Journal articles on the topic 'Translational and applied bioinformatics'
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Shen, Bairong, Hong-Bin Shen, Tianhai Tian, Qiang Lü, and Guang Hu. "Translational Bioinformatics and Computational Systems Medicine." Computational and Mathematical Methods in Medicine 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/375641.
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Smaïl-Tabbone, Malika, and Bastien Rance. "Contributions from the 2018 Literature on Bioinformatics and Translational Informatics." Yearbook of Medical Informatics 28, no. 01 (August 2019): 190–93. http://dx.doi.org/10.1055/s-0039-1677945.
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Objectives: To summarize recent research and select the best papers published in 2018 in the field of Bioinformatics and Translational Informatics (BTI) for the corresponding section of the International Medical Informatics Association (IMIA) Yearbook. Methods: A literature review was performed for retrieving from PubMed papers indexed with keywords and free terms related to BTI. Independent review allowed the two section editors to select a list of 14 candidate best papers which were subsequently peer-reviewed. A final consensus meeting gathering the whole IMIA Yearbook editorial committee was organized to finally decide on the selection of the best papers. Results: Among the 636 retrieved papers published in 2018 in the various subareas of BTI, the review process selected four best papers. The first paper presents a computational method to identify molecular markers for targeted treatment of acute myeloid leukemia using multi-omics data (genome-wide gene expression profiles) and in vitro sensitivity to 160 chemotherapy drugs. The second paper describes a deep neural network approach to predict the survival of patients suffering from glioma on the basis of digitalised pathology images and genomics biomarkers. The authors of the third paper adopt a pan-cancer approach to take benefit of multi-omics data for drug repurposing. The fourth paper presents a graph-based semi-supervised method to accurate phenotype classification applied to ovarian cancer. Conclusions: Thanks to the normalization of open data and open science practices, research in BTI continues to develop and mature. Noteworthy achievements are sophisticated applications of leading edge machine-learning methods dedicated to personalized medicine.
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Preisig, Carol L. "A systematic approach to companion diagnostic development: A case study for omacetaxine (OM) for the treatment of chronic myelogenous leukemia (CML)." Journal of Clinical Oncology 30, no. 30_suppl (October 20, 2012): 100. http://dx.doi.org/10.1200/jco.2012.30.30_suppl.100.
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100 Background: In the age of genomically informed medicine, therapeutic development carries with it the imperative to employ genomics in patient selection. Physicians expect genomics-based methods to identify treatments likely to be effective, and identify anomalies likely to cause adverse response for a given patient. Companion diagnostics should support such rule-in and rule-out decisions. We demonstrate a systematic approach to companion diagnostics that leverages new methods in translational bioinformatics and clinical economics. CML therapies have been at the forefront of genomically informed medicine. Early TKI inhibitors targeting the BCR-ABL fusion protein are highly effective. With time, however, they induce resistance-creating mutations in many patients. Omacetixine, a translation inhibitor, was expected to help this CML patient subpopulation which has few therapeutic alternatives. We use this well-characterized drug and publicly available data to demonstrate a prospective approach to companion diagnostics. Methods: We used translational bioinformatics, incorporating pathway, cell line, and patient data to identify biologically plausible biomarkers from which alternative companion diagnostic paths were constructed. These alternatives were analyzed using a modified version of the MIT Stratified Medicine Model to assess the clinical economics of each path. Results: From a systematic look at the biology of the disease, the unique mechanism of action of OM and the clinical need, we identified 3 alternative companion diagnostics for OM. Economic analyses quantified the trade-offs of targeting different subpopulations for the indication, clarifying the impact of biomarker selection based on clinical need or biology. Other analyses have shown that eNPV can be halved or doubled based on strategy choice. Conclusions: Combining applied translational bioinformatics and stratified medicine economics provides an effective approach to companion diagnostic selection. This approach can reduce drug-development cost and clinical risk while providing physicians with better genomics-based methods for clinical decision-making.
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Katoh, Masuko, and Masaru Katoh. "Bioinformatics for Cancer Management in the Post-Genome Era." Technology in Cancer Research & Treatment 5, no. 2 (April 2006): 169–75. http://dx.doi.org/10.1177/153303460600500208.
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Human cancer is caused by multiple factors, such as genetic predisposition, chronic persistent inflammation, environmental factors, life style, and aging. Dysregulated proliferation, dysregulated adhesion, resistance to apoptosis, resistance to senescence, and resistance to anti-cancer drugs are features of cancer cells. Accumulation of multiple epigenetic changes and genetic alterations of cancer-associated genes during multi-stage carcinogenesis results in more malignant phenotypes. Post-genome science is characterized by omics data related to genome, transcriptome, proteome, metabolome, interactome, and epigenome as well as by high-throughput technology, such as whole-genome tiling oligonucleotide array, array CGH with 32,433 overlapping BAC clones, transcriptome microarray, mass spectrometry, tissue-based expression array, and cell-based transfection array. Benchtop oncology supplies Desktop oncology with large amounts of omics data produced by high-throughput technology. Desktop oncology establishes knowledge on cancer-related biomarkers, such as predisposition markers, diagnostic markers, prognostic markers, and therapeutic markers, by using bioinformatics and human intelligence of experts for data mining and text mining. Bedside oncology applies the knowledge established by Desktop oncology to determine therapeutics for cancer patients. Antibody drugs (Trastuzumab/Herceptin, Cetuximab/Erbitux, Bevacizumab/Avastin, et cetera), small molecule inhibitors for tyrosine kinases (Gefitinib/Iressa, Erlotinib/Tarceva, Imatinib/Gleevec, et cetera), conventional cytotoxic drugs, and anti-hormonal drugs are used for cancer chemotherapy. Biomarker monitoring contributes to therapeutic optional choice and drug dosage determination for cancer patients. Knowledge on biomarkers is feedforwarded from desktop to bedside in the translational research, and then biomarker monitoring is feedbacked from bedside to desktop in the reverse translational research. Desktop oncology is indispensable for cancer research in the post-genome era. Combination of genetic screening for cancer predisposition in the general population and precise selection of therapeutic options during cancer management could contribute to the realization of personalized prevention and to dramatically improve the prognosis of cancer patients in the future.
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Karollus, Alexander, Žiga Avsec, and Julien Gagneur. "Predicting mean ribosome load for 5’UTR of any length using deep learning." PLOS Computational Biology 17, no. 5 (May 10, 2021): e1008982. http://dx.doi.org/10.1371/journal.pcbi.1008982.
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The 5’ untranslated region plays a key role in regulating mRNA translation and consequently protein abundance. Therefore, accurate modeling of 5’UTR regulatory sequences shall provide insights into translational control mechanisms and help interpret genetic variants. Recently, a model was trained on a massively parallel reporter assay to predict mean ribosome load (MRL)—a proxy for translation rate—directly from 5’UTR sequence with a high degree of accuracy. However, this model is restricted to sequence lengths investigated in the reporter assay and therefore cannot be applied to the majority of human sequences without a substantial loss of information. Here, we introduced frame pooling, a novel neural network operation that enabled the development of an MRL prediction model for 5’UTRs of any length. Our model shows state-of-the-art performance on fixed length randomized sequences, while offering better generalization performance on longer sequences and on a variety of translation-related genome-wide datasets. Variant interpretation is demonstrated on a 5’UTR variant of the gene HBB associated with beta-thalassemia. Frame pooling could find applications in other bioinformatics predictive tasks. Moreover, our model, released open source, could help pinpoint pathogenic genetic variants.
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Miao, Chenyun, Yun Chen, Xiaojie Fang, Ying Zhao, Ruye Wang, and Qin Zhang. "Identification of the shared gene signatures and pathways between polycystic ovary syndrome and endometrial cancer: An omics data based combined approach." PLOS ONE 17, no. 7 (July 13, 2022): e0271380. http://dx.doi.org/10.1371/journal.pone.0271380.
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Objective Polycystic ovary syndrome (PCOS) is a common endocrine disorder with high incidence. Recently it has been implicated as a significant risk factor for endometrial cancer (EC). Our study aims to detect shared gene signatures and biological mechanism between PCOS and EC by bioinformatics analysis. Methods Bioinformatics analysis based on GEO database consisted of data integration, network construction and functional enrichment analysis was applied. In addition, the pharmacological methodology and molecular docking was also performed. Results Totally 10 hub common genes, MRPL16, MRPL22, MRPS11, RPL26L1, ESR1, JUN, UBE2I, MRPL17, RPL37A, GTF2H3, were considered as shared gene signatures for EC and PCOS. The GO and KEGG pathway analysis of these hub genes showed that “mitochondrial translational elongation”, “ribosomal subunit”, “structural constituent of ribosome” and “ribosome” were highly correlated. Besides, associated transcription factors (TFs) and miRNAs network were constructed. We identified candidate drug molecules including fenofibrate, cinnarizine, propanil, fenthion, clindamycin, chloramphenicol, demeclocycline, hydrochloride, azacitidine, chrysene and artenimol according to these hub genes. Molecular docking analysis verified a good binding interaction of fenofibrate against available targets (JUN, ESR1, UBE2I). Conclusion Gene signatures and regulatory biological pathways were identified through bioinformatics analysis. Moreover, the molecular mechanisms of these signatures were explored and potential drug molecules associated with PCOS and EC were screened out.
