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Article ; Online: Circr, a Computational Tool to Identify miRNA:circRNA Associations.

Dori, Martina / Caroli, Jimmy / Forcato, Mattia

Frontiers in bioinformatics

2022 Volume 2, Page(s) 852834

Abstract: Circular RNAs (circRNAs) are known to act as important regulators of the microRNA (miRNA) activity. Yet, computational resources to identify miRNA:circRNA interactions are mostly limited to already annotated circRNAs or affected by high rates of false ... ...

Abstract	Circular RNAs (circRNAs) are known to act as important regulators of the microRNA (miRNA) activity. Yet, computational resources to identify miRNA:circRNA interactions are mostly limited to already annotated circRNAs or affected by high rates of false positive predictions. To overcome these limitations, we developed Circr, a computational tool for the prediction of associations between circRNAs and miRNAs. Circr combines three publicly available algorithms for
Language	English
Publishing date	2022-03-11
Publishing country	Switzerland
Document type	Journal Article
ISSN	2673-7647
ISSN (online)	2673-7647
DOI	10.3389/fbinf.2022.852834
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Article: popsicleR: a R Package for pre-processing and quality control analysis of single cell RNA-seq data

Grandi, Francesco / Caroli, Jimmy / Romano, Oriana / Marchionni, Matteo / Forcato, Mattia / Bicciato, Silvio

Journal of molecular biology. 2022 Mar. 18,

2022

Abstract: The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires ... ...

Abstract	The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Pre-processing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of pre-processing parameters. Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available at https://github.com/bicciatolab/popsicleR.
Keywords	genomics ; molecular biology ; quality control ; sequence analysis
Language	English
Dates of publication	2022-0318
Publishing place	Elsevier Ltd
Document type	Article
Note	Pre-press version
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2022.167560
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Article: Computational Methods for the Integrative Analysis of Genomics and Pharmacological Data.

Caroli, Jimmy / Dori, Martina / Bicciato, Silvio

Frontiers in oncology

2020 Volume 10, Page(s) 185

Abstract: Since the pioneering NCI-60 panel of the late'80's, several major screenings of genetic profiling and drug testing in cancer cell lines have been conducted to investigate how genetic backgrounds and transcriptional patterns shape cancer's response to ... ...

Abstract	Since the pioneering NCI-60 panel of the late'80's, several major screenings of genetic profiling and drug testing in cancer cell lines have been conducted to investigate how genetic backgrounds and transcriptional patterns shape cancer's response to therapy and to identify disease-specific genes associated with drug response. Historically, pharmacogenomics screenings have been largely heterogeneous in terms of investigated cell lines, assay technologies, number of compounds, type and quality of genomic data, and methods for their computational analysis. The analysis of this enormous and heterogeneous amount of data required the development of computational methods for the integration of genomic profiles with drug responses across multiple screenings. Here, we will review the computational tools that have been developed to integrate cancer cell lines' genomic profiles and sensitivity to small molecule perturbations obtained from different screenings.
Language	English
Publishing date	2020-02-27
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2020.00185
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Article ; Online: popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data.

Grandi, Francesco / Caroli, Jimmy / Romano, Oriana / Marchionni, Matteo / Forcato, Mattia / Bicciato, Silvio

Journal of molecular biology

2022 Volume 434, Issue 11, Page(s) 167560

Abstract	The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both advanced computational skills and biological sensibility. When dealing with single cell RNA-seq (scRNA-seq) data, the presence of technical artifacts, noise, and biological biases imposes to first identify, and eventually remove, unreliable signals from low-quality cells and unwanted sources of variation that might affect the efficacy of subsequent downstream modules. Pre-processing and quality control (QC) of scRNA-seq data is a laborious process consisting in the manual combination of different computational strategies to quantify QC-metrics and define optimal sets of pre-processing parameters. Here we present popsicleR, a R package to interactively guide skilled and unskilled command line-users in the pre-processing and QC analysis of scRNA-seq data. The package integrates, into several main wrapper functions, methods derived from widely used pipelines for the estimation of quality-control metrics, filtering of low-quality cells, data normalization, removal of technical and biological biases, and for cell clustering and annotation. popsicleR starts from either the output files of the Cell Ranger pipeline from 10X Genomics or from a feature-barcode matrix of raw counts generated from any scRNA-seq technology. Open-source code, installation instructions, and a case study tutorial are freely available at https://github.com/bicciatolab/popsicleR.
MeSH term(s)	Gene Expression Profiling/methods ; Quality Control ; RNA-Seq/methods ; Single-Cell Analysis/methods ; Software
Language	English
Publishing date	2022-03-24
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80229-3
ISSN	1089-8638 ; 0022-2836
ISSN (online)	1089-8638
ISSN	0022-2836
DOI	10.1016/j.jmb.2022.167560
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Article ; Online: A community Biased Signaling Atlas.

