day3, 20170517Wed Jackson Lab, Galaxy, IGV,

=> Paola Vera-Licona
gene network

time series gene expression data -> network

structure-based control of signaling networks (optimization of interaction? )

HER2-positive breast cancer

BiNoM           -> geneXplain --> OCSANA
gene expression -> list TFs ---> mapping pathways + master regulator --> identify optimal combination of intervention from network analysis

candidate genes with p-values
pick largest connected component
using random sampling permutation to evaluate the choice of p-value cutoff.

https://binom.curie.fr/
http://compsysmed.org/Software/OCSANA/OCSANA.html

Using annotated pathway to build a directed nework for intervention analysis and prediction.

How drugble? Drug reposition?

Q: KEGG?

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=> Reinhard Laubenbacher

https://www.ncbi.nlm.nih.gov/myncbi/browse/collection/46337356/?sort=date&direction=descending

http://www.sciencedirect.com/science/article/pii/S1040842813002308

================
Karl Broman, Reproducible research (should added to my REU bootcamp training).

biostatistics and medical informatics

http://kbroman.org/

https://github.com/QinLab/Talk_ReproRes

http://kbroman.org/steps2rr/


IGV: need *bam file for alignment, *bai file for index. 

vcf file can be visualized in IGV or Ensembl Variant Effect Predictor.

http://www.cbioportal.org/

Usually, large genes tend to have more mutations than small genes. Genes with repetitive elements tend to have more mutations.

genomespace.org