RNA-seq汇总(分析+工具+GATK流程)

RNA-seq分析流程

不得不提的一篇文献
Sahraeian, Sayed Mohammad Ebrahim, et al. "Gaining comprehensive biological insight into the transcriptome by performing a broad-spectrum RNA-seq analysis." Nature communications 8.1 (2017): 59.
文献地址:https://www.nature.com/articles/s41467-017-00050-4

简单描述:

15个样品(具有各种种系,癌症和干细胞数据集),39个分析工具,约120种组合,约490次分析。
针对各个工具,作者都描述了其性能表现,做出了庞大的比较分析。并在此基础上,作者构建了一个综合性的RNA-seq analysis protocol,即RNACocktail(https://bioinform.github.io/rnacocktail/),囊括了这些工具,免费提供给他人下载使用,以帮助研究人员更好地进行生物学分析。

文献流程:

1.比对
2.有参转录本组装
3.无参转录本组装
4.三代长reads获取转录本
5.转录本定量
6.差异表达
7.突变分析
8.RNA编辑
9.融合基因检测

再详细的内容可以看文献,这里就不在赘述了。

文献工具总结:

注:其中包含了一些软件或工具的重要参数设定

1.比对工具

1.TopHat2: –no-coverage-search http://ccb.jhu.edu/software/tophat/index.shtml

2.STAR: -twopassMode Basic –outFilterType BySJout https://github.com/alexdobin/STAR/releases

3.HISAT2 2.0.1-beta –dta (or –dta-cufflinks) http://www.ccb.jhu.edu/software/hisat/index.shtml

4.RASER 0.52 -b 0.03 https://www.ibp.ucla.edu/research/xiao/RASER.html

2.有参考转录本组装工具

1.Cufflinks 2.2.1 –frag-bias-correct http://cole-trapnell-lab.github.io/cufflinks/

2.StringTie 1.2.1 -v -B http://www.ccb.jhu.edu/software/stringtie/

3.无参考转录本组装工具

1.SOAPdenovoTrans 1.04 -K 25 https://github.com/aquaskyline/SOAPdenovo-Trans/

2.Oases 0.2.09(velveth haslength: 25) http://www.ebi.ac.uk/~zerbino/oases/

(需依赖软件Velvetv1.2.10) (velvetg options: -read trkg yes)

  1. Trinity 2.1.1 –normalize reads https://github.com/trinityrnaseq/trinityrnaseq/wiki

  2. Trimmomatic 0.35 http://www.usadellab.org/cms/index.php?page=trimmomatic

4.三代长read分析工具

1.LoRDEC 0.6 -k 23 -s 3 http://atgc.lirmm.fr/lordec/

2.GMAP 12/31/15 -f 1 http://research-pub.gene.com/gmap/

3. STARlong 2.5.1b https://github.com/alexdobin/STAR/releases

Followed the recommended options :

–outSAMattributes NH HI NM MD –readNameSeparator space

–outFilterMultimapScoreRange 1 –outFilterMismatchNmax 2000 –scoreGapNoncan -20

–scoreGapGCAG -4 –scoreGapATAC -8 –scoreDelOpen -1 –scoreDelBase -1

–scoreInsOpen -1 –scoreInsBase -1 –alignEndsType Local –seedSearchStartLmax 50

–seedPerReadNmax 100000 –seedPerWindowNmax 1000

–alignTranscriptsPerReadNmax 100000 –alignTranscriptsPerWindowNmax 10000

–outSAMstrandField intronMotif –outSAMunmapped Within

4. IDP 0.1.9 https://www.healthcare.uiowa.edu/labs/au/IDP/

5.定量工具

1. eXpress 1.5.1 (bowtie2 v2.2.7) https://pachterlab.github.io/eXpress/index.html

(bowtie2 options: -a -X 600 –rdg 6,5 –rfg 6,5 –score-min L,-.6,-.4 –no-discordant –no-mixed)