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Valenta, Annette L., Eta S. Berner, Suzanne A. Boren, Gloria J. Deckard, Christina Eldredge, Douglas B. Fridsma, Cynthia Gadd, et al. "AMIA Board White Paper: AMIA 2017 core competencies for applied health informatics education at the master’s degree level." Journal of the American Medical Informatics Association 25, no. 12 (October 26, 2018): 1657–68. http://dx.doi.org/10.1093/jamia/ocy132.
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Abstract This White Paper presents the foundational domains with examples of key aspects of competencies (knowledge, skills, and attitudes) that are intended for curriculum development and accreditation quality assessment for graduate (master’s level) education in applied health informatics. Through a deliberative process, the AMIA Accreditation Committee refined the work of a task force of the Health Informatics Accreditation Council, establishing 10 foundational domains with accompanying example statements of knowledge, skills, and attitudes that are components of competencies by which graduates from applied health informatics programs can be assessed for competence at the time of graduation. The AMIA Accreditation Committee developed the domains for application across all the subdisciplines represented by AMIA, ranging from translational bioinformatics to clinical and public health informatics, spanning the spectrum from molecular to population levels of health and biomedicine. This document will be periodically updated, as part of the responsibility of the AMIA Accreditation Committee, through continued study, education, and surveys of market trends.
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Klein, Joshua, Luis Carvalho, and Joseph Zaia. "Application of network smoothing to glycan LC-MS profiling." Bioinformatics 34, no. 20 (May 22, 2018): 3511–18. http://dx.doi.org/10.1093/bioinformatics/bty397.
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Abstract Motivation Glycosylation is one of the most heterogeneous and complex protein post-translational modifications. Liquid chromatography coupled mass spectrometry (LC-MS) is a common high throughput method for analyzing complex biological samples. Accurate study of glycans require high resolution mass spectrometry. Mass spectrometry data contains intricate sub-structures that encode mass and abundance, requiring several transformations before it can be used to identify biological molecules, requiring automated tools to analyze samples in a high throughput setting. Existing tools for interpreting the resulting data do not take into account related glycans when evaluating individual observations, limiting their sensitivity. Results We developed an algorithm for assigning glycan compositions from LC-MS data by exploring biosynthetic network relationships among glycans. Our algorithm optimizes a set of likelihood scoring functions based on glycan chemical properties but uses network Laplacian regularization and optionally prior information about expected glycan families to smooth the likelihood and thus achieve a consistent and more representative solution. Our method was able to identify as many, or more glycan compositions compared to previous approaches, and demonstrated greater sensitivity with regularization. Our network definition was tailored to N-glycans but the method may be applied to glycomics data from other glycan families like O-glycans or heparan sulfate where the relationships between compositions can be expressed as a graph. Availability and implementation Built Executable http://www.bumc.bu.edu/msr/glycresoft/ and Source Code: https://github.com/BostonUniversityCBMS/glycresoft. Supplementary information Supplementary data are available at Bioinformatics online.
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Sims, A. H. "Bioinformatics and breast cancer: what can high-throughput genomic approaches actually tell us?" Journal of Clinical Pathology 62, no. 10 (January 27, 2009): 879–85. http://dx.doi.org/10.1136/jcp.2008.060376.
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High-throughput genomic technology has rapidly become a major tool for the study of breast cancer. Gene expression profiling has been applied to many areas of research from basic science to translational studies, with the potential to identify new targets for treatment, mechanisms of resistance and to improve on current tools for the analysis of prognosis. However, the sheer scale of the data generated along with the number of different protocols, platforms and analysis methods can make these studies difficult for clinicians to comprehend. Similarly, computational scientists and statisticians that may be called upon to analyse the data generated are often unaware of the processes involved in sample collection or the relevance and impact of genetics and pathological characteristics. There is a pressing need for better understanding of the challenges and limitations of microarray approaches, both in experimental design and data analysis. Holistic, whole-genome approaches are still relatively new and critics have been quick to highlight non-overlapping results from groups testing similar hypotheses. However, it is often subtle differences in the experimental design and technology that underpin the variation between these studies. Rather than indicating that the data are meaningless, this suggests that many findings are real, but highly context dependent. This review explores both the current state and potential of bioinformatics to bring meaning to high-throughput genomic approaches in the understanding of breast cancer.
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Fan, Jason, Xuan Cindy Li, Mark Crovella, and Mark D. M. Leiserson. "Matrix (factorization) reloaded: flexible methods for imputing genetic interactions with cross-species and side information." Bioinformatics 36, Supplement_2 (December 2020): i866—i874. http://dx.doi.org/10.1093/bioinformatics/btaa818.
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Abstract Motivation Mapping genetic interactions (GIs) can reveal important insights into cellular function and has potential translational applications. There has been great progress in developing high-throughput experimental systems for measuring GIs (e.g. with double knockouts) as well as in defining computational methods for inferring (imputing) unknown interactions. However, existing computational methods for imputation have largely been developed for and applied in baker’s yeast, even as experimental systems have begun to allow measurements in other contexts. Importantly, existing methods face a number of limitations in requiring specific side information and with respect to computational cost. Further, few have addressed how GIs can be imputed when data are scarce. Results In this article, we address these limitations by presenting a new imputation framework, called Extensible Matrix Factorization (EMF). EMF is a framework of composable models that flexibly exploit cross-species information in the form of GI data across multiple species, and arbitrary side information in the form of kernels (e.g. from protein–protein interaction networks). We perform a rigorous set of experiments on these models in matched GI datasets from baker’s and fission yeast. These include the first such experiments on genome-scale GI datasets in multiple species in the same study. We find that EMF models that exploit side and cross-species information improve imputation, especially in data-scarce settings. Further, we show that EMF outperforms the state-of-the-art deep learning method, even when using strictly less data, and incurs orders of magnitude less computational cost. Availability Implementations of models and experiments are available at: https://github.com/lrgr/EMF. Supplementary information Supplementary data are available at Bioinformatics online.
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Juibari, Aref Doozandeh, Sina Ramezani, and Mohammad Hosein Rezadoust. "Bioinformatics analysis of various signal peptides for periplasmic expression of parathyroid hormone in E.coli." Journal of Medicine and Life 12, no. 2 (April 2019): 184–91. http://dx.doi.org/10.25122/jml-2018-0049.
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Hypoparathyroidism is a rare endocrine disease which is characterized by the deficiency of serum calcium levels. RhPTH is prescribed as a therapy for the management of refractory hypoparathyroidism. The aim of this study is to investigate 32 signal peptides of gram-negative bacterial origin and evaluate their potential for efficient secretion of recombinant human PTH (1–84)In E.coli to obtain higher expression of recombinant PTH in bacterial systems by using this fusion partner. SignalP and ProtParam servers were employed to predict the presence and location of signal peptide cleavage sites in protein sequence and computation of various physical and chemical parameters of protein respectively. Also, SOLpro server was applied for prediction of the protein solubility. Then ProtComp and SecretomeP online servers were employed to determine protein location. The evaluations showed that theoretically two signal peptides Lipopolysaccharide export system protein LptA (lptA) and Periplasmic pH-dependent serine endoprotease DegQ (degQ) are the most appropriate signal peptides examined. Due to the lack of post-translational modification in PTH, its periplasmic expression has preferences. Based on the results of this study, using bioinformatics and reliable servers signal peptides with appropriate secretory potential can be obtained which lead to the highest expression level.
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Mokhtaridoost, Milad, and Mehmet Gönen. "An efficient framework to identify key miRNA–mRNA regulatory modules in cancer." Bioinformatics 36, Supplement_2 (December 2020): i592—i600. http://dx.doi.org/10.1093/bioinformatics/btaa798.
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Abstract Motivation Micro-RNAs (miRNAs) are known as the important components of RNA silencing and post-transcriptional gene regulation, and they interact with messenger RNAs (mRNAs) either by degradation or by translational repression. miRNA alterations have a significant impact on the formation and progression of human cancers. Accordingly, it is important to establish computational methods with high predictive performance to identify cancer-specific miRNA–mRNA regulatory modules. Results We presented a two-step framework to model miRNA–mRNA relationships and identify cancer-specific modules between miRNAs and mRNAs from their matched expression profiles of more than 9000 primary tumors. We first estimated the regulatory matrix between miRNA and mRNA expression profiles by solving multiple linear programming problems. We then formulated a unified regularized factor regression (RFR) model that simultaneously estimates the effective number of modules (i.e. latent factors) and extracts modules by decomposing regulatory matrix into two low-rank matrices. Our RFR model groups correlated miRNAs together and correlated mRNAs together, and also controls sparsity levels of both matrices. These attributes lead to interpretable results with high predictive performance. We applied our method on a very comprehensive data collection by including 32 TCGA cancer types. To find the biological relevance of our approach, we performed functional gene set enrichment and survival analyses. A large portion of the identified modules are significantly enriched in Hallmark, PID and KEGG pathways/gene sets. To validate the identified modules, we also performed literature validation as well as validation using experimentally supported miRTarBase database. Availability and implementation Our implementation of proposed two-step RFR algorithm in R is available at https://github.com/MiladMokhtaridoost/2sRFR together with the scripts that replicate the reported experiments. Supplementary information Supplementary data are available at Bioinformatics online.
