{"id":1076,"date":"2020-11-10T16:28:21","date_gmt":"2020-11-10T15:28:21","guid":{"rendered":"https:\/\/itrop.ird.fr\/wordpress\/?page_id=1076"},"modified":"2022-04-06T14:50:08","modified_gmt":"2022-04-06T12:50:08","slug":"trainings-2019-trinity-practice-annotation","status":"publish","type":"page","link":"https:\/\/bioinfo.ird.fr\/index.php\/trainings-fr\/trainings-2019-trinity\/trainings-2019-trinity-practice-annotation\/","title":{"rendered":"Trainings 2019 &#8211; Trinity &#8211; Practice Annotation"},"content":{"rendered":"<h2>Annotation of Transcrits Assemblies Practice<\/h2>\n<table>\n<thead>\n<tr>\n<th style=\"text-align: left;\">Description<\/th>\n<th style=\"text-align: left;\">Hands On Lab Exercises for  RNASeq annotation<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"text-align: left;\">Related-course materials<\/td>\n<td style=\"text-align: left;\"><a href=\"https:\/\/itrop.ird.fr\/wordpress\/index.php\/trainings-2019-linux-for-dummies\/\">Linux for Dummies<\/a><\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: left;\">Authors<\/td>\n<td style=\"text-align: left;\">Julie Orjuela-Bouniol (julie.orjuela@ird.fr)  - i-trop platform (UMR BOREA \/ DIADE \/ IPME - IRD)<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: left;\">Creation Date<\/td>\n<td style=\"text-align: left;\">03\/08\/2018<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: left;\">Last Modified Date<\/td>\n<td style=\"text-align: left;\">19\/09\/2019<\/td>\n<\/tr>\n<tr>\n<td style=\"text-align: left;\">Modified by<\/td>\n<td style=\"text-align: left;\">...<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h1>Funtional Annotation of Transcrits using <code>Trinotate<\/code> package<\/h1>\n<h2>Summary<\/h2>\n<p><!-- TOC depthFrom:2 depthTo:2 withLinks:1 updateOnSave:1 orderedList:0 --><\/p>\n<ul>\n<li><a href=\"#practice-0\">Preambule : Connection to the i-Trop cluster - <code>ssh,srun,scp<\/code><\/a><\/li>\n<li><a href=\"#practice-1\">Practice 1: Trinotate pipeline : first part (run_trinotate.slurm script )<\/a><\/li>\n<li><a href=\"#practice-2\">Practice 2: Trinotate pipeline : second part (Annotation report script)<\/a><\/li>\n<li><a href=\"#license\">License<\/a><\/li>\n<\/ul>\n<hr \/>\n<h2>Preambule<\/h2>\n<p>Transcrits assembled using Trinity can be easily annotate using trinotate <a href=\"https:\/\/github.com\/Trinotate\/Trinotate.github.io\/wiki\">https:\/\/github.com\/Trinotate\/Trinotate.github.io\/wiki<\/a>.<\/p>\n<p>Trinotate use different methods for functional annotation including homology search to known sequence data (BLAST+\/SwissProt), protein domain identification (HMMER\/PFAM), protein signal peptide and transmembrane domain prediction (signalP\/tmHMM), and take advantage from annotation databases (eggNOG\/GO\/Kegg). These data are integrated into a SQLite database which allows to create an annotation report for a transcriptome.<\/p>\n<p>Two bash scripts were created to obtain the whole of files obligatories to build a Sqlite database and create reports.<\/p>\n<hr \/>\n<p><a name=\"practice-0\"><\/a><\/p>\n<h2>0. Connection to the i-Trop Cluster through <code>ssh<\/code> mode<\/h2>\n<p>We will work on the i-Trop Cluster with a &quot;supermem&quot; node using SLURM scheduler.