Veidlapa Nr. M-3 (8)
Study Course Description
Big Data in Biomedicine
Course Code
DN_181
Branch of Science
Clinical medicine
ECTS
3.00
Target Audience
Medicine; Pharmacy
LQF
Level 8
Study Type And Form
Full-Time
Study Course Implementer
Course Supervisor
Baiba Vilne
Structure Unit Manager
Dace Bandere
Structural Unit
Department of Doctoral Studies
Contacts
16 Dzirciema iela, Riga, LV-1007, baiba.vilne@rsu.lv
About Study Course
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.
Preliminary Knowledge
Study courses in medicine or biology, preferably with additional prior knowledge in statistics and computer programming.
Learning Outcomes
Knowledge
1.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
1.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.
Competences
1.The doctoral student is well familiar with the main big data analyses tools, methods and workflows and their basic principles, used by bioinformaticians.
Assessment
Individual work
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Individual 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.
|
||
Examination
|
Title
|
% from total grade
|
Grade
|
|---|---|---|
|
1.
Examination |
-
|
-
|
|
• 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.
|
||
Study Course Theme Plan
FULL-TIME
Part 1
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Overview: Multi-OMICS & bioinformatics for personalised medicine
Description
Annotation: We will start with an overview of what Multi-OMICS is, why it is of importance for personalized medicine (PM) and where and how bioinformatics analyses come into play. We will briefly consider several OMICs data types and their analysis, namely GENOME, EPIGENOME, TRANSCRIPTOME (looking at both arrays and sequencing data), as well as PROTEOME and METABOLOME (mainly mass spectrometry-derived), and MICROBIOME (16S and shotgun sequencing data). Finally, we will also discuss the role of clinical, environmental, and lifestyle data, or CLINOME/ENVIROME, and consider the existing data integration methods, from simple correlations and regressions to artificial intelligence (AI) / machine learning (ML) approaches.
During the hands-on training, we will look at the data analysis tools used by bioinformaticians.
Literature: 1. https://obamawhitehouse.archives.gov/the-press-office/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative
2. 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
3. https://www.fda.gov/medical-devices/in-vitro-diagnostics/direct-consumer-tests
4. 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
5. https://www.internationalgenome.org/
6. https://web.ornl.gov/sci/techresources/Human_Genome/
7. Mardis ER. 2010. The $1,000 genome, the $100,000 analysis? Genome Med. 2010 Nov 26;2(11):84. doi: 10.1186/gm205.
8. 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
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
Overview: Multi-OMICS & bioinformatics for personalised medicine
Description
Annotation: We will start with an overview of what Multi-OMICS is, why it is of importance for personalized medicine (PM) and where and how bioinformatics analyses come into play. We will briefly consider several OMICs data types and their analysis, namely GENOME, EPIGENOME, TRANSCRIPTOME (looking at both arrays and sequencing data), as well as PROTEOME and METABOLOME (mainly mass spectrometry-derived), and MICROBIOME (16S and shotgun sequencing data). Finally, we will also discuss the role of clinical, environmental, and lifestyle data, or CLINOME/ENVIROME, and consider the existing data integration methods, from simple correlations and regressions to artificial intelligence (AI) / machine learning (ML) approaches.
During the hands-on training, we will look at the data analysis tools used by bioinformaticians.
Literature: 1. https://obamawhitehouse.archives.gov/the-press-office/2015/01/30/fact-sheet-president-obama-s-precision-medicine-initiative
2. 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
3. https://www.fda.gov/medical-devices/in-vitro-diagnostics/direct-consumer-tests