2. kallisto 0.42.4 http://pachterlab.github.io/kallisto/about.html

3. Sailfish 0.9.0 http://www.cs.cmu.edu/~ckingsf/software/sailfish/

4. Salmon-Aln 0.6.1 https://github.com/COMBINE-lab/salmon

5. Salmon-SMEM 0.6.1 index: –type fmd quant: -k,19 https://github.com/COMBINE-lab/salmon

6. Salmon-Quasi 0.6.1 index: –type quasi -k 31 https://github.com/COMBINE-lab/salmon

7. featureCounts 1.5.0-p1 -p -B -C http://subread.sourceforge.net/

6.差异表达分析工具

1. DESeq2 1.14.1 http://bioconductor.org/packages/release/bioc/html/DESeq2.html

2. edgeR 3.16.5 http://www.bioconductor.org/packages/release/bioc/html/edgeR.html

3. limma 3.30.7 http://bioconductor.org/packages/release/bioc/html/limma.html

4. Cuffdiff 2.2.1 –frag-bias-correct –emit-count-tables http://cole-trapnell-lab.github.io/cufflinks/

  1. Ballgown 2.6.0 https://github.com/alyssafrazee/ballgown

6. sleuth 0.28.1 https://github.com/pachterlab/sleuth

7. 变异分析工具

1. SAMtools 1.2 samtools mpileup -C50 -d 100000 https://github.com/samtools/samtools

(bcftools v1.2) bcftools filter -s LowQual -e ’%QUAL<20 -DP>10000’ https://github.com/samtools/bcftools

2.GATK v3.5-0-g36282e4 (picard 1.129) https://software.broadinstitute.org/gatk/download/

Picard AddOrReplaceReadGroups: SO=coordinate

Picard MarkDuplicates: CREATE INDEX=true VALIDATION STRINGENCY=SILENTGATK

SplitNCigarReads: -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW N CIGAR READSGATK

HaplotypeCaller: -stand call conf 20.0 -stand emit conf 20.0 -A StrandBiasBySample -A StrandAlleleCountsBySampleGATK

VariantFiltration: -window 35 -cluster 3 -filterName FS -filter“FS >30.0” -filterName QD -filter “QD <2.0”

8.RNA编辑

1. GIREMI 0.2.1 https://github.com/zhqingit/giremi

2. Varsim 0.5.1 https://github.com/bioinform/varsim http://bioinform.github.io/varsim/

9.基因融合

1.FusionCatcher 0.99.5a beta https://github.com/ndaniel/fusioncatcher

2.JAFFA 1.0.6 https://github.com/Oshlack/JAFFA

3.SOAPfuse 1.27 http://soap.genomics.org.cn/soapfuse.html

4.STAR-Fusion 0.7.0 https://github.com/STAR-Fusion/STAR-Fusion

5.TopHat-Fusion 2.0.14 http://ccb.jhu.edu/software/tophat/fusion_index.shtml

RNA-seq GATK Workflow

1. Mapping to the reference

评估了所有专门用于RNAseq比对的主要软件包,发现使用STAR能够对SNP和重要的indels实现最高的灵敏度.同时使用STAR 2-pass方法

下面是STAR 2-pass 比对步骤:

  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:

genomeDir=/path/to/hg19\ mkdir $genomeDir
**STAR** --runMode genomeGenerate --genomeDir $genomeDir --genomeFastaFiles hg19.fa --runThreadN

  1. Alignment jobs were executed as follows:

runDir=/path/to/1pass\ mkdir $runDir \cd ​$runDir
**STAR** --genomeDir $genomeDir --readFilesIn mate1.fq mate2.fq --runThreadN

  1. 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:

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
注:sjdbOverhang 100
int>0: length of the donor/acceptor sequence on each side of the junctions, ideally = (mate_length - 1)

  1. The resulting index is then used to produce the final alignments as follows:

runDir=/path/to/2pass\ mkdir $runDir\ cd $runDir
**STAR** --genomeDir $genomeDir --readFilesIn mate1.fq mate2.fq --runThreadN

2. Add read groups, sort, mark duplicates, and create index

上面的步骤生成一个SAM文件,然后我们通过常规的Picard处理步骤:添加read组信息,排序,标记重复和索引.

