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DOI: 10.15265/IY-2015-026
Bioinformatics Methods and Tools to Advance Clinical Care
Findings from the Yearbook 2015 Section on Bioinformatics and Translational InformaticsPublikationsverlauf
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13. August 2015
Publikationsdatum:
10. März 2018 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00034612/201501/lookinside/thumbnails/10-15265-iy-2015-026-1.jpg)
Summary
Objectives: To summarize excellent current research in the field of Bioinformatics and Translational Informatics with application in the health domain and clinical care.
Method: We provide a synopsis of the articles selected for the IMIA Yearbook 2015, from which we attempt to derive a synthetic overview of current and future activities in the field. As last year, a first step of selection was performed by querying MEDLINE with a list of MeSH descriptors completed by a list of terms adapted to the section. Each section editor has evaluated separately the set of 1,594 articles and the evaluation results were merged for retaining 15 articles for peer-review.
Results: The selection and evaluation process of this Yearbook’s section on Bioinformatics and Translational Informatics yielded four excellent articles regarding data management and genome medicine that are mainly tool-based papers. In the first article, the authors present PPISURV a tool for uncovering the role of specific genes in cancer survival outcome. The second article describes the classifier PredictSNP which combines six performing tools for predicting disease-related mutations. In the third article, by presenting a high-coverage map of the human proteome using high resolution mass spectrometry, the authors highlight the need for using mass spectrometry to complement genome annotation. The fourth article is also related to patient survival and decision support. The authors present datamining methods of large-scale datasets of past transplants. The objective is to identify chances of survival.
Conclusions: The current research activities still attest the continuous convergence of Bioinformatics and Medical Informatics, with a focus this year on dedicated tools and methods to advance clinical care. Indeed, there is a need for powerful tools for managing and interpreting complex, large-scale genomic and biological datasets, but also a need for user-friendly tools developed for the clinicians in their daily practice. All the recent research and development efforts contribute to the challenge of impacting clinically the obtained results towards a personalized medicine.
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References
- 1 Lecroq T, Soualmia LF. From genome sequencing to bedside. Findings from the section on bioinformatics and translational informatics. Yearb Med Inform 2013; 8 (01) 175-7.
- 2 Lecroq T, Soualmia LF. Managing large-scale genomic datasets and translation into clinical practice. Yearb Med Inform 2014; 9 (01) 212-4.
- 3 Kohane IS, Churchill SE, Murphy SN. A translational engine at the national scale: informatics for integrating biology and the bedside. J Am Med Inform Assoc 2012; Mar-Apr 19 (02) 181-5.
- 4 Chute CG, Ullman-Cullere M, Wood GM, Lin SM, He M, Pathak J. Some experiences and opportunities for big data in translational research. Genetic Med 2013; 15 (10) 802-9.
- 5 Capriotti E, Nehrt NL, Kann MG, Bromberg Y. Bioinformatics for personal genome interpretation. Brief Bioinform 2012; 13 (04) 495-512.
- 6 Akan P, Alexeyenko A, Costea PI, Hedberg L, Solnestam BW, Lundin S. et al. Comprehensive analysis of the genome transcriptome and proteome landscapes of three tumor cell lines. Genome Med 2012; 4 (11) 86.
- 7 Leary RJ, Sausen M, Kinde I, Papadopoulos N, Carpten JD, Craig D. et al. Detection of chromosomal alterations in the circulation of cancer patients with whole-genome sequencing. Sci Transl Med 2012; 4 (162) 162ra154.
- 8 Bensimon A, Heck AJ, Aebersold R. Mass spec-trometry-based proteomics and network biology. Ann Rev Biochem 2012; 81: 379-405.
- 9 Cravatt BF, Simon GM, Yates JR. The biological impact of mass-spectrometry-based proteomics. Nature 2007; 450: 991-1000.
- 10 Wilhelm M, Schlegl J, Hahne H, Moghaddas Gholami A, Lieberenz M, Savitski MM. et al. Mass-spectrometry-based draft of the human proteome. Nature 2014; 509 7502 582-7.
