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Zhang Y, Krishnan S, Bao B, Chiang AWT, Sorrentino JT, Schinn SM, Kellman BP, Lewis NE. Preparing glycomics data for robust statistical analysis with GlyCompareCT. STAR Protoc 2023; 4:102162. [PMID: 36920914 PMCID: PMC10025275 DOI: 10.1016/j.xpro.2023.102162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 12/27/2022] [Accepted: 02/13/2023] [Indexed: 03/16/2023] Open
Abstract
GlyCompareCT is a portable command-line tool to facilitate downstream glycomic data analyses, by addressing data inherent sparsity and non-independence. Inputting glycan abundances, users can run GlyCompareCT with one line of code to obtain the abundances of a minimal substructure set, named glycomotif, thereby quantifying hidden biosynthetic relationships between measured glycans. Optional parameters tuning and annotation are supported for personal preference. For complete details on the use and execution of this protocol, please refer to Bao et al. (2021).1.
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Affiliation(s)
- Yujie Zhang
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - Sridevi Krishnan
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - Bokan Bao
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - Austin W T Chiang
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - James T Sorrentino
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - Song-Min Schinn
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA
| | - Benjamin P Kellman
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Augment Biologics, 9450 SW Gemini Dr. #46664, Beaverton, OR 97008, USA.
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA; Department of Bioengineering, University of California, San Diego, 9500 Gilman Drive MC 0760, La Jolla, CA 92093, USA.
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Marchetti L, Nifosì R, Martelli PL, Da Pozzo E, Cappello V, Banterle F, Trincavelli ML, Martini C, D’Elia M. Quantum computing algorithms: getting closer to critical problems in computational biology. Brief Bioinform 2022; 23:6758194. [PMID: 36220772 PMCID: PMC9677474 DOI: 10.1093/bib/bbac437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Revised: 08/15/2022] [Accepted: 09/08/2022] [Indexed: 12/14/2022] Open
Abstract
The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.
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Affiliation(s)
| | | | - Pier Luigi Martelli
- Corresponding authors: Pier Luigi Martelli. Tel.: +39 0512094005; Fax: +39 0512094005; E-mail: ; Claudia Martini. Tel.: +39 0502219522; Fax: +39 050 2210680; E-mail:
| | - Eleonora Da Pozzo
- University of Pisa, Department of Pharmacy, via Bonanno 6, 56126 Pisa Italy
| | - Valentina Cappello
- Italian Institute of Technology, Center for Materials Interfaces, Viale Rinaldo Piaggio 34, 56025 Pontedera (PI), Italy
| | | | | | - Claudia Martini
- Corresponding authors: Pier Luigi Martelli. Tel.: +39 0512094005; Fax: +39 0512094005; E-mail: ; Claudia Martini. Tel.: +39 0502219522; Fax: +39 050 2210680; E-mail:
| | - Massimo D’Elia
- University of Pisa, Department of Physics, Largo Bruno Pontecorvo 3, 56127, Pisa Italy
- INFN, Sezione di Pisa, Largo Bruno Pontecorvo 3, I-56127 Pisa, Italy
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