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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., (© 2024. The Author(s).)

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., (© 2024. The Author(s).)

Among the several mechanisms accounting for endocrine resistance in breast cancer, autophagy has emerged as an important player. Previous reports have evidenced that tamoxifen (Tam) induces autophagy and activates transcription factor EB (TFEB), which regulates the expression of genes controlling autophagy and lysosomal biogenesis. However, the mechanisms by which this occurs have not been elucidated as yet. This investigation aims at dissecting how TFEB is activated and contributes to Tam resistance in luminal A breast cancer cells. TFEB was overexpressed and prominently nuclear in Tam-resistant MCF7 cells (MCF7-TamR) compared with their parental counterpart, and this was not dependent on alterations of its nucleo-cytoplasmic shuttling. Tam promoted the release of lysosomal Ca 2+ through the major transient receptor potential cation channel mucolipin subfamily member 1 (TRPML1) and two-pore channels (TPCs), which caused the nuclear translocation and activation of TFEB. Consistently, inhibiting lysosomal calcium release restored the susceptibility of MCF7-TamR cells to Tam. Our findings demonstrate that Tam drives the nuclear relocation and transcriptional activation of TFEB by triggering the release of Ca 2+ from the acidic compartment, and they suggest that lysosomal Ca 2+ channels may represent new druggable targets to counteract the onset of autophagy-mediated endocrine resistance in luminal A breast cancer cells.

Objective: Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics., Methods: GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations., Results: To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months., Conclusion: GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Francesca Cordero reports financial support was provided by Horizon Europe. Marco Beccuti reports financial support was provided by CRT Foundation., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© The Author(s) 2023. Published by Oxford University Press.)

Single-cell RNA sequencing (scRNA-seq) allows 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 organization of a sample. In this chapter, we describe how to approach the clustering of single-cell RNAseq transcriptomics data using various clustering tools, and we provide some information on the limitations affecting the clustering procedure., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© The Author(s) 2022. Published by Oxford University Press GigaScience.)

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.