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  1. Article ; Online: iBioProVis: interactive visualization and analysis of compound bioactivity space.

    Donmez, Ataberk / Rifaioglu, Ahmet Sureyya / Acar, Aybar / Doğan, Tunca / Cetin-Atalay, Rengul / Atalay, Volkan

    Bioinformatics (Oxford, England)

    2020  Volume 36, Issue 17, Page(s) 4674

    Language English
    Publishing date 2020-10-19
    Publishing country England
    Document type Journal Article ; Published Erratum
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btaa666
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: iBioProVis: interactive visualization and analysis of compound bioactivity space.

    Donmez, Ataberk / Rifaioglu, Ahmet Sureyya / Acar, Aybar / Doğan, Tunca / Cetin-Atalay, Rengul / Atalay, Volkan

    Bioinformatics (Oxford, England)

    2020  Volume 36, Issue 14, Page(s) 4227–4230

    Abstract: Summary: iBioProVis is an interactive tool for visual analysis of the compound bioactivity space in the context of target proteins, drugs and drug candidate compounds. iBioProVis tool takes target protein identifiers and, optionally, compound SMILES as ... ...

    Abstract Summary: iBioProVis is an interactive tool for visual analysis of the compound bioactivity space in the context of target proteins, drugs and drug candidate compounds. iBioProVis tool takes target protein identifiers and, optionally, compound SMILES as input, and uses the state-of-the-art non-linear dimensionality reduction method t-Distributed Stochastic Neighbor Embedding (t-SNE) to plot the distribution of compounds embedded in a 2D map, based on the similarity of structural properties of compounds and in the context of compounds' cognate targets. Similar compounds, which are embedded to proximate points on the 2D map, may bind the same or similar target proteins. Thus, iBioProVis can be used to easily observe the structural distribution of one or two target proteins' known ligands on the 2D compound space, and to infer new binders to the same protein, or to infer new potential target(s) for a compound of interest, based on this distribution. Principal component analysis (PCA) projection of the input compounds is also provided, Hence the user can interactively observe the same compound or a group of selected compounds which is projected by both PCA and embedded by t-SNE. iBioProVis also provides detailed information about drugs and drug candidate compounds through cross-references to widely used and well-known databases, in the form of linked table views. Two use-case studies were demonstrated, one being on angiotensin-converting enzyme 2 (ACE2) protein which is Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Spike protein receptor. ACE2 binding compounds and seven antiviral drugs were closely embedded in which two of them have been under clinical trial for Coronavirus disease 19 (COVID-19).
    Availability and implementation: iBioProVis and its carefully filtered dataset are available at https://ibpv.kansil.org/ for public use.
    Contact: vatalay@metu.edu.tr.
    Supplementary information: Supplementary data are available at Bioinformatics online.
    MeSH term(s) Angiotensin-Converting Enzyme 2 ; Angiotensin-Converting Enzyme Inhibitors/chemistry ; Antiviral Agents/chemistry ; Betacoronavirus ; COVID-19 ; Coronavirus Infections ; Humans ; Internet ; Models, Molecular ; Pandemics ; Peptidyl-Dipeptidase A/chemistry ; Pneumonia, Viral ; Principal Component Analysis ; Receptors, Adrenergic, beta-2/chemistry ; Receptors, Adrenergic, beta-3/chemistry ; SARS-CoV-2 ; Software ; Spike Glycoprotein, Coronavirus/chemistry ; User-Computer Interface
    Chemical Substances ADRB2 protein, human ; ADRB3 protein, human ; Angiotensin-Converting Enzyme Inhibitors ; Antiviral Agents ; Receptors, Adrenergic, beta-2 ; Receptors, Adrenergic, beta-3 ; Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2 ; Peptidyl-Dipeptidase A (EC 3.4.15.1) ; ACE2 protein, human (EC 3.4.17.23) ; Angiotensin-Converting Enzyme 2 (EC 3.4.17.23)
    Keywords covid19
    Language English
    Publishing date 2020-05-14
    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/btaa496
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2374: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article: Severus: accurate detection and characterization of somatic structural variation in tumor genomes using long reads.

