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  • 1
    Online Resource
    Online Resource
    MERIDA: a novel Boolean logic-based integer linear program for personalized cancer therapy
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 37, No. 21 ( 2021-11-05), p. 3881-3888
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 21 ( 2021-11-05), p. 3881-3888
    Abstract: A major goal of personalized medicine in oncology is the optimization of treatment strategies given measurements of the genetic and molecular profiles of cancer cells. To further our knowledge on drug sensitivity, machine learning techniques are commonly applied to cancer cell line panels. Results We present a novel integer linear programming formulation, called MEthod for Rule Identification with multi-omics DAta (MERIDA), for predicting the drug sensitivity of cancer cells. The method represents a modified version of the LOBICO method and yields easily interpretable models amenable to a Boolean logic-based interpretation. Since the proposed altered logical rules lead to an enormous acceleration of the running times of MERIDA compared to LOBICO, we cannot only consider larger input feature sets integrated from genetic and molecular omics data but also build more comprehensive models that mirror the complexity of cancer initiation and progression. Moreover, we enable the inclusion of a priori knowledge that can either stem from biomarker databases or can also be newly acquired knowledge gathered iteratively by previous runs of MERIDA. Our results show that this approach does not only lead to an improved predictive performance but also identifies a variety of putative sensitivity and resistance biomarkers. We also compare our approach to state-of-the-art machine learning methods and demonstrate the superior performance of our method. Hence, MERIDA has great potential to deepen our understanding of the molecular mechanisms causing drug sensitivity or resistance. Availability and implementation The corresponding code is available on github (https://github.com/unisb-bioinf/MERIDA.git). Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btab546
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 2
    Online Resource
    Online Resource
    ClinOmicsTrailbc: a visual analytics tool for breast cancer treatment stratification
    Oxford University Press (OUP) ; 2019
    In:  Bioinformatics Vol. 35, No. 24 ( 2019-12-15), p. 5171-5181
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 35, No. 24 ( 2019-12-15), p. 5171-5181
    Abstract: Breast cancer is the second leading cause of cancer death among women. Tumors, even of the same histopathological subtype, exhibit a high genotypic diversity that impedes therapy stratification and that hence must be accounted for in the treatment decision-making process. Results Here, we present ClinOmicsTrailbc, a comprehensive visual analytics tool for breast cancer decision support that provides a holistic assessment of standard-of-care targeted drugs, candidates for drug repositioning and immunotherapeutic approaches. To this end, our tool analyzes and visualizes clinical markers and (epi-)genomics and transcriptomics datasets to identify and evaluate the tumor’s main driver mutations, the tumor mutational burden, activity patterns of core cancer-relevant pathways, drug-specific biomarkers, the status of molecular drug targets and pharmacogenomic influences. In order to demonstrate ClinOmicsTrailbc’s rich functionality, we present three case studies highlighting various ways in which ClinOmicsTrailbc can support breast cancer precision medicine. ClinOmicsTrailbc is a powerful integrated visual analytics tool for breast cancer research in general and for therapy stratification in particular, assisting oncologists to find the best possible treatment options for their breast cancer patients based on actionable, evidence-based results. Availability and implementation ClinOmicsTrailbc can be freely accessed at https://clinomicstrail.bioinf.uni-sb.de. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btz302
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2019
    detail.hit.zdb_id: 1468345-3
    SSG: 12
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  • 3
    Online Resource
    Online Resource
    GeneTrail 3: advanced high-throughput enrichment analysis
    Oxford University Press (OUP) ; 2020
