Selected Software

Below are listed the main software developed and published to bioinformatics community by InfOmics lab members. For a comprehensive and complete list of software developed by our lab click here


CRISPRme is a web based tool dedicated to perform predictive analysis and result assessement on CRISPR/Cas-9 experiments with a user-friendly GUI and the precise scope of searching individual variant in VCF dateset. With this aim in mind we created a simple package that takes care of any step, from downloading the necessary data, to execute a complete search and present to the user an exhaustive result report with images and tabulated targets to navigate with the included web-based GUI.



GRAFIMO (GRAph-based Finding of Individual Motif Occurrences) is a command-line tool that extends the traditional Position Weight Matrix (PWM) scanning procedure to pangenome variation graphs (VGs). GRAFIMO can search the occurrences of a given PWM in many genomes in a single run, accounting for the effects that SNPs, indels and potentially any structural variation (handled by VG) have on found potential motif occurrences. As result, GRAFIMO produces a report containing the statistically significant motif candidates found, reporting their frequency within the haplotypes embedded in the scanned VG and if they contain genomic variants or belong to the reference genome sequence.



Stardust in a clustering algorithm for Spatial Transcriptomics data. Its aim is, given as input an expression matrix, the positions of spots and a space weight configuration, to derive a vector of cluster identities for each spot in the input data. The similarity between spots is composed by integrating transcripts and physical distance of spots in a user-driven fashion.



CRISPRitz is software suite to perform in-silico CRISPR analysis and assessment. CRISPRitz suite contains 5 different tools dedicated to perform predictive analysis and result assessement on CRISPR/Cas experiments.



LErNet is a method to in silico define and predict the roles of IncRNAs. The core of the approach is a network expansion algorithm which enriches the genomic context of IncRNAs. The context is built by integrating the genes encoding proteins that are found next to the non-coding elements both at genomic and system level. The pipeline is particularly useful in situations where the functions of discovered IncRNAs are not yet known.



PanDelos is a stand alone tool for the discovery of pan-genome contents among phylogenetic distant genomes. The methodology is based on information theory and network analysis. It is parameter-free because thresholds are automatically deduced from the context.



cuRnet is a package that provides a wrap of parallel graph algorithms developed in CUDA for the R environment. It makes available GPU solutions to R end-users in a transparent way, by including basic data structures for representing graphs, and parallel implementation of of BFS (Breadth-First Search), SCC (Strongly Connected Components) and SSSP (Single-Source Shortest Paths) customized for biological network analysis on GPUs (Busato and Bombieri, 2016).



APPAGATO is a stochastic and parallel algorithm to find approximate occurrences of a query network in biological networks. APPAGATO allows nodes and edges mismatches. To speed-up the querying process, APPAGATO has been also implemented in parallel to run on graphics processing units (GPUs).



GRAPES is a querying system for parallel searching in databases of graphs, and single target graph, using symmetric multiprocessing (SMP) architectures. It implements a parallel version of well established graph searching algorithms providing efficient solutions for graphs indexing and matching. The GRAPES method consists of three main phases: indexing, filtering, and matching. In the indexing phase, features are extracted from target graphs and a database index is built off-line. A feature is a labeled path presents in a graph. GRAPES extracts all paths up to a fixed length (lp) and stores them in a compact trie structure. Moreover, starting nodes of such paths are also stored. Once the index is built, the system is able to find subgraph isomorphims between a query and all the target graphs. The filtering phase allows to a priori discard such database graphs which do not contain the query's features. It is also able to preliminarily recognize unmatching nodes, and entire regions, of database graphs. This useful behavior allows GRAPES to dial both with large database or single target graphs. Finally, an exact subgraph matching algorithm is run in parallel. See the related scientific paper for more details.



RI is a general purpose algorithm for one-to-one exact subgraph isomorphism problem maintaining topological constraints. It is both a C++ library and a standalone tool, providing developing API and a command line interface, with no dependencies out of standard GNU C++ library. RI works on Unix and Mac OS X systems with G++ installed, and it can be compiled under Windows using Gygwin. Working graphs may be directed, undirected, multigraphs with optional attributes both on nodes and edges. Customizable features allow user-defined behaviors for attribute comparisons and the algorithm's flow. RI aims to provide a better search strategy for the common used backtracking approach to the subgraph isomorphism problem. It can be integrated with additional preprocessing steps or it can be used for the verification of candidate structures coming from data mining, data indexing or other filtering techniques. RI is able to find graphs isomorphisms, subgraph isomorphisms and induced subgraph isomorphisms. It is distributed in several versions divide chiefly in two groups respectively for static or dynamically changing attributes. All proposed versions are developed taking into account trade-offs between time and memory requirements. Optional behaviors such as stop at first encountered match, processing of result matches, type of isomorphism and additional features may be enabled thanks to high modularity and library's API.


GraphGrepSX is a querying system for databases of graphs. It is based on its predecessor GraphGrep. The system implements efficient graph searching algorithms together with advanced filtering techniques that allow an i nitial approximate search. It allows users to select candidate subgraphs rather than entire graphs. The method searches for subgraph isomorphism (monomorphism) between a query graph (pattern) and graphs inside a database of graphs (targets).