    Keskus, Ayse / Bryant, Asher / Ahmad, Tanveer / Yoo, Byunggil / Aganezov, Sergey / Goretsky, Anton / Donmez, Ataberk / Lansdon, Lisa A / Rodriguez, Isabel / Park, Jimin / Liu, Yuelin / Cui, Xiwen / Gardner, Joshua / McNulty, Brandy / Sacco, Samuel / Shetty, Jyoti / Zhao, Yongmei / Tran, Bao / Narzisi, Giuseppe /
    Helland, Adrienne / Cook, Daniel E / Chang, Pi-Chuan / Kolesnikov, Alexey / Carroll, Andrew / Molloy, Erin K / Pushel, Irina / Guest, Erin / Pastinen, Tomi / Shafin, Kishwar / Miga, Karen H / Malikic, Salem / Day, Chi-Ping / Robine, Nicolas / Sahinalp, Cenk / Dean, Michael / Farooqi, Midhat S / Paten, Benedict / Kolmogorov, Mikhail

    medRxiv : the preprint server for health sciences

    2024  

    Abstract: Most current studies rely on short-read sequencing to detect somatic structural variation (SV) in cancer genomes. Long-read sequencing offers the advantage of better mappability and long-range phasing, which results in substantial improvements in ... ...

    Abstract Most current studies rely on short-read sequencing to detect somatic structural variation (SV) in cancer genomes. Long-read sequencing offers the advantage of better mappability and long-range phasing, which results in substantial improvements in germline SV detection. However, current long-read SV detection methods do not generalize well to the analysis of somatic SVs in tumor genomes with complex rearrangements, heterogeneity, and aneuploidy. Here, we present Severus: a method for the accurate detection of different types of somatic SVs using a phased breakpoint graph approach. To benchmark various short- and long-read SV detection methods, we sequenced five tumor/normal cell line pairs with Illumina, Nanopore, and PacBio sequencing platforms; on this benchmark Severus showed the highest F1 scores (harmonic mean of the precision and recall) as compared to long-read and short-read methods. We then applied Severus to three clinical cases of pediatric cancer, demonstrating concordance with known genetic findings as well as revealing clinically relevant cryptic rearrangements missed by standard genomic panels.
    Language English
    Publishing date 2024-03-26
    Publishing country United States
    Document type Preprint
    DOI 10.1101/2024.03.22.24304756
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Data Centric Molecular Analysis and Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools.

    Cetin-Atalay, Rengul / Kahraman, Deniz Cansen / Nalbat, Esra / Rifaioglu, Ahmet Sureyya / Atakan, Ahmet / Donmez, Ataberk / Atas, Heval / Atalay, M Volkan / Acar, Aybar C / Doğan, Tunca

    Journal of gastrointestinal cancer

    2021  Volume 52, Issue 4, Page(s) 1266–1276

    Abstract: Purpose: Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artificial ...

    Abstract Purpose: Computational approaches have been used at different stages of drug development with the purpose of decreasing the time and cost of conventional experimental procedures. Lately, techniques mainly developed and applied in the field of artificial intelligence (AI), have been transferred to different application domains such as biomedicine.
    Methods: In this study, we conducted an investigative analysis via data-driven evaluation of potential hepatocellular carcinoma (HCC) therapeutics in the context of AI-assisted drug discovery/repurposing. First, we discussed basic concepts, computational approaches, databases, modeling approaches, and featurization techniques in drug discovery/repurposing. In the analysis part, we automatically integrated HCC-related biological entities such as genes/proteins, pathways, phenotypes, drugs/compounds, and other diseases with similar implications, and represented these heterogeneous relationships via a knowledge graph using the CROssBAR system.
    Results: Following the system-level evaluation and selection of critical genes/proteins and pathways to target, our deep learning-based drug/compound-target protein interaction predictors DEEPScreen and MDeePred have been employed for predicting new bioactive drugs and compounds for these critical targets. Finally, we embedded ligands of selected HCC-associated proteins which had a significant enrichment with the CROssBAR system into a 2-D space to identify and repurpose small molecule inhibitors as potential drug candidates based on their molecular similarities to known HCC drugs.
    Conclusions: We expect that these series of data-driven analyses can be used as a roadmap to propose early-stage potential inhibitors (from database-scale sets of compounds) to both HCC and other complex diseases, which may subsequently be analyzed with more targeted in silico and experimental approaches.
    MeSH term(s) Antineoplastic Agents/pharmacology ; Artificial Intelligence ; Carcinoma, Hepatocellular/drug therapy ; Carcinoma, Hepatocellular/pathology ; Computational Biology ; Drug Development/methods ; Humans ; Liver Neoplasms/drug therapy ; Liver Neoplasms/pathology ; Molecular Targeted Therapy
    Chemical Substances Antineoplastic Agents
    Language English
    Publishing date 2021-12-15
    Publishing country United States
    Document type Journal Article ; Review
    ZDB-ID 2452514-5
    ISSN 1941-6636 ; 1559-0739 ; 1941-6628 ; 1537-3649
    ISSN (online) 1941-6636 ; 1559-0739
    ISSN 1941-6628 ; 1537-3649
    DOI 10.1007/s12029-021-00768-x
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2184: Show issues Location:
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    ab Jg. 2022: Lesesaal (EG)

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  5. Article: iBioProVis: interactive visualization and analysis of compound bioactivity space

    Donmez, Ataberk / Rifaioglu, Ahmet Sureyya / Acar, Aybar / Dogan, Tunca / Cetin-Atalay, Rengul / Atalay, Volkan

    Bioinformatics

    Abstract: SUMMARY: iBioProVis is an interactive tool for visual analysis of the compound bioactivity space in the context of target proteins, drugs and drug candidate compounds. iBioProVis tool takes target protein identifiers and, optionally, compound SMILES as ... ...

