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. RESULTS: CREDO, a Customizable, REproducible, DOcker file generator for bioinformatics applications, has been developed as a tool to moderate reproducibility issues by building and distributing docker containers with embedded bioinformatics tools. CREDO simplifies the process of generating Docker images, facilitating reproducibility and efficient research in bioinformatics. The crucial step in generating a Docker image is creating the Dockerfile, which requires incorporating heterogeneous packages and environments such as Bioconductor and Conda. CREDO stores all required package information and dependencies in a Github-compatible format to enhance Docker image reproducibility, allowing easy image creation from scratch. The user-friendly GUI and CREDO's ability to generate modular Docker images make it an ideal tool for life scientists to efficiently create Docker images. Overall, CREDO is a valuable tool for addressing reproducibility issues in bioinformatics research and promoting open science practices.

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. When used to analyze tumor growth kinetics over time in 1599 patient-derived xenograft growth curves from ovarian and colorectal cancers, CONNECTOR allowed the aggregation of time-series data through an unsupervised approach in informative clusters. We give a new perspective of mechanism interpretation, specifically, we define novel model aggregations and we identify unanticipated molecular associations with response to clinically approved therapies. AVAILABILITY AND IMPLEMENTATION: CONNECTOR is freely available under GNU GPL license at https://qbioturin.github.io/connector and https://doi.org/10.17504/protocols.io.8epv56e74g1b/v1.

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. In 2020 we introduced Laniakea, a software platform conceived to streamline the configuration and deployment of "on-demand" Galaxy instances over the cloud. By facilitating the set-up and configuration of Galaxy web servers, Laniakea provides researchers with a powerful and highly customisable platform for executing complex bioinformatics analyses. The system can be accessed through a dedicated and user-friendly web interface that allows the Galaxy web server's initial configuration and deployment. RESULTS: "Laniakea@ReCaS", the first instance of a Laniakea-based service, is managed by ELIXIR-IT and was officially launched in February 2020, after about one year of development and testing that involved several users. Researchers can request access to Laniakea@ReCaS through an open-ended call for use-cases. Ten project proposals have been accepted since then, totalling 18 Galaxy on-demand virtual servers that employ ~ 100 CPUs, ~ 250 GB of RAM and ~ 5 TB of storage and serve several different communities and purposes. Herein, we present eight use cases demonstrating the versatility of the platform. CONCLUSIONS: During this first year of activity, the Laniakea-based service emerged as a flexible platform that facilitated the rapid development of bioinformatics tools, the efficient delivery of training activities, and the provision of public bioinformatics services in different settings, including food safety and clinical research. Laniakea@ReCaS provides a proof of concept of how enabling access to appropriate, reliable IT resources and ready-to-use bioinformatics tools can considerably streamline researchers' work.

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. Here we isolated, immortalized, and subcloned podocytes from the urine of three different AS patients for molecular and functional characterization. AS podocytes expressed a typical podocyte signature and showed a collagen IV profile reflecting each patient's mutation. Furthermore, RNA-sequencing analysis revealed 348 genes differentially expressed in AS podocytes compared with control podocytes. Gene Ontology analysis underlined the enrichment in genes involved in cell motility, adhesion, survival, and angiogenesis. In parallel, AS podocytes displayed reduced motility. Finally, a functional permeability assay, using a podocyte-glomerular endothelial cell co-culture system, was established and AS podocyte co-cultures showed a significantly higher permeability of albumin compared to control podocyte co-cultures, in both static and dynamic conditions under continuous perfusion. In conclusion, our data provide a molecular characterization of immortalized AS podocytes, highlighting alterations in several biological processes related to extracellular matrix remodelling. Moreover, we have established an in vitro model to reproduce the altered podocyte permeability observed in patients with AS. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland..

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. An autoencoder is composed of an encoder and a decoder sub-model. An autoencoder is a very powerful tool in data compression and noise removal. However, the decoder model remains a black box from which is impossible to depict the contribution of the single input elements. We have recently developed a new class of autoencoders, called Sparsely Connected Autoencoders (SCA), which have the advantage of providing a controlled association among the input layer and the decoder module. This new architecture has the benefit that the decoder model is not a black box anymore and can be used to depict new biologically interesting features from single cell data. RESULTS: Here, we show that SCA hidden layer can grab new information usually hidden in single cell data, like providing clustering on meta-features difficult, i.e. transcription factors expression, or not technically not possible, i.e. miRNA expression, to depict in single cell RNAseq data. Furthermore, SCA representation of cell clusters has the advantage of simulating a conventional bulk RNAseq, which is a data transformation allowing the identification of similarity among independent experiments. CONCLUSIONS: In our opinion, SCA represents the bioinformatics version of a universal "Swiss-knife" for the extraction of hidden knowledgeable features from single cell omics data.