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Li, Kaiqiao, Sijie Yao, Zhenyu Zhang, Biwei Cao, Christopher M. Wilson, Denise Kalos, Pei Fen Kuan, Ruoqing Zhu, and Xuefeng Wang. "Efficient gradient boosting for prognostic biomarker discovery." Bioinformatics 38, no. 6 (January 3, 2022): 1631–38. http://dx.doi.org/10.1093/bioinformatics/btab869.
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Abstract Motivation A gradient boosting decision tree (GBDT) is a powerful ensemble machine-learning method that has the potential to accelerate biomarker discovery from high-dimensional molecular data. Recent algorithmic advances, such as extreme gradient boosting (XGB) and light gradient boosting (LGB), have rendered the GBDT training more efficient, scalable and accurate. However, these modern techniques have not yet been widely adopted in discovering biomarkers for censored survival outcomes, which are key clinical outcomes or endpoints in cancer studies. Results In this paper, we present a new R package ‘Xsurv’ as an integrated solution that applies two modern GBDT training frameworks namely, XGB and LGB, for the modeling of right-censored survival outcomes. Based on our simulations, we benchmark the new approaches against traditional methods including the stepwise Cox regression model and the original gradient boosting function implemented in the package ‘gbm’. We also demonstrate the application of Xsurv in analyzing a melanoma methylation dataset. Together, these results suggest that Xsurv is a useful and computationally viable tool for screening a large number of prognostic candidate biomarkers, which may facilitate future translational and clinical research. Availability and implementation ‘Xsurv’ is freely available as an R package at: https://github.com/topycyao/Xsurv. Supplementary information Supplementary data are available at Bioinformatics online.
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Moradi, Atieh, Shuaijian Dai, Emily Oi Ying Wong, Guang Zhu, Fengchao Yu, Hon-Ming Lam, Zhiyong Wang, et al. "Isotopically Dimethyl Labeling-Based Quantitative Proteomic Analysis of Phosphoproteomes of Soybean Cultivars." Biomolecules 11, no. 8 (August 16, 2021): 1218. http://dx.doi.org/10.3390/biom11081218.
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Isotopically dimethyl labeling was applied in a quantitative post-translational modification (PTM) proteomic study of phosphoproteomic changes in the drought responses of two contrasting soybean cultivars. A total of 9457 phosphopeptides were identified subsequently, corresponding to 4571 phosphoprotein groups and 3889 leading phosphoproteins, which contained nine kinase families consisting of 279 kinases. These phosphoproteins contained a total of 8087 phosphosites, 6106 of which were newly identified and constituted 54% of the current soybean phosphosite repository. These phosphosites were converted into the highly conserved kinase docking sites by bioinformatics analysis, which predicted six kinase families that matched with those newly found nine kinase families. The overly post-translationally modified proteins (OPP) occupies 2.1% of these leading phosphoproteins. Most of these OPPs are photoreceptors, mRNA-, histone-, and phospholipid-binding proteins, as well as protein kinase/phosphatases. The subgroup population distribution of phosphoproteins over the number of phosphosites of phosphoproteins follows the exponential decay law, Y = 4.13e−0.098X − 0.04. Out of 218 significantly regulated unique phosphopeptide groups, 188 phosphoproteins were regulated by the drought-tolerant cultivar under the water loss condition. These significantly regulated phosphoproteins (SRP) are mainly enriched in the biological functions of water transport and deprivation, methionine metabolic processes, photosynthesis/light reaction, and response to cadmium ion, osmotic stress, and ABA response. Seventeen and 15 SRPs are protein kinases/phosphatases and transcription factors, respectively. Bioinformatics analysis again revealed that three members of the calcium dependent protein kinase family (CAMK family), GmSRK2I, GmCIPK25, and GmAKINβ1 kinases, constitute a phosphor-relay-mediated signal transduction network, regulating ion channel activities and many nuclear events in this drought-tolerant cultivar, which presumably contributes to the development of the soybean drought tolerance under water deprivation process.
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Zhou, Mengshi, Chunlei Zheng, and Rong Xu. "Combining phenome-driven drug-target interaction prediction with patients’ electronic health records-based clinical corroboration toward drug discovery." Bioinformatics 36, Supplement_1 (July 1, 2020): i436—i444. http://dx.doi.org/10.1093/bioinformatics/btaa451.
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Abstract Motivation Predicting drug–target interactions (DTIs) using human phenotypic data have the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinical corroboration. Results We developed a network-based DTI prediction system (TargetPredict) by modeling 855 904 phenotypic and genetic relationships among 1430 drugs, 4251 side effects, 1059 diseases and 17 860 genes. We systematically evaluated TargetPredict in de novo cross-validation and compared it to a state-of-the-art phenome-driven DTI prediction approach. We applied TargetPredict in identifying novel repositioned candidate drugs for Alzheimer’s disease (AD), a disease affecting over 5.8 million people in the United States. We evaluated the clinical efficiency of top repositioned drug candidates using EHRs of over 72 million patients. The area under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs. TargetPredict outperformed a state-of-the-art phenome-driven DTI prediction system as measured by precision–recall curves [measured by average precision (MAP): 0.28 versus 0.23, P-value < 0.0001]. The EHR-based case–control studies identified that the prescriptions top-ranked repositioned drugs are significantly associated with lower odds of AD diagnosis. For example, we showed that the prescription of liraglutide, a type 2 diabetes drug, is significantly associated with decreased risk of AD diagnosis [adjusted odds ratios (AORs): 0.76; 95% confidence intervals (CI) (0.70, 0.82), P-value < 0.0001]. In summary, our integrated approach that seamlessly combines computational DTI prediction and large-scale patients’ EHRs-based clinical corroboration has high potential in rapidly identifying novel drug targets and drug candidates for complex diseases. Availability and implementation nlp.case.edu/public/data/TargetPredict.
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Bianchi, Laura, Annalisa Altera, Virginia Barone, Denise Bonente, Tommaso Bacci, Elena De Benedetto, Luca Bini, Gian Marco Tosi, Federico Galvagni, and Eugenio Bertelli. "Untangling the Extracellular Matrix of Idiopathic Epiretinal Membrane: A Path Winding among Structure, Interactomics and Translational Medicine." Cells 11, no. 16 (August 15, 2022): 2531. http://dx.doi.org/10.3390/cells11162531.
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Idiopathic epiretinal membranes (iERMs) are fibrocellular sheets of tissue that develop at the vitreoretinal interface. The iERMs consist of cells and an extracellular matrix (ECM) formed by a complex array of structural proteins and a large number of proteins that regulate cell–matrix interaction, matrix deposition and remodelling. Many components of the ECM tend to produce a layered pattern that can influence the tractional properties of the membranes. We applied a bioinformatics approach on a list of proteins previously identified with an MS-based proteomic analysis on samples of iERM to report the interactome of some key proteins. The performed pathway analysis highlights interactions occurring among ECM molecules, their cell receptors and intra- or extracellular proteins that may play a role in matrix biology in this special context. In particular, integrin β1, cathepsin B, epidermal growth factor receptor, protein-glutamine gamma-glutamyltransferase 2 and prolow-density lipoprotein receptor-related protein 1 are key hubs in the outlined protein–protein cross-talks. A section on the biomarkers that can be found in the vitreous humor of patients affected by iERM and that can modulate matrix deposition is also presented. Finally, translational medicine in iERM treatment has been summed up taking stock of the techniques that have been proposed for pharmacologic vitreolysis.
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Wang, Yutao, Jianfeng Wang, Kexin Yan, Jiaxing Lin, Zhenhua Zheng, and Jianbin Bi. "Identification of core genes associated with prostate cancer progression and outcome via bioinformatics analysis in multiple databases." PeerJ 8 (March 31, 2020): e8786. http://dx.doi.org/10.7717/peerj.8786.
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Abstract The morbidity and mortality of prostate carcinoma has increased in recent years and has become the second most common ale malignant carcinoma worldwide. The interaction mechanisms between different genes and signaling pathways, however, are still unclear. Methods Variation analysis of GSE38241, GSE69223, GSE46602 and GSE104749 were realized by GEO2R in Gene Expression Omnibus database. Function enrichment was analyzed by DAVID.6.8. Furthermore, the PPI network and the significant module were analyzed by Cytoscape, STRING and MCODE.GO. Pathway analysis showed that the 20 candidate genes were closely related to mitosis, cell division, cell cycle phases and the p53 signaling pathway. A total of six independent prognostic factors were identified in GSE21032 and TCGA PRAD. Oncomine database and The Human Protein Atlas were applied to explicit that six core genes were over expression in prostate cancer compared to normal prostate tissue in the process of transcriptional and translational. Finally, gene set enrichment were performed to identified the related pathway of core genes involved in prostate cancer. Result Hierarchical clustering analysis revealed that these 20 core genes were mostly related to carcinogenesis and development. CKS2, TK1, MKI67, TOP2A, CCNB1 and RRM2 directly related to the recurrence and prognosis of prostate cancer. This result was verified by TCGA database and GSE21032. Conclusion These core genes play a crucial role in tumor carcinogenesis, development, recurrence, metastasis and progression. Identifying these genes could help us to understand the molecular mechanisms and provide potential biomarkers for the diagnosis and treatment of prostate cancer.