<\/p>\n<pre><code>ssh formationX@bioinfo-master.ird.fr<\/code><\/pre>\n<h3>Connection to supermem partition<\/h3>\n<p>Connect you to node25 (supermem partition) without opening an interactive bash session<\/p>\n<pre><code>ssh node25<\/code><\/pre>\n<h3>Prepare input files<\/h3>\n<pre><code>#make directory to annotation\ncd \/scratch\/formationX\/\nmkdir ANNOTATION\ncd ANNOTATION\n\n# make a symbolic link for fasta assembly. now assembled sequences is called sacharomyces.fasta\nln -s \/scratch\/formationX\/TRINITY_OUT\/Trinity.fasta sacharomyces.fasta\n\n#copy script into this ANNOTATION repertory\nscp nas:\/data2\/formation\/TP-trinity\/scripts\/run_trinotate.slurm .\nscp nas:\/data2\/formation\/TP-trinity\/scripts\/build_sqlite_trinotateDB.sh .\n\n#open `run_trinotate.slurm` script with nano and adapt pathToScratch variable with your formation number\npathToScratch=&quot;\/tmp\/formationX\/ANNOTATION\/&quot;<\/code><\/pre>\n<p><a name=\"practice-1\"><\/a><\/p>\n<h2>1. Trinotate pipeline  : first part (run_trinotate.slurm script )<\/h2>\n<p>Let's run <code>run_trinotate.slurm<\/code> to obtain ORFs, seach sequence homology and conserved domains and others ...<\/p>\n<pre><code># run annotation script in slurm\nsbatch run_trinotate.slurm<\/code><\/pre>\n<p class=\"warning\"> WARNING !: This job can be run for about 12h <\/p>\n<p>\nYou can scp results from <code>nas:\/data2\/formation\/TP-trinity\/TRINITY_OUT\/ANNOTATION\/results_sacharomyces<\/code> to your <code>\/scratch\/formationX\/ANNOTATION<\/code> repertory.<\/p>\n<p>```<br \/>\nscp -r nas:\/data2\/formation\/TP-trinity\/TRINITY_OUT\/ANNOTATION\/results_sacharomyces\/ \/scratch\/formationX\/ANNOTATION\/<br \/>\n```<\/p>\n<p>What is doing this script ? Most important steps are explained here :\n<\/p>\n<h3>Determining longest Open Reading Frames (ORFs)<\/h3>\n<p>First step of the annotation of transcript is to determine open reading frame (ORFs), they will be then annotated. Use <code>TransDecoder<\/code> to identy likely coding sequences based on the following steps:<\/p>\n<p>Running TransDecoder is a two-step process. First run the TransDecoder step that identifies all long ORFs and then the step that predicts which ORFs are likely to be coding (TransDecoder.LongOrfs, TransDecoder.Predict). Once you have the sequences you can start looking for sequence or domain homologies.<\/p>\n<pre><code># 2 generation of peptide file (TransDecoder.LongOrfs)\n\n# 2.1 generation of longestOrf with minimum protein length 50aa\nTransDecoder.LongOrfs -t $pathToScratch\/sacharomyces.fasta \\\n--gene_trans_map $pathToScratch\/results_sacharomyces\/sacharomyces.fasta_gene_trans_map\\\n-m 50 <\/code><\/pre>\n<pre><code># 2.2a recherche d\u2019identit\u00e9 parmis les longorfs hmmscan\n# Let&#039;s run a HMMER search against the Pfam database using the longorfs and identify conserved \n# domains that might be indicative or suggestive of function\nhmmscan --cpu 2 --domtblout pfam_longorfs.domtblout\\\n$pathToScratch\/DB\/Pfam-A.hmm \\\n$pathToScratch\/results_sacharomyces\/sacharomyces.fasta.transdecoder_dir\/longest_orfs.pep <\/code><\/pre>\n<pre><code># 2.2b recherche d\u2019identit\u00e9 parmis les longorfs by blastp with diamond\n\/usr\/local\/diamond-0.8.29\/diamond blastp \\\n--query $pathToScratch\/results_sacharomyces\/sacharomyces.fasta.transdecoder_dir\/longest_orfs.pep\\\n--db $pathToScratch\/DB\/uniprot_sprot \\\n--out diamP_uniprot_longorfs.outfmt 6\\\n--outfmt 6 \\\n--max-target-seqs 1<\/code><\/pre>\n<p>Now, run the step that predicts which ORFs are likely to be coding.