Cellular and Molecular Biology

Publications

Selected publications

1. Pernice S, Sirovich R, Grassi E, Viviani M, Ferri M, Sassi F, Alessandri L, Tortarolo D, Calogero RA, Trusolino L, Bertotti A, Beccuti M, Olivero M, Cordero F. (2023) CONNECTOR, fitting and clustering of longitudinal data to reveal a new risk stratification system. Bioinformatics (Oxford, England) 39(5) [DOI  PMID]
2. Contaldo SG, Alessandri L, Colonnelli I, Beccuti M, Aldinucci M. (2023) Bringing Cell Subpopulation Discovery on a Cloud-HPC Using rCASC and StreamFlow. Methods in molecular biology (Clifton, N.J.) 2584 337-345 [DOI  PMID]
3. Beccuti M, Calogero RA. (2023) Single-Cell RNAseq Clustering. Methods in molecular biology (Clifton, N.J.) 2584 241-250 [DOI  PMID]
4. Avesani S, Viesi E, Alessandri L, Motterle G, Bonnici V, Beccuti M, Calogero R, Giugno R. (2022) Stardust: improving spatial transcriptomics data analysis through space-aware modularity optimization-based clustering. GigaScience 11 [DOI  PMID]
5. Alessandri L, Ratto ML, Contaldo SG, Beccuti M, Cordero F, Arigoni M, Calogero RA. (2021) Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis. International journal of molecular sciences 22(23) [DOI  PMID]
6. Genuardi E, Romano G, Beccuti M, Alessandria B, Mannina D, Califano C, Rota Scalabrini D, Cortelazzo S, Ladetto M, Ferrero S, Calogero RA, Cordero F. (2021) Application of the Euro Clonality next-generation sequencing-based marker screening approach to detect immunoglobulin heavy chain rearrangements in mantle cell lymphoma patients: first data from the Fondazione Italiana Linfomi MCL0208 trial. British journal of haematology 194(2) 378-381 [DOI  PMID]
7. Nosi V, Luca A, Milan M, Arigoni M, Benvenuti S, Cacchiarelli D, Cesana M, Riccardo S, Di Filippo L, Cordero F, Beccuti M, Comoglio PM, Calogero RA. (2021) MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning. International journal of molecular sciences 22(8) [DOI  PMID]
8. Licheri N, Bonnici V, Beccuti M, Giugno R. (2021) GRAPES-DD: exploiting decision diagrams for index-driven search in biological graph databases. BMC bioinformatics 22(1) 209 [DOI  PMID]
9. Alessandri L, Cordero F, Beccuti M, Arigoni M, Calogero RA. (2021) Computational Analysis of Single-Cell RNA-Seq Data. Methods in molecular biology (Clifton, N.J.) 2284 289-301 [DOI  PMID]
10. Ferrero G, Licheri N, De Bortoli M, Calogero RA, Beccuti M, Cordero F. (2021) Computational Analysis of circRNA Expression Data. Methods in molecular biology (Clifton, N.J.) 2284 181-192 [DOI  PMID]
11. Alessandri L, Cordero F, Beccuti M, Licheri N, Arigoni M, Olivero M, Di Renzo MF, Sapino A, Calogero R. (2021) Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining. NPJ systems biology and applications 7(1) 1 [DOI  PMID]
12. Pernice S, Follia L, Maglione A, Pennisi M, Pappalardo F, Novelli F, Clerico M, Beccuti M, Cordero F, Rolla S. (2020) Computational modeling of the immune response in multiple sclerosis using epimod framework. BMC bioinformatics 21(Suppl 17) 550 [DOI  PMID]
13. Pernice S, Castagno P, Marcotulli L, Maule MM, Richiardi L, Moirano G, Sereno M, Cordero F, Beccuti M. (2020) Impacts of reopening strategies for COVID-19 epidemic: a modeling study in Piedmont region. BMC infectious diseases 20(1) 798 [DOI  PMID]
14. Castagno P, Pernice S, Ghetti G, Povero M, Pradelli L, Paolotti D, Balbo G, Sereno M, Beccuti M. (2020) A computational framework for modeling and studying pertussis epidemiology and vaccination. BMC bioinformatics 21(Suppl 8) 344 [DOI  PMID]
15. Ferrero G, Licheri N, Coscujuela Tarrero L, De Intinis C, Miano V, Calogero RA, Cordero F, De Bortoli M, Beccuti M. (2019) Docker4Circ: A Framework for the Reproducible Characterization of circRNAs from RNA-Seq Data. International journal of molecular sciences 21(1) [DOI  PMID]
16. Pernice S, Pennisi M, Romano G, Maglione A, Cutrupi S, Pappalardo F, Balbo G, Beccuti M, Cordero F, Calogero RA. (2019) A computational approach based on the colored Petri net formalism for studying multiple sclerosis. BMC bioinformatics 20(Suppl 6) 623 [DOI  PMID]
17. Alessandri L, Cordero F, Beccuti M, Arigoni M, Olivero M, Romano G, Rabellino S, Licheri N, De Libero G, Pace L, Calogero RA. (2019) rCASC: reproducible classification analysis of single-cell sequencing data. GigaScience 8(9) [DOI  PMID]
18. Follia L, Ferrero G, Mandili G, Beccuti M, Giordano D, Spadi R, Satolli MA, Evangelista A, Katayama H, Hong W, Momin AA, Capello M, Hanash SM, Novelli F, Cordero F. (2019) Integrative Analysis of Novel Metabolic Subtypes in Pancreatic Cancer Fosters New Prognostic Biomarkers. Frontiers in oncology 9 115 [DOI  PMID]
19. Kulkarni N, Alessandri L, Panero R, Arigoni M, Olivero M, Ferrero G, Cordero F, Beccuti M, Calogero RA. (2018) Reproducible bioinformatics project: a community for reproducible bioinformatics analysis pipelines. BMC bioinformatics 19(Suppl 10) 349 [DOI  PMID]
