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Book: Computational Intelligence Methods for Bioinformatics and Biostatistics

Chicco, Davide / Facchiano, Angelo / Tavazzi, Erica / Cazzaniga, Paolo / Vettoretti, Martina / Bernasconi, Anna / Avesani, Simone / Longato, Enrico

17th International Meeting, CIBB 2021, Virtual Event, November 15¿17, 2021, Revised Selected Papers

(Lecture Notes in Bioinformatics)

2022

Series title	Lecture Notes in Bioinformatics
Keywords	artificial intelligence ; computational and systems biology ; computer networks ; Computer Systems ; Computer vision ; Correlation Analysis ; Data Mining ; education ; Image Analysis ; image processing ; Image segmentation ; Learning ; Machine Learning ; Neural Networks ; pattern recognition ; Signal Processing ; biostatistics ; computer systems ; computer vision ; correlation analysis ; data mining ; image analysis ; image segmentation ; learning ; machine learning ; neural networks ; signal processing
Language	English
Size	272 p.
Edition	1
Publisher	Springer International Publishing
Document type	Book
Note	PDA Manuell_17
Format	155 x 235 x 15
ISBN	9783031208362 ; 3031208366
Database	PDA

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Article ; Online: Data-driven recombination detection in viral genomes.

Alfonsi, Tommaso / Bernasconi, Anna / Chiara, Matteo / Ceri, Stefano

Nature communications

2024 Volume 15, Issue 1, Page(s) 3313

Abstract: Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than ninety SARS-CoV-2 lineages are designated as recombinant. In the wake of the ... ...

Abstract	Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than ninety SARS-CoV-2 lineages are designated as recombinant. In the wake of the COVID-19 pandemic, several methods for detecting recombination in SARS-CoV-2 have been proposed; however, none could faithfully confirm manual analyses by experts in the field. We hereby present RecombinHunt, an original data-driven method for the identification of recombinant genomes, capable of recognizing recombinant SARS-CoV-2 genomes (or lineages) with one or two breakpoints with high accuracy and within reduced turn-around times. ReconbinHunt shows high specificity and sensitivity, compares favorably with other state-of-the-art methods, and faithfully confirms manual analyses by experts. RecombinHunt identifies recombinant viral genomes from the recent monkeypox epidemic in high concordance with manually curated analyses by experts, suggesting that our approach is robust and can be applied to any epidemic/pandemic virus.
MeSH term(s)	Humans ; Pandemics ; COVID-19 ; SARS-CoV-2 ; Genome, Viral ; Recombination, Genetic ; Phylogeny
Language	English
Publishing date	2024-04-17
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/s41467-024-47464-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Editorial: Identification of phenotypically important genomic variants.

Heron, Elizabeth A / Valle, Giorgio / Bernasconi, Anna

Frontiers in bioinformatics

2023 Volume 3, Page(s) 1328945

Language	English
Publishing date	2023-11-10
Publishing country	Switzerland
Document type	Editorial
ISSN	2673-7647
ISSN (online)	2673-7647
DOI	10.3389/fbinf.2023.1328945
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Processing genome-wide association studies within a repository of heterogeneous genomic datasets.

Bernasconi, Anna / Canakoglu, Arif / Comolli, Federico

BMC genomic data

2023 Volume 24, Issue 1, Page(s) 13

Abstract: Background: Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants - typically single-nucleotide polymorphisms (SNPs) - in different individuals that are associated with phenotypic traits. Research ... ...

Abstract	Background: Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants - typically single-nucleotide polymorphisms (SNPs) - in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions. Results: To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals. Conclusions: As a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.
MeSH term(s)	Humans ; Genome-Wide Association Study ; Genomics ; Epigenomics ; Big Data ; Data Analysis
Language	English
Publishing date	2023-03-03
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	2730-6844
ISSN (online)	2730-6844
DOI	10.1186/s12863-023-01111-y
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Correction: Processing genome-wide association studies within a repository of heterogeneous genomic datasets.

Bernasconi, Anna / Canakoglu, Arif / Comolli, Federico

BMC genomic data

2023 Volume 24, Issue Suppl 1, Page(s) 32

Language	English
Publishing date	2023-06-05
Publishing country	England
Document type	Published Erratum
ISSN	2730-6844
ISSN (online)	2730-6844
DOI	10.1186/s12863-023-01135-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Processing genome-wide association studies within a repository of heterogeneous genomic datasets

Bernasconi, Anna / Canakoglu, Arif / Comolli, Federico

BMC Genom Data. 2023 Dec., v. 24, no. 1 p.13-13

2023

Abstract: BACKGROUND: Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research ... ...

