.

Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis.

Background: Biological processes are based on complex networks of cells and molecules. Single cell multi-omics is a new tool aiming to provide new incites in the complex network of events controlling the functionality of the cell.

Methods: Since single cell technologies provide many sample measurements, they are the ideal environment for the application of Deep Learning and Machine Learning approaches.

Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service.

Background: Improving the availability and usability of data and analytical tools is a critical precondition for further advancing modern biological and biomedical research. For instance, one of the many ramifications of the COVID-19 global pandemic has been to make even more evident the importance of having bioinformatics tools and data readily actionable by researchers through convenient access points and supported by adequate IT infrastructures. One of the most successful efforts in improving the availability and usability of bioinformatics tools and data is represented by the Galaxy workflow manager and its thriving community.

MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning.

Background: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable.

Methods: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data.

Computational Analysis of Single-Cell RNA-Seq Data.

Single-cell RNAseq data can be generated using various technologies, spanning from isolation of cells by FACS sorting or droplet sequencing, to the use of frozen tissue sections retaining spatial information of cells in their morphological context. The analysis of single cell RNAseq data is mainly focused on the identification of cell subpopulations characterized by specific gene markers that can be used to purify the population of interest for further biological studies. This chapter describes the steps required for dataset clustering and markers detection using a droplet dataset and a spatial transcriptomics dataset.

Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining.

Single-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA).

Molecular and functional characterization of urine-derived podocytes from patients with Alport syndrome.

Alport syndrome (AS) is a genetic disorder involving mutations in the genes encoding collagen IV α3, α4 or α5 chains, resulting in the impairment of glomerular basement membrane. Podocytes are responsible for production and correct assembly of collagen IV isoforms; however, data on the phenotypic characteristics of human AS podocytes and their functional alterations are currently limited. The evident loss of viable podocytes into the urine of patients with active glomerular disease enables their isolation in a non-invasive way.

Salt or fish (or salted fish)? The Bronze Age specialised sites along the Tyrrhenian coast of Central Italy: New insights from Caprolace settlement.

In 2017, an excavation led by the Groningen Institute of Archaeology and in collaboration with the Tor Vergata University of Rome, took place on two small islands in the Caprolace lagoon (Sabaudia, Italy), where Middle Bronze Age layers had previously been reported. Combining the results of an environmental reconstruction of the surroundings and a detailed study of the pottery assemblages, we were able to trace a specialised area on the southern island, in all probability devoted to salt production by means of the briquetage technique. The latter basically consists of boiling a brine through which a salt cake is obtained.

Apoptotic Cell Exclusion and Bias-Free Single-Cell Selection Are Important Quality Control Requirements for Successful Single-Cell Sequencing Applications.

Single-cell sequencing experiments are a new mainstay in biology and have been advancing science especially in the biomedical field. The high pressure to integrate the technology into daily laboratory live requires solid knowledge with respect to potential limitations and precautions to be taken care of before applying it to complex research questions. In the past, we have identified two issues with quality measures neglected by the growing community involving SmartSeq and droplet or micro-well-based scRNASeq methods (1) how to ensure that only single cells are introduced without biasing on light scattering when handling complex cell mixtures and organ preparations or (2) how best to control for (pro-)apoptotic cell contaminations in single-cell sequencing approaches.

rCASC: reproducible classification analysis of single-cell sequencing data.

Background: Single-cell RNA sequencing is essential for investigating cellular heterogeneity and highlighting cell subpopulation-specific signatures. Single-cell sequencing applications have spread from conventional RNA sequencing to epigenomics, e.g.

Differential Expression Analysis in Single-Cell Transcriptomics.

Differential expression analysis is an important aspect of bulk RNA sequencing (RNAseq). A lot of tools are available, and among them DESeq2 and edgeR are widely used. Since single-cell RNA sequencing (scRNAseq) expression data are zero inflated, single-cell data are quite different from those generated by conventional bulk RNA sequencing.

Reproducible bioinformatics project: a community for reproducible bioinformatics analysis pipelines.

Background: Reproducibility of a research is a key element in the modern science and it is mandatory for any industrial application. It represents the ability of replicating an experiment independently by the location and the operator. Therefore, a study can be considered reproducible only if all used data are available and the exploited computational analysis workflow is clearly described.