這么多工具和基因組版本，選擇困難症犯了，到底用哪個好呢？
2018 nature - Developmental diversification of cortical inhibitory interneurons : ENSEMBL release 84 Mus musculus genome
2017 Molecular Cell - Single-Cell Alternative Splicing Analysis with Expedition Reveals Splicing Dynamics during Neuron Differentiation : STAR, human genome (hg19), using GENCODE (v19) gene annotations; sailfish - GENCODE v19 protein-coding and long non-coding RNA annotation. Outrigger
2017 - Science - Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors : UCSC hg19 transcriptome; RSEM; TPM; 可行但是不完美，建議用count
2017 - Cell - Single-Cell Analysis of Human Pancreas Reveals Transcriptional Signatures of Aging and Somatic Mutation Patterns : cutadapt; hg19;
2015 - Cell Stem Cell - Single-Cell Transcriptome Analysis Reveals Dynamic Changes in lncRNA Expression during Reprogramming : TopHat; mm9; Cufflinks; DESeq
2017 - Nature - : UCSC mm10 mouse transcriptome using Bowtie; RSEM
小結：
QC: cutadaptb不錯哦
如果只想進行定量，那就用bowtie、bowtie2比對，再用RSEM定量，這CNS用得最多；但是，單細胞能用TPM嗎？顯然不行，因為表達基因的數量差異太大了，這會帶來很嚴重的偏差。
如果想要Reads count，那還是用FeatureCounts吧。（網上貌似說FeatureCounts比HTseq算法更好一些，但是HTseq2015年發表以來，引用了3000多次了，真是糾結選哪個！！！）
參考：Compariosn Htseq And Feature Count
http://bioinformatics.cvr.ac.uk/blog/featurecounts-or-htseq-count/
http://genomespot.blogspot.hk/2014/09/read-counting-with-featurecounts.html
如果想鑒定可變剪切，那就必須Tophat、Hisat2和STAR中選了，Hisat2引用少得可憐；為什么大家都不用呢？STAR的引用秒殺它，Tophat就太老了，不用也罷。