比对软件很多，首先大家去收集一下，因为我们是带大家入门，请统一用hisat2，并且搞懂它的用法。
直接去hisat2的主页下载index文件即可，然后把fastq格式的reads比对上去得到sam文件。
接着用samtools把它转为bam文件，并且排序(注意N和P两种排序区别)索引好，载入IGV，再截图几个基因看看！
顺便对bam文件进行简单QC，参考直播我的基因组系列。

HISAT2安装：

linux版Hisat2下载，解压，可以使用了：
$ wget ftp://ftp.ccb.jhu.edu/pub/infphilo/hisat2/downloads/hisat2-2.1.0-Linux_x86_64.zip
解压（-d 解压到指定文件）：
$ unzip -d /work/LXJ/software/ hisat2-2.1.0-Linux_x86_64.zip
检查是否可以运行：
$ ./hisat2
(ERR): hisat2-align exited with value 1：可以忽略

环境路径设置:
$ sudo vi /etc/environment
添加：/work/LXJ/software/hisat2-2.1.0
$ source /etc/environment

HISAT2使用

基因组索引

自行建立基因组索引：
Command Line ： hisat2-build [options]* <reference_in> <ht2_base>
Usage : hisat2-build –p 8 genome.fa genome
如果想分析关于snp、exon、剪切位点新的信息,HISAT2建立基因组索引时，需要加入注释过的snp、exon、剪切位点后，再信息建立基因组索引；(hisat2包中有程序帮你解决)
下载基因组索引：
从HISAT2的官网中下载现成的基因组索引，这样子比较省事，也可以防止出错：

这是老鼠的基因组索引，根据需要下载合适的版本：
$ wget ftp://ftp.ccb.jhu.edu/pub/infphilo/hisat2/data/mm10.tar.gz tar zxvf mm10.tar.gz

HISAT2比对RNA-Seq到基因组：
hisat2 [options]* -x <hisat2-idx> {-1 <m1> -2 <m2> | -U <r> | --sra-acc <SRA accession number>} [-S <hit>]
<ht2-idx> Index filename prefix (minus trailing .X.ht2).
<m1> Files with #1 mates, paired with files in <m2>.
Could be gzip'ed (extension: .gz) or bzip2'ed (extension: .bz2).
<m2> Files with #2 mates, paired with files in <m1>.
Could be gzip'ed (extension: .gz) or bzip2'ed (extension: .bz2).
<r> Files with unpaired reads.
Could be gzip'ed (extension: .gz) or bzip2'ed (extension: .bz2).
<SRA accession number> Comma-separated list of SRA accession numbers, e.g. --sra-acc SRR353653,SRR353654.
<sam> File for SAM output (default: stdout)

<m1>, <m2>, <r> can be comma-separated lists (no whitespace) and can be
specified many times. E.g. '-U file1.fq,file2.fq -U file3.fq'.

HISAT2比对：

for i in {59..62};
do
echo $i
hisat2 -t -p 8 -x /work/LXJ/Genome/M.musculus/mm10.hisat2.index/genome -1 SRR35899${i}.sra_1.fastq.gz -2 SRR35899${i}.sra_2.fastq.gz -S /mnt/hgfs/Labubuntu_data/GSE81916.RNAseq/hisat2.mm10/SRR35899${i}.sam;
done