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. Sighting of concurrent literature involving single-cell sequencing technologies revealed that these topics are generally neglected or simply approached in silico but not at the bench before generating single-cell data sets. We fear that those important quality aspects are overlooked due to reduced awareness of their importance for guaranteeing the quality of experiments. In this Cytometry rigor issue, we provide experimentally supported guidance on how to circumvent those critical shortcomings in order to promote a better use of the fantastic single-cell sequencing toolbox in biology. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

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. However, today for reproducing a complex bioinformatics analysis, the raw data and the list of tools used in the workflow could be not enough to guarantee the reproducibility of the results obtained. Indeed, different releases of the same tools and/or of the system libraries (exploited by such tools) might lead to sneaky reproducibility issues. RESULTS: To address this challenge, we established the Reproducible Bioinformatics Project (RBP), which is a non-profit and open-source project, whose aim is to provide a schema and an infrastructure, based on docker images and R package, to provide reproducible results in Bioinformatics. One or more Docker images are then defined for a workflow (typically one for each task), while the workflow implementation is handled via R-functions embedded in a package available at github repository. Thus, a bioinformatician participating to the project has firstly to integrate her/his workflow modules into Docker image(s) exploiting an Ubuntu docker image developed ad hoc by RPB to make easier this task. Secondly, the workflow implementation must be realized in R according to an R-skeleton function made available by RPB to guarantee homogeneity and reusability among different RPB functions. Moreover she/he has to provide the R vignette explaining the package functionality together with an example dataset which can be used to improve the user confidence in the workflow utilization. CONCLUSIONS: Reproducible Bioinformatics Project provides a general schema and an infrastructure to distribute robust and reproducible workflows. Thus, it guarantees to final users the ability to repeat consistently any analysis independently by the used UNIX-like architecture.

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. Efficient algorithms are therefore needed to unravel the intricate biological processes of cancer. To address these challenges, benchmarking datasets are essential to validate bioinformatics methodologies for analysing single-cell omics in oncology. Here, we present a 10XGenomics scRNA-seq experiment, providing a controlled heterogeneous environment using lung cancer cell lines characterised by the expression of seven different driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1), leading to partially overlapping functional pathways. Our dataset provides a comprehensive framework for the development and validation of methodologies for analysing cancer heterogeneity by means of scRNA-seq.

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). The collected record suggested the potential use of plant taxa by the people living in Central Italy during the Copper-Middle Bronze Age and expanded the body of evidence reported by previous palynological and palaeoecological studies. The application of a microscopic approach provided information about domesticated crops and/or gathered wild plants and inferred considerations on ancient environments, water sources, and past health and diseases. Moreover, the research supplied data to define the natural resources (e.g., C4-plant intake) and the social use of the space during that period. Another important aspect was the finding of plant clues referable to woody habitats, characterised by broad-leaved deciduous taxa and generally indicative of a warm-temperate climate and grassy vegetation. Other unusual records (e.g., diatoms, brachysclereids) participated in defining the prehistoric ecological framework. Thus, this work provides an overview on the potential of the human dental calculus analysis to delineate some features of the ancient plant ecology and biodiversity.

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. In this chapter, we describe a functional feature-driven data reduction approach, which could provide a better link among cell clusters and their underlying cell biology.

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. In this chapter, we describe the use of sparsely connected autoencoder, as a tool to convert single-cell clusters in pseudo-RNAseq experiments to be used as input for differential expression analysis, and the use of COMET, as a tool to depict cluster-specific gene markers.

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.To allow the use of rCASC through a graphical user interface on the client side and to harness the many advantages provided by the Galaxy platform, we have made rCASC available as a Galaxy set of tools, also providing a dedicated public instance of Galaxy named "Galaxy-rCASC." To integrate rCASC into Galaxy, all its functions, originally implemented as a set of Docker containers to maximize reproducibility, have been extensively reworked to become independent from the R package functions that launch them in the original implementation. Furthermore, suitable Galaxy wrappers have been developed for most functions of rCASC. We provide a detailed reference document to the use of Galaxy-rCASC with insights and explanations on the platform functionalities, parameters, and output while guiding the reader through the typical rCASC analysis workflow of a scRNA-Seq dataset.