Abstract	BACKGROUND: Genome Wide Association Studies (GWAS) are based on the observation of genome-wide sets of genetic variants – typically single-nucleotide polymorphisms (SNPs) – in different individuals that are associated with phenotypic traits. Research efforts have so far been directed to improving GWAS techniques rather than on making the results of GWAS interoperable with other genomic signals; this is currently hindered by the use of heterogeneous formats and uncoordinated experiment descriptions. RESULTS: To practically facilitate integrative use, we propose to include GWAS datasets within the META-BASE repository, exploiting an integration pipeline previously studied for other genomic datasets that includes several heterogeneous data types in the same format, queryable from the same systems. We represent GWAS SNPs and metadata by means of the Genomic Data Model and include metadata within a relational representation by extending the Genomic Conceptual Model with a dedicated view. To further reduce the gap with the descriptions of other signals in the repository of genomic datasets, we perform a semantic annotation of phenotypic traits. Our pipeline is demonstrated using two important data sources, initially organized according to different data models: the NHGRI-EBI GWAS Catalog and FinnGen (University of Helsinki). The integration effort finally allows us to use these datasets within multi-sample processing queries that respond to important biological questions. These are then made usable for multi-omic studies together with, e.g., somatic and reference mutation data, genomic annotations, epigenetic signals. CONCLUSIONS: As a result of the our work on GWAS datasets, we enable 1) their interoperable use with several other homogenized and processed genomic datasets in the context of the META-BASE repository; 2) their big data processing by means of the GenoMetric Query Language and associated system. Future large-scale tertiary data analysis may extensively benefit from the addition of GWAS results to inform several different downstream analysis workflows.
Keywords	data collection ; epigenetics ; genomics ; metadata ; models ; mutation ; phenotype
Language	English
Dates of publication	2023-12
Size	p. 13.
Publishing place	BioMed Central
Document type	Article ; Online
ISSN	2730-6844
DOI	10.1186/s12863-023-01111-y
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Scenarios for the Integration of Microarray Gene Expression Profiles in COVID-19-Related Studies.

Bernasconi, Anna / Cascianelli, Silvia

Methods in molecular biology (Clifton, N.J.)

2021 Volume 2401, Page(s) 195–215

Abstract: The COVID-19 pandemic has hit heavily many aspects of our lives. At this time, genomic research is concerned with exploiting available datasets and knowledge to fuel discovery on this novel disease. Studies that can precisely characterize the gene ... ...

Abstract	The COVID-19 pandemic has hit heavily many aspects of our lives. At this time, genomic research is concerned with exploiting available datasets and knowledge to fuel discovery on this novel disease. Studies that can precisely characterize the gene expression profiles of human hosts infected by SARS-CoV-2 are of significant relevance. However, not many such experiments have yet been produced to date, nor made publicly available online. Thus, it is of paramount importance that data analysts explore all possibilities to integrate information coming from similar viruses and related diseases; interestingly, microarray gene profile experiments become extremely valuable for this purpose. This chapter reviews the aspects that should be considered when integrating transcriptomics data, considering mainly samples infected by different viruses and combining together various data types and also the extracted knowledge. It describes a series of scenarios from studies performed in literature and it suggests possible other directions of noteworthy integration.
MeSH term(s)	COVID-19/genetics ; Gene Expression Profiling ; Genomics ; Humans ; Pandemics ; Transcriptome
Language	English
Publishing date	2021-12-13
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1940-6029
ISSN (online)	1940-6029
DOI	10.1007/978-1-0716-1839-4_13
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Genomic data integration and user-defined sample-set extraction for population variant analysis.

Alfonsi, Tommaso / Bernasconi, Anna / Canakoglu, Arif / Masseroli, Marco

BMC bioinformatics

2022 Volume 23, Issue 1, Page(s) 401

Abstract: Background: Population variant analysis is of great importance for gathering insights into the links between human genotype and phenotype. The 1000 Genomes Project established a valuable reference for human genetic variation; however, the integrative ... ...