    In:  Nucleic Acids Research Vol. 48, No. W1 ( 2020-07-02), p. W515-W520
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 48, No. W1 ( 2020-07-02), p. W515-W520
    Abstract: We present GeneTrail 3, a major extension of our web service GeneTrail that offers rich functionality for the identification, analysis, and visualization of deregulated biological processes. Our web service provides a comprehensive collection of biological processes and signaling pathways for 12 model organisms that can be analyzed with a powerful framework for enrichment and network analysis of transcriptomic, miRNomic, proteomic, and genomic data sets. Moreover, GeneTrail offers novel workflows for the analysis of epigenetic marks, time series experiments, and single cell data. We demonstrate the capabilities of our web service in two case-studies, which highlight that GeneTrail is well equipped for uncovering complex molecular mechanisms. GeneTrail is freely accessible at: http://genetrail.bioinf.uni-sb.de.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkaa306
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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  • 4
    Online Resource
    Online Resource
    DataSheet2.xlsx
    Frontiers Media SA ; 2021
    In:  Frontiers in Molecular Biosciences Vol. 8 ( 2021-9-16)
    Details
    In: Frontiers in Molecular Biosciences, Frontiers Media SA, Vol. 8 ( 2021-9-16)
    Abstract: Experimental high-throughput techniques, like next-generation sequencing or microarrays, are nowadays routinely applied to create detailed molecular profiles of cells. In general, these platforms generate high-dimensional and noisy data sets. For their analysis, powerful bioinformatics tools are required to gain novel insights into the biological processes under investigation. Here, we present an overview of the GeneTrail tool suite that offers rich functionality for the analysis and visualization of (epi-)genomic, transcriptomic, miRNomic, and proteomic profiles. Our framework enables the analysis of standard bulk, time-series, and single-cell measurements and includes various state-of-the-art methods to identify potentially deregulated biological processes and to detect driving factors within those deregulated processes. We highlight the capabilities of our web service with an analysis of a single-cell COVID-19 data set that demonstrates its potential for uncovering complex molecular mechanisms. GeneTrail can be accessed freely and without login requirements at http://genetrail.bioinf.uni-sb.de .
    Type of Medium: Online Resource
    ISSN: 2296-889X
    URL: Article
    URL: Article
    URL: Article
    DOI: 10.3389/fmolb.2021.716544
    DOI: 10.3389/fmolb.2021.716544.s001
    DOI: 10.3389/fmolb.2021.716544.s002
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2814330-9
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  • 5
    Online Resource
    Online Resource
    Simultaneous regression and classification for drug sensitivity prediction using an advanced random forest method
    Springer Science and Business Media LLC ; 2022
    In:  Scientific Reports Vol. 12, No. 1 ( 2022-08-05)
    Details
    In: Scientific Reports, Springer Science and Business Media LLC, Vol. 12, No. 1 ( 2022-08-05)
    Abstract: Machine learning methods trained on cancer cell line panels are intensively studied for the prediction of optimal anti-cancer therapies. While classification approaches distinguish effective from ineffective drugs, regression approaches aim to quantify the degree of drug effectiveness. However, the high specificity of most anti-cancer drugs induces a skewed distribution of drug response values in favor of the more drug-resistant cell lines, negatively affecting the classification performance (class imbalance) and regression performance (regression imbalance) for the sensitive cell lines. Here, we present a novel approach called SimultAneoUs Regression and classificatiON Random Forests (SAURON-RF) based on the idea of performing a joint regression and classification analysis. We demonstrate that SAURON-RF improves the classification and regression performance for the sensitive cell lines at the expense of a moderate loss for the resistant ones. Furthermore, our results show that simultaneous classification and regression can be superior to regression or classification alone.
