day2, moring, 20170516

=> Sheng Li, RNAseq
RNAseq library contruction

Kukurba KR, montgomery SB, Cold Spring Harbo Protoc, 2015,
https://www.ncbi.nlm.nih.gov/pubmed/25870306

For microRNA, ~20nt, special protocol is required.

stranded and non-stranded library (to distinguish overlapping exons or genes on opposite DNA strands)

minimal reads: 20-25 millions reads  for mammalian transcriptiome

Illumina Hiseq-4000, ~ 4000 millions per lane. 4-8 libraries per lane. Often, double indexing can be used for high number of multiplexing libraries.


2nd step, Gene annotation: GenCode gencode-help@sanger.ac.uk
Ensembl88,
GTF format

3rd step, gene expression quantification

RNAseq metric,
single-end RPKM, reads per kilobase per million reads
paired-end, FPKM, fragments per kilobase per million reads
nomalize read counts for sequencing depth, length of gene
TPM, transipts per million
 pro: sum of total normalized reads is the same for all samples.(not for  R/FPKM)

before 1st step, Quality check step.
 genebody coverage, (with genes)
 insert sizes
 GC content
 reads distribution
 adaptor enrichment (containmination or PCR amplification bias?)
 read quality

RSeQC, Liguo Wang, Bioinformatics 2012
FastQC
  polyA selected 3' UTR, so 5'UTR degradation can be a problem.

Public data:
GEO
RNA-seq blog

http://rpubs.com/shelly1436/274304

combatR, correct of batch effect

biological degradation of mRNA during aging, using sva latent variable, to distinguish biological degradation from non-biological degradation.

https://gist.github.com/slowkow/6e34ccb4d1311b8fe62e#file-rpkm_versus_tpm-r


=======================
Single cell RNAseq, Ion Mandoiu

psuedotemporal order of cells
https://www.nature.com/nbt/journal/v32/n4/pdf/nbt.2859.pdf

single cell mutaional profieing and clonal phylogeny in cancer
Potter, Genome Re
http://genome.cshlp.org/content/23/12/2115.long

cell type identification in primary visual cortex


Fluidigm
https://www.ncbi.nlm.nih.gov/gds/?term=fluidigm

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE77477

challenges in single-cell RNAseq: low RT and sequencing depth, "zero inflated" data

DAVID
GeneMania

Matching clusters to cell types or organism parts

10X genomics . https://www.10xgenomics.com/ .
neuron cortex
https://support.10xgenomics.com/single-cell-gene-expression/datasets

https://support.10xgenomics.com/single-cell-gene-expression/datasets/1M_neurons