跟着Nature Communications学习Hisat-Trinity-PASA等分析流程

本文主要是介绍跟着Nature Communications学习Hisat-Trinity-PASA等分析流程，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

一边学习，一边总结，一边分享！

详细教程请访问：
组学分析流程

本期分析流程

1. Hisat2-Samtools
2. Trinity_GG_denovo
3. PASA

…

本期教程文章

题目：Genomic insights into local adaptation and future climate-induced vulnerability of a keystone forest tree in East Asia

Hisat2-samtools分析流程

#!/bin/bashgenome=$1
index=${genome%.*}
rna_1_fq=`cat $2|grep 1P|sed ":a;N;s/\n/,/g;ta"` #1.fq path list
rna_2_fq=`cat $2|grep 2P|sed ":a;N;s/\n/,/g;ta"` #2.fq path list#echo $index
hisat2-build -p 20 $genome $indexhisat2 -x $index \-1 $rna_1_fq\-2 $rna_2_fq\--threads 20 \--min-intronlen 20 \--max-intronlen 20000 \--dta \--score-min L,0.0,-0.4 \-S ${index}.samsamtools sort -@ 20 \-o ${index}.sorted.bam \-O BAM \${index}.sam

PSSA_align

#!/bin/bashexport PATH="$PATH:/usr_storage/jcf/.conda/envs/PASA"
source  /pub_storage2/new_PASA/.bashrc#cat $Trinity_GG $Trinity_denovo >transcripts.fasta #
transcripts_fasta="$1" # transcripts.fasta generated from merging fasta file of Trinity denovo and Trinity genome guided mode#perl -e 'while(<>) { print "$1\n" if />(\S+)/ }' Trinity.fasta >tdn.accs #
denovo_transcript_id="$2" 
alignAssembly_config="$3"
genome="$4" #reference fasta fileseqclean $transcripts_fasta \-v /pub_storage2/PASA/UniVecLaunch_PASA_pipeline.pl -c $alignAssembly_config \-C -R -T \-g $genome \ -t $transcripts_fasta.clean \-u ${transcripts_fasta} \--ALIGNERS gmap,blat \--CPU 8 \ --TDN $denovo_transcript_id

Trinity GG denovo

#!/bin/bash#conda activate trinityexport PATH="$PATH:/usr_storage/jcf/.conda/envs/trinity"rna_1_fq="cat $1|sed ":a;N;s/\n/,/g;ta"" #1.fq path list 
rna_2_fq="cat $2|sed ":a;N;s/\n/,/g;ta"" #2.fq path list
bam="$3"  #sorted.bam from hisat
out=${bam%.*}Trinity --left $rna_1_fq \--right $rna_2_fq \--seqType fq  \--max_memory 100G \--no_normalize_reads \--CPU 20 \--bflyCalculateCPU  \--output trinity_denovo_$outTrinity --genome_guided_bam $bam  \--genome_guided_max_intron 10000 \--max_memory 100G \--no_normalize_reads \--CPU 20 \--bflyCalculateCPU\--output trinity_GG_$out

ab homo

#!/bin/bashexport PATH="$PATH:/usr_storage/jcf/.conda/envs/BUSCO"
source /usr_storage/jcf/geta-user204/.bashrcrna_1_fq="cat $1|sed ":a;N;s/\n/,/g;ta"" #1.fq path list 
rna_2_fq="cat $2|sed ":a;N;s/\n/,/g;ta"" #2.fq path list
genome="$3" #genome fasta file 
conf="$4" #small genome conf.txt of geta pipepline setting as default parameters
out=${genome%.*}
homo_pro="$5"geta.pl \--RM_species Embryophyta\--out_prefix `pwd`/$out \--config $conf \--cpu 20 \--protein $homo_pro\-genome $genome \-1 $rna_1_fq \-2 $rna_2_fq \--augustus_species $out

Evm

#!/bin/bashexport PATH="/usr_storage/xyf/jcf/genewise/EVM/EVidenceModeler-1.1.1/EvmUtils/:$PATH"genome="$1" #genome fasta file 
augustus_gff3="$2" #gff3 generated from augutus 
genewise_gff3="$3" #gff3 generated from tblastn and genewise
pasa_align_gff3="$4" #gff3 generated from PASA 
repeat_gff3="$5" #repeat gff3 generated from repeatemasker
partition="$6" #partition path for evmpartition_EVM_inputs.pl \--genome $genome\--gene_predictions $augustus_gff3 \--protein_alignments $genewise_gff3 \--transcript_alignments $pasa_align_gff3 \--repeats $repeat_gff3 \--segmentSize 5000000 \--overlapSize 10000 \--partition_listing $partitionwrite_EVM_commands.pl \--genome $genome \--gene_predictions $augustus_gff3 \--protein_alignments $genewise_gff3 \--transcript_alignments $pasa_align_gff3 \--repeats $repeat_gff3 \--output_file_name evm.out \--weights $weight >command.listParaFly -c command.list -CPU 32 recombine_EVM_partial_outputs.pl \--partitions $partition \--output_file_name evm.out convert_EVM_outputs_to_GFF3.pl \--partitions $partition \--output_file_name evm.out \--genome  $genome cat */evm.out.gff3 >evm.out.gff3

PASA update

#!/bin/bashexport PATH="$PATH:/usr_storage/jcf/.conda/envs/PASA "
source  /pub_storage2/new_PASA/.bashrcgenome="$1" #genome fasta file
annotation_conf="$2" #pasa annotation compare conf 
transcripts_fasta="$3" #transcripts_fasta file for PASA seqclean step
gff3="$4" #gff3 for PASA updataLaunch_PASA_pipeline.pl \-c $annotation_conf\-A -T -L \-g $genome\-t ${transcripts_fasta}.clean \-u $transcripts_fasta \--annots $gff3

这里只是提供了各个分析流程的脚本，对于初学者来说是比较有好的。我们在转录组上游分析教程[零基础]中提供了详细转录组上游分析的参数，对于初学者来说是比较友好的。

往期文章：

1. 复现SCI文章系列专栏

2. 《生信知识库订阅须知》,同步更新，易于搜索与管理。

3. 最全WGCNA教程（替换数据即可出全部结果与图形）

• WGCNA分析 | 全流程分析代码 | 代码一

• WGCNA分析 | 全流程分析代码 | 代码二

• WGCNA分析 | 全流程代码分享 | 代码三

• WGCNA分析 | 全流程分析代码 | 代码四

• WGCNA分析 | 全流程分析代码 | 代码五(最新版本)

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4. 精美图形绘制教程

• 精美图形绘制教程

5. 转录组分析教程

转录组上游分析教程[零基础]

小杜的生信筆記 ，主要发表或收录生物信息学的教程，以及基于R的分析和可视化（包括数据分析，图形绘制等）；分享感兴趣的文献和学习资料!!

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