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Article ; Online: Ranking of cell clusters in a single-cell RNA-sequencing analysis framework using prior knowledge.

Oulas, Anastasis / Savva, Kyriaki / Karathanasis, Nestoras / Spyrou, George M

2024 Volume 20, Issue 4, Page(s) e1011550

Abstract: Prioritization or ranking of different cell types in a Single-cell RNA Sequencing (scRNA-Seq) framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different ... ...

Abstract	Prioritization or ranking of different cell types in a Single-cell RNA Sequencing (scRNA-Seq) framework can be performed in a variety of ways, some of these include: i) obtaining an indication of the proportion of cell types between the different conditions under study, ii) counting the number of differentially expressed genes (DEGs) between cell types and conditions in the experiment or, iii) prioritizing cell types based on prior knowledge about the conditions under study (i.e., a specific disease). These methods have drawbacks and limitations thus novel methods for improving cell ranking are required. Here we present a novel methodology that exploits prior knowledge in combination with expert-user information to accentuate cell types from a scRNA-seq analysis that yield the most biologically meaningful results with respect to a disease under study. Our methodology allows for ranking and prioritization of cell-types based on how well their expression profiles relate to the molecular mechanisms and drugs associated with a disease. Molecular mechanisms, as well as drugs, are incorporated as prior knowledge in a standardized, structured manner. Cell-types are then ranked/prioritized based on how well results from data-driven analysis of scRNA-seq data match the predefined prior knowledge. In additional cell-cell communication perturbations between disease and control networks are used to further prioritize/rank cell-types. Our methodology has substantial advantages to more traditional cell ranking techniques and provides an informative complementary methodology that utilizes prior knowledge in a rapid and automated manner, that has previously not been attempted by other studies. The current methodology is also implemented as an R package entitled Single Cell Ranking Analysis Toolkit (scRANK) and is available for download and installation via GitHub (https://#hub.com/aoulas/scRANK).
Language	English
Publishing date	2024-04-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	2193340-6
ISSN	1553-7358 ; 1553-734X
ISSN (online)	1553-7358
ISSN	1553-734X
DOI	10.1371/journal.pcbi.1011550
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Bacterial Wars-a tool for the prediction of bacterial predominance based on network analysis measures.

Oulas, Anastasis / Minadakis, George / Zachariou, Margarita / Tomazou, Marios / Vlamis-Gardikas, Alexios / Spyrou, George M

NAR genomics and bioinformatics

2023 Volume 5, Issue 2, Page(s) lqad049

Abstract: Bacterial Wars (BW) is a network-based tool that applies a two-step pipeline to display information on the competition of bacterial species found in the same microbiome. It utilizes antimicrobial peptide (AMP) sequence similarities to obtain a ... ...

Abstract	Bacterial Wars (BW) is a network-based tool that applies a two-step pipeline to display information on the competition of bacterial species found in the same microbiome. It utilizes antimicrobial peptide (AMP) sequence similarities to obtain a relationship between species. The working hypothesis (putative AMP defense) is that friendly species share sequence similarity among the putative AMPs of their proteomes and are therefore immune to their AMPs. This may not happen in competing bacterial species with dissimilar putative AMPs. Similarities in the putative AMPs of bacterial proteomes may be thus used to predict predominance. The tool provides insights as to which bacterial species are more likely to 'die' in a competing environmental niche.
Language	English
Publishing date	2023-05-30
Publishing country	England
Document type	Journal Article
ISSN	2631-9268
ISSN (online)	2631-9268
DOI	10.1093/nargab/lqad049
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters.

Baltoumas, Fotis A / Karatzas, Evangelos / Paez-Espino, David / Venetsianou, Nefeli K / Aplakidou, Eleni / Oulas, Anastasis / Finn, Robert D / Ovchinnikov, Sergey / Pafilis, Evangelos / Kyrpides, Nikos C / Pavlopoulos, Georgios A

Frontiers in bioinformatics

2023 Volume 3, Page(s) 1157956

Abstract: Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods ... ...