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Jang, Ho, and Hyunju Lee. "Multiresolution correction of GC bias and application to identification of copy number alterations." Bioinformatics 35, no. 20 (March 13, 2019): 3890–97. http://dx.doi.org/10.1093/bioinformatics/btz174.
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Abstract Motivation Whole-genome sequencing (WGS) data are affected by various sequencing biases such as GC bias and mappability bias. These biases degrade performance on detection of genetic variations such as copy number alterations. The existing methods use a relation between the GC proportion and depth of coverage (DOC) of markers by means of regression models. Nonetheless, severity of the GC bias varies from sample to sample. We developed a new method for correction of GC bias on the basis of multiresolution analysis. We used a translation-invariant wavelet transform to decompose biased raw signals into high- and low-frequency coefficients. Then, we modeled the relation between GC proportion and DOC of the genomic regions and constructed new control DOC signals that reflect the GC bias. The control DOC signals are used for normalizing genomic sequences by correcting the GC bias. Results When we applied our method to simulated sequencing data with various degrees of GC bias, our method showed more robust performance on correcting the GC bias than the other methods did. We also applied our method to real-world cancer sequencing datasets and successfully identified cancer-related focal alterations even when cancer genomes were not normalized to normal control samples. In conclusion, our method can be employed for WGS data with different degrees of GC bias. Availability and implementation The code is available at http://gcancer.org/wabico. Supplementary information Supplementary data are available at Bioinformatics online.
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Hu, Alex, William S. Noble, and Alejandro Wolf-Yadlin. "Technical advances in proteomics: new developments in data-independent acquisition." F1000Research 5 (March 31, 2016): 419. http://dx.doi.org/10.12688/f1000research.7042.1.
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The ultimate aim of proteomics is to fully identify and quantify the entire complement of proteins and post-translational modifications in biological samples of interest. For the last 15 years, liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-dependent acquisition (DDA) mode has been the standard for proteomics when sampling breadth and discovery were the main objectives; multiple reaction monitoring (MRM) LC-MS/MS has been the standard for targeted proteomics when precise quantification, reproducibility, and validation were the main objectives. Recently, improvements in mass spectrometer design and bioinformatics algorithms have resulted in the rediscovery and development of another sampling method: data-independent acquisition (DIA). DIA comprehensively and repeatedly samples every peptide in a protein digest, producing a complex set of mass spectra that is difficult to interpret without external spectral libraries. Currently, DIA approaches the identification breadth of DDA while achieving the reproducible quantification characteristic of MRM or its newest version, parallel reaction monitoring (PRM). In comparative de novo identification and quantification studies in human cell lysates, DIA identified up to 89% of the proteins detected in a comparable DDA experiment while providing reproducible quantification of over 85% of them. DIA analysis aided by spectral libraries derived from prior DIA experiments or auxiliary DDA data produces identification and quantification as reproducible and precise as that achieved by MRM/PRM, except on low‑abundance peptides that are obscured by stronger signals. DIA is still a work in progress toward the goal of sensitive, reproducible, and precise quantification without external spectral libraries. New software tools applied to DIA analysis have to deal with deconvolution of complex spectra as well as proper filtering of false positives and false negatives. However, the future outlook is positive, and various researchers are working on novel bioinformatics techniques to address these issues and increase the reproducibility, fidelity, and identification breadth of DIA.
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Islam, Mohammad, and Shirin Sultana. "Codon usage bias and purifying selection identified in Cirrhinus reba mitogenome." Journal of Advanced Biotechnology and Experimental Therapeutics 5, no. 3 (2022): 605. http://dx.doi.org/10.5455/jabet.2022.d139.
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In our previous study, we described sequence assembly and organization of the complete mitochondrial genome of a threatened labeonine fish, Cirrhinus reba (GenBank accession no.: MN862482). In this study, our attempts were to find out any mutation or selection pressures and codon usage patterns existing in the mitogenome of the same fish. We applied bioinformatics tools to measure important gene parameters including AT/GC skewness, codon adaptation index (CAI), the effective number of codons (ENc) and GC percentages of each protein coding gene. We found an overrepresentation of A and C resulting a lower number of T and G bases, respectively, where AT-skew was slightly positive and GC-skew was slightly negative. Except for ND6, all protein coding genes (PCGs) had negative GC-skew, which indicated the higher occurrence of Cs. With reference to other two mitogenomes, the dN/dS or Ka/Ks ratios ranged from the lowest value (0.016) for ND4L to the highest value (0.694) for ND1 gene which indicated that PCGs of this fish evolved under strong purifying selection. We further analyzed the codon frequency and relative synonymous codon usage (RSCU) and observed a total of 3802 codons which were used for coding 20 amino acids by a standard set of 64 codons. The amino acids Leucine and Serine were encoded each by six different codons, whereas rest of the amino acids was encoded by either two or four codons. We identified a total of 25 RSCU values (> 1) and revealed 12 codons as “overpresented” that implied for codon usage bias to engage in highly expressed genes for efficient protein synthesis via translational selection. The existence of codon usage biasness rolling in translational selection and the signs of purifying selection identified in PCGs suggest obvious conservation of this threatened fish species.
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Ma, Baoshan, Mingkun Fang, and Xiangtian Jiao. "Inference of gene regulatory networks based on nonlinear ordinary differential equations." Bioinformatics 36, no. 19 (October 1, 2020): 4885–93. http://dx.doi.org/10.1093/bioinformatics/btaa032.
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Abstract Motivation Gene regulatory networks (GRNs) capture the regulatory interactions between genes, resulting from the fundamental biological process of transcription and translation. In some cases, the topology of GRNs is not known, and has to be inferred from gene expression data. Most of the existing GRNs reconstruction algorithms are either applied to time-series data or steady-state data. Although time-series data include more information about the system dynamics, steady-state data imply stability of the underlying regulatory networks. Results In this article, we propose a method for inferring GRNs from time-series and steady-state data jointly. We make use of a non-linear ordinary differential equations framework to model dynamic gene regulation and an importance measurement strategy to infer all putative regulatory links efficiently. The proposed method is evaluated extensively on the artificial DREAM4 dataset and two real gene expression datasets of yeast and Escherichia coli. Based on public benchmark datasets, the proposed method outperforms other popular inference algorithms in terms of overall score. By comparing the performance on the datasets with different scales, the results show that our method still keeps good robustness and accuracy at a low computational complexity. Availability and implementation The proposed method is written in the Python language, and is available at: https://github.com/lab319/GRNs_nonlinear_ODEs Supplementary information Supplementary data are available at Bioinformatics online.
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Xu, Yan, Yingxi Yang, Zu Wang, and Yuanhai Shao. "Prediction of Acetylation and Succinylation in Proteins Based on Multilabel Learning RankSVM." Letters in Organic Chemistry 16, no. 4 (March 20, 2019): 275–82. http://dx.doi.org/10.2174/1570178615666180830101540.
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In vivo, one of the most efficient biological mechanisms for expanding the genetic code and regulating cellular physiology is protein post-translational modification (PTM). Because PTM can provide very useful information for both basic research and drug development, identification of PTM sites in proteins has become a very important topic in bioinformatics. Lysine residue in protein can be subjected to many types of PTMs, such as acetylation, succinylation, methylation and propionylation and so on. In order to deal with the huge protein sequences, the present study is devoted to developing computational techniques that can be used to predict the multiple K-type modifications of any uncharacterized protein timely and effectively. In this work, we proposed a method which could deal with the acetylation and succinylation prediction in a multilabel learning. Three feature constructions including sequences and physicochemical properties have been applied. The multilabel learning algorithm RankSVM has been first used in PTMs. In 10-fold cross-validation the predictor with physicochemical properties encoding got accuracy 73.86%, abslute-true 64.70%, respectively. They were better than the other feature constructions. We compared with other multilabel algorithms and the existing predictor iPTM-Lys. The results of our predictor were better than other methods. Meanwhile we also analyzed the acetylation and succinylation peptides which could illustrate the results.
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Behzadipour and Hemmati. "Considerations on the Rational Design of Covalently Conjugated Cell-Penetrating Peptides (CPPs) for Intracellular Delivery of Proteins: A Guide to CPP Selection Using Glucarpidase as the Model Cargo Molecule." Molecules 24, no. 23 (November 26, 2019): 4318. http://dx.doi.org/10.3390/molecules24234318.