<\/p>\n<pre><code># #2.3 Prediction peptides (TransDecoder.Predict)\nTransDecoder.Predict --cpu 2 \\\n-t $pathToScratch\/sacharomyces.fasta \\\n--retain_pfam_hits $pathToScratch\/results_sacharomyces\/pfam_longorfs.domtblout \\\n--retain_blastp_hits $pathToScratch\/results_sacharomyces\/diamP_uniprot_longorfs.outfmt6 <\/code><\/pre>\n<h3>Sequence homology searches from predicted protein sequences<\/h3>\n<p>Now, let's look for sequence homologies by just searching our predicted protein sequences rather than using the entire transcript as a target<\/p>\n<pre><code># 3 Recherche de similarit\u00e9 en utilisant Diamond\n\n# blastp diamP_uniprot\n\/usr\/local\/diamond-0.8.29\/diamond blastp \\\n--query $pathToScratch\/results_sacharomyces\/sacharomyces.fasta.transdecoder.pep \\\n--threads 2 \\\n--db $pathToScratch\/DB\/uniprot_sprot \\\n--out $pathToScratch\/results_sacharomyces\/diamP_uniprot.outfmt6\\\n--outfmt 6 \\\n--max-target-seqs 1\\\n--more-sensitive <\/code><\/pre>\n<pre><code># blastp diamP_uniref90 \n\/usr\/local\/diamond-0.8.29\/diamond blastp \\\n--query $pathToScratch\/results_sacharomyces\/sacharomyces.fasta.transdecoder.pep \\\n--threads 2 \\\n--db $pathToScratch\/DB\/uniref90.fasta \\\n--out $pathToScratch\/results_sacharomyces\/diamP_uniref90.outfmt6 \\\n--outfmt 6 \\\n--max-target-seqs 1 \\\n--more-sensitive <\/code><\/pre>\n<pre><code># blastx diamX_uniprot \n\/usr\/local\/diamond-0.8.29\/diamond blastx \\\n--query $pathToScratch\/sacharomyces.fasta \\\n--threads 2 \\\n--db $pathToScratch\/DB\/uniprot_sprot \\\n--out diamX_uniprot.outfmt6 \\\n--outfmt 6 \\\n--max-target-seqs 1 \\\n--more-sensitive <\/code><\/pre>\n<pre><code># blastx diamX_uniref90 \n\/usr\/local\/diamond-0.8.29\/diamond blastx \\\n--query $pathToScratch\/sacharomyces.fasta \\\n--threads 2\\\n--db $pathToScratch\/DB\/uniref90.fasta \\\n--out diamX_uniref90.outfmt6 \\\n--outfmt 6 \\\n--max-target-seqs 1 \\\n--more-sensitive<\/code><\/pre>\n<h3>Search conserved domains<\/h3>\n<p>Using our predicted protein sequences, let's also run a HMMER search against the Pfam database, and identify conserved domains that might be indicative or suggestive of function<\/p>\n<pre><code># 3 recherche de domaines \nhmmscan --cpu 2 --domtblout $pathToScratch\/results_sacharomyces\/sacharomyces_PFAM.out\\\n$pathToScratch\/DB\/Pfam-A.hmm \\\n$pathToScratch\/results_sacharomyces\/sacharomyces.fasta.transdecoder.pep &gt; $pathToScratch\/results_sacharomyces\/pfam.log<\/code><\/pre>\n<h3>Computational prediction of sequence features<\/h3>\n<h4>Recheche de peptides signaux<\/h4>\n<p>The signalP and tmhmm software tools are very useful for predicting signal peptides (secretion signals) and transmembrane domains, respectively.<\/p>\n<pre><code># 4 recheche de peptides signaux \nperl \/usr\/local\/signalp-4.1\/signalp \\\n-f short \\\n-n $pathToScratch\/results_sacharomyces\/sacharomyces_signalp.out \\\n$pathToScratch\/results_sacharomyces\/sacharomyces.fasta.transdecoder.pep <\/code><\/pre>\n<pre><code># 5 recherche de domaines transmembranaires # undetected in this dataset\ntmhmm --short &lt;  $pathToScratch\/results_sacharomyces\/sacharomyces.fasta.transdecoder.pep &gt; $pathToScratch\/results_sacharomyces\/sacharomyces.tmhmm.out  <\/code><\/pre>\n<p>question : How many of your proteins are predicted to encode signal peptides?