4. 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
5. https://www.internationalgenome.org/
6. https://web.ornl.gov/sci/techresources/Human_Genome/
7. Mardis ER. 2010. The $1,000 genome, the $100,000 analysis? Genome Med. 2010 Nov 26;2(11):84. doi: 10.1186/gm205.
8. 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
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
GENOME data analysis
Description
Annotation: In this second lecture, we will focus on the analysis of GENOME data in the context of personalized medicine, looking at the available bioinformatics tools and workflows. We will consider both genome arrays and whole exome / genome sequencing (WES/WGS) as the possible data sources. In the context of genome arrays, we will take a look at the, so called, genome-wide association studies (GWAS) for both disease risk prediction (e.g. coronary artery disease) and in the context of pharmacogenomics, to investigate whether and how specific genetic variations may affect an individual's response to certain drugs. Finally, we will talk about the analysis of the whole exome/genome sequencing data. In general, the main focus will be germline single nucleotide variations (SNVs), but when considering the analyses of WES/WGS data, we will also briefly look at the other classes of variations (e.g. structural variations), as well as somatic variations.
During the hands-on training, we will explore the genome data analysis tool PLINK (https://zzz.bwh.harvard.edu/plink/).
Literature: 1. 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
2. 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
3. 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
4. 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
5. Orrico K.B. 2019. Basic Concepts in Genetics and Pharmacogenomics for Pharmacists.
Drug Target Insights. 2019 Dec 3. doi: 10.1177/1177392819886875
6. 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
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
GENOME data analysis
Description
Annotation: In this second lecture, we will focus on the analysis of GENOME data in the context of personalized medicine, looking at the available bioinformatics tools and workflows. We will consider both genome arrays and whole exome / genome sequencing (WES/WGS) as the possible data sources. In the context of genome arrays, we will take a look at the, so called, genome-wide association studies (GWAS) for both disease risk prediction (e.g. coronary artery disease) and in the context of pharmacogenomics, to investigate whether and how specific genetic variations may affect an individual's response to certain drugs. Finally, we will talk about the analysis of the whole exome/genome sequencing data. In general, the main focus will be germline single nucleotide variations (SNVs), but when considering the analyses of WES/WGS data, we will also briefly look at the other classes of variations (e.g. structural variations), as well as somatic variations.
During the hands-on training, we will explore the genome data analysis tool PLINK (https://zzz.bwh.harvard.edu/plink/).
Literature: 1. 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
2. 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
3. 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
4. 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
5. Orrico K.B. 2019. Basic Concepts in Genetics and Pharmacogenomics for Pharmacists.
Drug Target Insights. 2019 Dec 3. doi: 10.1177/1177392819886875
6. 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
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
EPIGENOME data analysis
Description
Annotation: The third lecture will be devoted to the EPIGENOME data analysis in the context of personalized medicine, looking at the available bioinformatics tools and workflows. In general, the main focus will be on DNA methylation and its detection using both arrays and sequencing, but we will also briefly consider other types of epigenetic modifications and their analysis methods. We will also look at the epigenome-wide association studies (EWAS).
During the hands-on training, we will explore the EWAS data analysis and interpretation tools, such as eFORGE (http://eforge.cs.ucl.ac.uk/).
Literature: 1. 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
2. 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
3. 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
4. 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
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
EPIGENOME data analysis
Description
Annotation: The third lecture will be devoted to the EPIGENOME data analysis in the context of personalized medicine, looking at the available bioinformatics tools and workflows. In general, the main focus will be on DNA methylation and its detection using both arrays and sequencing, but we will also briefly consider other types of epigenetic modifications and their analysis methods. We will also look at the epigenome-wide association studies (EWAS).
During the hands-on training, we will explore the EWAS data analysis and interpretation tools, such as eFORGE (http://eforge.cs.ucl.ac.uk/).
Literature: 1. 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
2. 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
3. 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
4. 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
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
TRANSCRIPTOME data analysis
Description
Annotation: In this lecture, we will focus on the analysis of TRANSCRIPTOME data in the context of personalized medicine, looking at the available bioinformatics tools and workflows, from the quality control and raw read filtering to the alignment with the reference genome/transcriptome and the identification of relevant transcripts. We will consider the standard, gene-level analysis, as well as cases, when it is necessary to quantify isoforms for alternative splicing studies, identify single nucleotide variations in the transcriptome, quantify allele-specific expression, identify gene fusion or expression quantitative trait loci. We will focus mainly on bulk RNA-seq data, but single-cell RNA-seq analysis will also be briefly discussed.