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Access of proteins to their intracellular targets is limited by a hydrophobic barrier called the cellular membrane. Conjugation with cell-penetrating peptides (CPPs) has been shown to improve protein transduction into the cells. This conjugation can be either covalent or non-covalent, each with its unique pros and cons. The CPP-protein covalent conjugation may result in undesirable structural and functional alterations in the target protein. Therefore, we propose a systematic approach to evaluate different CPPs for covalent conjugations. This guide is presented using the carboxypeptidase G2 (CPG2) enzyme as the target protein. Seventy CPPs —out of 1155— with the highest probability of uptake efficiency were selected. These peptides were then conjugated to the N- or C-terminus of CPG2. Translational efficacy of the conjugates, robustness and thermodynamic properties of the chimera, aggregation possibility, folding rate, backbone flexibility, and aspects of in vivo administration such as protease susceptibility were predicted. The effect of the position of conjugation was evaluated using unpaired t-test (p < 0.05). It was concluded that N-terminal conjugation resulted in higher quality constructs. Seventeen CPP-CPG2/CPG2-CPP constructs were identified as the most promising. Based on this study, the bioinformatics workflow that is presented may be universally applied to any CPP-protein conjugate design.
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Gouripeddi, Ram, Danielle Groat, Samir E. Abdelrahman, Tom Cheatham, Mollie Cummins, Karen Eilbeck, Bernie LaSalle, Katherine Sward, and Julio C. Facelli. "3339 Development of a Competency-based Informatics Course for Translational Researchers." Journal of Clinical and Translational Science 3, s1 (March 2019): 66–67. http://dx.doi.org/10.1017/cts.2019.156.
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OBJECTIVES/SPECIFIC AIMS: Translational researchers often require the use of informatics methods in their work. Lack of an understanding of key informatics principles and methods limits the abilities of translational researchers to successfully implement Findable, Accessible, Interoperable, Reusable (FAIR) principles in grant proposal submissions and performed studies. In this study we describe our work in addressing this limitation in the workforce by developing a competency-based, modular course in informatics to meet the needs of diverse translational researchers. METHODS/STUDY POPULATION: We established a Translational Research Informatics Education Collaborative (TRIEC) consisting of faculty at the University of Utah (UU) with different primary expertise in informatics methods, and working in different tiers of the translational spectrum. The TRIEC, in collaboration with the Foundation of Workforce Development of the Utah Center for Clinical and Translational Science (CCTS), gathered informatics needs of early investigators by consolidating requests for informatics services, assistance provided in grant writing, and consultations. We then reviewed existing courses and literature for informatics courses that focused on clinical and translational researchers [3–9]. Using the structure and content of the identified courses, we developed an initial draft of a syllabus for a Translational Research Informatics (TRI) course which included key informatics topics to be covered and learning activities, and iteratively refined it through discussions. The course was approved by the UU Department of Biomedical Informatics, UU Graduate School and the CCTS. RESULTS/ANTICIPATED RESULTS: The TRI course introduces informatics PhD students, clinicians, and public health practitioners who have a demonstrated interest in research, to fundamental principles and tools of informatics. At the completion of the course, students will be able to describe and identify informatics tools and methods relevant to translational research and demonstrate inter-professional collaboration in the development of a research proposal addressing a relevant translational science question that utilizes the state-of-the-art in informatics. TRI covers a diverse set of informatics content presented as modules: genomics and bioinformatics, electronic health records, exposomics, microbiomics, molecular methods, data integration and fusion, metadata management, semantics, software architectures, mobile computing, sensors, recruitment, community engagement, secure computing environments, data mining, machine learning, deep learning, artificial intelligence and data science, open source informatics tools and platforms, research reproducibility, and uncertainty quantification. The teaching methods for TRI include (1) modular didactic learning consisting of presentations and readings and face-to-face discussions of the content, (2) student presentations of informatics literature relevant to their final project, and (3) a final project consisting of the development, critique and chalk talk and formal presentations of informatics methods and/or aims of an National Institutes of Health style K or R grant proposal. For (3), the student presents their translational research proposal concept at the beginning of the course, and works with members of the TRIEC with corresponding expertise. The final course grade is a combination of the final project, paper presentations and class participation. We offered TRI to a first cohort of students in the Fall semester of 2018. DISCUSSION/SIGNIFICANCE OF IMPACT: Translational research informatics is a sub-domain of biomedical informatics that applies and develops informatics theory and methods for translational research. TRI covers a diverse set of informatics topics that are applicable across the translational spectrum. It covers both didactic material and hands-on experience in using the material in grant proposals and research studies. TRI’s course content, teaching methodology and learning activities enable students to initially learn factual informatics knowledge and skills for translational research correspond to the ‘Remember, Understand, and Apply’ levels of the Bloom’s taxonomy [10]. The final project provides opportunity for applying these informatics concepts corresponding to the ‘Analyze, Evaluate, and Create’ levels of the Bloom’s taxonomy [10]. This inter-professional, competency-based, modular course will develop an informatics-enabled workforce trained in using state-of-the-art informatics solutions, increasing the effectiveness of translational science and precision medicine, and promoting FAIR principles in research data management and processes. Future work includes opening the course to all Clinical and Translational Science Award hubs and publishing the course material as a reference book. While student evaluations for the first cohort will be available end of the semester, true evaluation of TRI will be the number of trainees taking the course and successful grant proposal submissions. References: 1. Wilkinson MD, Dumontier M, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016 Mar 15. 2. National Center for Advancing Translational Sciences. Translational Science Spectrum. National Center for Advancing Translational Sciences. 2015 [cited 2018 Nov 15]. Available from: https://ncats.nih.gov/translation/spectrum 3. Hu H, Mural RJ, Liebman MN. Biomedical Informatics in Translational Research. 1 edition. Boston: Artech House; 2008. 264 p. 4. Payne PRO, Embi PJ, Niland J. Foundational biomedical informatics research in the clinical and translational science era: a call to action. J Am Med Inform Assoc JAMIA. 2010;17(6):615–6. 5. Payne PRO, Embi PJ, editors. Translational Informatics: Realizing the Promise of Knowledge-Driven Healthcare. Softcover reprint of the original 1st ed. 2015 edition. Springer; 2016. 196 p. 6. Richesson R, Andrews J, editors. Clinical Research Informatics. 2nd ed. Springer International Publishing; 2019. (Health Informatics). 7. Robertson D, MD GHW, editors. Clinical and Translational Science: Principles of Human Research. 2 edition. Amsterdam: Academic Press; 2017. 808 p. 8. Shen B, Tang H, Jiang X, editors. Translational Biomedical Informatics: A Precision Medicine Perspective. Softcover reprint of the original 1st ed. 2016 edition. S.l.: Springer; 2018. 340 p. 9. Valenta AL, Meagher EA, Tachinardi U, Starren J. Core informatics competencies for clinical and translational scientists: what do our customers and collaborators need to know? J Am Med Inform Assoc. 2016 Jul 1;23(4):835–9. 10. Anderson LW, Krathwohl DR, Airasian PW, Cruikshank KA, Mayer RE, Pintrich PR, Raths J, Wittrock MC. A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives, Abridged Edition. 1 edition. New York: Pearson; 2000.
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Zhao, Dengke, William D. Baez, Kurt Fredrick, and Ralf Bundschuh. "RiboProP: a probabilistic ribosome positioning algorithm for ribosome profiling." Bioinformatics 35, no. 9 (October 10, 2018): 1486–93. http://dx.doi.org/10.1093/bioinformatics/bty854.
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Abstract Motivation Ribosome profiling has been widely used to study translation in a genome-wide fashion. It requires deep sequencing of ribosome protected mRNA fragments followed by mapping of fragments to the reference genome. For applications such as identification of ribosome pausing sites, it is not enough to map a fragment to a given gene, but the exact position of the ribosome represented by the fragment must be identified for each mRNA fragment. The assignment of the correct ribosome position is complicated by the broad length distribution of the ribosome protected fragments caused by the known sequence bias of micrococcal nuclease (MNase), the most widely used nuclease for digesting mRNAs in bacteria. Available mapping algorithms suffer from either MNase bias or low accuracy in characterizing the ribosome pausing kinetics. Results In this paper, we introduce a new computational method for mapping the ribosome protected fragments to ribosome locations. We first develop a mathematical model of the interplay between MNase digestion and ribosome protection of the mRNAs. We then use the model to reconstruct the ribosome occupancy profile on a per gene level. We demonstrate that our method has the capability of mitigating the sequence bias introduced by MNase and accurately locating ribosome pausing sites at codon resolution. We believe that our method can be broadly applied to ribosome profiling studies on bacteria where codon resolution is necessary. Availability and implementation Source code implementing our approach can be downloaded under GPL3 license at http://bioserv.mps.ohio-state.edu/RiboProP. Supplementary information Supplementary data are available at Bioinformatics online.
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Dvorkina, Tatiana, Andrey V. Bzikadze, and Pavel A. Pevzner. "The string decomposition problem and its applications to centromere analysis and assembly." Bioinformatics 36, Supplement_1 (July 1, 2020): i93—i101. http://dx.doi.org/10.1093/bioinformatics/btaa454.
