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Article: Framing Apache Spark in life sciences.

Manconi, Andrea / Gnocchi, Matteo / Milanesi, Luciano / Marullo, Osvaldo / Armano, Giuliano

2023 Volume 9, Issue 2, Page(s) e13368

Abstract: Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, ...

Abstract	Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities.
Language	English
Publishing date	2023-02-09
Publishing country	England
Document type	Journal Article ; Review
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e13368
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Framing Apache Spark in life sciences

Manconi, Andrea / Gnocchi, Matteo / Milanesi, Luciano / Marullo, Osvaldo / Armano, Giuliano

Heliyon. , p.e13368-

2023

Abstract	Advances in high-throughput and digital technologies have required the adoption of big data for handling complex tasks in life sciences. However, the drift to big data led researchers to face technical and infrastructural challenges for storing, sharing, and analysing them. In fact, this kind of tasks requires distributed computing systems and algorithms able to ensure efficient processing. Cutting edge distributed programming frameworks allow to implement flexible algorithms able to adapt the computation to the data over on-premise HPC clusters or cloud architectures. In this context, Apache Spark is a very powerful HPC engine for large-scale data processing on clusters. Also thanks to specialised libraries for working with structured and relational data, it allows to support machine learning, graph-based computation, and stream processing. This review article is aimed at helping life sciences researchers to ascertain the features of Apache Spark and to assess whether it can be successfully used in their research activities.
Keywords	algorithms ; architecture ; artificial intelligence ; cluster analysis ; engines ; information processing ; researchers ; streams ; 00-01 ; 99-00 ; Apache Spark ; Big data ; Parallel computing ; HPC
Language	English
Publishing place	Elsevier Ltd
Document type	Article ; Online
Note	Pre-press version ; Use and reproduction
ZDB-ID	2835763-2
ISSN	2405-8440
ISSN	2405-8440
DOI	10.1016/j.heliyon.2023.e13368
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Article: Network Diffusion Promotes the Integrative Analysis of Multiple Omics.

Di Nanni, Noemi / Bersanelli, Matteo / Milanesi, Luciano / Mosca, Ettore

Frontiers in genetics

2020 Volume 11, Page(s) 106

Abstract: The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the ... ...

Abstract	The development of integrative methods is one of the main challenges in bioinformatics. Network-based methods for the analysis of multiple gene-centered datasets take into account known and/or inferred relations between genes. In the last decades, the mathematical machinery of network diffusion-also referred to as network propagation-has been exploited in several network-based pipelines, thanks to its ability of amplifying association between genes that lie in network proximity. Indeed, network diffusion provides a quantitative estimation of network proximity between genes associated with one or more different data types, from simple binary vectors to real vectors. Therefore, this powerful data transformation method has also been increasingly used in integrative analyses of multiple collections of biological scores and/or one or more interaction networks. We present an overview of the state of the art of bioinformatics pipelines that use network diffusion processes for the integrative analysis of omics data. We discuss the fundamental ways in which network diffusion is exploited, open issues and potential developments in the field. Current trends suggest that network diffusion is a tool of broad utility in omics data analysis. It is reasonable to think that it will continue to be used and further refined as new data types arise (e.g. single cell datasets) and the identification of system-level patterns will be considered more and more important in omics data analysis.
Language	English
Publishing date	2020-02-27
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2606823-0
ISSN	1664-8021
ISSN	1664-8021
DOI	10.3389/fgene.2020.00106
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Article ; Online: Frailness and resilience of gene networks predicted by detection of co-occurring mutations via a stochastic perturbative approach.

Bersanelli, Matteo / Mosca, Ettore / Milanesi, Luciano / Bazzani, Armando / Castellani, Gastone

Scientific reports

2020 Volume 10, Issue 1, Page(s) 2643

Abstract: In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the ... ...

