STAR manual

来源：STARmanual.pdf

来源：Calling variants in RNAseq

PART0 准备工作

    #STAR 安装前的依赖的工具
    #Red Hat, CentOS, Fedora.
     sudo yum update
     sudo yum install make
     sudo yum install gcc-c++
     sudo yum install glibc-static

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PART1 Quick start

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#STAR Basic workflow
###1. Generating genome indexes files
###构建索引
###2. Mapping reads to the genome
###将reads 比对到基因组上

#范例
##1) STAR uses genome index files that must be saved in unique directories. The human genome index was built from the FASTA file hg19.fa as follows:
##(以人类基因组为例，构建索引)
genomeDir=/path/to/hg19
mkdir $genomeDir
STAR --runMode genomeGenerate --genomeDir $genomeDir \
--genomeFastaFiles hg19.fa --runThreadN <n>

##2) Alignment jobs were executed as follows:
##（比对）
runDir=/path/to/1pass
mkdir $runDir
cd $runDir
STAR --genomeDir $genomeDir --readFilesIn mate1.fq mate2.fq \
 --runThreadN <n>

##3) For the 2-pass STAR, a new index is then created using splice junction information contained in the file SJ.out.tab from the first pass:
##（从1-pass STAR运行结果中提取可变剪切位点信息，再次构建索引）
genomeDir=/path/to/hg19_2pass
mkdir $genomeDir
STAR --runMode genomeGenerate --genomeDir $genomeDir \
--genomeFastaFiles hg19.fa \
--sjdbFileChrStartEnd /path/to/1pass/SJ.out.tab \
--sjdbOverhang 75 --runThreadN <n>

##4) The resulting index is then used to produce the final alignments as follows:
##（利用索引文件信息将reads进行比对，生成结果文件）
runDir=/path/to/2pass
mkdir $runDir
cd $runDir
STAR --genomeDir $genomeDir --readFilesIn mate1.fq mate2.fq \
--runThreadN <n>

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PART2 Generating genome indexes files

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 #构建索引使用的基本参数
--runThreadN NumberOfThreads（线程数）

--runMode genomeGenerate （生成index的运行模式）

--genomeDir /path/to/genomeDir （index 存储的目录）

--genomeFastaFiles /path/to/genome/fasta1 /path/to/genome/fasta2 ... （参考基因组序列文件Ref.fa）

--sjdbGTFfile /path/to/annotations.gtf （参考基因组注释文件Ref.gtf）
    ### STAR will extract splice junctions from this file and use them to greatly improve accuracy of the mapping. While this is optional, and STAR can be run without annotations, using annotations is highly recommended whenever they are available. Starting from 2.4.1a, the annotations can also be included on the fly at the mapping step.（可选项；如果应用此项，STAR 将会从注释文件中提取可变剪切位点的信息，从而使后面的比对更为准确，一般如果有注释文件的话，则推荐使用；但是如果没有文件，STAR也能运行）

--sjdbOverhang ReadLength-1

    ###specifies the length of the genomic sequence around the annotated junction to be used in constructing the splice junctions database. Ideally, this length should be equal to the ReadLength-1, where ReadLength is the length of the reads. For instance, for Illumina 2x100b paired-end reads, the ideal value is 100-1=99. In case of reads of varying length, the ideal value is max(ReadLength)-1. In most cases, the default value of 100 will work as well as the ideal value.（在构建可变剪切位点数据库时，应用这个参数可指定在已被注释的剪切位点附近的基因组序列的长度。理想情况下，这个长度等于（ReadLength-1），注意，这里的ReadLength指的是reads 的长度。例如，对于Illumina 2x100b 双端reads，理论值应该是100-1=99。但是假设reads长度不一致，那么其理论值则是max(ReadLength)-1。大多情况下，默认值100的运行效果和理论值几乎相同 ）

    ###Genome files comprise binary genome sequence, suffix arrays, text chromosome names/lengths,splice junctions coordinates, and transcripts/genes information.

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PART3 mapping jobs

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 #比对（mapping jobs）使用的基本参数
--runThreadN NumberOfThreads    （线程数）
--genomeDir /path/to/genomeDir  （index存储的目录）
--readFilesIn /path/to/read1 [/path/to/read2 ]
（RNA-seq FASTQ/FASTA files）
    ###如果提供的是压缩文件，则可使用此参数：
    #####--readFilesCommand UncompressionCommand
    #####例如：针对gzipped 文件 (*.gz)
    --readFilesCommand zcat
or
    --readFilesCommand gunzip -c

    #####例如：针对bzip2-compressed 文件
    --readFilesCommand bunzip2 -c

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PART4 Output files

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#STAR 生成多个输出文件，一般默认会自动存储在当前工作目录下，但可以利用参数指定生成目录和文件前缀。如下：
--outFileNamePrefix /path/to/output/dir/prefix.

#log files
###log.out/log.process.out/log.final.out

#SAM
###Aligned.out.sam - alignments in standard SAM format.

