lab meeting 11/29/2016

1)a-DE gene lists for RNAseq project

Q1: there are various time points between control and treatment. Should we use the consensus DEG list?

 IN the BGI report go to /Differential.../DEGList/
(There is an R package NOISeq)
BGI already did the analysis, we need to do a better job @11,12

check NOISeq in the report page 16/23-17/23-...


do a good job on that parts for a manuscript:

11-Pathway Analysis of DEG: functional enrichment phyer, hypergeormetric_distribution
12- PPI Analysis of DEG

It seems "GeneID" in BGI report are from NCBI. Example GeneID: 57573 is a standard ID.

https://www.ncbi.nlm.nih.gov/gene/?term=NP_065864 

http://pantherdb.org/genes/gene.jsp?acc=HUMAN|HGNC=23226|UniProtKB=Q9BX82&showAllAlt=yes

1)b-Pathway analysis plan for DE gene lists
TODO: There are different sources of human gene/protein networks. We should try several for comparisons
TODO: We should try different clustering method, such as hlcust, mcl, etc (refer to Qin's previous paper for clustering analysis).

  2)a-time-lapsed image analysis for yeast replicative lifespan
       softwares:ImageJ, MATLAB, R

https://www.mendeley.com/groups/ gene -pathways/ pathway analysis

gene set analysis is a basic thing, we also need to do that.

data visualization course would be good for animations of .gifs

RNA-seq further reading

Marioni JC et al (2008) RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res 18:1509–17. 

Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biol. 11:R106. 

Auer PL, Doerge RW (2010) Statistical design and analysis of RNA sequencing data. Genetics 185:405-416. Z. 

Wang et al. 2009. RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics 10:57-63.

Theorem of the day

http://www.theoremoftheday.org/Theorems.html#112

tutorial RNA-seq

How to analyze RNA-Seq data? Find differentially expressed genes in your research

https://www.youtube.com/watch?v=xh_wpWj0AzM

What is RNA-seq?
"using next generation sequencing to reveal the presence and quantity of RNA in a biological sample at a given moment in time."(wikipedia)

* differential expression of RNA-seq and pathway analysis is important
here is a nice set of slides:

http://www.mi.fu-berlin.de/wiki/pub/ABI/GenomicsLecture13Materials/rnaseq2.pdf

* nice discussion FPKM vs RPKM

https://www.biostars.org/p/124826/

RNA-seq lab meeting notes

*Stringtie got a bug.

*make sure you can follow the home path$ echo $PATH

*rnaseq_hisat2 /ballgown/chrx_genes*.csv
                                        /hisat
                                   
* FPKM : stands for Fragments Per Kilobase of transcript per Million mapped reads. In RNA-Seq, the relative expression of a transcript is proportional to the number of cDNA fragments that originate from it.

*Why FPKM ?

* Lior Pachter is a professor of computational biology follow his blog.

*focus on pathway analysis in RNA-seq 
*differential expression analysis

language and scientific research editing

https://secure.authorservices.springernature.com/en/default/submit

methods for parameter estimations

1)Method of moments (MM)
2)maximum likelihood estimation (MLE)
3)least squares estimation (LSE)
4)simulated annealing (MCMC) or Bayesian statistics

article published

https://peerj.com/articles/2671/

Congrats to our team. And special thanks to our lab PI Dr. Hong Qin for designing this work.