    Type of Medium: Online Resource
    ISSN: 2045-2322
    URL: Article
    DOI: 10.1038/s41598-022-17609-x
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2615211-3
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  • 6
    Online Resource
    Online Resource
    Quantitative and time-resolved miRNA pattern of early human T cell activation
    Oxford University Press (OUP) ; 2020
    In:  Nucleic Acids Research Vol. 48, No. 18 ( 2020-10-09), p. 10164-10183
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 48, No. 18 ( 2020-10-09), p. 10164-10183
    Abstract: T cells are central to the immune response against various pathogens and cancer cells. Complex networks of transcriptional and post-transcriptional regulators, including microRNAs (miRNAs), coordinate the T cell activation process. Available miRNA datasets, however, do not sufficiently dissolve the dynamic changes of miRNA controlled networks upon T cell activation. Here, we established a quantitative and time-resolved expression pattern for the entire miRNome over a period of 24 h upon human T-cell activation. Based on our time-resolved datasets, we identified central miRNAs and specified common miRNA expression profiles. We found the most prominent quantitative expression changes for miR-155-5p with a range from initially 40 molecules/cell to 1600 molecules/cell upon T-cell activation. We established a comprehensive dynamic regulatory network of both the up- and downstream regulation of miR-155. Upstream, we highlight IRF4 and its complexes with SPI1 and BATF as central for the transcriptional regulation of miR-155. Downstream of miR-155-5p, we verified 17 of its target genes by the time-resolved data recorded after T cell activation. Our data provide comprehensive insights into the range of stimulus induced miRNA abundance changes and lay the ground to identify efficient points of intervention for modifying the T cell response.
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkaa788
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 1472175-2
    SSG: 12
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    Online Resource
  • 7
    Online Resource
    Online Resource
    Genetic alterations of the SUMO isopeptidase SENP6 drive lymphomagenesis and genetic instability in diffuse large B-cell lymphoma
    Springer Science and Business Media LLC ; 2022
    In:  Nature Communications Vol. 13, No. 1 ( 2022-01-12)
    Details
    In: Nature Communications, Springer Science and Business Media LLC, Vol. 13, No. 1 ( 2022-01-12)
    Abstract: SUMOylation is a post-translational modification of proteins that regulates these proteins’ localization, turnover or function. Aberrant SUMOylation is frequently found in cancers but its origin remains elusive. Using a genome-wide transposon mutagenesis screen in a MYC-driven B-cell lymphoma model, we here identify the SUMO isopeptidase (or deconjugase) SENP6 as a tumor suppressor that links unrestricted SUMOylation to tumor development and progression. Notably, SENP6 is recurrently deleted in human lymphomas and SENP6 deficiency results in unrestricted SUMOylation. Mechanistically, SENP6 loss triggers release of DNA repair- and genome maintenance-associated protein complexes from chromatin thereby impairing DNA repair in response to DNA damages and ultimately promoting genomic instability. In line with this hypothesis, SENP6 deficiency drives synthetic lethality to Poly-ADP-Ribose-Polymerase (PARP) inhibition. Together, our results link SENP6 loss to defective genome maintenance and reveal the potential therapeutic application of PARP inhibitors in B-cell lymphoma.
    Type of Medium: Online Resource
    ISSN: 2041-1723
    URL: Article
    DOI: 10.1038/s41467-021-27704-8
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 2553671-0
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  • 8
    Online Resource
    Online Resource
    Glycosylation patterns of human chorionic gonadotropin revealed by liquid chromatography‐mass spectrometry and bioinformatics
    Wiley ; 2006
    In:  ELECTROPHORESIS Vol. 27, No. 13 ( 2006-07), p. 2734-2746
    Details
    In: ELECTROPHORESIS, Wiley, Vol. 27, No. 13 ( 2006-07), p. 2734-2746
    Abstract: Due to their extensive structural heterogeneity, the elucidation of glycosylation patterns in glycoproteins such as the subunits of human chorionic gonadotropin (hCG), hCG‐α, and hCG‐β, remains one of the most challenging problems in the proteomic analysis of post‐translational modifications. In consequence, glycosylation is usually studied after decomposition of the intact proteins to the proteolytic peptide level. However, by this approach all information about the combination of the different glycopeptides in the intact protein is lost. In this study we have, therefore, attempted to combine the results of glycan identification after tryptic digestion with molecular mass measurements on the native starting material of the new first WHO Reference Reagents (RR) for hCG‐α (99/720) and hCG‐β (99/650). Despite the extremely high number of possible combinations of the glycans identified in the tryptic peptides by HPLC‐MS ( 〉 1000 for hCG‐α and 〉 10 000 for hCG‐β), the mass spectra of intact hCG‐α and hCG‐β revealed only a limited number of glycoforms present in hCG preparations from pools of pregnancy urines. Peak annotations for hCG‐α were performed with the help of a bioinformatic algorithm that generated a database containing all possible modifications of the proteins, including modifications possibly introduced during sample preparation such as oxidation or truncation, for subsequent searches for combinations fitting the mass difference between the polypeptide backbone and the measured molecular masses. Fourteen different glycoforms of hCG‐α, containing biantennary, partly sialylized hybrid‐type glycans, including methionine‐oxidized and N ‐terminally truncated forms, were identified. Mass spectra of high quality were also obtained for hCG‐β, however, a database search mass accuracy of ±5 Da was insufficient to unambiguously assign the possible combinations of post‐translational modifications. In summary, mass spectrometric fingerprints of intact molecules were shown to be highly useful for the characterization of glycosylation patterns of different hCG preparations such as the new first WHO RR for immunoassays and could be the first step in establishing biophysical reference methods for hCG and related molecules.