Caroli, Jimmy / Mamyrbekov, Alibek / Harpsøe, Kasper / Gardizi, Sahar / Dörries, Linda / Ghosh, Eshan / Hauser, Alexander S / Kooistra, Albert J / Gloriam, David E

Nature chemical biology

2023 Volume 19, Issue 5, Page(s) 531–535

MeSH term(s)	Signal Transduction
Language	English
Publishing date	2023-03-22
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2202962-X
ISSN	1552-4469 ; 1552-4450
ISSN (online)	1552-4469
ISSN	1552-4450
DOI	10.1038/s41589-023-01292-8
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Article ; Online: GproteinDb in 2024: new G protein-GPCR couplings, AlphaFold2-multimer models and interface interactions.

Pándy-Szekeres, Gáspár / Taracena Herrera, Luis P / Caroli, Jimmy / Kermani, Ali A / Kulkarni, Yashraj / Keserű, György M / Gloriam, David E

Nucleic acids research

2023 Volume 52, Issue D1, Page(s) D466–D475

Abstract: G proteins are the major signal proteins of ∼800 receptors for medicines, hormones, neurotransmitters, tastants and odorants. GproteinDb offers integrated genomic, structural, and pharmacological data and tools for analysis, visualization and experiment ... ...

Abstract	G proteins are the major signal proteins of ∼800 receptors for medicines, hormones, neurotransmitters, tastants and odorants. GproteinDb offers integrated genomic, structural, and pharmacological data and tools for analysis, visualization and experiment design. Here, we present the first major update of GproteinDb greatly expanding its coupling data and structural templates, adding AlphaFold2 structure models of GPCR-G protein complexes and advancing the interactive analysis tools for their interfaces underlying coupling selectivity. We present insights on coupling agreement across datasets and parameters, including constitutive activity, agonist-induced activity and kinetics. GproteinDb is accessible at https://gproteindb.org.
MeSH term(s)	Computational Biology ; Databases, Protein ; GTP-Binding Proteins/chemistry ; GTP-Binding Proteins/genetics ; Internet ; Models, Molecular ; Receptors, G-Protein-Coupled/chemistry ; Receptors, G-Protein-Coupled/metabolism ; Humans
Chemical Substances	GTP-Binding Proteins (EC 3.6.1.-) ; Receptors, G-Protein-Coupled
Language	English
Publishing date	2023-11-24
Publishing country	England
Document type	Journal Article
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkad1089
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Article ; Online: APTANI2: update of aptamer selection through sequence-structure analysis.

Caroli, Jimmy / Forcato, Mattia / Bicciato, Silvio

Bioinformatics (Oxford, England)

2019 Volume 36, Issue 7, Page(s) 2266–2268

Abstract: Summary: Here we present APTANI2, an expanded and optimized version of APTANI, a computational tool for selecting target-specific aptamers from high-throughput-Systematic Evolution of Ligands by Exponential Enrichment data through sequence-structure ... ...

Abstract	Summary: Here we present APTANI2, an expanded and optimized version of APTANI, a computational tool for selecting target-specific aptamers from high-throughput-Systematic Evolution of Ligands by Exponential Enrichment data through sequence-structure analysis. As compared to its original implementation, APTANI2 ranks aptamers and identifies relevant structural motifs through the calculation of a score that combines frequency and structural stability of each secondary structure predicted in any aptamer sequence. In addition, APTANI2 comprises modules for a deeper investigation of sequence motifs and secondary structures, a graphical user interface that enhances its usability, and coding solutions that improve performances. Availability and implementation: Source code, documentation and example command lines can be downloaded from http://aptani.unimore.it. APTANI2 is implemented in Python 3.4, released under the GNU GPL3.0 License, and compatible with Linux, Mac OS and the MS Windows subsystem for Linux. Supplementary information: Supplementary information is available at Bioinformatics online.
MeSH term(s)	Documentation ; High-Throughput Nucleotide Sequencing ; Software
Language	English
Publishing date	2019-11-28
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btz897
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Article ; Online: GPCRdb in 2023: state-specific structure models using AlphaFold2 and new ligand resources.

Pándy-Szekeres, Gáspár / Caroli, Jimmy / Mamyrbekov, Alibek / Kermani, Ali A / Keserű, György M / Kooistra, Albert J / Gloriam, David E

Nucleic acids research

2022 Volume 51, Issue D1, Page(s) D395–D402

Abstract: G protein-coupled receptors (GPCRs) are physiologically abundant signaling hubs routing hundreds of extracellular signal substances and drugs into intracellular pathways. The GPCR database, GPCRdb supports >5000 interdisciplinary researchers every month ... ...

Abstract	G protein-coupled receptors (GPCRs) are physiologically abundant signaling hubs routing hundreds of extracellular signal substances and drugs into intracellular pathways. The GPCR database, GPCRdb supports >5000 interdisciplinary researchers every month with reference data, analysis, visualization, experiment design and dissemination. Here, we present our fifth major GPCRdb release setting out with an overview of the many resources for receptor sequences, structures, and ligands. This includes recently published additions of class D generic residue numbers, a comparative structure analysis tool to identify functional determinants, trees clustering GPCR structures by 3D conformation, and mutations stabilizing inactive/active states. We provide new state-specific structure models of all human non-olfactory GPCRs built using AlphaFold2-MultiState. We also provide a new resource of endogenous ligands along with a larger number of surrogate ligands with bioactivity, vendor, and physiochemical descriptor data. The one-stop-shop ligand resources integrate ligands/data from the ChEMBL, Guide to Pharmacology, PDSP Ki and PubChem database. The GPCRdb is available at https://gpcrdb.org.
MeSH term(s)	Humans ; Ligands ; Mutation ; Receptors, G-Protein-Coupled/chemistry ; Sequence Alignment ; Signal Transduction ; Databases, Protein ; Protein Conformation
Chemical Substances	Ligands ; Receptors, G-Protein-Coupled
Language	English
Publishing date	2022-11-14
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkac1013
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Article ; Online: RNA aptamers specific for transmembrane p24 trafficking protein 6 and Clusterin for the targeted delivery of imaging reagents and RNA therapeutics to human β cells.