Abstract	Background: Population variant analysis is of great importance for gathering insights into the links between human genotype and phenotype. The 1000 Genomes Project established a valuable reference for human genetic variation; however, the integrative use of the corresponding data with other datasets within existing repositories and pipelines is not fully supported. Particularly, there is a pressing need for flexible and fast selection of population partitions based on their variant and metadata-related characteristics. Results: Here, we target general germline or somatic mutation data sources for their seamless inclusion within an interoperable-format repository, supporting integration among them and with other genomic data, as well as their integrated use within bioinformatic workflows. In addition, we provide VarSum, a data summarization service working on sub-populations of interest selected using filters on population metadata and/or variant characteristics. The service is developed as an optimized computational framework with an Application Programming Interface (API) that can be called from within any existing computing pipeline or programming script. Provided example use cases of biological interest show the relevance, power and ease of use of the API functionalities. Conclusions: The proposed data integration pipeline and data set extraction and summarization API pave the way for solid computational infrastructures that quickly process cumbersome variation data, and allow biologists and bioinformaticians to easily perform scalable analysis on user-defined partitions of large cohorts from increasingly available genetic variation studies. With the current tendency to large (cross)nation-wide sequencing and variation initiatives, we expect an ever growing need for the kind of computational support hereby proposed.
MeSH term(s)	Computational Biology ; Genomics ; Genotype ; Humans ; Metadata ; Software
Language	English
Publishing date	2022-09-29
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/s12859-022-04927-0
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Article ; Online: META-BASE: A Novel Architecture for Large-Scale Genomic Metadata Integration.

Bernasconi, Anna / Canakoglu, Arif / Masseroli, Marco / Ceri, Stefano

IEEE/ACM transactions on computational biology and bioinformatics

2022 Volume 19, Issue 1, Page(s) 543–557

Abstract: The integration of genomic metadata is, at the same time, an important, difficult, and well-recognized challenge. It is important because a wealth of public data repositories is available to drive biological and clinical research; combining information ... ...

Abstract	The integration of genomic metadata is, at the same time, an important, difficult, and well-recognized challenge. It is important because a wealth of public data repositories is available to drive biological and clinical research; combining information from various heterogeneous and widely dispersed sources is paramount to a number of biological discoveries. It is difficult because the domain is complex and there is no agreement among the various metadata definitions, which refer to different vocabularies and ontologies. It is well-recognized in the bioinformatics community because, in the common practice, repositories are accessed one-by-one, learning their specific metadata definitions as result of long and tedious efforts, and such practice is error-prone. In this paper, we describe META-BASE, an architecture for integrating metadata extracted from a variety of genomic data sources, based upon a structured transformation process. We present a variety of innovative techniques for data extraction, cleaning, normalization and enrichment. We propose a general, open and extensible pipeline that can easily incorporate any number of new data sources, and propose the resulting repository-already integrating several important sources-which is exposed by means of practical user interfaces to respond biological researchers' needs.
MeSH term(s)	Computational Biology ; Genomics ; Information Storage and Retrieval ; Metadata
Language	English
Publishing date	2022-02-03
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ISSN	1557-9964
ISSN (online)	1557-9964
DOI	10.1109/TCBB.2020.2998954
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Article ; Online: Data-driven recombination detection in viral genomes

Alfonsi, Tommaso / Bernasconi, Anna / Chiara, Matteo / Ceri, Stefano

bioRxiv

Abstract: Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than seventy SARS-CoV-2 lineages are designated as recombinant. In the wake of the ...

Abstract	Recombination is a key molecular mechanism for the evolution and adaptation of viruses. The first recombinant SARS-CoV-2 genomes were recognized in 2021; as of today, more than seventy SARS-CoV-2 lineages are designated as recombinant. In the wake of the COVID-19 pandemic, several methods for detecting recombination in SARS-CoV-2 have been proposed; however, none could faithfully reproduce manual analyses by experts in the field. We hereby present RecombinHunt, a novel, automated method for the identification of recombinant genomes purely based on a data-driven approach. RecombinHunt compares favorably with other state-of-the-art methods and recognizes recombinant SARS-CoV-2 genomes (or lineages) with one or two breakpoints with high accuracy, within reduced turn-around times and small discrepancies with respect to the expert manually-curated standard nomenclature. Strikingly, applied to the complete collection of viral sequences from the recent monkeypox epidemic, RecombinHunt identifies recombinant viral genomes in high concordance with manually curated analyses by experts, suggesting that our approach is robust and can be applied to any epidemic/pandemic virus. Although RecombinHunt does not substitute manual expert curation based on phylogenetic analysis, we believe that our method represents a breakthrough for the detection of recombinant viral lineages in pandemic/epidemic scenarios.
Keywords	covid19
Language	English
Publishing date	2023-06-07
Publisher	Cold Spring Harbor Laboratory
Document type	Article ; Online
DOI	10.1101/2023.06.05.543733
Database	COVID19

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