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Abstract Motivation Recent attempts to assemble extra-long tandem repeats (such as centromeres) faced the challenge of translating long error-prone reads from the nucleotide alphabet into the alphabet of repeat units. Human centromeres represent a particularly complex type of high-order repeats (HORs) formed by chromosome-specific monomers. Given a set of all human monomers, translating a read from a centromere into the monomer alphabet is modeled as the String Decomposition Problem. The accurate translation of reads into the monomer alphabet turns the notoriously difficult problem of assembling centromeres from reads (in the nucleotide alphabet) into a more tractable problem of assembling centromeres from translated reads. Results We describe a StringDecomposer (SD) algorithm for solving this problem, benchmark it on the set of long error-prone Oxford Nanopore reads generated by the Telomere-to-Telomere consortium and identify a novel (rare) monomer that extends the set of known X-chromosome specific monomers. Our identification of a novel monomer emphasizes the importance of identification of all (even rare) monomers for future centromere assembly efforts and evolutionary studies. To further analyze novel monomers, we applied SD to the set of recently generated long accurate Pacific Biosciences HiFi reads. This analysis revealed that the set of known human monomers and HORs remains incomplete. SD opens a possibility to generate a complete set of human monomers and HORs for using in the ongoing efforts to generate the complete assembly of the human genome. Availability and implementation StringDecomposer is publicly available on https://github.com/ablab/stringdecomposer. Supplementary information Supplementary data are available at Bioinformatics online.
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Mourragui, Soufiane, Marco Loog, Mark A. van de Wiel, Marcel J. T. Reinders, and Lodewyk F. A. Wessels. "PRECISE: a domain adaptation approach to transfer predictors of drug response from pre-clinical models to tumors." Bioinformatics 35, no. 14 (July 2019): i510—i519. http://dx.doi.org/10.1093/bioinformatics/btz372.
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Abstract Motivation Cell lines and patient-derived xenografts (PDXs) have been used extensively to understand the molecular underpinnings of cancer. While core biological processes are typically conserved, these models also show important differences compared to human tumors, hampering the translation of findings from pre-clinical models to the human setting. In particular, employing drug response predictors generated on data derived from pre-clinical models to predict patient response remains a challenging task. As very large drug response datasets have been collected for pre-clinical models, and patient drug response data are often lacking, there is an urgent need for methods that efficiently transfer drug response predictors from pre-clinical models to the human setting. Results We show that cell lines and PDXs share common characteristics and processes with human tumors. We quantify this similarity and show that a regression model cannot simply be trained on cell lines or PDXs and then applied on tumors. We developed PRECISE, a novel methodology based on domain adaptation that captures the common information shared amongst pre-clinical models and human tumors in a consensus representation. Employing this representation, we train predictors of drug response on pre-clinical data and apply these predictors to stratify human tumors. We show that the resulting domain-invariant predictors show a small reduction in predictive performance in the pre-clinical domain but, importantly, reliably recover known associations between independent biomarkers and their companion drugs on human tumors. Availability and implementation PRECISE and the scripts for running our experiments are available on our GitHub page (https://github.com/NKI-CCB/PRECISE). Supplementary information Supplementary data are available at Bioinformatics online.
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Bendezu-Quispe, Guido, L. Max Labán-Seminario, Miguel Ángel Arce-Huamani, Ramón R. Cámara-Reyes, Daniel Fernandez-Guzman, Brenda Caira-Chuquineyra, Diego Urrunaga-Pastor, and Andrés Guido Bendezú-Martínez. "Biomedical informatics: characterization of the offer of massive open online courses." Medwave 22, no. 11 (December 5, 2022): e2631-e2631. http://dx.doi.org/10.5867/medwave.2022.11.2631.
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Introduction Informatics applied to health sciences has brought cutting-edge solutions to healthcare problems. However, the number of health professionals trained in "Health Informatics" is low. Virtual education, such as massive online open courses, provide the opportunity for training in this field. Objective To estimate the global offer of massive online open biomedical informatics courses and characterize their content. Methods A search for massive online open courses was conducted throughout December 2021 on 25 platforms offering these courses. The search strategy included the terms “health informatics” and “biomedical informatics”. The application areas of biomedical informatics, platform, institution, duration, time required per week, language, and subtitles available for each course were evaluated. Data were analyzed descriptively, reporting absolute and relative frequencies. Results Our search strategy identified 1333 massive online open courses. Of these, only 79 were related to health informatics. Most of these courses (n = 44; 55.7%) were offered through Coursera. More than half (n = 55; 69.6%) were conducted by U.S. institutions in english (n = 76; 96.2%). Most courses focused on areas of translational bioinformatics (n = 27; 34.2%), followed by public health informatics (n = 23; 29.1%), and clinical research informatics (n = 13, 16.5%). Conclusions We found a significant supply of massive online open courses on health informatics. These courses favor the training of more professionals worldwide, mostly addressing competencies to apply informatics in clinical practice, public health, and health research.
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Brisdelli, Fabrizia, Laura Di Francesco, Alessandra Giorgi, Anna Rita Lizzi, Carla Luzi, Giuseppina Mignogna, Argante Bozzi, and M. Eugenia Schininà. "Proteomic Analysis of Quercetin-Treated K562 Cells." International Journal of Molecular Sciences 21, no. 1 (December 19, 2019): 32. http://dx.doi.org/10.3390/ijms21010032.
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Among natural products under investigation for their additive potential in cancer prevention and treatment, the flavonoid quercetin has received attention for its effects on the cell cycle arrest and apoptosis. In the past, we addressed this issue in K562 cells, a cellular model of the human chronic myeloid leukemia. Here, we applied stable isotope labeling by amino acids in cell culture (SILAC) proteomics with the aim to increase knowledge on the regulative and metabolic pathways modulated by quercetin in these cells. After 24 h of quercetin treatment, we observed that apoptosis was not completely established, thus we selected this time range to capture quantitative data. As a result, we were able to achieve a robust identification of 1703 proteins, and to measure fold changes between quercetin-treated and untreated cells for 1206 proteins. Through a bioinformatics functional analysis on a subset of 112 proteins, we propose that the apoptotic phenotype of K562 cells entails a significant modulation of the translational machinery, RNA metabolism, antioxidant defense systems, and enzymes involved in lipid metabolism. Finally, we selected eight differentially expressed proteins, validated their modulated expression in quercetin-treated K562 cells, and discussed their possible role in flavonoid cytotoxicity. This quantitative profiling, performed for the first time on this type of tumor cells upon treatment with a flavonoid, will contribute to revealing the molecular basis of the multiplicity of the effects selectively exerted by quercetin on K562 cells.
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Lénon, Marine, Na Ke, Cecily Szady, Hassan Sakhtah, Guoping Ren, Bruno Manta, Bryce Causey, and Mehmet Berkmen. "Improved production of Humira antibody in the genetically engineered Escherichia coli SHuffle, by co-expression of human PDI-GPx7 fusions." Applied Microbiology and Biotechnology 104, no. 22 (September 30, 2020): 9693–706. http://dx.doi.org/10.1007/s00253-020-10920-5.
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Abstract Microbial production of antibodies offers the promise of cheap, fast, and efficient production of antibodies at an industrial scale. Limiting this capacity in prokaryotes is the absence of the post-translational machinery, present in dedicated antibody producing eukaryotic cell lines, such as B cells. There has been few and limited success in producing full-length, correctly folded, and assembled IgG in the cytoplasm of prokaryotic cell lines. One such success was achieved by utilizing the genetically engineered Escherichia coli strain SHuffle with an oxidative cytoplasm. Due to the genetic disruption of reductive pathways, SHuffle cells are under constant oxidative stress, including increased levels of hydrogen peroxide (H2O2). The oxidizing capacity of H2O2 was linked to improved disulfide bond formation, by expressing a fusion of two endoplasmic reticulum-resident proteins, the thiol peroxidase GPx7 and the protein disulfide isomerase, PDI. In concert, these proteins mediate disulfide transfer from H2O2 to target proteins via PDI-Gpx7 fusions. The potential of this new strain was tested with Humira, a blockbuster antibody usually produced in eukaryotic cells. Expression results demonstrate that the new engineered SHuffle strain (SHuffle2) could produce Humira IgG four-fold better than the parental strain, both in shake-flask and in high-density fermentation. These preliminary studies guide the field in genetically engineering eukaryotic redox pathways in prokaryotes for the production of complex macromolecules. Key points • A eukaryotic redox pathway was engineered into the E. coli strain SHuffle in order to improve the yield of the blockbuster antibody Humira. • The best peroxidase-PDI fusion was selected using bioinformatics and in vivo studies. • Improved yields of Humira were demonstrated at shake-flask and high-density fermenters.
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Mokgautsi, Ntlotlang, Yu-Cheng Kuo, Yan-Jiun Huang, Chien-Hsin Chen, Debabrata Mukhopadhyay, Alexander T. H. Wu, and Hsu-Shan Huang. "Preclinical Evaluation of a Novel Small Molecule LCC-21 to Suppress Colorectal Cancer Malignancy by Inhibiting Angiogenic and Metastatic Signatures." Cells 12, no. 2 (January 9, 2023): 266. http://dx.doi.org/10.3390/cells12020266.
