Difference between revisions of "Main Page"

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	==== Papers ====		==== Papers ====
−	===== ''Methods for genome-wide association studies (GWAS)'' =====	+	===== ''Population Genetics'' =====
−	* [https://www.ncbi.nlm.nih.gov/pubmed/16415888 Skol AD et al. (2006) "Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies" ''Nat. Genet''] <br /> - Useful to understand basic methods for GWAS and study design	+	* 1000GenomesProject.pdf
−	* [https://www.ncbi.nlm.nih.gov/pubmed/20616382 Willer CJ et al. (2010) "METAL: fast and efficient meta-analysis of genomewide association scans." ''Nat Genet''] <br /> - Software tool for meta-analysis	+	* CavalliSforzaHGDP2005.pdf
		+	* LiHGDP2008.pdf
		+	* NovemberNature2008
−	===== ''DNA sequencing and De-novo assembly'' =====	+	===== ''Single Cell RNA'' =====
−	* [https://www.ncbi.nlm.nih.gov/pubmed/20981092 The 1000 Genomes Project Consortium (2010) A map of human genome variation from population-scale sequencing ''Nature''] <br /> First 1000 genomes paper	+	* Macosko_MouseRetinaDropSeq.pdf
−	* [https://www.ncbi.nlm.nih.gov/pubmed/26432245 The 1000 Genomes Project Consortium (2015) A global reference for human genetic variation ''Nature''] <br /> Final release of the 1000 Genomes Project	+	* Single cells make big data.pdf
−	* [https://www.ncbi.nlm.nih.gov/pubmed/22231483 Iqbal Z. et al (2012) De novo assembly and genotyping of variants using colored de Bruijn graphs. ''Nature''] <br /> Variant caller using de-novo assembly graphs
−	* [https://www.ncbi.nlm.nih.gov/pubmed/19451168 Li et al (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. ] <br /> Sequence alignment algorithm using BWT
−	===== ''Single Cell RNA Sequencing'' =====	+	===== ''Transcriptomics'' =====
−	* [https://www.ncbi.nlm.nih.gov/pubmed/26000488 Macosko E et al (2015) Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. ''Cell'] <br /> Landmark paper for DropSeq method	+	* GamazonPrediXcan2015.pdf
−	* [https://www.ncbi.nlm.nih.gov/pubmed/28091601 Zheng G et al (2017) Massively parallel digital transcriptional profiling of single cells. ''Nat Comm''] <br /> Paper from 10x genomics	+	* GusevTWAS2016.pdf
−	* [https://lvdmaaten.github.io/publications/papers/JMLR_2008.pdf van der Maaten LJP and Hinton GE (2008) Visualizing Data using t-SNE ''J Machine Learning Research''] <br /> First paper of t-SNE method
−
−	===== ''Prediction of Gene Expression and/or Complex Phenotypes'' =====
−	* [https://www.ncbi.nlm.nih.gov/pubmed/26258848 Gamazon et al (2015) A gene-based association method for mapping traits using reference transcriptome data ''Nat Genet''] PrediXcan paper for elasticNet-based prediction of expression
−	* [https://www.ncbi.nlm.nih.gov/pubmed/24037378 Lappalainen T et al (2013) Transcriptome and genome sequencing uncovers functional variation in humans. ''Nat Genet''] Paper describing GEUVADIS data
−	* [https://www.ncbi.nlm.nih.gov/pubmed/21167468 Yang J et al (2011) GCTA: a tool for genome-wide complex trait analysis ''Am J Hum Genet''] GCTA paper that has BLUP method
−	* [https://www.ncbi.nlm.nih.gov/pubmed/23408905 Zhou X et al (2013) Polygenic modeling with bayesian sparse linear mixed models] BSLMM method as a more accurate alternatives to BLUP
	==== Online videos to better understand genetics and genomics ====		==== Online videos to better understand genetics and genomics ====

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Revision as of 16:57, 12 June 2018

Welcome to the U-M Big Data Summer Institute 2018 Wiki!

Consult the User's Guide for information on using the wiki software.

Reading Material

Data Mining

• Project Outline

Machine Learning Group

• Project Outline

Papers

• Bush et al. (2016) Unravelling the Human Genome
• AAndreu-Perez et al. (2015) Big Data for Health
• Madigan et al. (2014) A Systematic Approach to Evaluating Evidence from Observational Studies
• Collins et al. (2015) A New Initiative on Precision Medicine

Genomics Group

• Project Outline

Papers

Population Genetics

• 1000GenomesProject.pdf
• CavalliSforzaHGDP2005.pdf
• LiHGDP2008.pdf
• NovemberNature2008

Single Cell RNA

• Macosko_MouseRetinaDropSeq.pdf
• Single cells make big data.pdf

Transcriptomics

• GamazonPrediXcan2015.pdf
• GusevTWAS2016.pdf

Online videos to better understand genetics and genomics

Genetics

• Introduction to Genetics by 23andMe (5 videos)
• TED-Ed : How Mendel's pea plants helped us understand genetics - Hortensia Jiménez Díaz
• Genetic Recombination and Gene Mapping by Bozeman Science
• Useful Genetics : A college-level comprehensive genetics course with 292 lectures offered by Rosie Redfield at UBC

Useful 3D Animations

• From DNA to protein - 3D Animation
• DNA Transcription - 3D Animation
• DNA splicing - 3D Animation
• mRNA Translation - 3D Animation
• How DNA is packaged - 3D Animation
• The Central Dogma - 3D Animation

Gene Regulation and Epigenetics

• Epigenetics Lecture by SciShow
• Hi-C Technique : A 3D map of the Human Genome
• The ENCODE Project
• RNAi by Nature Video

Sequencing Technologies

• TED-Ed : The race to sequence the human genome - Tien Nguyen
• DropSeq - Droplet-based Single Cell Sequencing by McCarroll Lab

Imaging Group

• Project Outline

2018 Presentations

Week 1

Day 0: June 17

• Orientation Slides 2018 - Bhramar Mukherjee, PhD

Day 1: June 18

• Welcome Slides 2018 - Bhramar Mukherjee, PhD

Day 2: June 19

Day 3: June 20

Day 4: June 21

Day 5: June 22

Week 2

Day 6: June 25

Day 7: June 26

Day 8: June 27

Day 9: June 28

Day 10: June 29

Week 3

Day 11: July 2

Day 12: July 3

Day 13: July 4 (NO CLASS)

Day 14: July 5

Day 15: July 6

Week 4

Day 16: July 9

Day 17: July 10

Day 18: July 11

Day 19: July 12

Day 20: July 13

Week 5

Day 21: July 16

Day 22: July 17

Day 23: July 18

Day 24: July 19

Day 25: July 20

Week 6

Day 26: July 23

Day 27: July 24

Day 28: July 25

Day 29: Symposium

Student Poster Presentations

Additional Resources

• DataCamp Resources