20. Coscujuela Tarrero L, Ferrero G, Miano V, De Intinis C, Ricci L, Arigoni M, Riccardo F, Annaratone L, Castellano I, Calogero RA, Beccuti M, Cordero F, De Bortoli M. (2018) Luminal breast cancer-specific circular RNAs uncovered by a novel tool for data analysis. Oncotarget 9(18) 14580-14596 [DOI  PMID]
21. Beccuti M, Genuardi E, Romano G, Monitillo L, Barbero D, Boccadoro M, Ladetto M, Calogero R, Ferrero S, Cordero F. (2017) HashClone: a new tool to quantify the minimal residual disease in B-cell lymphoma from deep sequencing data. BMC bioinformatics 18(1) 516 [DOI  PMID]
22. Beccuti M, Cordero F, Arigoni M, Panero R, Amparore EG, Donatelli S, Calogero RA. (2018) SeqBox: RNAseq/ChIPseq reproducible analysis on a consumer game computer. Bioinformatics (Oxford, England) 34(5) 871-872 [DOI  PMID]
23. Ferrero G, Miano V, Beccuti M, Balbo G, De Bortoli M, Cordero F. (2017) Dissecting the genomic activity of a transcriptional regulator by the integrative analysis of omics data. Scientific reports 7(1) 8564 [DOI  PMID]
24. Martina F, Beccuti M, Balbo G, Cordero F. (2017) Peculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data sets. PloS one 12(8) e0177475 [DOI  PMID]
25. Miglio G, Sabatino AD, Veglia E, Giraudo MT, Beccuti M, Cordero F. (2016) A computational analysis of S-(2-succino)cysteine sites in proteins. Biochimica et biophysica acta 1864(2) 211-8 [DOI  PMID]
26. Munoz-Amatriain M, Lonardi S, Luo M, Madishetty K, Svensson JT, Moscou MJ, Wanamaker S, Jiang T, Kleinhofs A, Muehlbauer GJ, Wise RP, Stein N, Ma Y, Rodriguez E, Kudrna D, Bhat PR, Chao S, Condamine P, Heinen S, Resnik J, Wing R, Witt HN, Alpert M, Beccuti M, Bozdag S, Cordero F, Mirebrahim H, Ounit R, Wu Y, You F, Zheng J, Simkova H, Dolezel J, Grimwood J, Schmutz J, Duma D, Altschmied L, Blake T, Bregitzer P, Cooper L, Dilbirligi M, Falk A, Feiz L, Graner A, Gustafson P, Hayes PM, Lemaux P, Mammadov J, Close TJ. (2015) Sequencing of 15 622 gene-bearing BACs clarifies the gene-dense regions of the barley genome. The Plant journal : for cell and molecular biology 84(1) 216-27 [DOI  PMID]
27. Carrara M, Lum J, Cordero F, Beccuti M, Poidinger M, Donatelli S, Calogero RA, Zolezzi F. (2015) Alternative splicing detection workflow needs a careful combination of sample prep and bioinformatics analysis. BMC bioinformatics 16 Suppl 9(Suppl 9) S2 [DOI  PMID]
28. Fornari C, Balbo G, Halawani SM, Ba-Rukab O, Ahmad AR, Calogero RA, Cordero F, Beccuti M. (2015) A versatile mathematical work-flow to explore how Cancer Stem Cell fate influences tumor progression. BMC systems biology 9 Suppl 3(Suppl 3) S1 [DOI  PMID]
29. Medico E, Russo M, Picco G, Cancelliere C, Valtorta E, Corti G, Buscarino M, Isella C, Lamba S, Martinoglio B, Veronese S, Siena S, Sartore-Bianchi A, Beccuti M, Mottolese M, Linnebacher M, Cordero F, Di Nicolantonio F, Bardelli A. (2015) The molecular landscape of colorectal cancer cell lines unveils clinically actionable kinase targets. Nature communications 6 7002 [DOI  PMID]
30. Beccuti M, Carrara M, Cordero F, Lazzarato F, Donatelli S, Nadalin F, Policriti A, Calogero RA. (2014) Chimera: a Bioconductor package for secondary analysis of fusion products. Bioinformatics (Oxford, England) 30(24) 3556-7 [DOI  PMID]
31. Fornari C, Beccuti M, Lanzardo S, Conti L, Balbo G, Cavallo F, Calogero RA, Cordero F. (2014) A mathematical-biological joint effort to investigate the tumor-initiating ability of Cancer Stem Cells. PloS one 9(9) e106193 [DOI  PMID]
32. Carrara M, Beccuti M, Cavallo F, Donatelli S, Lazzarato F, Cordero F, Calogero RA. (2013) State of art fusion-finder algorithms are suitable to detect transcription-induced chimeras in normal tissues? BMC bioinformatics 14 Suppl 7(Suppl 7) S2 [DOI  PMID]
33. Cordero F, Beccuti M, Fornari C, Lanzardo S, Conti L, Cavallo F, Balbo G, Calogero R. (2013) Multi-level model for the investigation of oncoantigen-driven vaccination effect. BMC bioinformatics 14 Suppl 6(Suppl 6) S11 [DOI  PMID]
34. Lonardi S, Duma D, Alpert M, Cordero F, Beccuti M, Bhat PR, Wu Y, Ciardo G, Alsaihati B, Ma Y, Wanamaker S, Resnik J, Bozdag S, Luo MC, Close TJ. (2013) Combinatorial pooling enables selective sequencing of the barley gene space. PLoS computational biology 9(4) e1003010 [DOI  PMID]
35. Carrara M, Beccuti M, Lazzarato F, Cavallo F, Cordero F, Donatelli S, Calogero RA. (2013) State-of-the-art fusion-finder algorithms sensitivity and specificity. BioMed research international 2013 340620 [DOI  PMID]
36. Mayer KF, Waugh R, Brown JW, Schulman A, Langridge P, Platzer M, Fincher GB, Muehlbauer GJ, Sato K, Close TJ, Wise RP, Stein N. (2012) A physical, genetic and functional sequence assembly of the barley genome. Nature 491(7426) 711-6 [DOI  PMID]
37. Cordero F, Beccuti M, Donatelli S, Calogero RA. (2012) Large disclosing the nature of computational tools for the analysis of next generation sequencing data. Current topics in medicinal chemistry 12(12) 1320-30 [DOI  PMID]
38. Cordero F, Beccuti M, Arigoni M, Donatelli S, Calogero RA. (2012) Optimizing a massive parallel sequencing workflow for quantitative miRNA expression analysis. PloS one 7(2) e31630 [DOI  PMID]

