Team:Heidelberg/Notebook modeling

From 2009.igem.org

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== October ==
== October ==

Revision as of 22:49, 19 October 2009

Notebook HEARTBEAT

Welcome to the notebook of the HEARTBEAT (Heidelberg Artificial Transcription Factor Binding Sites Assembly and Engineering Tool) project. This notebook comprises the work on three sublanes: HEARTBEAT database (DB), HEARTBEAT graphical user interface (GUI) and HEARTBEAT fuzzy modeling (FN) as well as some additional work on logo as well as wiki design. Have fun!


Contents

July

7-27-2009

  • Meeting with Oliver Pelz
    • Discuss general ideas of our Database Structure and Content
    • An introduction into PromoterSweep (LINK). PromoterSweep screens a given sequence for conserved regions giving us consensus sequences and moreover screens them for TFBS by using database search (TRANSFAC, Jasper) (LINK)
    • Our new database should contain following informations: promoter sequence, TFs, TFBS, position of TFBS, number of binding TFBS, "host organism"
    • We decide to choose MySQL as a appropiate language solving this challenge which allows us also a graphical representation of the database on the web later.
    • GUI on wiki: which language? php? javascript?
    • Problems: access to PromoterSweep (Husar Bioinformatics Group, DKFZ), choice of Promoter Database (DoOP, UCSC, EnsEMBL) (LINK)
  • aim: create database until end of August

[TOP]

August

Week Days
Mon Tue Wed Thu Fri Sat Sun
31 - - - - - 1 2
32 3 4 5 6 7 8 9
33 10 11 12 13 14 15 16
34 17 18 19 20 21 22 23
35 24 25 26 27 28 29 30
36 31 - - - - - -

[TOP]

8-3-2009

  • First contact with MySQL
  • Start making an overview of other team's projects
  • Configuring our Virtual Server

8-4-2009

  • Official Team Meeting (LINK) @ BQ seminar room 43: preparaing presentation & writing meeting report
  • Start installing developing environment on our internal server
    • GNOME
    • Mediawiki

8-5-2009

  • Meeting with Tobias Bauer & Anna-Lena Kranz (Theoretical Bioinformatics, DKFZ) @ TP3, DKFZ
    • Integrating ideas of PromoterSweep, Transfac as well as DoOP/CisRED
    • select "interesting" TFs (e.g. HIF, NFkB, c-myc, p53) for Wetlab
    • select "interesting" pathways (e.g. cell cycle, inflammation, metabolism etc)
    • future experimental validation: ChIP-on-Chip
      • for this we need a TFBS-free sequence
    • idea: plot histogram of TFBS relative to TSS
      • problem: choice of sequence: upstream only? inculde downstream?
    • new programming language: R and perl
    • next meeting: Friday after team meeting
  • Meeting with Karl-Heinz Glatting (HUSAR, DKFZ) @ TP3, DKFZ
    • An introduction into PromoterSweep
    • Structure and analysis principles of PromoterSweep
    • Output is stored in an XML file. This means we have to parse the xml code.
    • Oliver Pelz will give help for us in programming
  • Protocol of the meeting can be downloaded from here.
  • Start working with MySQL
  • request UNIX/HUSAR/HPC access at DKFZ (Nao)
  • first contact with several databases: EmsEMBL, Compara, cisRED, DoOP, TiProD, contra (LINKS)

8-6-2009

  • Meeting with Oliver Pelz
    • defining workflow with PromoterSweep, Matrix Profile Search and introduction into different Motif Discovery Algorithms
  • installation of NX server for access onto internal server from Windows
  • configure developing environment (printing from Linux, configure Mediawiki)
  • defining basic concept of database construction
    • we select annotated promoter sequences in DoOP
    • we make a selection of pathway of interest using KEGG
    • narrow down number of target promoter sequences <10000.

8-7-2009

  • Official Team Meeting on Scheduling
  • Meeting with Anna-Lena and Tobias
    • Introduction into R
    • Tobias will give us access to their computing cluster (Group Roland Eils)
    • Promoter Selection: DoOP, EnsEMBL, or UCSC?
  • HUSAR account arrived
  • installation of R, R editor and perl editor
  • further configuration of our internal server / mediawiki

[TOP]

8-10-2009

  • first contact with R and perl
  • playing around with R and perl
  • playing around with R library: Biobase
  • check working on DKFZ cluster

8-11-2009

  • defining programming languages: perl, R, MySQL
  • retrieving first Promotersweep output files
  • Meeting with Marti
    • ideas for modeling
      • we will have at least three colors which overlap in their spectra.
      • a very nice approach will be Fuzzy Logic Modeling.
      • idea 1: error checking of affinity: compare expectation to experimental results and figure out where the error is hiding
      • idea 2: create&visualize fancy and fuzzy data from in silico simulation
    • combine: promoter, output and graphic representation (GRAFIK!)
    • next meeting with Marti: end of next week.
  • extract NCBI Entrez Gene IDs with R and perl
  • MAC adresses registered for bioquant network

8-12-2009

  • configure perl working environment
  • study structure of DoOP database
  • download DoOP and load DoOP database into MySQL

8-13-2009

  • trying out some DoOP queries
  • download fasta sequences from UCSC gene browser (LINK)
  • mapping of NCBI Entrez Gene IDs with RefSeq IDs
  • configure perl working environment on Windows XP
  • contact Endre Sebestyen concerning the perl module Bio-DoOP-DoOP (LINK)

8-14-2009

  • start PromoterSweep Analysis over Weekend

[TOP]

8-18-2009

Tim, Stephen, ab hier müsst ihr eure Sachen selber eintragen!

