Trinity学习笔记

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1:分析流程图如下

Trinity学习笔记

2: 首先就是将样本的reads合并在一起命令如下:

cat 1M_READS_sample/*.left.fq > reads.ALL.left.fq
cat 1M_READS_sample/*.right.fq > reads.ALL.right.fq

3:开始拼接

$TRINITY_HOME/Trinity.pl --seqType fq --JM 10G --left reads.ALL.left.fq --right reads.ALL.right.fq --SS_lib_type RF --CPU 6 --seqType fq —-output ./trinity_out_dir

输出文件:Trinity.fasta

4:拼接统计

$TRINITY_HOME/util/TrinityStats.pl trinity_out_dir/Trinity.fasta>./assembly_report.txt

输出文件:assembly_report.txt

5:比对reads评估表达量(每个样本都需要单独比对)

$TRINITY_HOME/util/align_and_estimate_abundance.pl --transcripts Trinity.fasta --seqType fq --left reads_1.fq --right reads_2.fq --est_method RSEM --aln_method bowtie --trinity_mode —prep_reference

比对输出:bowtie.csorted.bam 

RSEM输出

RSEM.isoforms.results  : EM read counts per Trinity transcript

RSEM.genes.results     : EM read counts on a per-Trinity-component (aka... gene) basis, gene used loosely here.

过滤比对

$TRINITY_HOME/util/filter_fasta_by_rsem_values.pl --rsem_output=/path/to/RSEM.isoforms.results[,...] --fasta=/path/to/Trinity.fasta --output=/path/to/output.fasta --fpkm_cutoff=1200

过滤值需要根据需求自己设定。

6:差异表达分析(edgeR)

假定有四个样本,转录本定量输出为:

LOG.isoforms.results 

DS.isoforms.results 

HS.isoforms.results

PLAT.isoforms.results

注意:--samples_file为样本分组信息文件 group.txt ,例如:

Throat sample2.sam

Saliva sample3.sam

Throat sample4.sam

Vaginal sample5.sam

--contrasts 为样本不同条件下比较compare.txt:

Throat Saliva

Vaginal  Saliva

Throat  Vaginal

7:提取最好的OFR

$TRINITY_HOME/trinity-plugins/transdecoder/TransDecoder -t  transcripts.fasta -m  100 —search_pfam /path/to/pfam_db.hmm to search —CPU 6

输出文件:

  • Trinity.fasta.transdecoder.pep
  • Trinity.fasta.transdecoder.cds
  • Trinity.fasta.transdecoder.bed
  • Trinity.fasta.transdecoder.gff3

8:功能注释

下载的软件:Trinotate、Trinity、sqlite、NCBI Blast、HMMER、signalP v4、tmhmm v2、RNAMMER

比对数据库:SwissProt、Uniref90、Pfam domains 

标准化数据:

makeblastdb -in uniprot_sprot.fasta -dbtype prot
makeblastdb -in uniref90.fasta -dbtype prot
hmmpress Pfam-A.hmm

blast比对(比对的数据库可以换成nr/Uniref90

# search Trinity transcripts

blastx -query Trinity.fasta -db uniprot_sprot.fasta -num_threads 8 -max_target_seqs 1 -outfmt 6 -evalue 1e-5 > blastx.outfmt6

# search Transdecoder-predicted proteins

blastp -query transdecoder.pep -db uniprot_sprot.fasta -num_threads 8 -max_target_seqs 1 -outfmt 6 -evalue 1e-5 > blastp.outfmt6

功能域

hmmscan --cpu 8 --domtblout TrinotatePFAM.out Pfam-A.hmm transdecoder.pep > pfam.log

信号肽

signalp -f short -n signalp.out transdecoder.pep

跨膜结构

tmhmm --short < transdecoder.pep > tmhmm.out

识别rRNA

$TRINOTATE_HOME/util/rnammer_support/RnammerTranscriptome.pl --transcriptome Trinity.fasta  --path_to_rnammer /usr/bin/software/rnammer_v1.2/rnammer

输出:Trinity.fasta.rnammer.gff

9:Load transcripts and coding regions

$TRINITY_HOME/util/support_scripts/get_Trinity_gene_to_trans_map.pl Trinity.fasta >Trinity.fasta.gene_trans_map

Trinotate Trinotate.sqlite init --gene_trans_map Trinity.fasta.gene_trans_map --transcript_fasta Trinity.fasta --transdecoder_pep transdecoder.pep

10:Output an Annotation Report

Trinotate Trinotate.sqlite LOAD_swissprot_blastp blastp.outfmt6
Trinotate Trinotate.sqlite LOAD_swissprot_blastx blastx.outfmt6
Trinotate Trinotate.sqlite LOAD_pfam TrinotatePFAM.out
Trinotate Trinotate.sqlite LOAD_tmhmm tmhmm.out
Trinotate Trinotate.sqlite LOAD_signalp signalp.out
Trinotate Trinotate.sqlite report >trinotate_annotation_report.xls

输出文件:trinotate_annotation_report.xls

原文来自:http://blog.sina.com.cn/s/blog_83f77c940102v7xu.html

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