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Colorectal cancer (CRC) is one of the most common cancers, and it frequently metastasizes to the liver and lymph nodes. Despite major advances in treatment modalities, CRC remains a poorly characterized biological malignancy, with high reported cases of deaths globally. Moreover, cancer stem cells (CSCs) and their microenvironment have been widely shown to promote colon cancer development, progression, and metastasis. Therefore, an understanding of the underlying mechanisms that contribute to the maintenance of CSCs and their markers in CRC is crucial in efforts to treat cancer metastasis and develop specific therapeutic targets for augmenting current standard treatments. Herein, we applied computational simulations using bioinformatics to identify potential theranostic markers for CRC. We identified the overexpression of vascular endothelial growth factor-α (VEGFA)/β-catenin/matrix metalloproteinase (MMP)-7/Cluster of Differentiation 44 (CD44) in CRC to be associated with cancer progression, stemness, resistance to therapy, metastasis, and poor clinical outcomes. To further investigate, we explored in silico molecular docking, which revealed potential inhibitory activities of LCC-21 as a potential multitarget small molecule for VEGF-A/CTNNB1/MMP7/CD44 oncogenic signatures, with the highest binding affinities displayed. We validated these finding in vitro and demonstrated that LCC-21 inhibited colony and sphere formation, migration, and invasion, and these results were further confirmed by a Western blot analysis in HCT116 and DLD-1 cells. Thus, the inhibitory effects of LCC-21 on these angiogenic and onco-immunogenic signatures could be of translational relevance as potential CRC biomarkers for early diagnosis.
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Zhu, Ying, Bo Hu, Long Xu, Lili Yang, Congjie Wang, Siyu Zhang, Xufeng He, Tesheng Gao, Yong Guo, and Changxin Huang. "Tumor-Specific Nascent Nine-Peptide-Epitopes Prediction and Bioinformatics Characterization in Human Colorectal Cancer." Journal of Medical Imaging and Health Informatics 10, no. 6 (June 1, 2020): 1338–45. http://dx.doi.org/10.1166/jmihi.2020.3018.
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Background/Aims: Neoantigens are peptides produced by translation of mutant exons and existed in tumor tissues instead of normal tissues, thus, we desired to investigate the antigenic peptide epitopes of cancerspecific neoantigen, to detect the affinity of the nonapeptide with the corresponding Human Leukocyte Antigen I (HLA I) allele molecule, in order to understand the relationship of mutant exon genes of Colorectal Cancer (CRC) patients and the drive genes that are currently known for tumorigenesis of CRC. Methods: The next generation sequencing (NGS) method was used to detect the whole genome sequence and HLA I allele types of tumor tissues and adjacent tissues of 5 CRC patients. pVAC-Seq was applied to identify the nascent nonapeptides generated from exon mutations. The affinity of polypeptides with respective HLA I molecules in CRC was calculated by using NETMHC 4.0 Server. The molecular localization, molecular function, and signal pathways of mutant genes in 5 CRC patients were performed by FunRich 3.1.3 software. The TIMER website was applied to predict the analysis of intratumoral immune cell infiltration associated with the gene mutations in 5 CRC patients. Results: Fifty-six tumor-specific neo-nonapeptides were predicted from 54 different exon gene mutations. The 56 tumor new nonapeptide sequences were different from the shared motif of the HLA I allele. We explored that the tumor-specific nascent nonapeptide mainly bound to HLA-A.02*03, HLA-B.58*01 and HLA-B.11*01, and the affinity analysis results suggested that 14 of the nonapeptides had strong binding force, 20 nonapeptides had weak binding force, and 22 nonapeptides had no binding force. 54 mutant exons of 5 CRC patients were chiefly located in Leading edge membrane, Fanconi anaemia nuclear complex, and Azurophil granule. The molecular functions of these genes were involved in DNA-directed DNA polymerase activity, Vitamin or cofactor transporter activity, and Receptor signaling protein tyrosine kinase activity. 54 gene mutations had key roles in Translesion synthesis by Pol zeta, Translesion synthesis by DNA polymerases bypassing lesion on DNA template, and DNA Damage Bypass. We found that the mutant FMN2 had more infiltration of CD8+ T cells in the tumor than the wild type, and the mutant ZNF717 had more infiltration of CD8+ T cells and neutrophils in the tumor than the wild type, the difference was statistically significant. Conclusion: This study provides a preliminary result to illustrate that the prediction and bioinformatics feature of tumor-specific new nine-peptide-epitopes in CRC. It is hoped that the cancer-specific neoantigen will be used for adjuvant immunotherapy after radical surgery of colorectal cancer, and the mutant genes of CRC can also be used as landmarks for postoperative recurrence and metastasis of colorectal cancer.
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Brown Epstein, Helen-Ann. "Successful Support of Bioinformatics and Translational Bioinformatics." Journal of Hospital Librarianship 12, no. 3 (July 2012): 266–71. http://dx.doi.org/10.1080/15323269.2012.692272.
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Bellazzi, Riccardo. "Big Data and Biomedical Informatics: A Challenging Opportunity." Yearbook of Medical Informatics 23, no. 01 (August 2014): 08–13. http://dx.doi.org/10.15265/iy-2014-0024.
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SummaryBig data are receiving an increasing attention in biomedicine and healthcare. It is therefore important to understand the reason why big data are assuming a crucial role for the biomedical informatics community. The capability of handling big data is becoming an enabler to carry out unprecedented research studies and to implement new models of healthcare delivery. Therefore, it is first necessary to deeply understand the four elements that constitute big data, namely Volume, Variety, Velocity, and Veracity, and their meaning in practice. Then, it is mandatory to understand where big data are present, and where they can be beneficially collected. There are research fields, such as translational bioinformatics, which need to rely on big data technologies to withstand the shock wave of data that is generated every day. Other areas, ranging from epidemiology to clinical care, can benefit from the exploitation of the large amounts of data that are nowadays available, from personal monitoring to primary care. However, building big data-enabled systems carries on relevant implications in terms of reproducibility of research studies and management of privacy and data access; proper actions should be taken to deal with these issues. An interesting consequence of the big data scenario is the availability of new software, methods, and tools, such as map-reduce, cloud computing, and concept drift machine learning algorithms, which will not only contribute to big data research, but may be beneficial in many biomedical informatics applications. The way forward with the big data opportunity will require properly applied engineering principles to design studies and applications, to avoid preconceptions or over-enthusiasms, to fully exploit the available technologies, and to improve data processing and data management regulations.
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Londin, Eric R., and Carol Isaacson Barash. "What is translational bioinformatics?" Applied & Translational Genomics 6 (September 2015): 1–2. http://dx.doi.org/10.1016/j.atg.2015.08.003.
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Rodrigues, Kenneth Francis, Wilson Thau Lym Yong, Md Safiul Alam Bhuiyan, Shafiquzzaman Siddiquee, Muhammad Dawood Shah, and Balu Alagar Venmathi Maran. "Current Understanding on the Genetic Basis of Key Metabolic Disorders: A Review." Biology 11, no. 9 (September 2, 2022): 1308. http://dx.doi.org/10.3390/biology11091308.
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Advances in data acquisition via high resolution genomic, transcriptomic, proteomic and metabolomic platforms have driven the discovery of the underlying factors associated with metabolic disorders (MD) and led to interventions that target the underlying genetic causes as well as lifestyle changes and dietary regulation. The review focuses on fourteen of the most widely studied inherited MD, which are familial hypercholesterolemia, Gaucher disease, Hunter syndrome, Krabbe disease, Maple syrup urine disease, Metachromatic leukodystrophy, Mitochondrial encephalopathy lactic acidosis stroke-like episodes (MELAS), Niemann-Pick disease, Phenylketonuria (PKU), Porphyria, Tay-Sachs disease, Wilson’s disease, Familial hypertriglyceridemia (F-HTG) and Galactosemia based on genome wide association studies, epigenetic factors, transcript regulation, post-translational genetic modifications and biomarker discovery through metabolomic studies. We will delve into the current approaches being undertaken to analyze metadata using bioinformatic approaches and the emerging interventions using genome editing platforms as applied to animal models.
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Mammen, Manoj J., Chengjian Tu, Matthew C. Morris, Spencer Richman, William Mangione, Zackary Falls, Jun Qu, Gordon Broderick, Sanjay Sethi, and Ram Samudrala. "Proteomic Network Analysis of Bronchoalveolar Lavage Fluid in Ex-Smokers to Discover Implicated Protein Targets and Novel Drug Treatments for Chronic Obstructive Pulmonary Disease." Pharmaceuticals 15, no. 5 (May 1, 2022): 566. http://dx.doi.org/10.3390/ph15050566.
