hSNP project, 0822 notes

Current goal: 
 Parse dbSNP XML data file into csv files, map SNP into genes, and then perform network permutation analysis. Are there network clustering difference of diseases between races?

 dbSNP XML -> parse coordindates --> genes --> map to gene network --> association patterns?

Tasks
0) Figure out collaboration on project using GitHub.

1a) Download dbSNP data from ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/genotype/

1b) Download the human reference genome. http://www.ncbi.nlm.nih.gov/genome/guide/human/
(There are probably better sources for this. Hong needs to check the right version for dbSNP).

1c) Register and download the OMIM database http://omim.org/downloads. OMIM cannot be shared with third party. This is another database for human disease information.

2a) Parse dbSNP XML files into csv format or tab-delimited format. This probably can be done using Python and bioPython.

2b) Map SNPs coordinates and disease association into genes using the human reference genomes. 

2c) Cross-valiatation of dbSNP disease associations with OMIM.

3) Network permutation test of disease clustering by races. Are there racial differences from network perspectives?



Hong needs to check
dbGaP,  Genetics association database

Which human reference genome should we use for dbSNP? Or, it should not matter?

Create hSNP and disease Github repository

cd hSNP_disease/ 

git init
git add *

git remote add origin  https://github.com/hongqin/hSNP_disease.git

git config http.postBuffer 209715200

git commit -m 'first'

git push --force origin master 

rwwltdsmac110:hSNP_disease hongqin$ git push  --force origin master
Counting objects: 7, done.
Delta compression using up to 2 threads.
Compressing objects: 100% (5/5), done.
Writing objects: 100% (7/7), 225.04 MiB | 39.85 MiB/s, done.
Total 7 (delta 0), reused 0 (delta 0)

human disease genes

http://www.omim.org/


<sup>http://www.genecards.org/cgi-bin/listdiseasecards.pl?type=full

6548 "disease genes" are currently present in the GeneCards database</sup>  

human genome facts

From wikipedia:

The haploid human genome (23 chromosomes) is estimated to be about 3.2 billion base pairs long and to contain 20,000–25,000 distinct genes.^[1][2][3]

Biopython and SNP

References:
http://comments.gmane.org/gmane.comp.python.bio.devel/8928

https://github.com/ngopal/23andMe


http://biopython.org/pipermail/biopython/2010-April/006416.html

2010/4/13 Tiago Antão <tiagoantao at gmail.com>:
> Hi,
>
> Just a simple question:
> Entrez SNP seems to return ASN.1 format only.
> Is there any way to parse this in biopython? I've looked at SeqIO and
> found nothing...
> I can think of tools to process this outside, but I am just curious if
> this is processed natively with Biopython (being an exposed NCBI
> format...)
>
> Many thanks,
> Tiago
> PS - You can easily try this with:
> hdl = Entrez.efetch(db="snp", id="3739022")
> print hdl.read()

Hi Tiago,

No, we don't support ASN.1, and I don't see any good reason to - I
think it would only be NCBI ASN.1 we'd we interested in, and I think
that all their resources are available in other easier to use formats
like XML these days.

See also http://en.wikipedia.org/wiki/Abstract_Syntax_Notation_One

Instead ask Entrez to give you the SNP data as XML:

Entrez.efetch(db="snp", id="3739022", retmode="xml")

Hopefully the SNP XML file has everything in it.

You have a choice of Python XML parsers to use. However, the
Bio.Entrez parser doesn't like this XML. This appears to be related
(or caused by) a known NCBI bug. See
http://bugzilla.open-bio.org/show_bug.cgi?id=2771

Peter

cell size control

cell cycle regulation OR growth rate regulation?

Do smaller cells have longer G1 phases?
negative correlation bw G1 length and cell size

antibody screen for size sencors

identify drugs that weakens negative correlation bw size and cell cycle, size and growth rate.

mTOR affect size and growth rate.

some kind of integral feed back loops might be involved.

Albert Goldbeter, Vatech talk

Gerard and Goldbeter 2012 argued that cell cycle is a limit cycle. This seems to imply that cell cycle would go indefinitly.  An aging model would require a damned cycle.
Gerard Goldbeter Math Model Nat Phenom, 2012

Inquiry based learning

Some practices:

This seems to be rooted in the Socractic method.

Literature review on a research topic.  Writing a term paper.


Case studies.

Capstone experiences

Blended classrooms, worksheet for a group of 4-5 students, with information. The worksheet will guide students to develop concepts.

Case: Dr. Oz's show, are there arsenic in our juice?

SWOT analysis


References:
http://en.wikipedia.org/wiki/Socratic_method
http://en.wikipedia.org/wiki/Inquiry-based_learning
Lee, Virginia S. What is inquiry-guided learning?

Micro UN symposium

Microbiology,  yeast genetics,

setting policies of the microbe world

signal transduction

war and peace theme

shopping list, food recipe

Banner tutorial

How do I create a new requisition? Please use the link below which will give you step by step directions:

Banner Tutorial:
http://speldoc.spelman.edu/itc/AdminServices/index.htm

Who can I buy goods and services from? Please use the preferred vendor list for a list by commodities and vendors. Click on commodity and all information is there to assist you in completing your requisition and acquiring a Purchase Order.

Preferred Vendor List
http://princess.spelman.edu/vendorprofile.nsf/PreferredVendorList

How does a new vendor get approved to do business with Spelman College? How do I requisition a Guest Speaker? The first step is to complete the attached vendor profile. After the vendor or Speaker is approved a Banner requisition is required and a PO must be created. Special Note: Completion of a profile does not guarantee approval.

Vendor Profile
http://princess.spelman.edu/vendorprofile.nsf/frontpage

To look for vendor codes, go to banner database,

Form FTIIDEN

enter ARMARK%, F8 to execute the query

F7 to clear the form

Tony Hui, UCSD

Schaechter, J gen Microbio 1958, faction of proteins (R-proteins) ~ growth rate

Bacterial growth laws



Three modes of growth limitation:

C-limitation -| catabolic section
A-lim  --| anabolic section
R-lim  ---| polymerization section

Mass spec -> changes of groups of proteins

Proteome partition model
 Scott, Science 2010,

  You, Nature 2013

6 Coarse grained proteome sections.

Spencer, qbio, prolieferation-quiscence decision

Spencer, Tobias Meyer, Cell, in press

Live-cell sensor for CDK2 activity,


G1/G0,
CDK2-Venus green in nucleus

S, G2/M
CDK2-Venus in cytosol

p21 levels are different in CDK2-inc and CDD2-low cells.
  p21---| CDK2


CDK2-low is in transient G0 state.

E coli aging !!

Suckjoon Jun and Minsu Kim: 

CFU viability is exponential, but RLS is Gompertz. When SOS is knocked out, RLS become exponential.

RLS for E coli is measured in good growth condition, and CLS for E coli is measured in depleted nutrient condition. It is possible that CLS condition overwhelm network buffering and resulted in a network with too little redundancy.  On the other hand,  aging was able to manifest itself during growth, because the external 'insults' are below the 'critical' point for network robustness.

Craig Skinner and Su-ju Lin, 2010 AMB review on CR

CR on E coli aging
http://precedings.nature.com/documents/2071/version/1

Unscripted classrooms

Zip-Zap, introduction

Promotion commercials for a bacteria, pathogen, virus.

Benjamin Machta, Princeton, information fow in plasma membrane.

Membrane is nearly critical.

Phase transition in synthetic lipid membran
 2nd order critical point

Allosteric regulation

General anesthesia -> membrane involvement? However, proteins are unquestionably involved.