Van Simaeys, Dimitri / De La Fuente, Adriana / Zilio, Serena / Zoso, Alessia / Kuznetsova, Victoria / Alcazar, Oscar / Buchwald, Peter / Grilli, Andrea / Caroli, Jimmy / Bicciato, Silvio / Serafini, Paolo

Nature communications

2022 Volume 13, Issue 1, Page(s) 1815

Abstract: The ability to detect and target β cells in vivo can substantially refine how diabetes is studied and treated. However, the lack of specific probes still hampers a precise characterization of human β cell mass and the delivery of therapeutics in clinical ...

Abstract	The ability to detect and target β cells in vivo can substantially refine how diabetes is studied and treated. However, the lack of specific probes still hampers a precise characterization of human β cell mass and the delivery of therapeutics in clinical settings. Here, we report the identification of two RNA aptamers that specifically and selectively recognize mouse and human β cells. The putative targets of the two aptamers are transmembrane p24 trafficking protein 6 (TMED6) and clusterin (CLUS). When given systemically in immune deficient mice, these aptamers recognize the human islet graft producing a fluorescent signal proportional to the number of human islets transplanted. These aptamers cross-react with endogenous mouse β cells and allow monitoring the rejection of mouse islet allografts. Finally, once conjugated to saRNA specific for X-linked inhibitor of apoptosis (XIAP), they can efficiently transfect non-dissociated human islets, prevent early graft loss, and improve the efficacy of human islet transplantation in immunodeficient in mice.
MeSH term(s)	Animals ; Aptamers, Nucleotide/genetics ; Clusterin/genetics ; Graft Rejection ; Humans ; Indicators and Reagents ; Islets of Langerhans/metabolism ; Islets of Langerhans Transplantation ; Mice ; RNA/metabolism ; Vesicular Transport Proteins/genetics
Chemical Substances	Aptamers, Nucleotide ; Clusterin ; Indicators and Reagents ; TMED6 protein, mouse ; Vesicular Transport Proteins ; RNA (63231-63-0)
Language	English
Publishing date	2022-04-05
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-022-29377-3
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: GDA, a web-based tool for Genomics and Drugs integrated analysis.

Caroli, Jimmy / Sorrentino, Giovanni / Forcato, Mattia / Del Sal, Giannino / Bicciato, Silvio

Nucleic acids research

2018 Volume 46, Issue W1, Page(s) W148–W156

Abstract: Several major screenings of genetic profiling and drug testing in cancer cell lines proved that the integration of genomic portraits and compound activities is effective in discovering new genetic markers of drug sensitivity and clinically relevant ... ...

Abstract	Several major screenings of genetic profiling and drug testing in cancer cell lines proved that the integration of genomic portraits and compound activities is effective in discovering new genetic markers of drug sensitivity and clinically relevant anticancer compounds. Despite most genetic and drug response data are publicly available, the availability of user-friendly tools for their integrative analysis remains limited, thus hampering an effective exploitation of this information. Here, we present GDA, a web-based tool for Genomics and Drugs integrated Analysis that combines drug response data for >50 800 compounds with mutations and gene expression profiles across 73 cancer cell lines. Genomic and pharmacological data are integrated through a modular architecture that allows users to identify compounds active towards cancer cell lines bearing a specific genomic background and, conversely, the mutational or transcriptional status of cells responding or not-responding to a specific compound. Results are presented through intuitive graphical representations and supplemented with information obtained from public repositories. As both personalized targeted therapies and drug-repurposing are gaining increasing attention, GDA represents a resource to formulate hypotheses on the interplay between genomic traits and drug response in cancer. GDA is freely available at http://gda.unimore.it/.
MeSH term(s)	Antineoplastic Agents/pharmacology ; Cell Line, Tumor ; Genomics/methods ; Humans ; Internet ; Mutation ; Neoplasms/genetics ; Neoplasms/metabolism ; Protein Kinase Inhibitors/pharmacology ; Proto-Oncogene Proteins B-raf/antagonists & inhibitors ; Proto-Oncogene Proteins B-raf/genetics ; Signal Transduction ; Software ; Transcriptional Activation ; Transcriptome/drug effects
Chemical Substances	Antineoplastic Agents ; Protein Kinase Inhibitors ; Proto-Oncogene Proteins B-raf (EC 2.7.11.1)
Language	English
Publishing date	2018-05-25
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gky434
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