Abstract	Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.
Language	English
Publishing date	2023-03-03
Publishing country	Switzerland
Document type	Journal Article ; Review
ISSN	2673-7647
ISSN (online)	2673-7647
DOI	10.3389/fbinf.2023.1157956
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Novel clinical, molecular and bioinformatics insights into the genetic background of autism.

Talli, Ioanna / Dovrolis, Nikolas / Oulas, Anastasis / Stavrakaki, Stavroula / Makedou, Kali / Spyrou, George M / Maroulakou, Ioanna

Human genomics

2022 Volume 16, Issue 1, Page(s) 39

Abstract: Background: Clinical classification of autistic patients based on current WHO criteria provides a valuable but simplified depiction of the true nature of the disorder. Our goal is to determine the biology of the disorder and the ASD-associated genes ... ...

Abstract	Background: Clinical classification of autistic patients based on current WHO criteria provides a valuable but simplified depiction of the true nature of the disorder. Our goal is to determine the biology of the disorder and the ASD-associated genes that lead to differences in the severity and variability of clinical features, which can enhance the ability to predict clinical outcomes. Method: Novel Whole Exome Sequencing data from children (n = 33) with ASD were collected along with extended cognitive and linguistic assessments. A machine learning methodology and a literature-based approach took into consideration known effects of genetic variation on the translated proteins, linking them with specific ASD clinical manifestations, namely non-verbal IQ, memory, attention and oral language deficits. Results: Linear regression polygenic risk score results included the classification of severe and mild ASD samples with a 81.81% prediction accuracy. The literature-based approach revealed 14 genes present in all sub-phenotypes (independent of severity) and others which seem to impair individual ones, highlighting genetic profiles specific to mild and severe ASD, which concern non-verbal IQ, memory, attention and oral language skills. Conclusions: These genes can potentially contribute toward a diagnostic gene-set for determining ASD severity. However, due to the limited number of patients in this study, our classification approach is mostly centered on the prediction and verification of these genes and does not hold a diagnostic nature per se. Substantial further experimentation is required to validate their role as diagnostic markers. The use of these genes as input for functional analysis highlights important biological processes and bridges the gap between genotype and phenotype in ASD.
MeSH term(s)	Autism Spectrum Disorder/diagnosis ; Autism Spectrum Disorder/genetics ; Autistic Disorder/complications ; Autistic Disorder/diagnosis ; Computational Biology ; Genetic Background ; Humans ; Phenotype
Language	English
Publishing date	2022-09-18
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2147618-4
ISSN	1479-7364 ; 1479-7364
ISSN (online)	1479-7364
ISSN	1479-7364
DOI	10.1186/s40246-022-00415-x
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Article: Putative Antimicrobial Peptides Within Bacterial Proteomes Affect Bacterial Predominance: A Network Analysis Perspective.

Oulas, Anastasis / Zachariou, Margarita / Chasapis, Christos T / Tomazou, Marios / Ijaz, Umer Z / Schmartz, Georges Pierre / Spyrou, George M / Vlamis-Gardikas, Alexios

Frontiers in microbiology

2021 Volume 12, Page(s) 752674

Abstract: The predominance of bacterial taxa in the gut, was examined in view of the putative antimicrobial peptide sequences (AMPs) within their proteomes. The working assumption was that compatible bacteria would share homology and thus immunity to their ... ...

Abstract	The predominance of bacterial taxa in the gut, was examined in view of the putative antimicrobial peptide sequences (AMPs) within their proteomes. The working assumption was that compatible bacteria would share homology and thus immunity to their putative AMPs, while competing taxa would have dissimilarities in their proteome-hidden AMPs. A network-based method ("Bacterial Wars") was developed to handle sequence similarities of predicted AMPs among
Language	English
Publishing date	2021-11-12
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2587354-4
ISSN	1664-302X
ISSN	1664-302X
DOI	10.3389/fmicb.2021.752674
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Article ; Online: Selecting variants of unknown significance through network-based gene-association significantly improves risk prediction for disease-control cohorts.

