Big Data in Biomedicine

Study Course Description

Course Description Statuss:Approved

Course Description Version:2.00

Study Course Accepted:09.02.2023 15:41:16

Study Course Information
Course Code:		DN_181		LQF level:		Level 8
Credit Points:		2.00		ECTS:		3.00
Branch of Science:		Clinical Medicine		Target Audience:		Pharmacy; Medicine
Study Course Supervisor
Course Supervisor:		Baiba Vilne
Study Course Implementer
Structural Unit:		Department of Doctoral Studies
The Head of Structural Unit:
Contacts:		16 Dzirciema iela, Riga, LV-1007, baiba[pnkts]vilnersu[pnkts]lv
Study Course Planning
Full-Time - Semester No.1
Lectures (count)		8	Lecture Length (academic hours)		1	Total Contact Hours of Lectures		8
Classes (count)		8	Class Length (academic hours)		1	Total Contact Hours of Classes		8
Total Contact Hours								16
Study course description
Preliminary Knowledge:
Study courses in medicine or biology, preferably with additional prior knowledge in statistics and computer programming.
Objective:
To acquaint doctoral students with the sources and types of big data in the modern biomedicine, as well as to give the first insight into the processing and interpretation of this data. The main focus of the course will be on the so-called OMICS data, namely GENOME, EPIGENOME, TRANSCRIPTOME, PROTEOME, METABOLOME, MICROBIOME data, as well as the integration of these data with clinical, environmental and life-style data or CLINOME/ENVIROME.
Topic Layout (Full-Time)
No.	Topic			Type of Implementation		Number	Venue
1	Overview: Multi-OMICS & bioinformatics for personalised medicine			Lectures		1.00	computer room
				Classes		1.00	computer room
2	GENOME data analysis			Lectures		1.00	computer room
				Classes		1.00	computer room
3	EPIGENOME data analysis			Lectures		1.00	computer room
				Classes		1.00	computer room
4	TRANSCRIPTOME data analysis			Lectures		1.00	computer room
				Classes		1.00	computer room
5	PROTEOME data analysis			Lectures		1.00	computer room
				Classes		1.00	computer room
6	METABOLOME data analysis			Lectures		1.00	computer room
				Classes		1.00	computer room
7	MICROBIOME data analysis			Lectures		1.00	computer room
				Classes		1.00	computer room
8	CLINOME/ENVIROME data analysis and integration with other OMICS data			Lectures		1.00	computer room
				Classes		1.00	computer room
Assessment
Unaided Work:
Journal Club presentation: Pick a recent and, in your opinion, important article profiling at least one of the omics data types (e.g. genome) and integrating those with the clinical and/or life-style information in the context of personal medicine. Carefully read and digest the material, make sure you have understood the background of the article, the experimental methods used for the generation of the data, as well as the bioinformatics tools and workflows used for data analyses, the results and the conclusions. Prepare a 10-15 minute-Journal Club presentation in PowerPoint, accompanied by an audio recording of your narrations on the above mentioned and send to Dr. Baiba Vilne by the end of the term. The student's contribution to the improvement of the study process is the provision of meaningful feedback on the study course by filling out its evaluation questionnaire.
Assessment Criteria:
• A recent and appropriate article was chosen • The slides and talk were clear, well organized and well communicated to the audience, considering the time constraints • Exhibited a clear understanding of the study background information • Understood the experimental methods used for the generation of the data, as well as the bioinformatics tools and workflows used for data analyses • Demonstrated keen insights into the results and understood the conclusions • Assessment: Pass / fail.
Final Examination (Full-Time):		Test
Final Examination (Part-Time):
Learning Outcomes
Knowledge:		The doctoral student has gained understanding of the sources and types of large data in modern biomedicine (GENOME, EPIGENOME, TRANSCRIPTOME, PROTEOME, METABOLOME, MICROBIOME and CLINOME / ENVIROME).
Skills:		The doctoral student has basic skills in handling big data. The doctoral student is able to critically analyse and explain the results obtained from big data.
Competencies:		The doctoral student is well familiar with the main big data analyses tools, methods and workflows and their basic principles, used by bioinformaticians.
Bibliography
No.	Reference
Required Reading
1	Vilne B. 2018. Integrating Genes Affecting Coronary Artery Disease in Functional Networks by Multi-OMICs Approach. Front Cardiovasc Med. 2018; 5: 89. Jul 17. doi: 10.3389/fcvm.2018.00089
2	Dainis AM. 2018. Cardiovascular Precision Medicine in the Genomics Era. Review JACC Basic Transl Sci. 2018 May 30;3(2):313-326. doi: 10.1016/j.jacbts.2018.01.003
3	Mardis ER. 2010. The $1,000 genome, the $100,000 analysis? Genome Med. 2010 Nov 26;2(11):84. doi: 10.1186/gm205. (akceptējams izdevums)
4	Hwang KB. 2019. Comparative analysis of whole-genome sequencing pipelines to minimize false negative findings. Sci Rep. 2019; 9: 3219. Published online 2019 Mar 1. doi: 10.1038/s41598-019-39108-2
5	Marees A.T. 2018. A tutorial on conducting genome‐wide association studies: Quality control and statistical analysis. Int J Methods Psychiatr Res. 2018 Jun; 27(2). doi: 10.1002/mpr.1608
6	Visscher P.M. 2017. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017 Jul 6; 101(1): 5–22. doi: 10.1016/j.ajhg.2017.06.005
7	Loos R.J.F. 2020. 15 years of genome-wide association studies and no signs of slowing down. Nat Commun. 2020; 11: 5900. Published online 2020 Nov 19. doi: 10.1038/s41467-020-19653-5
8	Lehne B. 2015. A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies. Genome Biol. 2015; 16(1): 37. Feb 15. doi: 10.1186/s13059-015-0600-x
9	Triche TJ Jr. 2013. Low-level processing of Illumina Infinium DNA Methylation BeadArrays. Nucleic Acids Res. 2013 Apr; 41(7): e90. Mar 9. doi: 10.1093/nar/gkt090
10	Amin N. 2019. Evaluation of deep learning in non-coding RNA classification. Nature Machine Intelligence volume 1, pages246–256(2019).
11	Yang I.S. 2015. Analysis of Whole Transcriptome Sequencing Data: Workflow and Software. Genomics Inform. 2015 Dec; 13(4): 119-125. Dec 31. doi: 10.5808/GI.2015.13.4.119
12	Doll S. 2017. Region and cell-type resolved quantitative proteomic map of the human heart. Nat Commun. 2017 Nov 13;8(1):1469. doi: 10.1038/s41467-017-01747-2.
13	Tyanova S. 2016. The MaxQuant computational platform for mass spectrometry-based shotgun proteomics. Nat Protoc. 2016 Dec;11(12):2301-2319. doi: 10.1038/nprot.2016.136. Epub 2016 Oct 27.
14	Stevens V.L. 2019. Pre-Analytical Factors that Affect Metabolite Stability in Human Urine, Plasma, and Serum: A Review. Metabolites. 2019 Jul 25;9(8):156. doi: 10.3390/metabo9080156
15	Yarza P. 2014. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014 Sep;12(9):635-45. doi: 10.1038/nrmicro3330