    Type of Medium: Online Resource
    ISSN: 0173-0835 , 1522-2683
    URL: Issue
    URL: Article
    DOI: 10.1002/elps.v27:13
    DOI: 10.1002/elps.200600022
    Language: English
    Publisher: Wiley
    Publication Date: 2006
    detail.hit.zdb_id: 1475486-1
    SSG: 12
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  • 9
    Online Resource
    Online Resource
    Network-based interactive navigation and analysis of large biological datasets
    Walter de Gruyter GmbH ; 2015
    In:  it - Information Technology Vol. 57, No. 1 ( 2015-2-28), p. 37-48
    Details
    In: it - Information Technology, Walter de Gruyter GmbH, Vol. 57, No. 1 ( 2015-2-28), p. 37-48
    Abstract: Over the last decade, advances in high-throughput technologies have resulted in a flood of new biological data. Here, individual samples can extend up into terabyte size. While potential applications are broad, ranging from biotechnology to medical applications, the analysis of these datasets poses massive challenges. In order to make use of the produced terabytes of data, these datasets need to be integrated, need to be mapped onto existing biological knowledge, and need to be explored by experts. We present UniPAX and BiNA, a scalable system for the integration and analysis of high-throughput data (genomics, transcriptomics, proteomics, and metabolomics) in a network context. A central data warehouse holds the core dataset. A flexible middleware can execute custom queries on this dataset and communicate with our visual analytics tool BiNA, the Biological Network Analyzer. We demonstrate how the combination of these tools permits an efficient analysis of large-scale datasets for medical applications.
    Type of Medium: Online Resource
    ISSN: 1611-2776 , 2196-7032
    URL: Article
    DOI: 10.1515/itit-2014-1076
    RVK:
    SQ 1100
    RVK:
    SA 5445
    Language: English
    Publisher: Walter de Gruyter GmbH
    Publication Date: 2015
    detail.hit.zdb_id: 2102301-3
    detail.hit.zdb_id: 144419-0
    detail.hit.zdb_id: 165820-7
    detail.hit.zdb_id: 2028598-X
    detail.hit.zdb_id: 6242-X
    detail.hit.zdb_id: 1146417-3
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  • 10
    Online Resource
    Online Resource
    Simulating synthetic polymer chains in parallel
    Elsevier BV ; 2000
    In:  Future Generation Computer Systems Vol. 16, No. 5 ( 2000-3), p. 513-522
    Details
    In: Future Generation Computer Systems, Elsevier BV, Vol. 16, No. 5 ( 2000-3), p. 513-522
    Type of Medium: Online Resource
    ISSN: 0167-739X
    URL: Article
    DOI: 10.1016/S0167-739X(99)00138-7
    RVK:
    SA 5000
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2000
    detail.hit.zdb_id: 48781-8
    detail.hit.zdb_id: 2020551-X
    detail.hit.zdb_id: 1100390-X
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