Omics Sciences
Laboratory of Advanced Methods in Bioinformatics and Data Analysis
The Laboratory of Advanced Methods in Bioinformatics and Data Analysis (λ-lab) deploys innovative strategies to analyze biological data. We develop the computational methods needed to process data generated with novel technologies developed at the COSR. We have a special interest in single cell technologies and the related computational challenges. We engage with other scientific groups to provide out-of-the-box solutions.
Research activity
- We partner with the Innovation lab @ COSR to provide an appropriate analytical framework for data produced with novel multimodal technologies integrating scGETseq and scRNA-seq; we are interested in defining robust ways to associate the changes in chromatin structure to gene expression in single cells. In addition, we continuously screen and update the standards for the analysis of scGET-seq data.
- We study the application of Stochastic Block Models to identify differential modules in biological networks; we are interested in differential modules of coexpression and coaccessibility that can be linked to drug resistance in colorectal cancer.
- We use spatial transcriptomics data to identify structural variants in tumor genomes and to study the topological properties of cancer, with respect to its surrounding environment.
- We study the properties of DNA language models in the context of evolutionary biology.
- We continously update schist, a python library to analyze single cell data using Stochastic Block Models.
Perelli L, Zhang L, Mangiameli S, Giannese F, Mahadevan KK, Peng F, Citron F, Khan H, Le C, Gurreri E, Carbone F, Russell AJC, Soeung M, Lam TNA, Lundgren S, Marisetty S, Zhu C, Catania D, Mohamed AMT, Feng N, Augustine JJ, Sgambato A, Tortora G, Draetta GF, Tonon G, Futreal A, Giuliani V, Carugo A, Viale A, Kim MP, Heffernan TP, Wang L, Kalluri R, Cittaro D, Chen F, Genovese G — Evolutionary fingerprints of epithelial-to-mesenchymal transition. Nature. 2025 Mar 5. doi: 10.1038/s41586-025-08671-2
Punzi S, Cittaro D, Gatti G, Crupi G, Botrugno OA, Cartalemi AA, Gutfreund A, Oneto C, Giansanti V, Battistini C, Santacatterina G, Patruno L, Villanti I, Palumbo M, Laverty DJ, Giannese F, Graudenzi A, Caravagna G, Antoniotti M, Nagel Z, Cavallaro U, Lanfrancone L, Yap TA, Draetta G, Balaban N, Tonon G — Early tolerance and late persistence as alternative drug responses in cancer. Nat Commun. 2025 Feb 3;16(1):1291. doi: 10.1038/s41467-024-54728-7
Giansanti V, Giannese F, Botrugno OA, Gandolfi G, Balestrieri C, Antoniotti M, Tonon G, Cittaro D — Scalable Integration of Multiomic Single Cell Data Using Generative Adversarial Networks. Bioinformatics.. 2024 May 2. doi: 10.1093/bioinformatics/btae300
Cittaro D, Lazarević D, Tonon G, Giannese F — Analyzing genomic and epigenetic profiles in single cells by hybrid transposase (scGET-seq). Star Prot. 2023 Jun; 4(2) 102176. doi: 10.1016/j.xpro.2023.102176
Lanciano T, Savino A, Porcu F, Cittaro D, Bonchi F, Provero P — Contrast subgraphs allow comparing homogeneous and heterogeneous networks derived from omics data. Gigascience. 2022 Dec 28;12:giad010. doi: 10.1093/gigascience/giad010
Draetta EL, Lazarević D, Provero P, Cittaro D — The frequency of somatic mutations in cancer predicts the phenotypic relevance of germline mutations. Front. Genet. 2023 Jan 9; 13:1045301. doi: 10.3389/fgene.2022.1045301
Tedesco M, Giannese F, Lazarevic D, Giansanti V, Rosano D, Monzani S, Catalano I, Grassi E, Zanella E, Botrugno O, Morelli L, Panina Bordignon P, Caravagna G, Bertotti A, Martino G, Aldrighetti L, Pasqualato S, Trusolino L, Cittaro D, Tonon G — Chromatin Velocity reveals epigenetic dynamics by single-cell profiling of heterochromatin and euchromatin. Nat Biotechnol. 2022 Feb;40(2):235-244. doi: 10.1038/s41587-021-01031-1
de Pretis S, Cittaro D — Dimensionality reduction and statistical modeling of scGET-seq data. bioRxiv(2022); doi 10.1101/2022.06.29.498092
Morelli L, Giansanti V, Cittaro D — Nested Stochastic Block Models applied to the analysis of single cell data. BMC Bioinformatics. 2021 Nov 30;22(1):576. doi: 10.1186/s12859-021-04489-7
Giansanti V, Tang M, Cittaro D — Fast analysis of scATAC-seq data using a predefined set of genomic regions. F1000Research 2020, 9:199. doi:10.12688/f1000research.22731.2
