nih genomcis

 encoder, imputing, 

GAN to generate diverse data set, using European people to GAN under-represented data. 

there are thing that we know we don't know, there are things that we do not know we don't know. Calibration, test, re-calibrate

race, socio-economical, life style, 

genomic deep learning tutorial

 

Deep Learning in Genomics Primer (Tutorial)

https://colab.research.google.com/drive/160h26Egm0M0jguLg80zkolMjNkzyJUhr?usp=sharing

*** covid 19

https://github.com/CSSEGISandData/COVID-19/blob/master/csse_covid_19_data/csse_covid_19_daily_reports/03-20-2020.csv

https://www.gisaid.org/registration/register/

weather data,
https://ldas.gsfc.nasa.gov/data
GED DISC NASA
https://disc.gsfc.nasa.gov/datasets?page=1&subject=Atmospheric%20Temperature

1day ML2T: MLS/Aura Level 2 Temperature V004
https://disc.gsfc.nasa.gov/datasets/ML2T_004/summary

death, age information

sequences

https://platform.gisaid.org/epi3/frontend#5748f6

meta-information

https://nextstrain.org/ncov

communal transmission ~ weather

https://www.viprbrc.org/brc/vipr_genome_search.spg?method=ShowCleanSearch&decorator=corona


GFF-version 3 file, meta information can be downloaded
https://bigd.big.ac.cn/ncov/genome/accession?q=hCoV-19/USA/CA-CDPH-UC3/2020
https://bigd.big.ac.cn/ncov/

geospatial, weather ~ evolution rate?
spatial phylogeny (use weather data as landscape data? )?

cancer phylogeny
https://www.youtube.com/watch?v=rnx-UI3h0MU
pathTimMEx: mutually exclusive cancer pathways and their dependencies in tumor progression.

bioimage training, deep learning?
https://www.sirm.org/category/senza-categoria/covid-19/

COVIDbase
https://covidbase.com/7d1a6f8ef0b9434b87e68cbe05d8a9d6?v=1d23e01e433147edb0ee7b49474734eb

George town open research  covid19 data sets
https://pages.semanticscholar.org/coronavirus-research

academic data science alliance
https://www.academicdatascience.org/covid

Coronavirus Open Data Sources, CAD models and more

https://docs.google.com/document/d/1agNknLycm9o6UfFCW_5P-0RUuubwf4M-XoTQvQe4pnM/edit

integration of hegerogenous genomic data sets

Discovery of multi-dimensional modules by integrative analysis of cancer genomic data

https://academic.oup.com/nar/article/40/19/9379/2414808

Gene prioritization through genomic data fusion

https://www.ncbi.nlm.nih.gov/pubmed/16680138

semantic similarity, 

https://academic.oup.com/bioinformatics/article/23/10/1274/197095 

Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM 

https://academic.oup.com/bioinformatics/article/26/12/i237/282591

Integrative genomics analyses unveil downstream biological effectors of disease-specific polymorphisms buried in intergenic regions

https://www.nature.com/articles/npjgenmed20166

MNMF, modified non-negative matrix Factorization = mNMF 

*** repositive.io genomics datasets

https://agingresearchbiobank.nia.nih.gov/studies/life/


repositive.io

include 23&me, 1000 genomes, Allen institute, arrayexpress, corpasome

danish diabetes study, human DNA methylation

dbGap

EMI metagenomics

Encylopedia of DNA elements

Estonis biocentre
Europoean Aggregation consoritum

Expression atlas

Babriel

GEO
Genome Austria
Genome Asia 100K
Genome in a bottle
Genomes Unzipped
Giant consoritum
Horizon
Human genome diversity project
Human knockout project
insilicoDB
integrative japanese genome variant project

IHGC, international headache genetics consortium

DDBJ

Kadoorie biobank

Majic consoritum
Methylome DB

Mike's Genome Mike Lin

MTB, mouse tumor biology database

NGSmethDB
NIAGADS, NIA Alzheimer's Disease

Open Humans
Personal genome project
Repositive
SRA
Simons genome diversity project
Singapore Genome Variation project
Steven's Keating's genome
TACCF
Texax biobank
GONL
Biobank Finland
UCSC
Cancer Methylome system
Expressomics

Carl Zimmer, personal genome

60G raw data

http://scopeblog.stanford.edu/2017/03/09/what-can-you-learn-from-your-own-genome-science-writer-carl-zimmer-found-out/

docker deepsea error on macpro

https://github.com/gifford-lab/deepsea-docker

docker run -it --rm \
--device /dev/nvidia0 \
--device /dev/nvidia1 \
--device /dev/nvidia2 \
--device /dev/nvidia-uvm \
--device /dev/nvidiactl \
giffordlab/deepsea-docker

Shedden et al, 2008, Nature medicine, gene expression based survival prediction in lung adenocarcinoma

Shedden et al, 2008, Nature medicine, gene expression based survival prediction in lung adenocarcinoma
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2667337/

health disparity, expression dataset, supposed to have ethnic group information. 

several classifier algorithms were used. cross-validation were applied, ROC curves used. 

good reference for my lifespan prediction study. 

Method A (Gene clusters and ridge regression)

binary tree-structured vecgor quantization -> binary cluster tree using expression profiles

Method B (Stratified Cox model on univariately selected genes)

Method C (clustering of samples combined with minimum gene selection).

Method D. (clustering of samples combined with minimum gene selection).