- 11 1000 Genomes Project Consortium, Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012; 491 7422 56-65.
- 12 Gonzalez MA, Lebrigio RF, Van Booven D, Ulloa RH, Powell E, Speziani F. et al. Genomes Management Application (GEM.app): a new software tool for large-scale collaborative genome analysis. Hum Mutat 2013; 34 (06) 842-6.
- 13 Harrow J, Frankish A, Gonzalez JM, Tapanari E, Diekhans M, Kokocinski F. et al. GENCODE: the reference human genome annotation for The ENCODE Project. Genome Res 2012; 22 (09) 1760-74.
- 14 Kim MS, Pinto SM, Getnet D, Nirujogi RS, Manda SS, Chaerkady R. et al. A draft map of the human proteome. Nature 2014; 509 7502 575-81.
- 15 Antonov AV, Krestyaninova M, Knight RA, Rodchenkov I, Melino G, Barlev NA. PPISURV: a novel bioinformatics tool for uncovering the hidden role of specific genes in cancer survival outcome. Oncogene 2014; 33 (13) 1621-8.
- 16 Garcia-Albornoz M, Thankaswamy-Kosalai S, Nilsson A, Väremo L, Nookaew I, Nielsen J. BioMet Toolbox 2.0: genome-wide analysis of metabolism and omics data. Nucleic Acids Res 2014; 42 Web Server issue W175-81.
- 17 Bendl J, Stourac J, Salanda O, Pavelka A, Wieben ED, Zendulka J. et al. PredictSNP: robust and accurate consensus classifier for prediction of disease-related mutations. PLoS Comput Biol 2014; 10 (01) e1003440.
- 18 Lamy JB, Séroussi B, Griffon N, Kerdelhué G, Jaulent MC, Bouaud J. Toward a formalization of the process to select IMIA Yearbook best papers. Methods Inf Med 2015; 54 (02) 135-44.
- 19 Ammenwerth E, Wolff AC, Knaup P, Ulmer H, Skonetzki S, van Bemmel JH, Mc Cray AT, Haux R, Kulikowski C. et al. Developing and evaluating criteria to help reviewers of biomedical informatics manuscripts. JAMIA 2003; 10: 512-4.
- 20 Taati B, Snoek J, Aleman D, Ghavamzadeh A. Data mining in bone marrow transplant records to identify patients with high odds of survival. IEEE J Biomed Health Inform 2014; 18 (01) 21-7.
- 21 Tarczy-Hornoch P, Amendola L, Aronson SJ, Garraway L, Gray S, Grundmeier RW. et al. A survey of informatics approaches to whole-exome and whole-genome clinical reporting in the electronic health record. Genet Med 2013; 15 (10) 824-32.
- 22 Sleiman P, Bradfield J, Mentch F, Almoguera B, Connolly J, Hakonarson H. Assessing the functional consequence of loss of function variants using electronic medical record and large-scale genomics consortium efforts. Front Genet 2014; 5: 105.
- 23 Buggert M, Frederiksen J, Noyan K, Svärd J, Barqasho B, Sönnerborg A. et al. Multiparametric bioinformatics distinguish the CD4/CD8 ratio as a suitable laboratory predictor of combined T cell pathogenesis in HIV infection. J Immunol 2014; Mar 1 192 (05) 2099-108.
- 24 McClay JL, Aberg KA, Clark SL, Nerella S, Kumar G, Xie LY. et al. A methylome-wide study of aging using massively parallel sequencing of the methyl-CpG-enriched genomic fraction from blood in over 700 subjects. Hum Mol Genet 2014; Mar 1 23 (05) 1175-85.
- 25 Kamaraj B, Purohit R. Computational screening of disease-associated mutations in OCA2 gene. Cell Biochem Biophys 2014; Jan 68 (01) 97-109.
- 26 Kohl M, Megger DA, Trippler M, Meckel H, Ahrens M, Bracht T. et al. A practical data processing workflow for multi-OMICS projects. Biochim Biophys Acta 2014; Jan 1844 1 Pt A 52-62.