Oulas, Anastasis / Minadakis, George / Zachariou, Margarita / Spyrou, George M

Scientific reports

2019 Volume 9, Issue 1, Page(s) 3266

Abstract: Variants of unknown/uncertain significance (VUS) pose a huge dilemma in current genetic variation screening methods and genetic counselling. Driven by methods of next generation sequencing (NGS) such as whole exome sequencing (WES), a plethora of VUS are ...

Abstract	Variants of unknown/uncertain significance (VUS) pose a huge dilemma in current genetic variation screening methods and genetic counselling. Driven by methods of next generation sequencing (NGS) such as whole exome sequencing (WES), a plethora of VUS are being detected in research laboratories as well as in the health sector. Motivated by this overabundance of VUS, we propose a novel computational methodology, termed VariantClassifier (VarClass), which utilizes gene-association networks and polygenic risk prediction models to shed light into this grey area of genetic variation in association with disease. VarClass has been evaluated using numerous validation steps and proves to be very successful in assigning significance to VUS in association with specific diseases of interest. Notably, using VUS that are deemed significant by VarClass, we improved risk prediction accuracy in four large case-studies involving disease-control cohorts from GWAS as well as WES, when compared to traditional odds ratio analysis. Biological interpretation of selected high scoring VUS revealed interesting biological themes relevant to the diseases under investigation. VarClass is available as a standalone tool for large-scale data analyses, as well as a web-server with additional functionalities through a user-friendly graphical interface.
MeSH term(s)	DNA Mutational Analysis ; Gene Regulatory Networks ; Genetic Predisposition to Disease ; Genetic Variation ; Genome-Wide Association Study ; Genotype ; High-Throughput Nucleotide Sequencing ; Humans ; Models, Genetic
Language	English
Publishing date	2019-03-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-019-39796-w
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Article ; Online: Exploring microbial functional biodiversity at the protein family level—From metagenomic sequence reads to annotated protein clusters

Fotis A. Baltoumas / Evangelos Karatzas / David Paez-Espino / Nefeli K. Venetsianou / Eleni Aplakidou / Anastasis Oulas / Robert D. Finn / Sergey Ovchinnikov / Evangelos Pafilis / Nikos C. Kyrpides / Georgios A. Pavlopoulos

Frontiers in Bioinformatics, Vol

2023 Volume 3

Abstract	Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.
Keywords	protein clustering ; metagenomes ; metatranscriptomes ; cluster annotation ; biodiversity ; microbial dark matter ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	612
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Frontiers Media S.A.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: In depth analysis of Cyprus-specific mutations of SARS-CoV-2 strains using computational approaches

Oulas, Anastasis / Richter, Jan / Zanti, Maria / Tomazou, Marios / Michailidou, Kyriaki / Christodoulou, Kyproula / Christodoulou, Christina / Spyrou, George M.

BMC genomic data. 2021 Dec., v. 22, no. 1

2021

Abstract: BACKGROUND: This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. METHODS: ...