Leek group genomics paper

https://github.com/jtleek/genomicspapers

Network resources, human, (in progress)

*** GeneSigDB (used by Li14BMC, should contain disease genes). This site provide download for R analysis.   http://compbio.dfci.harvard.edu/genesigdb/

List of human disease genes in gene Card
http://www.genecards.org/cgi-bin/listdiseasecards.pl?type=full
(not sure whether this includes haploid type association)

Genome Research Genome-wide map of regulatory interactions in the human genome,

http://genome.cshlp.org/content/early/2014/09/15/gr.176586.114.abstract

Farmington study
http://videocast.nih.gov/Summary.asp?File=18760&bhcp=1

Network analysis of GWAS data, 2013 Current Opinion in Genetics and Development

Mark DM Leiserson1,2, Jonathan V Eldridge1,2, Sohini Ramachandran2,3 and Benjamin J Raphael1,2 

http://www.psidev.info/index.php?q=node/60#tools

MIF Parsers


The human protein interaction network data can be found from http://www.hprd.org/download

Reference: Peri S, Navarro JD, Amanchy R, Kristiansen TZ, Jonnalagadda CK,
Surendranath V, Niranjan V, Muthusamy B, Gandhi TKB, GronborgM,
Ibarrola N, Deshpande N, Shanker K, Shivashankar HN, Rashmi BP,
Ramya MA, Zhao ZX, Chandrika KN, Padma N, Harsha HC et al
(2003) Development of human protein reference database as an initial
platform for approaching systems biology in humans. Genome Res 13:
2363–237

Gerstein lab's human networks
  human multinet 
http://homes.gersteinlab.org/Khurana-PLoSCompBio-2013/
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002886#s4

http://encodenets.gersteinlab.org/


dbCline
UChicago

peptieAtlas
http://www.nature.com/embor/journal/v9/n5/pdf/embor200856.pdf

ensemble

human twin aging expression
http://genomebiology.com/2013/14/7/R75?utm_campaign=10_12_13_genomebiol_Article_Mailing_Reg&utm_content=7387379393&utm_medium=BMCemail&utm_source=Emailvision


http://hongqinlab.blogspot.com/2013/10/mutation-tolerance-in-human-genes-data.html

http://hongqinlab.blogspot.com/2013/03/candidate-rojects-for-r-based-data.html

expression, aging, human, kidny,
http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.0020427

9. Keshava Prasad TS, Goel R, Kandasamy K, Keerthikumar S, Kumar S, Mathivanan S, Telikicherla D, Raju R, Shafreen B, Venugopal A et al.: Human Protein Reference Database- 2009 update. [Internet]. Nucleic Acids Res 2009, 37: D767-D772. 

10. Stark C, Breitkreutz B-J, Reguly T, Boucher L, Breitkreutz A, Tyers M: BioGRID: a general repository for interaction datasets. [Internet]. Nucleic Acids Res 2006, 34:D535-D539. 

11. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C et al.: STRING v9.1: protein–protein interaction networks, with increased coverage and integration. [Internet]. Nucleic Acids Res 2013, 41:D808-D815. 

12. Razick S, Magklaras G, Donaldson IM: iRefIndex: a consolidated protein interaction database with provenance. [Internet]. BMC Bioinformatics 2008, 9:405. 

13. Croft D, O’Kelly G, Wu G, Haw R, Gillespie M, Matthews L, Caudy M, Garapati P, Gopinath G, Jassal B et al.: Reactome: a database of reactions, pathways and biological processes. [Internet]. Nucleic Acids Research 2011, 39:D691-D697. 

14. Ewing RM et al.: Large-scale mapping of human protein– protein interactions by mass spectrometry. Molecular Systems Biology 2007, 3:89. 

15. Hutchins JRa et al.: Systematic analysis of human protein complexes identifies chromosome segregation proteins. Science 2010, 328:593-599. 

16. Rual J-F et al.: Towards a proteome-scale map of the human protein–protein interaction network. Nature 2005, 437:1173-1178. 

17. Stelzl U et al.: A human protein–protein interaction network: a resource for annotating the proteome. Cell 2005, 122:957-968. 

18. Yu H et al.: Next-generation sequencing to generate interactome datasets. Nat Methods 2011, 8:478-480. 608 Genetics of system biology Current Opinion 

From Gilman 2011 Neuron, netbag on autism

Downloaded Information

The data described in previous sections was downloaded from the following public

resources:

 GeneOntology annotations from NCBI (01/2009 – ftp://ftp.ncbi.nlm.nih.gov/gene/)

 Pathways and enzyme codes from Kyoto Encyclopedia of Genes and Genomes (KEGG)

database (01/2009 – ftp://ftp.genome.jp/pub/kegg/genes/organisms/hsa/)

 Domains from InterPro database (01/2009 – ftp://ftp.ebi.ac.uk/pub/databases/interpro/)

 Tissue indicators from the TiGER database (09/2009 – http://bioinfo.wilmer.jhu.edu/tiger/)

 Protein-protein Interactions:

o BIND with protein complexes (08/2009 – http://bond.unleashedinformatics.com/)

o BioGRID interactions (08/2009 – http://www.thebiogrid.org/downloads.php)

o DIP interactions (10/2009 – http://dip.doe-mbi.ucla.edu/dip/)

o HPRD interactions (10/2009 – http://www.hprd.org/)

o InNetDB interactions (05/2009 – http://hanlab.genetics.ac.cn/sys/intnetdb)

o IntAct (10/2009 – http://www.ebi.ac.uk/intact/)

o BiGG metabolic interactions (04/2009 – http://gcrg.ucsd.edu/Downloads )

o MINT (05/2009 – http://mint.bio.uniroma2.it/mint/)

o MIPS (05/2009 – http://mips.gsf.de/proj/ppi/)

gene expression between human and mouse, PNAS, mike snyder lab, Standford
http://www.pnas.org/content/111/48/17224.full