Abstract	In recent years complex networks have been identified as powerful mathematical frameworks for the adequate modeling of many applied problems in disparate research fields. Assuming a Master Equation (ME) modeling the exchange of information within the network, we set up a perturbative approach in order to investigate how node alterations impact on the network information flow. The main assumption of the perturbed ME (pME) model is that the simultaneous presence of multiple node alterations causes more or less intense network frailties depending on the specific features of the perturbation. In this perspective the collective behavior of a set of molecular alterations on a gene network is a particularly adapt scenario for a first application of the proposed method, since most diseases are neither related to a single mutation nor to an established set of molecular alterations. Therefore, after characterizing the method numerically, we applied as a proof of principle the pME approach to breast cancer (BC) somatic mutation data downloaded from Cancer Genome Atlas (TCGA) database. For each patient we measured the network frailness of over 90 significant subnetworks of the protein-protein interaction network, where each perturbation was defined by patient-specific somatic mutations. Interestingly the frailness measures depend on the position of the alterations on the gene network more than on their amount, unlike most traditional enrichment scores. In particular low-degree mutations play an important role in causing high frailness measures. The potential applicability of the proposed method is wide and suggests future development in the control theory context.
MeSH term(s)	Apoptosis/genetics ; Breast Neoplasms/genetics ; Female ; Gene Regulatory Networks ; Humans ; Models, Genetic ; Mutation/genetics ; Stochastic Processes
Language	English
Publishing date	2020-02-14
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-020-59036-w
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Article: Transcriptomic Analysis of

Zampolli, Jessica / Di Canito, Alessandra / Manconi, Andrea / Milanesi, Luciano / Di Gennaro, Patrizia / Orro, Alessandro

Frontiers in microbiology

2020 Volume 11, Page(s) 1808

Abstract: Xylenes are considered one of the most common hazardous sources of environmental contamination. The biodegradation of these compounds has been often reported, rarer the ability to oxidize ... ...

Abstract	Xylenes are considered one of the most common hazardous sources of environmental contamination. The biodegradation of these compounds has been often reported, rarer the ability to oxidize the
Language	English
Publishing date	2020-08-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587354-4
ISSN	1664-302X
ISSN	1664-302X
DOI	10.3389/fmicb.2020.01808
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Article ; Online: scMuffin: an R package to disentangle solid tumor heterogeneity by single-cell gene expression analysis.

Nale, Valentina / Chiodi, Alice / Di Nanni, Noemi / Cifola, Ingrid / Moscatelli, Marco / Cocola, Cinzia / Gnocchi, Matteo / Piscitelli, Eleonora / Sula, Ada / Zucchi, Ileana / Reinbold, Rolland / Milanesi, Luciano / Mezzelani, Alessandra / Pelucchi, Paride / Mosca, Ettore

BMC bioinformatics

2023 Volume 24, Issue 1, Page(s) 445

Abstract: Introduction: Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a ... ...

Abstract	Introduction: Single-cell (SC) gene expression analysis is crucial to dissect the complex cellular heterogeneity of solid tumors, which is one of the main obstacles for the development of effective cancer treatments. Such tumors typically contain a mixture of cells with aberrant genomic and transcriptomic profiles affecting specific sub-populations that might have a pivotal role in cancer progression, whose identification eludes bulk RNA-sequencing approaches. We present scMuffin, an R package that enables the characterization of cell identity in solid tumors on the basis of a various and complementary analyses on SC gene expression data. Results: scMuffin provides a series of functions to calculate qualitative and quantitative scores, such as: expression of marker sets for normal and tumor conditions, pathway activity, cell state trajectories, Copy Number Variations, transcriptional complexity and proliferation state. Thus, scMuffin facilitates the combination of various evidences that can be used to distinguish normal and tumoral cells, define cell identities, cluster cells in different ways, link genomic aberrations to phenotypes and identify subtle differences between cell subtypes or cell states. We analysed public SC expression datasets of human high-grade gliomas as a proof-of-concept to show the value of scMuffin and illustrate its user interface. Nevertheless, these analyses lead to interesting findings, which suggest that some chromosomal amplifications might underlie the invasive tumor phenotype and the presence of cells that possess tumor initiating cells characteristics. Conclusions: The analyses offered by scMuffin and the results achieved in the case study show that our tool helps addressing the main challenges in the bioinformatics analysis of SC expression data from solid tumors.
MeSH term(s)	Humans ; DNA Copy Number Variations ; Single-Cell Gene Expression Analysis ; Neoplasms/genetics ; Transcriptome ; Sequence Analysis, RNA/methods ; Single-Cell Analysis/methods
Language	English
Publishing date	2023-11-27
Publishing country	England
Document type	Journal Article
ZDB-ID	2041484-5
ISSN	1471-2105 ; 1471-2105
ISSN (online)	1471-2105
ISSN	1471-2105
DOI	10.1186/s12859-023-05563-y
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Article: How computer science can help in understanding the 3D genome architecture

