Susanna Donatelli

Bio: Susanna Donatelli is an academic researcher from University of Turin. The author has contributed to research in topics: Petri net & Markov chain. The author has an hindex of 10, co-authored 21 publications receiving 253 citations.

30 Years of GreatSPN

Abstract: GreatSPN is a tool for the stochastic analysis of systems modeled as (stochastic) Petri nets. This chapter describes the evolution of the GreatSPN framework over its life span of 30 years, from the first stochastic Petri net analyzer implemented in Pascal, to the current, fancy, graphical interface that supports a number of different model analyzers. This chapter reviews, with the help of a manufacturing system example, how GreatSPN is currently used for an integrated qualitative and quantitative analysis of Petri net systems, ranging from symbolic model checking techniques to a stochastic analysis whose efficiency is boosted by lumpability.

Stochastic) Model Checking in GreatSPN

Abstract: GreatSPN is a tool for the definition and solution of Generalized Stochastic Petri Nets (GSPN). This paper presents the model checking features that have been recently introduced in GreatSPN. Through a new (Java-based) graphical interface for the GSPN model definition, the user can now access model checking of three different logics: the classical branching temporal logic CTL, and two stochastic logics, CSL and its superset CSLTA. This allows to integrate easily classical and probabilistic verification. A distinctive feature of the CTL model checker is the ability of generating counterexamples and witnesses. The CTL model checker employs symbolic data structures (decision diagrams) implemented in the Meddly library [6], developed Iowa State University, while the CSLTA model checker uses advanced solution methods, recently published, for Markov Renewal Processes.

Alternative splicing detection workflow needs a careful combination of sample prep and bioinformatics analysis

Abstract: RNA-Seq provides remarkable power in the area of biomarkers discovery and disease characterization. Two crucial steps that affect RNA-Seq experiment results are Library Sample Preparation (LSP) and Bioinformatics Analysis (BA). This work describes an evaluation of the combined effect of LSP methods and BA tools in the detection of splice variants. Different LSPs (TruSeq unstranded/stranded, ScriptSeq, NuGEN) allowed the detection of a large common set of splice variants. However, each LSP also detected a small set of unique transcripts that are characterized by a low coverage and/or FPKM. This effect was particularly evident using the low input RNA NuGEN v2 protocol. A benchmark dataset, in which synthetic reads as well as reads generated from standard (Illumina TruSeq 100) and low input (NuGEN) LSPs were spiked-in was used to evaluate the effect of LSP on the statistical detection of alternative splicing events (AltDE). Statistical detection of AltDE was done using as prototypes for splice variant-quantification Cuffdiff2 and RSEM-EBSeq. As prototype for exon-level analysis DEXSeq was used. Exon-level analysis performed slightly better than splice variant-quantification approaches, although at most only 50% of the spiked-in transcripts was detected. The performances of both splice variant-quantification and exon-level analysis improved when raising the number of input reads. Data, derived from NuGEN v2, were not the ideal input for AltDE, especially when the exon-level approach was used. We observed that both splice variant-quantification and exon-level analysis performances were strongly dependent on the number of input reads. Moreover, the ribosomal RNA depletion protocol was less sensitive in detecting splicing variants, possibly due to the significant percentage of the reads mapping to non-coding transcripts.

DSPN-Tool: A New DSPN and GSPN Solver for GreatSPN

Abstract: Generalized and Deterministic Stochastic Petri Nets (GSPN and DSPN) and their relative solvers have been around for a while. In the last years research on GSPN solution has mainly concentrated on efficient data structure for state space representation, while little has been published on DSPN. Strangely enough although, the DSPN solvers available do still exhibit a number of limitations, most notably that no steady state solver is available for non-ergodic DSPNs. This demo tool presentation introduces the new DSPN solver that was developed, and which has been inserted in GreatSPN. With the occasion also a new GSPN solver has been added, which allows for more flexibility in the net definition, and an easier integration with other tools.

Detecting circular RNAs: bioinformatic and experimental challenges

Abstract: The pervasive expression of circular RNAs (circRNAs) is a recently discovered feature of gene expression in highly diverged eukaryotes. Numerous algorithms that are used to detect genome-wide circRNA expression from RNA sequencing (RNA-seq) data have been developed in the past few years, but there is little overlap in their predictions and no clear gold-standard method to assess the accuracy of these algorithms. We review sources of experimental and bioinformatic biases that complicate the accurate discovery of circRNAs and discuss statistical approaches to address these biases. We conclude with a discussion of the current experimental progress on the topic.

A Storm is Coming: A Modern Probabilistic Model Checker

Abstract: We launch the new probabilistic model checker Storm. It features the analysis of discrete- and continuous-time variants of both Markov chains and MDPs. It supports the Prism and JANI modeling languages, probabilistic programs, dynamic fault trees and generalized stochastic Petri nets. It has a modular set-up in which solvers and symbolic engines can easily be exchanged. It offers a Python API for rapid prototyping by encapsulating Storm’s fast and scalable algorithms. Experiments on a variety of benchmarks show its competitive performance.

Advanced Applications of RNA Sequencing and Challenges

Abstract: Next-generation sequencing technologies have revolutionarily advanced sequence-based research with the advantages of high-throughput, high-sensitivity, and high-speed. RNA-seq is now being used widely for uncovering multiple facets of transcriptome to facilitate the biological applications. However, the large-scale data analyses associated with RNA-seq harbors challenges. In this study, we present a detailed overview of the applications of this technology and the challenges that need to be addressed, including data preprocessing, differential gene expression analysis, alternative splicing analysis, variants detection and allele-specific expression, pathway analysis, co-expression network analysis, and applications combining various experimental procedures beyond the achievements that have been made. Specifically, we discuss essential principles of computational methods that are required to meet the key challenges of the RNA-seq data analyses, development of various bioinformatics tools, challenges associated with the RNA-seq applications, and examples that represent the advances made so far in the characterization of the transcriptome.