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.In this chapter, we describe how cell subpopulation discovery algorithms, integrated into rCASC, could be efficiently executed on cloud-HPC infrastructure. To achieve this task, we focus on the StreamFlow framework which provides container-native runtime support for scientific workflows in cloud/HPC environments.

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. This diachronic study documents an increased dietary variability due to the introduction of novel resources in these protohistoric societies, possibly related to the southward spread of northern human groups into Central Italy. This contact between different cultures is also testified by the pottery typology found in the cave. The latter shows an increase in cultural intermingling starting during the beginning of the middle Bronze Age. The local mobility during this phase likely involved multiple communities scattered throughout an area of a few kilometers around the cave, which used the latter as a burial site both in the Copper and Bronze ages.

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. State-of-the-art approaches group data by taking into account transcriptional similarity and some by exploiting spatial information as well. However, it is not yet clear how much the spatial information combined with transcriptomics improves the clustering result. RESULTS: We propose a new clustering method, Stardust, that easily exploits the combination of space and transcriptomic information in the clustering procedure through a manual or fully automatic tuning of algorithm parameters. Moreover, a parameter-free version of the method is also provided where the spatial contribution depends dynamically on the expression distances distribution in the space. We evaluated the proposed methods results by analyzing ST data sets available on the 10x Genomics website and comparing clustering performances with state-of-the-art approaches by measuring the spots' stability in the clusters and their biological coherence. Stability is defined by the tendency of each point to remain clustered with the same neighbors when perturbations are applied. CONCLUSIONS: Stardust is an easy-to-use methodology allowing to define how much spatial information should influence clustering on different tissues and achieving more stable results than state-of-the-art approaches.

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). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.

Ancient genomes reveal structural shifts after the arrival of Steppe-related ancestry in the Italian Peninsula.

Across Europe, the genetics of the Chalcolithic/Bronze Age transition is increasingly characterized in terms of an influx of Steppe-related ancestry. The effect of this major shift on the genetic structure of populations in the Italian Peninsula remains underexplored. Here, genome-wide shotgun data for 22 individuals from commingled cave and single burials in Northeastern and Central Italy dated between 3200 and 1500 BCE provide the first genomic characterization of Bronze Age individuals (n = 8; 0.001-1.2× coverage) from the central Italian Peninsula, filling a gap in the literature between 1950 and 1500 BCE. Our study confirms a diversity of ancestry components during the Chalcolithic and the arrival of Steppe-related ancestry in the central Italian Peninsula as early as 1600 BCE, with this ancestry component increasing through time. We detect close patrilineal kinship in the burial patterns of Chalcolithic commingled cave burials and a shift away from this in the Bronze Age (2200-900 BCE) along with lowered runs of homozygosity, which may reflect larger changes in population structure. Finally, we find no evidence that the arrival of Steppe-related ancestry in Central Italy directly led to changes in frequency of 115 phenotypes present in the dataset, rather that the post-Roman Imperial period had a stronger influence, particularly on the frequency of variants associated with protection against Hansen's disease (leprosy). Our study provides a closer look at local dynamics of demography and phenotypic shifts as they occurred as part of a broader phenomenon of widespread admixture during the Chalcolithic/Bronze Age transition.

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. Comparative analysis of tools used to detect differentially expressed genes between two groups of single cells showed that edgeR with quasi-likelihood F-test (QLF) outperforms other methods.In bulk RNAseq, differential expression is mainly used to compare limited number of replicates of two or more biological conditions. However, scRNAseq differential expression analysis might be also instrumental to identify the main players of cells subpopulation organization, thus requiring the use of multiple comparisons tools. Nowadays, edgeR is one of the few tools that are able to handle both zero inflated matrices and multiple comparisons. Here, we provide a guide to the use of edgeR as a tool to detect differential expression in single-cell data.

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. The technique was widespread all over Europe, from Neolithic to Roman Times. Since the evidence points to an elite-driven workshop, this result has deep implications for the development of the Bronze Age socio-economic framework of Central Italy. Pottery evidence also suggests that in the Bronze Age sites along the Tyrrhenian coast of Central Italy where briquetage has already been hypothesised, more complex processes may have taken place. On the northern island, we collected a large number of so-called pedestals, which are characteristic features of briquetage, while chemical analyses point to salt or fish sauce production, like the roman liquamen, in a Middle Bronze Age domestic context.