Motivation: Multi-omics data in molecular biology has accumulated rapidly over the years. Such data contains valuable information for research in medicine and drug discovery. Unfortunately, data-driven research in medicine and drug discovery is challenging for a majority of small research labs due to the large volume of data and the complexity of analysis pipeline.
Results: We present BioVLAB-Cancer-Pharmacogenomics, a bioinformatics system that facilitates analysis of multi-omics data from breast cancer to analyze and investigate intratumor heterogeneity and pharmacogenomics on Amazon Web Services. Our system takes multi-omics data as input to perform tumor heterogeneity analysis in terms of TCGA data and deconvolve-and-match the tumor gene expression to cell line data in CCLE using DNA methylation profiles. We believe that our system can help small research labs perform analysis of tumor multi-omics without worrying about computational infrastructure and maintenance of databases and tools.
Availability And Implementation: http://biohealth.snu.ac.kr/software/biovlab_cancer_pharmacogenomics.
Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btab478 | DOI Listing |
Nat Commun
December 2024
Laboratory of Cellular Biophysics, The Rockefeller University, New York, NY, USA.
Fibrolamellar Hepatocellular Carcinoma (FLC) is a rare liver cancer characterized by a fusion oncokinase of the genes DNAJB1 and PRKACA, the catalytic subunit of protein kinase A (PKA). A few FLC-like tumors have been reported showing other alterations involving PKA. To better understand FLC pathogenesis and the relationships among FLC, FLC-like, and other liver tumors, we performed a massive multi-omics analysis.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Medicine, Institute of Biomedicine, University of Eastern Finland, Yliopistonranta 1, PO Box 1627, 70211 Kuopio, Finland.
The selection of biomarker panels in omics data, challenged by numerous molecular features and limited samples, often requires the use of machine learning methods paired with wrapper feature selection techniques, like genetic algorithms. They test various feature sets-potential biomarker solutions-to fine-tune a machine learning model's performance for supervised tasks, such as classifying cancer subtypes. This optimization process is undertaken using validation sets to evaluate and identify the most effective feature combinations.
View Article and Find Full Text PDFFront Bioinform
December 2024
Department of Immunology and Molecular Biology, College of Health Sciences, School of Biomedical Sciences, Makerere University, Kampala, Uganda.
World J Surg Oncol
December 2024
Chongqing Cancer Multiomics Big Data Application Engineering Research Center, Chongqing University Cancer Hospital, Chongqing, 400030, China.
Objective: Postoperative venous thromboembolism (VTE) is a potentially life-threatening complication. This study aimed to develop a predictive model to identify independent risk factors and estimate the likelihood of VTE in patients undergoing surgery for cervical cancer.
Methods: We conducted a retrospective cohort study involving 1,174 patients who underwent surgery for cervical carcinoma between 2019 and 2022.
Hum Genomics
December 2024
Department of Clinical Neurosciences, 'Carol Davila' University of Medicine and Pharmacy, Bucharest, Romania.
Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models.
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