59
Time loading forward index: 00:00:25
Time loading reference: 00:00:04
Multiseed full-index search: 00:15:41
30468155 reads; of these:
  30468155 (100.00%) were paired; of these:
    2722598 (8.94%) aligned concordantly 0 times
    24300848 (79.76%) aligned concordantly exactly 1 time
    3444709 (11.31%) aligned concordantly >1 times
    ----
    2722598 pairs aligned concordantly 0 times; of these:
      156872 (5.76%) aligned discordantly 1 time
    ----
    2565726 pairs aligned 0 times concordantly or discordantly; of these:
      5131452 mates make up the pairs; of these:
        3276583 (63.85%) aligned 0 times
        1334447 (26.01%) aligned exactly 1 time
        520422 (10.14%) aligned >1 times
94.62% overall alignment rate
Time searching: 00:15:45
Overall time: 00:16:11
60
Time loading forward index: 00:00:29
Time loading reference: 00:00:04
Multiseed full-index search: 00:29:01
52972617 reads; of these:
  52972617 (100.00%) were paired; of these:
    4438954 (8.38%) aligned concordantly 0 times
    42836426 (80.87%) aligned concordantly exactly 1 time
    5697237 (10.76%) aligned concordantly >1 times
    ----
    4438954 pairs aligned concordantly 0 times; of these:
      268939 (6.06%) aligned discordantly 1 time
    ----
    4170015 pairs aligned 0 times concordantly or discordantly; of these:
      8340030 mates make up the pairs; of these:
        5335211 (63.97%) aligned 0 times
        2173091 (26.06%) aligned exactly 1 time
        831728 (9.97%) aligned >1 times
94.96% overall alignment rate
Time searching: 00:29:05
Overall time: 00:29:34
61
Time loading forward index: 00:00:31
Time loading reference: 00:00:05
Multiseed full-index search: 00:21:39
36763726 reads; of these:
  36763726 (100.00%) were paired; of these:
    3102153 (8.44%) aligned concordantly 0 times
    29382458 (79.92%) aligned concordantly exactly 1 time
    4279115 (11.64%) aligned concordantly >1 times
    ----
    3102153 pairs aligned concordantly 0 times; of these:
      173349 (5.59%) aligned discordantly 1 time
    ----
    2928804 pairs aligned 0 times concordantly or discordantly; of these:
      5857608 mates make up the pairs; of these:
        3596954 (61.41%) aligned 0 times
        1595531 (27.24%) aligned exactly 1 time
        665123 (11.35%) aligned >1 times
95.11% overall alignment rate
Time searching: 00:21:44
Overall time: 00:22:15
62
Time loading forward index: 00:00:28
Time loading reference: 00:00:05
Multiseed full-index search: 00:22:33
43802631 reads; of these:
  43802631 (100.00%) were paired; of these:
    3816434 (8.71%) aligned concordantly 0 times
    35462440 (80.96%) aligned concordantly exactly 1 time
    4523757 (10.33%) aligned concordantly >1 times
    ----
    3816434 pairs aligned concordantly 0 times; of these:
      209180 (5.48%) aligned discordantly 1 time
    ----
    3607254 pairs aligned 0 times concordantly or discordantly; of these:
      7214508 mates make up the pairs; of these:
        4769954 (66.12%) aligned 0 times
        1806461 (25.04%) aligned exactly 1 time
        638093 (8.84%) aligned >1 times
94.56% overall alignment rate
Time searching: 00:22:38
Overall time: 00:23:06

Samtools

samtools view:

Sam文件转换为bam文件：

for i in {59..62};
do
echo $i
samtools view -S SRR35899${i}.sam -b > SRR35899${i}.bam;
done

samtools sort:

sort对bam文件排序，而不是sam文件；对比对结果按reads名称排序（默认根据染色体上对应位置排序）;此处依据reads名字排序是为了满足后面HTseq的计算，如果此处使用默认的chr position会增大HTseq生成count文件时的工作量。

for i in {59..62};
do
echo $i
samtools sort -n SRR35899${i}.bam -@ 8 SRR35899${i}_n.sorted;
done

默认按照染色体位置进行排序，而-n参数则是根据read名进行排序; -t，首先根据tag TAG排序，然后根据染色体位置或reads名字排序。

IGV查看

比对结果质控：
常用工具有
Picard https://broadinstitute.github.io/picard/
RSeQC http://rseqc.sourceforge.net/
Qualimap http://qualimap.bioinfo.cipf.es/
此处使用RseQC，RseQC下属各式各样的工具，并且RseQC官网中有测试数据和运行实例
RseQC
安装：pip install RseQC
可使用程序：

• bam2fq.py
• bam2wig.py
• bam_stat.py
• clipping_profile.py
• deletion_profile.py
• divide_bam.py
• FPKM_count.py
• geneBody_coverage.py
• geneBody_coverage2.py
• infer_experiment.py
• inner_distance.py
• insertion_profile.py
• junction_annotation.py
• junction_saturation.py
• mismatch_profile.py
• normalize_bigwig.py
• overlay_bigwig.py
• read_distribution.py
• read_duplication.py
• read_GC.py
• read_hexamer.py
• read_NVC.py
• read_quality.py
• RNA_fragment_size.py
• RPKM_count.py
• RPKM_saturation.py
• spilt_bam.py
• split_paired_bam.py
• tin.py
  bam_stat.py统计reads的mapping情况
  $ bam_stat.py -i SRR3589959.sort.bam
  

参考：
转录组入门（5）： 序列比对