Abstract	BACKGROUND: This study aims to characterize SARS-CoV-2 mutations which are primarily prevalent in the Cypriot population. Moreover, using computational approaches, we assess whether these mutations are associated with changes in viral virulence. METHODS: We utilize genetic data from 144 sequences of SARS-CoV-2 strains from the Cypriot population obtained between March 2020 and January 2021, as well as all data available from GISAID. We combine this with countries’ regional information, such as deaths and cases per million, as well as COVID-19-related public health austerity measure response times. Initial indications of selective advantage of Cyprus-specific mutations are obtained by mutation tracking analysis. This entails calculating specific mutation frequencies within the Cypriot population and comparing these with their prevalence world-wide throughout the course of the pandemic. We further make use of linear regression models to extrapolate additional information that may be missed through standard statistical analysis. RESULTS: We report a single mutation found in the ORF1ab gene (nucleotide position 18,440) that appears to be significantly enriched within the Cypriot population. The amino acid change is denoted as S6059F, which maps to the SARS-CoV-2 NSP14 protein. We further analyse this mutation using regression models to investigate possible associations with increased deaths and cases per million. Moreover, protein structure prediction tools show that the mutation infers a conformational change to the protein that significantly alters its structure when compared to the reference protein. CONCLUSIONS: Investigating Cyprus-specific mutations for SARS-CoV-2 can lead to a better understanding of viral pathogenicity. Researching these mutations can generate potential links between viral-specific mutations and the unique genomics of the Cypriot population. This can not only lead to important findings from which to battle the pandemic on a national level, but also provide insights into viral virulence worldwide.
Keywords	Severe acute respiratory syndrome coronavirus 2 ; amino acids ; genes ; genomics ; mutation ; pandemic ; prediction ; protein structure ; public health ; regression analysis ; virulence
Language	English
Dates of publication	2021-12
Size	p. 48.
Publishing place	BioMed Central
Document type	Article
ISSN	2730-6844
DOI	10.1186/s12863-021-01007-9
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Selecting variants of unknown significance through network-based gene-association significantly improves risk prediction for disease-control cohorts

Anastasis Oulas / George Minadakis / Margarita Zachariou / George M. Spyrou

Scientific Reports, Vol 9, Iss 1, Pp 1-

2019 Volume 15

Abstract: Abstract Variants of unknown/uncertain significance (VUS) pose a huge dilemma in current genetic variation screening methods and genetic counselling. Driven by methods of next generation sequencing (NGS) such as whole exome sequencing (WES), a plethora ... ...

Abstract	Abstract Variants of unknown/uncertain significance (VUS) pose a huge dilemma in current genetic variation screening methods and genetic counselling. Driven by methods of next generation sequencing (NGS) such as whole exome sequencing (WES), a plethora of VUS are being detected in research laboratories as well as in the health sector. Motivated by this overabundance of VUS, we propose a novel computational methodology, termed VariantClassifier (VarClass), which utilizes gene-association networks and polygenic risk prediction models to shed light into this grey area of genetic variation in association with disease. VarClass has been evaluated using numerous validation steps and proves to be very successful in assigning significance to VUS in association with specific diseases of interest. Notably, using VUS that are deemed significant by VarClass, we improved risk prediction accuracy in four large case-studies involving disease-control cohorts from GWAS as well as WES, when compared to traditional odds ratio analysis. Biological interpretation of selected high scoring VUS revealed interesting biological themes relevant to the diseases under investigation. VarClass is available as a standalone tool for large-scale data analyses, as well as a web-server with additional functionalities through a user-friendly graphical interface.
Keywords	Medicine ; R ; Science ; Q
Language	English
Publishing date	2019-03-01T00:00:00Z
Publisher	Nature Publishing Group
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: PathwayConnector: finding complementary pathways to enhance functional analysis.

Minadakis, George / Zachariou, Margarita / Oulas, Anastasis / Spyrou, George M

Bioinformatics (Oxford, England)

2018 Volume 35, Issue 5, Page(s) 889–891

Abstract: Summary: PathwayConnector is a web-tool that facilitates the construction of complementary pathway-to-pathway networks and subnetworks of them, based on a reference pathway network derived from the rich information available either in KEGG or Reactome ... ...

Abstract	Summary: PathwayConnector is a web-tool that facilitates the construction of complementary pathway-to-pathway networks and subnetworks of them, based on a reference pathway network derived from the rich information available either in KEGG or Reactome database for pathway mapping. Specifically, for a given set of pathways, PathwayConnector (i) finds all the direct connections between them, (ii) adds a minimum set of complementary pathways required to achieve connectivity between the pathways, leading to informative fully connected networks and (ii) provides a series of clustering methods for the further grouping of pathways in to sub-clusters. The proposed web-tool is a simple yet informative tool towards identifying connected groups of pathways that are significantly related to specific diseases. Availability and implementation: http://bioinformatics.cing.ac.cy/PathwayConnector. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Cluster Analysis ; Databases, Factual ; Software
Language	English
Publishing date	2018-08-20
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/bty693
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