    Abstract SUMMARY: iBioProVis is an interactive tool for visual analysis of the compound bioactivity space in the context of target proteins, drugs and drug candidate compounds. iBioProVis tool takes target protein identifiers and, optionally, compound SMILES as input, and uses the state-of-the-art non-linear dimensionality reduction method t-Distributed Stochastic Neighbor Embedding (t-SNE) to plot the distribution of compounds embedded in a 2D map, based on the similarity of structural properties of compounds and in the context of compounds' cognate targets. Similar compounds, which are embedded to proximate points on the 2D map, may bind the same or similar target proteins. Thus, iBioProVis can be used to easily observe the structural distribution of one or two target proteins' known ligands on the 2D compound space, and to infer new binders to the same protein, or to infer new potential target(s) for a compound of interest, based on this distribution. Principal component analysis (PCA) projection of the input compounds is also provided, Hence the user can interactively observe the same compound or a group of selected compounds which is projected by both PCA and embedded by t-SNE. iBioProVis also provides detailed information about drugs and drug candidate compounds through cross-references to widely used and well-known databases, in the form of linked table views. Two use-case studies were demonstrated, one being on angiotensin-converting enzyme 2 (ACE2) protein which is Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Spike protein receptor. ACE2 binding compounds and seven antiviral drugs were closely embedded in which two of them have been under clinical trial for Coronavirus disease 19 (COVID-19). AVAILABILITY AND IMPLEMENTATION: iBioProVis and its carefully filtered dataset are available at https://ibpv.kansil.org/ for public use. CONTACT: vatalay@metu.edu.tr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #260277
    Database COVID19

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  6. Article ; Online: iBioProVis

    Donmez, Ataberk / Rifaioglu, Ahmet Sureyya / Acar, Aybar / Doğan, Tunca / Cetin-Atalay, Rengul / Atalay, Volkan

    Bioinformatics

    interactive visualization and analysis of compound bioactivity space

    2020  Volume 36, Issue 14, Page(s) 4227–4230

    Abstract: Abstract Summary iBioProVis is an interactive tool for visual analysis of the compound bioactivity space in the context of target proteins, drugs and drug candidate compounds. iBioProVis tool takes target protein identifiers and, optionally, compound ... ...

    Abstract Abstract Summary iBioProVis is an interactive tool for visual analysis of the compound bioactivity space in the context of target proteins, drugs and drug candidate compounds. iBioProVis tool takes target protein identifiers and, optionally, compound SMILES as input, and uses the state-of-the-art non-linear dimensionality reduction method t-Distributed Stochastic Neighbor Embedding (t-SNE) to plot the distribution of compounds embedded in a 2D map, based on the similarity of structural properties of compounds and in the context of compounds’ cognate targets. Similar compounds, which are embedded to proximate points on the 2D map, may bind the same or similar target proteins. Thus, iBioProVis can be used to easily observe the structural distribution of one or two target proteins’ known ligands on the 2D compound space, and to infer new binders to the same protein, or to infer new potential target(s) for a compound of interest, based on this distribution. Principal component analysis (PCA) projection of the input compounds is also provided, Hence the user can interactively observe the same compound or a group of selected compounds which is projected by both PCA and embedded by t-SNE. iBioProVis also provides detailed information about drugs and drug candidate compounds through cross-references to widely used and well-known databases, in the form of linked table views. Two use-case studies were demonstrated, one being on angiotensin-converting enzyme 2 (ACE2) protein which is Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Spike protein receptor. ACE2 binding compounds and seven antiviral drugs were closely embedded in which two of them have been under clinical trial for Coronavirus disease 19 (COVID-19). Availability and implementation iBioProVis and its carefully filtered dataset are available at https://ibpv.kansil.org/ for public use. Contact vatalay@metu.edu.tr Supplementary information Supplementary data are available at Bioinformatics online.
    Keywords Statistics and Probability ; Computational Theory and Mathematics ; Biochemistry ; Molecular Biology ; Computational Mathematics ; Computer Science Applications ; covid19
    Language English
    Publisher Oxford University Press (OUP)
    Publishing country uk
    Document type Article ; Online
    ZDB-ID 1422668-6
    ISSN 1367-4803
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btaa496
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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    ab Jg. 2022: Lesesaal (EG)

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