Courses

• BIOINFORMATICS (SVB0047)
  Cellular and Molecular Biology
• Bioinformatics and Computational Models
  PhD in Complex Systems for Quantitative Biomedicine
• PROGRAMMING FOR DATA SCIENCE (SEM0144)
  Two - Year Master Degree in Economics
• Python programming for data science (MAT0338)
  Laurea Magistrale (M.Sc.) in Stochastics and Data Science
• R Programming for data science (MAT0337)
  Laurea Magistrale (M.Sc.) in Stochastics and Data Science

Research topics

Marco Beccuti is scientific coordinator of InfoLife National Laboratory of CINI, technical coordinator of ELIXIR Node of the Università degli Studi di Torino, scientific coordinator of the laboratory "HPC for biomed and AI" in ICxT, Chair of the Scientific Committee of the "HPC4AI" laboratory" at the Università degli Studi di Torino, and scientific co-coordinator of "Quantitative Biology" (q-Bio) group. He is also member of the "Performance Evaluation and System Validation" group (QMIPS), of "Bioinformatics ITalian Society" (BITS), and of the "Consorzio Nazionale Interuniversitario per le Telecomunicazioni" (CNIT).

His research is currently mainly focused on:

• designing FAIR (Findable, Accessible, Interoperable, Re-usable) workflows for the analysis
  of deep sequencing data (i.e. genomic, transcriptomic and single cell data);
• developing modeling and analysis techniques for studying complex biological systems.

Research groups

• Quantitative Biology (q-Bio)

• Quantitative Biology (q-Bio)
• System and Service Quality

Research projects

• Sperimentazioni e studio di modelli per la valutazione di Calcolo dell’Università di Torino

Activities in agenda

See my calendar

Academic bodies

• Commissione Terza Missione
• Consiglio di Dipartimento

Office hours