Shavit, Yoli / Merelli, Ivan / Milanesi, Luciano / Lio’, Pietro

Briefings in bioinformatics. 2016 Sept., v. 17, no. 5

2016

Abstract: Chromosome conformation capture techniques are producing a huge amount of data about the architecture of our genome. These data can provide us with a better understanding of the events that induce critical regulations of the cellular function from small ... ...

Abstract	Chromosome conformation capture techniques are producing a huge amount of data about the architecture of our genome. These data can provide us with a better understanding of the events that induce critical regulations of the cellular function from small changes in the three-dimensional genome architecture. Generating a unified view of spatial, temporal, genetic and epigenetic properties poses various challenges of data analysis, visualization, integration and mining, as well as of high performance computing and big data management. Here, we describe the critical issues of this new branch of bioinformatics, oriented at the comprehension of the three-dimensional genome architecture, which we call ‘Nucleome Bioinformatics', looking beyond the currently available tools and methods, and highlight yet unaddressed challenges and the potential approaches that could be applied for tackling them. Our review provides a map for researchers interested in using computer science for studying ‘Nucleome Bioinformatics', to achieve a better understanding of the biological processes that occur inside the nucleus.
Keywords	bioinformatics ; chromosomes ; computer science ; epigenetics ; genome ; information management
Language	English
Dates of publication	2016-09
Size	p. 733-744.
Publishing place	Oxford University Press
Document type	Article
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbv085
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: A Boron Delivery Antibody (BDA) with Boronated Specific Residues: New Perspectives in Boron Neutron Capture Therapy from an In Silico Investigation.

Rondina, Alessandro / Fossa, Paola / Orro, Alessandro / Milanesi, Luciano / De Palma, Antonella / Perico, Davide / Mauri, Pier Luigi / D'Ursi, Pasqualina

Cells

2021 Volume 10, Issue 11

Abstract: Boron Neutron Capture Therapy (BNCT) is a tumor cell-selective radiotherapy based on a nuclear reaction that occurs when the isotope boron-10 ( ...

Abstract	Boron Neutron Capture Therapy (BNCT) is a tumor cell-selective radiotherapy based on a nuclear reaction that occurs when the isotope boron-10 (
MeSH term(s)	Antibodies, Monoclonal/administration & dosage ; Antibodies, Monoclonal/chemistry ; Antibodies, Monoclonal/genetics ; Boron/administration & dosage ; Boron Neutron Capture Therapy ; Boronic Acids/chemistry ; Computer Simulation ; Molecular Docking Simulation ; Molecular Dynamics Simulation ; Mutation/genetics
Chemical Substances	Antibodies, Monoclonal ; Boronic Acids ; Boron (N9E3X5056Q)
Language	English
Publishing date	2021-11-18
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2661518-6
ISSN	2073-4409 ; 2073-4409
ISSN (online)	2073-4409
ISSN	2073-4409
DOI	10.3390/cells10113225
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Article ; Online: Network-based analysis of omics with multi-objective optimization.