16	Callahan BJ. 2017. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 2017 Dec;11(12):2639-2643. doi: 10.1038/ismej.2017.119. Epub 2017 Jul 21.
17	Denny J.C. 2016. Phenome-Wide Association Studies as a Tool to Advance Precision Medicine. Annu Rev Genomics Hum Genet. 2016 Aug 31;17:353-73. doi: 10.1146/annurev-genom-090314-024956. Epub 2016 May 4.
18	Millard LA. 2015. MR-PheWAS: hypothesis prioritization among potential causal effects of body mass index on many outcomes, using Mendelian randomization. Sci Rep. 2015 Nov 16;5:16645. doi: 10.1038/srep16645.
19	Patel CJ. 2010. An Environment-Wide Association Study (EWAS) on type 2 diabetes mellitus. PLoS One. 2010 May 20;5(5):e10746. doi: 10.1371/journal.pone.0010746. (akceptējams izdevums)
Additional Reading
1	Adams S.M. 2018. Clinical Pharmacogenomics: Applications in Nephrology. Clin J Am Soc Nephrol. 2018 Oct 8; 13(10): 1561–1571. doi: 10.2215/CJN.02730218
2	Orrico K.B. 2019. Basic Concepts in Genetics and Pharmacogenomics for Pharmacists. Drug Target Insights. 2019 Dec 3. doi: 10.1177/1177392819886875
3	Edwards S. L. 2013. Beyond GWASs: Illuminating the Dark Road from Association to Function. Am J Hum Genet. 2013 Nov 7; 93(5): 779–797. doi: 10.1016/j.ajhg.2013.10.012
4	Fortin J-P. 2014. Functional normalization of 450k methylation array data improves replication in large cancer studies. Genome Biol. 2014; 15(11): 503. Published online 2014 Dec 3. doi: 10.1186/s13059-014-0503-2
5	Xie T. 2019. Epigenome-Wide Association Study (EWAS) of Blood Lipids in Healthy Population from STANISLAS Family Study (SFS). Int J Mol Sci. 2019 Mar; 20(5): 1014. Published online 2019 Feb 26. doi: 10.3390/ijms20051014
6	Zappia L. 2018. Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database. PLoS Comput Biol. 2018 Jun 25;14(6):e1006245. doi: 10.1371/journal.pcbi.1006245
7	Cox J. 2011. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011 Apr 1;10(4):1794-805. doi: 10.1021/pr101065j. Epub 2011 Feb 22.
8	Tyanova S. 2018. Perseus: A Bioinformatics Platform for Integrative Analysis of Proteomics Data in Cancer Research. Methods Mol Biol. 2018;1711:133-148. doi: 10.1007/978-1-4939-7493-1_7.
9	Dettmer K. 2007. Mass spectrometry-based metabolomics. Mass Spectrom Rev. Jan-Feb 2007;26(1):51-78. doi: 10.1002/mas.20108.
10	Pietzner M. 2018. A Thyroid Hormone-Independent Molecular Fingerprint of 3,5-Diiodothyronine Suggests a Strong Relationship with Coffee Metabolism in Humans. Thyroid. 2019 Dec;29(12):1743-1754. doi: 10.1089/thy.2018.0549. Epub 2019 Nov 11.
11	Rognes T. 2016. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016 Oct 18;4:e2584. doi: 10.7717/peerj.2584
12	Uritskiy G.V. 2018. MetaWRAP-a flexible pipeline for genome-resolved metagenomic data analysis. Microbiome. 2018 Sep 15;6(1):158. doi: 10.1186/s40168-018-0541-1.
13	Millard LAC. 2018. Software Application Profile: PHESANT: a tool for performing automated phenome scans in UK Biobank. Int J Epidemiol. 2018 Feb;47(1):29-35. doi: 10.1093/ije/dyx204. Epub 2017 Oct 5.
Other Information Sources
1	https://obamawhitehouse.archives.gov/the-press-office/2015/…
2	https://www.fda.gov/medical-devices/in-vitro-diagnostics/di…
3	https://www.internationalgenome.org/
4	https://web.ornl.gov/sci/techresources/Human_Genome/
5	http://www.mirbase.org/
6	https://www.maxquant.org/summer_school/
7	https://www.metabolon.com
8	https://biocrates.com
9	https://www.arb-silva.de/
10	https://www.ukbiobank.ac.uk/