During the hands-on training, we will explore the sequencing data quality control tool FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/).
Literature: 1. Amin N. 2019. Evaluation of deep learning in non-coding RNA classification. Nature Machine Intelligence volume 1, pages246–256(2019).
2. http://www.mirbase.org/
3. 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
4. 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.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
TRANSCRIPTOME data analysis
Description
Annotation: In this lecture, we will focus on the analysis of TRANSCRIPTOME data in the context of personalized medicine, looking at the available bioinformatics tools and workflows, from the quality control and raw read filtering to the alignment with the reference genome/transcriptome and the identification of relevant transcripts. We will consider the standard, gene-level analysis, as well as cases, when it is necessary to quantify isoforms for alternative splicing studies, identify single nucleotide variations in the transcriptome, quantify allele-specific expression, identify gene fusion or expression quantitative trait loci. We will focus mainly on bulk RNA-seq data, but single-cell RNA-seq analysis will also be briefly discussed.
During the hands-on training, we will explore the sequencing data quality control tool FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/).
Literature: 1. Amin N. 2019. Evaluation of deep learning in non-coding RNA classification. Nature Machine Intelligence volume 1, pages246–256(2019).
2. http://www.mirbase.org/
3. 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
4. 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.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
PROTEOME data analysis
Description
Annotation: The fifth lecture will be devoted to the PROTEOME (including single-cell) data analyses in the context of personalized medicine, looking at the available bioinformatics tools and workflows. The main focus will be the so-called shotgun proteomics, i.e. the identification of proteins by liquid chromatography in combination with mass spectrometry.
During the hands-on training, we will explore the MaxQuant/Perseus (https://www.maxquant.org/) software packages for the analysis of this type of data, from raw data to the quantification of all proteins in the sample.
Literature: 1. 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.
2. 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.
3. 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.
4. 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.
5. https://www.maxquant.org/summer_school/
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
PROTEOME data analysis
Description
Annotation: The fifth lecture will be devoted to the PROTEOME (including single-cell) data analyses in the context of personalized medicine, looking at the available bioinformatics tools and workflows. The main focus will be the so-called shotgun proteomics, i.e. the identification of proteins by liquid chromatography in combination with mass spectrometry.
During the hands-on training, we will explore the MaxQuant/Perseus (https://www.maxquant.org/) software packages for the analysis of this type of data, from raw data to the quantification of all proteins in the sample.
Literature: 1. 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.
2. 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.
3. 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.
4. 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.
5. https://www.maxquant.org/summer_school/
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
METABOLOME data analysis
Description
Annotation: The sixth lecture will be devoted to the analysis of the METABOLOME data (including single-cell) in the context of personalized medicine, looking at the available bioinformatics tools and workflows. The main focus will be the so-called shotgun proteomics, i.e. the identification of proteins by liquid chromatography in combination with mass spectrometry. The main focus will be on the so-called shotgun identification of metabolites by liquid chromatography in combination with mass spectrometry. We will look at both the non-targeted (i.e. all metabolites in a sample) identification and quantification of metabolites using the METABOLON (https://www.metabolon.com/) platform, and the identification and quantification of targeted (pre-selected) metabolites using BIOCRATES (https://biocrates.com/) kits.
During the hands-on training, we will explore the omu R package of metabolome data analysis
(https://cran.r-project.org/web/packages/omu/index.html).
Literature: 1. https://www.metabolon.com
2. 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.
3. Dettmer K. 2007. Mass spectrometry-based metabolomics. Mass Spectrom Rev. Jan-Feb 2007;26(1):51-78. doi: 10.1002/mas.20108.