#STAR可以指定输出的比对文件的格式
--outSAMtype BAM Unsorted
#仅将SAM转变成BAM格式，不排序，输出Aligned.out.bam
--outSAMtype BAM SortedByCoordinate
#既将SAM转变成BAM格式，也对文件按名称排序，输出Aligned.sortedByCoord.out.bam，类似samtools sort 命令
--outSAMtype BAM Unsorted SortedByCoordinate
#生成两个文件，即未经过排序的Aligned.out.bam和经过排序的Aligned.sortedByCoord.out.bam

#Splice junctions
#SJ.out.tab 文件包含以下9列信息
column 1: chromosome
#（染色体）
column 2: first base of the intron (1-based)
#(内含子的第一个碱基)
column 3: last base of the intron (1-based)
#（内含子最后一个碱基）
column 4: strand (0: undened, 1: +, 2: -)
#（链的方向）
column 5: intron motif: 0: non-canonical; 1: GT/AG, 2: CT/AC, 3: GC/AG, 4: CT/GC, 5:AT/AC, 6: GT/AT
#（内含子序列）
column 6: 0: unannotated, 1: annotated (only if splice junctions database is used)
#（剪切位点是否已被注释）
column 7: number of uniquely mapping reads crossing the junction
column 8: number of multi-mapping reads crossing the junction
column 9: maximum spliced alignment overhang

STAR-Fusion is a software package for detecting fusion transcript from STAR chimeric output.

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PART5 与下游分析相关的参数

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With –quantMode TranscriptomeSAM option STAR will output alignments translated into transcript coordinates in the Aligned.toTranscriptome.out.bam file (in addition to alignments in genomic coordinates in Aligned.*.sam/bam files).

With –quantMode GeneCounts option STAR will count number reads per gene while mapping.

The counts coincide with those produced by htseq-count with default parameters.(这个参数的作用与htseq-count作用相同)

这个参数对应生成的文件是ReadsPerGene.out.tab

- column 1: gene ID

- column 2: counts for unstranded RNA-seq

- column 3: counts for the 1st read strand aligned with RNA (htseq-count option -s yes)

- column 4: counts for the 2nd read strand aligned with RNA (htseq-count option -s reverse)

With –quantMode TranscriptomeSAM GeneCounts

生成两个文件：

1）Aligned.toTranscriptome.out.bam

2）ReadsPerGene.out.tab

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PART6 2-pass mapping

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为了能准确发现新的剪切位点，力推使用STAR的 2-pass mode。

这并不是说增加检测的新剪切位点的数目，而是增强了检测到可变剪切reads比对到新剪切位点的能力。（即通过reads比对从而发现新的剪切位点）

It does not increase the number of detected novel junctions, but allows to detect more splices reads mapping to novel junctions.

基本思想是：

首先进行1-pass STAR mapping（基本参数即可），收集可变剪切位点信息；

其次利用上一步的变剪切位点信息，进行2-pass STAR mapping

#For a study with multiple samples, it is recommended to collect 1st pass junctions from all samples.
    ###1. Run 1st mapping pass for all samples with "usual" parameters. Using annotations is recommended either a the genome generation step, or mapping step.
    ###2. Run 2nd mapping pass for all samples , listing SJ.out.tab files from all samples in --sjdbFileChrStartEnd /path/to/sj1.tab /path/to/sj2.tab ....

#Per-sample 2-pass mapping.
    ###Annotated junctions will be included in both the 1st and 2nd passes. To run STAR 2-pass mapping for each sample separately, use --twopassMode Basic option. STAR will perform the 1st pass mapping, then it will automatically extract junctions, insert them into the genome index, and, finally, re-map all reads in the 2nd mapping pass. This option can be used with annotations, which can be included either at the run-time (see #1), or at the genome generation step.
    ###若每个样本分开运行 2-pass mapping，则推荐使用参数：--twopassMode
    #####其基本思想是，先运行1st pass mapping，自动提取剪切位点信息，再将其插入genome index中，最后，将所有reads重新运行2nd mapping pass。
    ### --twopass1readsN denefines the number of reads to be mapped in the 1st pass.(该参数指定在1st pass mapping过程中进行比对的reads数)
    #####The default and most sensitive approach is to set it to -1 (or make it bigger than the number of reads in the sample) in which case all reads in the input read file(s) are used in the 1st pass.

2-pass mapping with re-generated genome

1. Run 1st pass STAR for all samples with “usual” parameters. Genome indices generated with annotations are recommended.
2. Collect all junctions detected in the 1st pass by merging SJ.out.tab files from all runs. Filter the junctions by removing likelie false positives, e.g. junctions in the mitochondrion genome,
   
   or non-canonical junctions supported by a few reads. If you are using annotations, only novel junctions need to be considered here, since annotated junctions will be re-used in the 2nd pass
   
   anyway.
3. Use the filtered list of junctions from the 1st pass with –sjdbFileChrStartEnd option, together with annotations (via –sjdbGTFfile option) to generate the new genome indices for the 2nd pass mapping. This needs to be done only once for all samples.
4. Run the 2nd pass mapping for all samples with the new genome index.