java -jar picard.jar **AddOrReplaceReadGroups**I=star_output.sam O=rg_added_sorted.bam SO=coordinate RGID=id RGLB=library RGPL=platform RGPU=machine RGSM=sample
java -jar picard.jar **MarkDuplicates** I=rg_added_sorted.bam O=dedupped.bam CREATE_INDEX=true VALIDATION_STRINGENCY=SILENT M=output.metrics

3. Split'N'Trim and reassign mapping qualities

接下来,我们使用专为RNAseq开发的名为SplitNCigarReads的新GATK工具,该工具将read分成外显子片段(除去Ns但保持分组信息)并硬切割任何突出到内含子区域的序列。

在这一步,我们还添加了一个重要的调整:我们需要重新分配映射质量,因为STAR指定了良好的对齐,MAPQ为255(技术上意味着“未知”,因此对GATK毫无意义)。 所以我们使用GATK的ReassignOneMappingQuality读取过滤器将所有良好的比对重新分配到默认值60.这不是理想的,我们希望将来RNAseq的映射器会发出有意义的质量分数,但同时这是我们能做的最好的。 实际上,我们通过将ReassignOneMappingQuality读取过滤器添加到splitter命令来实现此目的。

最后,请务必指定允许使用ALLOW_N_CIGAR_READS。目前这仍然被归类为“不安全”选项,但这种分类将改变以反映这一事实,现在这是RNAseq处理的支持选项.

java -jar GenomeAnalysisTK.jar -T **SplitNCigarReads** -R ref.fasta -I dedupped.bam -o split.bam -rf ReassignOneMappingQuality -RMQF 255 -RMQT 60 -U ALLOW_N_CIGAR_READS
注:-U,--unsafe
Enable unsafe operations: nothing will be checked at runtime (ALLOW_N_CIGAR_READS|ALLOW_UNINDEXED_BAM|ALLOW_UNSET_BAM_SORT_ORDER|NO_READ_ORDER_VERIFICATION|ALLOW_SEQ_DICT_INCOMPATIBILITY|LENIENT_VCF_PROCESSING|ALL)

4. Indel Realignment (optional)

可选项.
java -jar GenomeAnalysisTK.jar -T **RealignerTargetCreator** -I split.bam -Rref.fasta-o split.intervals
java -jar GenomeAnalysisTK.jar -T **IndelRealigner** -I split.bam-R ref.fasta-targetIntervals split.intervals -o split.intervals.bam

5. Base Recalibration

可选项,建议运行BQSR.
java -jar GenomeAnalysisTK.jar -T **BaseRecalibrator** -R ref.fasta-I split.intervals.bam -knownSites dbsnp_138.hg19.vcf -mte -nct 4 -o split.recal.table
java -jar GenomeAnalysisTK.jar -T **PrintReads -**R ref.fasta-BQSR split.recal.table -I split.intervals.bam -o split.intervals.real.bam

6. Variant calling

我们为变体调用代码添加了一些功能,它将智能地考虑由SplitNCigarReads嵌入在BAM文件中的内含子 - 外显子分裂区域的信息。简而言之,新代码将执行“悬空头合并”操作,并避免使用软剪辑基础(这是一个临时解决方案),以尽量减少假阳和假阴。要调用此新功能,只需将-dontUseSoftClippedBases添加到常规HC命令行即可。此外,我们发现如果我们设置最小phred-scaled置信度阈值20去Call突变,我们会得到更好的结果,但如果需要,可以降低此值以提高灵敏度。.

java -jar GenomeAnalysisTK.jar -T **HaplotypeCaller** -R ref.fasta -I input.bam -dontUseSoftClippedBases -stand_call_conf 20.0 -o output.vcf

7. Variant filtering

用hard filters过滤生成的突变,因为还没有RNAseq运行VQSR所需的training/truth资源.

java -jar GenomeAnalysisTK.jar -T **VariantFiltration** -R hg_19.fasta -V input.vcf -window 35 -cluster 3 -filterName FS -filter "FS > 30.0" -filterName QD -filter "QD < 2.0" -o output.vcf

如果更关心灵敏度并且愿意容忍更多假阳,可以选择不进行过滤(或使用限制较少的阈值).

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