4. 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.
5. https://biocrates.com
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
METABOLOME data analysis
Description
Annotation: The sixth lecture will be devoted to the analysis of the METABOLOME data (including single-cell) in the context of personalized medicine, looking at the available bioinformatics tools and workflows. The main focus will be the so-called shotgun proteomics, i.e. the identification of proteins by liquid chromatography in combination with mass spectrometry. The main focus will be on the so-called shotgun identification of metabolites by liquid chromatography in combination with mass spectrometry. We will look at both the non-targeted (i.e. all metabolites in a sample) identification and quantification of metabolites using the METABOLON (https://www.metabolon.com/) platform, and the identification and quantification of targeted (pre-selected) metabolites using BIOCRATES (https://biocrates.com/) kits.
During the hands-on training, we will explore the omu R package of metabolome data analysis
(https://cran.r-project.org/web/packages/omu/index.html).
Literature: 1. https://www.metabolon.com
2. 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.
3. Dettmer K. 2007. Mass spectrometry-based metabolomics. Mass Spectrom Rev. Jan-Feb 2007;26(1):51-78. doi: 10.1002/mas.20108.
4. 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.
5. https://biocrates.com
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
MICROBIOME data analysis
Description
Annotation: In this lecture, we will consider the analysis of the MICROBIOME data in the context of personalized medicine. The main focus will be on the qualitative and quantitative composition of the human gut microbiome, as well as its changes as a result of various environmental factors (e.g. diet and medications) and disease. We will compare 16S rRNA amplicon sequencing, which allows taxonomic profiling of bacteria with shotgun metagenomics, which allows both taxonomic and functional profiling of all classes of microorganisms (bacteria, fungi, viruses).
During the hands-on training, we will explore the QIIME2 (https://qiime2.org/) microbiome data analysis platform.
Literature: 1. 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.
2. 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.
3. Callahan BJ. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016 Jul;13(7):581-3. doi: 10.1038/nmeth.3869. Epub 2016 May 23.
4. https://www.arb-silva.de/
5. Rognes T. 2016. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016 Oct 18;4:e2584. doi: 10.7717/peerj.2584.
6. 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.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
MICROBIOME data analysis
Description
Annotation: In this lecture, we will consider the analysis of the MICROBIOME data in the context of personalized medicine. The main focus will be on the qualitative and quantitative composition of the human gut microbiome, as well as its changes as a result of various environmental factors (e.g. diet and medications) and disease. We will compare 16S rRNA amplicon sequencing, which allows taxonomic profiling of bacteria with shotgun metagenomics, which allows both taxonomic and functional profiling of all classes of microorganisms (bacteria, fungi, viruses).
During the hands-on training, we will explore the QIIME2 (https://qiime2.org/) microbiome data analysis platform.
Literature: 1. 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.
2. 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.
3. Callahan BJ. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016 Jul;13(7):581-3. doi: 10.1038/nmeth.3869. Epub 2016 May 23.
4. https://www.arb-silva.de/
5. Rognes T. 2016. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016 Oct 18;4:e2584. doi: 10.7717/peerj.2584.
6. 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.
|
-
Lecture
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
CLINOME/ENVIROME data analysis and integration with other OMICS data
Description
Annotation: The final lecture in this lecture series will be devoted to clinical, lifestyle and environmental (CLINOME / ENVIROME) data analysis and INTEGRATION with other OMICS data in the context of personalized medicine, looking at the available bioinformatics tools and workflows. We will explore data sources like UK Biobank (https://www.ukbiobank.ac.uk/) and the opportunities it offers for researchers, including phenome-wide association studies; pheWAS). We will also talk about the Electronic Health Records (EHR), medical images, wearable medical devices and biosensors, including the brain-machine interfaces. Finally, we will discuss the existing data integration techniques, from simple correlations and regressions to the usage of artificial intelligence (MI)/machine learning (MM) approaches.