Mosca, Ettore / Milanesi, Luciano

Molecular bioSystems

2013 Volume 9, Issue 12, Page(s) 2971–2980

Abstract: Nowadays, computational and statistical methods focusing on integrated analysis of omics data are necessary. A few approaches have been recently described in the literature and a small number of software packages are available. We have developed a new ... ...

Abstract	Nowadays, computational and statistical methods focusing on integrated analysis of omics data are necessary. A few approaches have been recently described in the literature and a small number of software packages are available. We have developed a new method to generate networks of biological components that incorporate multi-omics information. The novelty of this method relies on using a multi-objective (MO) optimization procedure in order to drive the identification of networks that are enriched according to several statistical estimators. The network-based analysis of omics with MO optimization described in this work can be applied to different types of omics and biological interactions. By using this approach we found protein networks that participate in the establishment of the increased basal differentiation observed in breast tumors of BRCA1-mutation carriers. Additionally, we showed how MO optimization can be used to carry out a network-based comparison among several omic data sets: using transcriptomic data from two types of breast tumors and the corresponding epithelial cells from which tumors were generated, we found a protein network that shows a strong and coherent (the same direction) differential expression when comparing each tumor with its respective epithelial tissue. We have also compared the transcriptional variation detected in three different types of tumors originated in breast, colon and pancreas with the corresponding healthy tissues. Despite the global low correlation observed in the three pairs of tumors, we found more similar networks regulated in the same direction in colon and pancreas tumor cells. In conclusion, we propose the network-based analysis of omics with MO optimization as a valid tool for integrated analysis of omics data.
MeSH term(s)	Databases, Genetic ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Genomics/methods ; Humans ; Neoplasms/genetics ; Transcriptome
Language	English
Publishing date	2013-12
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2188635-0
ISSN	1742-2051 ; 1742-206X
ISSN (online)	1742-2051
ISSN	1742-206X
DOI	10.1039/c3mb70327d
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Article ; Online: Characterization and comparison of gene-centered human interactomes.

Mosca, Ettore / Bersanelli, Matteo / Matteuzzi, Tommaso / Di Nanni, Noemi / Castellani, Gastone / Milanesi, Luciano / Remondini, Daniel

Briefings in bioinformatics

2021 Volume 22, Issue 6

Abstract: The complex web of macromolecular interactions occurring within cells-the interactome-is the backbone of an increasing number of studies, but a clear consensus on the exact structure of this network is still lacking. Different genome-scale maps of human ... ...

Abstract	The complex web of macromolecular interactions occurring within cells-the interactome-is the backbone of an increasing number of studies, but a clear consensus on the exact structure of this network is still lacking. Different genome-scale maps of human interactome have been obtained through several experimental techniques and functional analyses. Moreover, these maps can be enriched through literature-mining approaches, and different combinations of various 'source' databases have been used in the literature. It is therefore unclear to which extent the various interactomes yield similar results when used in the context of interactome-based approaches in network biology. We compared a comprehensive list of human interactomes on the basis of topology, protein complexes, molecular pathways, pathway cross-talk and disease gene prediction. In a general context of relevant heterogeneity, our study provides a series of qualitative and quantitative parameters that describe the state of the art of human interactomes and guidelines for selecting interactomes in future applications.
MeSH term(s)	Algorithms ; Computational Biology/methods ; Databases, Genetic ; Gene Expression Profiling/methods ; Gene Ontology ; Gene Regulatory Networks ; Genetic Association Studies ; Genetic Predisposition to Disease ; Humans ; Protein Interaction Mapping/methods ; Protein Interaction Maps ; Reproducibility of Results ; Signal Transduction ; Software ; Transcriptome ; Web Browser
Language	English
Publishing date	2021-05-19
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2068142-2
ISSN	1477-4054 ; 1467-5463
ISSN (online)	1477-4054
ISSN	1467-5463
DOI	10.1093/bib/bbab153
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