  • study outputfile of PromoterSweep. check out general structure and pick up useful information.
  • result is grouped in: General Info, Best Genomic Mapping, Promoter DB Search Result, Graphical Overview, Combined Binding Sites, TSS and Exon Info, Profile Matrices and Generated Output Files.
  • upon selection, sections of interest will be collected and made ready for entry into MySQL DB
  • discuss table structure of our database
  • How should our database be called? - Brainstorming -
    • SHOULD contain: iGEM, Transcription Factor, Binding Site, Promoter, synthetic biology, Heidelberg
    • MAY contain: position, heartbeat, prediction, assembly, eukaryotes
    • and still more keywords to come

8-19-2009

  • parse Promotersweep xml file into tab-separated text file (PERL CODE?)
    • the text file should contain: RefSeq ID, TF name, TFBS position, TF motif sequence, TFBS Quality, TSS, Entrez ID, EnsEMBL ID, further gene description.
    • this provided us with several programming problems concerning working with multiple arrays, hashes and their combinations (arrays of hashes, hashes of hashes, etc.) thus
  • studying structure and basic concepts of hash & key

8-20-2009

  • pre-decision for our table-structure
    • Table: Main_Info
      • RefSeq ID, TF, TF motif start & end position, TFBS motif score, TFBS quality, TSS database info
    • Table: Gene_Info
      • Ensembl_ID, Gene Symbol, Gene Description.
    • we go for the RefSeq ID to be the key connecting these two tables.

8-21-2009

  • update script for parsing the Promotersweep output files due to unexpected errors
  • we forgot to include "weak" as a category for the TFBS quality - added!
  • PromoterSweep result contains information about TSS derived from different promoter databases. On which should we rely, if they differ from each other?
    • We set our highest priority to DoOP database since they show a good accordance within the RefseqID results when compared to other databases (e.g. DBTSS).
  • order [http://www.mathworks.com/| Matlab] iGEM licence
  • search for a tool to use MySQL in R programming environment
  • wiki: write an short article about the German Cancer Research Center (DKFZ)
  • Meeting with Anna-Lena: once we established our database... then
    • two strategies:
      • manually select interesting transcription factors and analyse them using database queries
      • plot histograms of TFBS occurance within the target promoter sequence (TSS - 1000bp upstream) for each TF and make systematic analysis
    • we go for both!
    • idea for the future: we can analyze combinatorial appearance of distinct TF pairs
  • We have a name for our database - we call it -


- wait for it -


HEARTBEAT database (Heidelberg Artificial Transcription Factor Binding Site Engineering and Assembly Tool)


[TOP]

8-24-2009

  • Meeting with Marti: defining output modeling strategies
    • "exclusive promoters"
      • a model for predicting the behaviour of activation of one, two, three... promoters at the same time.
      • the potential of this model lies in the possibility to model single as well as many pathways in combination and even check for synergistic effects
      • modeling logic: quantitative ODE VS. quantitative & qualitative fuzzy logic
    • "error checking"
      • what to capture/measure: affinity of transcription factor binding to DNA
        • calculate score / reliabilty
        • phenotypic measurement
      • if we have time in the end: model/experiment optimization by wetlab-drylab-rounds (GRAFIK)
      • if we do not have much time: figure out where is catch
    • modeling layers & final visualization
      • (i) capture affinity - (ii) model gene expression - (iii) pathway activity - (iv) fancy visualization (Mathworks Simulink?)
      • plot: time course, dynamic affinity
      • keep in mind the possible high amount of False Positives using promoter search/analysis


8-25-2009

  • official Team Meeting also with Mr. Kai Ludwig (LANGE + PFLANZ) as guest for Logo / Title Claim discussion
  • so far we have 1753 promoter sequences analyzed by PromoterSweep!
  • Meeting with Daniela (Nao): Cell Profiler for capturing biological images & data analysis based on MATLAB
  • working with R module RMySQL (LINK) for using the pipeline between R and MySQL
  • create a list of useful RMySQL commands

8-26-2009

  • Workflow for plotting histogram - workflow (SOURCE CODE/S?)
    • make MySQL query using R
    • make list of TFs, avoid duplicates using perl
    • pick up each TF (perl/R) and plot histogram (R)
  • create MySQL command list including combinatorial queries

8-27-2009

  • check HEARTBEAT DB for duplicate entries
  • how should we plot the histogram?
    • (a) histogram - how "wide" should be each bin? 100bp? 50bp? 20bp?
    • (b) plot probability density
  • study Transfac PWM (position weight matrices) for
    • difference in consensus sequences (also ask Anna-Lena)
    • different PWM types (vertebrates, plant, insect, fungi, bacteria, nematodes...)
    • positive control: when histograms are generated and plotted, check distribution of Sp1 (LINK)
  • so far we have 3640 promoter sequences "sweeped"!


8-28-2009

[TOP]


8-31-2009

[TOP]

September

Week Days
Mon Tue Wed Thu Fri Sat Sun
36 - 1 2 3 4 5 6
37 7 8 9 10 11 12 13
38 14 15 16 17 18 19 20
39 21 22 23 24 25 26 27
40 28 29 30 - - - -

[TOP]

9-1-2009

9-2-2009

9-3-2009

9-4-2009

[TOP]

9-7-2009

9-8-2009

9-9-2009

[TOP]

October

10-1-2009

10-2-2009

[TOP]

10-5-2009

10-6-2009

10-7-2009

10-8-2009

10-9-2009

[TOP]

10-12-2009

10-13-2009

[TOP]