During the hands-on training, we will explore the R package for machine learning caret (https://cran.r-project.org/web/packages/omu/index.html).
Literature: 1. https://www.ukbiobank.ac.uk/
2. 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.
3. 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.
4. 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.
5. 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.
|
-
Class/Seminar
|
Modality
|
Location
|
Contact hours
|
|---|---|---|
|
On site
|
Computer room
|
1
|
Topics
|
CLINOME/ENVIROME data analysis and integration with other OMICS data
Description
Annotation: The final lecture in this lecture series will be devoted to clinical, lifestyle and environmental (CLINOME / ENVIROME) data analysis and INTEGRATION with other OMICS data in the context of personalized medicine, looking at the available bioinformatics tools and workflows. We will explore data sources like UK Biobank (https://www.ukbiobank.ac.uk/) and the opportunities it offers for researchers, including phenome-wide association studies; pheWAS). We will also talk about the Electronic Health Records (EHR), medical images, wearable medical devices and biosensors, including the brain-machine interfaces. Finally, we will discuss the existing data integration techniques, from simple correlations and regressions to the usage of artificial intelligence (MI)/machine learning (MM) approaches.
During the hands-on training, we will explore the R package for machine learning caret (https://cran.r-project.org/web/packages/omu/index.html).
Literature: 1. https://www.ukbiobank.ac.uk/
2. 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.
3. 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.
4. 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.
5. 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.
|
Total ECTS (Creditpoints):
3.00
Contact hours:
16 Academic Hours
Final Examination:
Test
Bibliography
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.2018.00089Suitable for English stream
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.2018.01.003Suitable for English stream
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)Suitable for English stream
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/s41598-019-39108-2Suitable for English stream
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.1608Suitable for English stream
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.2017.06.005Suitable for English stream
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/s41467-020-19653-5Suitable for English stream
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-xSuitable for English stream
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 (akcpetējams izdevums)Suitable for English stream
10.
Amin N. 2019. Evaluation of deep learning in non-coding RNA classification. Nature Machine Intelligence volume 1, pages246–256(2019).Suitable for English stream
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.2015.13.4.119Suitable for English stream
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.Suitable for English stream
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.Suitable for English stream
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/metabo9080156Suitable for English stream
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 (akcpetējams izdevums)Suitable for English stream
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.Suitable for English stream
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.Suitable for English stream
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.Suitable for English stream
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)Suitable for English stream
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.02730218Suitable for English stream
2.
Orrico K.B. 2019. Basic Concepts in Genetics and Pharmacogenomics for Pharmacists. Drug Target Insights. 2019 Dec 3. doi: 10.1177/1177392819886875Suitable for English stream
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.2013.10.012Suitable for English stream
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/s13059-014-0503-2Suitable for English stream
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/ijms20051014Suitable for English stream
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.1006245Suitable for English stream
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.Suitable for English stream
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.Suitable for English stream
9.
Dettmer K. 2007. Mass spectrometry-based metabolomics. Mass Spectrom Rev. Jan-Feb 2007;26(1):51-78. doi: 10.1002/mas.20108.Suitable for English stream
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.Suitable for English stream
11.
Rognes T. 2016. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016 Oct 18;4:e2584. doi: 10.7717/peerj.2584Suitable for English stream
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.Suitable for English stream
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.Suitable for English stream
Other Information Sources
1.
FACT SHEET: President Obama’s Precision Medicine InitiativeSuitable for English stream
2.
Direct-to-Consumer TestsSuitable for English stream
3.
The International Genome Sample ResourceSuitable for English stream
4.
Human Genome ProjectSuitable for English stream
5.
miRBase: the microRNA databaseSuitable for English stream
6.
MaxQuant Summer SchoolSuitable for English stream
7.
MetabolonSuitable for English stream
8.
https://biocrates.comSuitable for English stream
9.
SILVA rRNA databaseSuitable for English stream
10.
UK biobankSuitable for English stream