<\/p>\n<h4>\ufffcRunning Rnammer to detected rRNA<\/h4>\n<p>The program uses hidden Markov models trained on data from the 5S ribosomal RNA database and the European ribosomal RNA database project<\/p>\n<pre><code># 6 recherche de rRNA \n\/usr\/local\/Trinotate-3.0.1\/util\/rnammer_support\/RnammerTranscriptome.pl \\\n--transcriptome $pathToScratch\/sacharomyces.fasta \\\n--org_type euk \\\n--path_to_rnammer \/usr\/local\/rnammer-1.2\/rnammer<\/code><\/pre>\n<hr \/>\n<p><a name=\"practice-2\"><\/a><\/p>\n<h1>Trinotate pipeline : second part (Annotation report script)<\/h1>\n<p>Now, we need allocate 10 RAM memory and 2 CPU with <code>srun<\/code> to continue with this practical. <\/p>\n<pre><code>srun -p supermem --mem 10G --time 08:00:00 --cpus-per-task 2 --pty bash -i<\/code><\/pre>\n<p class=\"warning\"> WARNING !:  Don't forget that you can scp results from first part of this TP from <code>nas:\/data2\/formation\/TP-trinity\/TRINITY_OUT\/ANNOTATION\/results_sacharomyces<\/code>!\n <\/p>\n<pre><code>cd \/scratch\/formationX\/ANNOTATION\/\nscp -r nas:\/data2\/formation\/TP-trinity\/TRINITY_OUT\/ANNOTATION\/results_sacharomyces\/ .<\/code><\/pre>\n<h4>Loading results into a Trinotate SQLite database and generating a report.<\/h4>\n<p>Generating a Trinotate annotation report involves first loading all of our bioinformatics computational results into a Trinotate SQLite database. The Trinotate software provides a boilerplate SQLite database called <code>Trinotate.sqlite<\/code> that comes pre-populated with a lot of generic data about SWISSPROT records and Pfam domains. This database is populated with all computes obtained before and the expression data to build a final report.<\/p>\n<ul>\n<li>Run the second bash script <code>build_sqlite_trinotateDB.sh<\/code> . This script needs as input the assembled transcrits and the repertory containing the whole of results obtained by <code>run_trinotate.slurm<\/code> (option -r) and the transcripts assembled by trinity file (option -f).<\/li>\n<\/ul>\n<pre><code>#go to annotation results directory\ncd \/scratch\/formationX\/ANNOTATION\/results_sacharomyces\n#run script\nbash ..\/build_sqlite_trinotateDB.sh -f \/scratch\/formationX\/ANNOTATION\/sacharomyces.fasta -r \/scratch\/formationX\/ANNOTATION\/results_sacharomyces\/<\/code><\/pre>\n<p>What is running in <code>build_sqlite_trinotateDB.sh <\/code> script?<\/p>\n<pre><code>#7 recuperation de la base Trinotate\necho &quot;7 Recuperation de la base Trinotate&quot;\nwget &quot;https:\/\/data.broadinstitute.org\/Trinity\/Trinotate_v3_RESOURCES\/Trinotate_v3.sqlite.gz&quot; -O Trinotate.sqlite.gz\ngunzip Trinotate.sqlite.gz\n\n# 8 chargement des analyses dans la base\necho &quot;8 chargement des analyses dans la base&quot;\n\/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite init --gene_trans_map $geneTransMap --transcript_fasta $fasta --transdecoder_pep $transdecoderPep\n\n#charging swissprot\/uniprot P and X results\necho &quot; --&gt;charging swissprot\/uniprot P and X results&quot;\n\/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite LOAD_swissprot_blastp diamP_uniprot.outfmt6\n\/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite LOAD_swissprot_blastx diamX_uniprot.outfmt6\n\n#charging uniref90 P and X results\necho &quot; --&gt;charging  uniref90 P and X results&quot; \n\/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite LOAD_custom_blast --outfmt6 diamP_uniref90.outfmt6 --prog blastp --dbtype uniref90\n\/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite LOAD_custom_blast --outfmt6 diamX_uniref90.outfmt6 --prog blastx --dbtype uniref90\n\n#charging pfam, tmhmm, signalp and rnammer\necho &quot; --&gt;charging  pfam, tmhmm, signalp and rnammer&quot;\n\/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite LOAD_pfam $shortName&quot;_PFAM.out&quot; \n\/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite LOAD_tmhmm $shortName&quot;.tmhmm.out&quot;\n\/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite LOAD_rnammer $shortName&quot;.fasta.rnammer.gff&quot;\n\nif [ -s $signalp ]; then\n    #charging signalp\n    \/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite LOAD_signalp $shortName&quot;.signalp.out&quot;\n    ls $signalp\nfi\n\n# 9 generation du report\necho &quot;9 generation du report&quot;\n\/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite report &gt; $shortName&quot;_annotation_report.xls&quot;\n\n#filtr\u00e9 sur les e-value des annotations\necho &quot; --&gt;filtr\u00e9 sur les e-value des annotations&quot;\n\/usr\/local\/Trinotate-3.0.1\/Trinotate $resultsDir\/Trinotate.sqlite report -E 10e-10 &gt; $shortName&quot;_annotation_report_filtered.xls&quot;\n\n#10 generation de statistiques\necho &quot;10 generation de statistiques&quot;\n\/usr\/local\/Trinotate-3.0.1\/util\/count_table_fields.pl $resultsDir\/$shortName&quot;_annotation_report_filtered.xls&quot; &gt; $shortName&quot;_table_fields.txt&quot;\n\n#11 extract GO terms\necho &quot;extract GO terms&quot;\n\/usr\/local\/Trinotate-3.0.1\/util\/extract_GO_assignments_from_Trinotate_xls.pl --Trinotate_xls $shortName&quot;_annotation_report_filtered.xls&quot; -G --include_ancestral_terms &gt; $shortName&quot;_go_annotations.txt&quot;\n\necho &quot; pour visualiser les GO va sur le site : &quot; \necho &quot;site wego : http:\/\/wego.genomics.org.cn\/&quot;<\/code><\/pre>\n<pre><code>[orjuela@node25 results_sacharomyces_last]$ more sacharomyces_table_fields.txt\n#gene_id    15839\ntranscript_id   15839\nuniref90_BLASTX 15178\nprot_id 14898\nprot_coords 14898\nsprot_Top_BLASTX_hit    14852\nuniref90_BLASTP 13086\nsprot_Top_BLASTP_hit    12641\ngene_ontology_blast 11842\nKegg    11592\nPfam    9327\ngene_ontology_pfam  6120\nTmHMM   3096\neggnog  2988\nSignalP 42\nRNAMMER 34\npeptide 0\ntranscript  0<\/code><\/pre>\n<p>Report can be found in <code>sacharomyces_annotation_report_filtered.xls<\/code> file. For details of report generated go to <a href=\"https:\/\/github.com\/Trinotate\/Trinotate.github.io\/wiki\/Loading-generated-results-into-a-Trinotate-SQLite-Database-and-Looking-the-Output-Annotation-Report\">https:\/\/github.com\/Trinotate\/Trinotate.github.io\/wiki\/Loading-generated-results-into-a-Trinotate-SQLite-Database-and-Looking-the-Output-Annotation-Report<\/a><\/p>\n<p>If you want to visualise GO go to wego site : <a href=\"http:\/\/wego.genomics.org.cn\/\">http:\/\/wego.genomics.org.cn\/<\/a> and import your <code>sacharomyces_go_annotations_rfm.txt<\/code> file after replace comma by tabulations :<\/p>\n<pre><code>sed &#039;s\/,\/\\t\/g&#039; sacharomyces_go_annotations.txt &gt; sacharomyces_go_annotations_rfm.tx<\/code><\/pre>\n<hr \/>\n<h3>License<\/h3>\n<p><a name=\"license\"><\/a><\/p>\n<div>\nThe resource material is licensed under the Creative Commons Attribution 4.0 International License (<a href=\"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/\">here<\/a>).<br \/>\n<center><img decoding=\"async\" width=\"25%\" class=\"img-responsive\" src=\"http:\/\/creativecommons.org.nz\/wp-content\/uploads\/2012\/05\/by-nc-sa1.png\"\/><br \/>\n<\/center>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Annotation of Transcrits Assemblies Practice Description Hands On Lab Exercises for RNASeq annotation Related-course materials Linux for Dummies Authors Julie&hellip; 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