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Bronchoalveolar lavage of the epithelial lining fluid (BALF) can sample the profound changes in the airway lumen milieu prevalent in chronic obstructive pulmonary disease (COPD). We compared the BALF proteome of ex-smokers with moderate COPD who are not in exacerbation status to non-smoking healthy control subjects and applied proteome-scale translational bioinformatics approaches to identify potential therapeutic protein targets and drugs that modulate these proteins for the treatment of COPD. Proteomic profiles of BALF were obtained from (1) never-smoker control subjects with normal lung function (n = 10) or (2) individuals with stable moderate (GOLD stage 2, FEV1 50–80% predicted, FEV1/FVC < 0.70) COPD who were ex-smokers for at least 1 year (n = 10). After identifying potential crucial hub proteins, drug–proteome interaction signatures were ranked by the computational analysis of novel drug opportunities (CANDO) platform for multiscale therapeutic discovery to identify potentially repurposable drugs. Subsequently, a literature-based knowledge graph was utilized to rank combinations of drugs that most likely ameliorate inflammatory processes. Proteomic network analysis demonstrated that 233 of the >1800 proteins identified in the BALF were significantly differentially expressed in COPD versus control. Functional annotation of the differentially expressed proteins was used to detail canonical pathways containing the differential expressed proteins. Topological network analysis demonstrated that four putative proteins act as central node proteins in COPD. The drugs with the most similar interaction signatures to approved COPD drugs were extracted with the CANDO platform. The drugs identified using CANDO were subsequently analyzed using a knowledge-based technique to determine an optimal two-drug combination that had the most appropriate effect on the central node proteins. Network analysis of the BALF proteome identified critical targets that have critical roles in modulating COPD pathogenesis, for which we identified several drugs that could be repurposed to treat COPD using a multiscale shotgun drug discovery approach.
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Oikkonen, Jaana, Kaiyang Zhang, Liina Salminen, Ingrid Schulman, Kari Lavikka, Noora Andersson, Erika Ojanperä, et al. "Prospective Longitudinal ctDNA Workflow Reveals Clinically Actionable Alterations in Ovarian Cancer." JCO Precision Oncology, no. 3 (December 2019): 1–12. http://dx.doi.org/10.1200/po.18.00343.
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PURPOSE Circulating tumor DNA (ctDNA) detection is a minimally invasive technique that offers dynamic molecular snapshots of genomic alterations in cancer. Although ctDNA markers can be used for early detection of cancers or for monitoring treatment efficacy, the value of ctDNA in guiding treatment decisions in solid cancers is controversial. Here, we monitored ctDNA to detect clinically actionable alterations during treatment of high-grade serous ovarian cancer, the most common and aggressive form of epithelial ovarian cancer with a 5-year survival rate of 43%. PATIENTS AND METHODS We implemented a clinical ctDNA workflow to detect clinically actionable alterations in more than 500 cancer-related genes. We applied the workflow to a prospective cohort consisting of 78 ctDNA samples from 12 patients with high-grade serous ovarian cancer before, during, and after treatment. These longitudinal data sets were analyzed using our open-access ctDNA-tailored bioinformatics analysis pipeline and in-house Translational Oncology Knowledgebase to detect clinically actionable genomic alterations. The alterations were ranked according to the European Society for Medical Oncology scale for clinical actionability of molecular targets. RESULTS Our results show good concordance of mutations and copy number alterations in ctDNA and tumor samples, and alterations associated with clinically available drugs were detected in seven patients (58%). Treatment of one chemoresistant patient was changed on the basis of detection of ERBB2 amplification, and this ctDNA-guided decision was followed by significant tumor shrinkage and complete normalization of the cancer antigen 125 tumor marker. CONCLUSION Our results demonstrate a proof of concept for using ctDNA to guide clinical decisions. Furthermore, our results show that longitudinal ctDNA samples can be used to identify poor-responding patients after first cycles of chemotherapy. We provide what we believe to be the first comprehensive, open-source ctDNA workflow for detecting clinically actionable alterations in solid cancers.
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Schröder, Christopher, Bernhard Horsthemke, and Christel Depienne. "GC-rich repeat expansions: associated disorders and mechanisms." Medizinische Genetik 33, no. 4 (December 1, 2021): 325–35. http://dx.doi.org/10.1515/medgen-2021-2099.
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Abstract Noncoding repeat expansions are a well-known cause of genetic disorders mainly affecting the central nervous system. Missed by most standard technologies used in routine diagnosis, pathogenic noncoding repeat expansions have to be searched for using specific techniques such as repeat-primed PCR or specific bioinformatics tools applied to genome data, such as ExpansionHunter. In this review, we focus on GC-rich repeat expansions, which represent at least one third of all noncoding repeat expansions described so far. GC-rich expansions are mainly located in regulatory regions (promoter, 5′ untranslated region, first intron) of genes and can lead to either a toxic gain-of-function mediated by RNA toxicity and/or repeat-associated non-AUG (RAN) translation, or a loss-of-function of the associated gene, depending on their size and their methylation status. We herein review the clinical and molecular characteristics of disorders associated with these difficult-to-detect expansions.
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Altman, Russ B. "Introduction to Translational Bioinformatics Collection." PLoS Computational Biology 8, no. 12 (December 27, 2012): e1002796. http://dx.doi.org/10.1371/journal.pcbi.1002796.
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Zhao, Xing-Ming, Jean X. Gao, and Jose C. Nacher. "Data Mining in Translational Bioinformatics." BioMed Research International 2014 (2014): 1–2. http://dx.doi.org/10.1155/2014/656519.
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Butte, Atul J., and Lucila Ohno-Machado. "Making it personal: translational bioinformatics." Journal of the American Medical Informatics Association 20, no. 4 (July 2013): 595–96. http://dx.doi.org/10.1136/amiajnl-2013-002028.
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Lussier, Yves A., and Haiquan Li. "The rise of translational bioinformatics." Genome Biology 13, no. 8 (2012): 319. http://dx.doi.org/10.1186/gb-2012-13-8-319.
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Lesko, L. J. "Drug Research and Translational Bioinformatics." Clinical Pharmacology & Therapeutics 91, no. 6 (June 2012): 960–62. http://dx.doi.org/10.1038/clpt.2012.45.
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Butte, A. J. "Translational Bioinformatics: Coming of Age." Journal of the American Medical Informatics Association 15, no. 6 (November 1, 2008): 709–14. http://dx.doi.org/10.1197/jamia.m2824.
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Shah, N. H. "Translational Bioinformatics Embraces Big Data." Yearbook of Medical Informatics 21, no. 01 (August 2012): 130–34. http://dx.doi.org/10.1055/s-0038-1639443.
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SummaryWe review the latest trends and major developments in translational bioinformatics in the year 2011-2012. Our emphasis is on highlighting the key events in the field and pointing at promising research areas for the future. The key take-home points are:• Translational informatics is ready to revolutionize human health and healthcare using large-scale measurements on individuals.• Data–centric approaches that compute on massive amounts of data (often called “Big Data”) to discover patterns and to make clinically relevant predictions will gain adoption.• Research that bridges the latest multimodal measurement technologies with large amounts of electronic healthcare data is increasing; and is where new breakthroughs will occur.
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Lussier, Yves A., Atul J. Butte, and Lawrence Hunter. "Current methodologies for translational bioinformatics." Journal of Biomedical Informatics 43, no. 3 (June 2010): 355–57. http://dx.doi.org/10.1016/j.jbi.2010.05.002.
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Dai, Hong-Jie, Chih-Hsuan Wei, Hung-Yu Kao, Rey-Long Liu, Richard Tzong-Han Tsai, and Zhiyong Lu. "Text Mining for Translational Bioinformatics." BioMed Research International 2015 (2015): 1–2. http://dx.doi.org/10.1155/2015/368264.
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Fan, Zhenwei, Xuan Wang, Peng Li, Chunli Mei, Min Zhang, Chunshan Zhao, and Yan Song. "Systematic Identification of lncRNA-Associated ceRNA Networks in Immune Thrombocytopenia." Computational and Mathematical Methods in Medicine 2020 (June 30, 2020): 1–8. http://dx.doi.org/10.1155/2020/6193593.
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Primary immune thrombocytopenia (ITP) is an autoimmune disease. However, the molecular mechanisms underlying ITP remained to be further investigated. In the present study, we analyzed a series of public datasets (including GSE43177 and GSE43178) and identified 468 upregulated mRNAs, 272 downregulated mRNAs, 134 upregulated lncRNAs, 23 downregulated lncRNAs, 29 upregulated miRNAs, and 39 downregulated miRNAs in ITP patients. Then, we constructed protein-protein interaction networks, miRNA-mRNA and lncRNA coexpression networks in ITP. Bioinformatics analysis showed these genes regulated multiple biological processes in ITP, such as mRNA nonsense-mediated decay, translation, cell-cell adhesion, proteasome-mediated ubiquitin, and mRNA splicing. We thought the present study could broaden our insights into the mechanism underlying the progression of ITP and provide a potential biomarker for the prognosis of ITP.
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Altman, Russ B., and Katharine S. Miller. "2010 Translational bioinformatics year in review." Journal of the American Medical Informatics Association 18, no. 4 (July 2011): 358–66. http://dx.doi.org/10.1136/amiajnl-2011-000328.
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