User Manual for MTide
Contents
1. Mir The name of the mature miRNA Gene The gene name of the transcript GO GO ids Validated V detected from degradome seq P predicted by TAPIR MF _score Molecular function similarity score BP_score Biological process similarity score CC_score Cellular component similarity score All_score Add all the scores 7 SRNA_cleaned_vs_Rfam_for_miRDeep fa This is the small RNA sequencing reads file that could be mapped to Rfam_for_miRDeep fa 10 13 Script reference 1 Integrated wrapper MTide pl Description This script is an integrated wrapper of all the scripts listed down It only contains one option which is a configure file and users can modify this file for controlling the running procedure Usage MTide pl c mtide conf Options c conf string configure file of MTide pl default mtide conf 2 Processing the reads CleanReads pl Description This script removes the adapters from 5 and 3 of the reads ends discards the reads shorter than 18 or longer than 30 by default and then removes the redundancy such that reads with identical sequence are represented with a single FASTA entry Therefore each sequence identifier must like gt rd1_read1_x15 rd1 means the three letter suffix of the sample and x15 means the number of the unique read Usage CleanReads pl q f fasta i reads1 fa rd1 i reads2 fa rd2 Options h help Print help message and quit v version2. bowtie and bowtie build See http bowtie bio sourceforge net index shtml Note do NOT Pi erga ad use bowtie2 as it is a very different type of aligner and bowtie is used for small fragment reads 6 RNAplex and RNAfold These are part of the Vienna RNA package See http www tbi univie ac at ronny RNA index html 7 SQUID See http selab janelia org software html Goto Squid and download it 8 randfold See http bioinformatics psb ugent be software details Randfold 9 csbl go This is a module of R used for GO similarity analysis See http csbi ltdk helsinki fi csbl go You also need to install some Bioconductor packages Biobase annotate GO db It s used in prioritization pl You have no need to install this dependency if you don t want to do GO similarity analysis for prioritization of target predicted by other tools 10 TAPIR See http bioinformatics psb ugent be webtools tapir It s used in tapir_wrapper pl You have no need to install this dependency if you don t want to predict target of miRNA 11 plyr This is a module of R 12 DESeq This is a module of R for differential expression analysis Installation Example All the packages have been tested on Ubuntu 12 04 and Fedora 8 x64 platforms and should work on similar system that support perl python and R First download all necessary packages listed here a cutadapt http code google com p cutadapt Ver
3. maximum_category 4 pvalue 0 step7_allowed yes transcriptome_from_cleaveland yes tapir_score_cutoff 4 tapir_mfe_ratio 0 65 ste8_allowed yes All you need is just edit this configure file copy to the tutorial directory and run MTide pl with it 6 13 The result directory tree illustration The important file will be denoted with red color MTide_run13_06_2014_t_10_15_24 result directory with the running start time CleaveLand_for_samplel like CleaveLand4 directory GSM278370 fasta_dd txt signature density file all_mirna_samplel fa_GSTAr txt after running GSTAr pl cleaveland_samplel result final result list of CleaveLand4 tx_for_GSTAr tmp fasta temporary transcriptome with degradome signature all_mirna_samplel fa all miRNA identified from small RNA seq bowtie_index bowtie index directory filtered_sRNA_cleaned bwt bwt format of genome mapped file filtered_sRNA_cleaned fa small RNA seg data after filtering other non coding RNA filtered_sRNA_cleaned_vs_genome arf arf format of genome mapped file miRDeep_for_samplel like miRDeep2 directory chr_length error_13_06_2014_t_10_22_06 log expression_13_06_2014 t_10_22 _06 html expression_analyses miRNAs_expressed_all_samples_13_06_2014 t_10_22 _06 csv know miRNA expression file mirdeep_runs output mrd predicted secondary structure with mapped reads run_13_06_2014_t_10_22_06_pa
4. Print version and quit q quiet Quiet mode no log progress information to STDERR i intput input1 fa rd1 input files should be in format like reads1 fa rd1 rd1 denotes the three letter prefix of the input file and this option can exist many times a adap3 string 3 adapter sequence default TCGTATGCCGTCTTCTGCTTG g adap5 string 5 adapter sequence default GTTCAGAGTTCTACAGTCCGACGATC e error float gt 0 1 Maximum allowed error rate no of errors divided by the length of the matching region default 0 1 m min integer Discard trimmed reads that are shorter than m default 18 x max integer Discard trimmed reads that are longer than x default 30 f format string Input file format can be either fastq fasta default fastq 3 Filtering reads filter pl Description This script takes as input a file with collapsed reads The script then processes the reads and or maps them to the reference library filtering the reads that mapped to exons or other non coding RNAs like rRNA snRNA snoRNA and tRNA Usage filter pl i reads fa l Rfam fa l Repbase fa v 2 m 4 11 13 Options i string Collapsed reads file in fasta format l string Library for reads filtering users can appoint one or more libraries and the library must be in fasta format v int Report alignments with at most lt int gt mismatches default 2 k Keep the matched ali
5. by step 1 1 1 Remove adaptor and collapse reads 1 1 2 Filter reads mapped to other non coding RNA 1 1 3 Map to genome and parse to arf format 1 1 4 Run miRDeep2 for miRNA identification 1 1 5 Run CleaveLand4 for target identification 1 1 6 Predict target of miRNA 1 1 7 Prioritize target of predicted target 1 2 Integrated way 1 2 1 Edit the configure file 1 2 2 Run MTide pl 2 Two samples experiment 2 1 Step by step 2 1 1 Like 1 1 in one sample experiment 2 1 2 Run de_for_miRNA pl for differential expression analysis 2 2 Integrated way 2 2 1 Edit the configure file 2 2 2 Run MTide pl The tutorial example runs in an integrated way for one sample experiment As the small RNA sequencing file has been cleaned and collapsed the first step will be skipped The configure file looks like 5 13 MTide conf Threads_number 20 quiet_mode no genome_file TAIR10_genome fa transcriptome_file TAIR10_cdna fa go_annotation_file ath go miRNA_mature_file ath_mature fasta miRNA_precursor_file ath_hairpin fasta nonRNA_libs_file Rfam_for_miRDeep fa samplel_prefix seq samplel_srna_file sRNA_cleaned fa samplel_deg_file GSM278370 fasta step1l_allowed no step2_allowed yes filter_mismatches_allowed 2 step3_allowed yes seed_length 18 genome_mismatches_allowed 2 maximal_alignments 15 step4_allowed yes disable_pdf yes optimal_length 250 maximum_pre_number 50000 step5_allowed no step6_allowed yes t_plots_allowed no cleaveland_mfe_ratio 0 65
6. is the same as part of the cleaveland_sample1 result file 3 GSM278370 fasta_dd txt This is the degradome density file of all the transcripts with reads mapped to them 4 tx_for_GSTAr tmp fasta It is the transcripts sequence file but excludes transcripts having no reads mapped to them This strategy could speed up the overall running time compared to original CleaveLand4 For other files 1 all_mirna_samplel1 fa It includes all the miRNAs sequences detected by miRDeep2 in step4 and is generated from result_13_06_2014 t_10_22 06 csv 2 filtered_sRNA_cleaned fa The small RNA reads file after filtering reads that could be mapped to other non RNA libraries like Rfam 3 filtered SRNA_cleaned bwt The result bowtie format file after mapping the filtered file to genome 4 filtered_sRNA_cleaned_vs_genome arf The arf format file transformed from bwt file 5 predicted_sample1 result It is the predicting file of all the miRNAs in all_mirna_sample1 fa file using TAPIR Some of the lines miRNA target score mfe mfe_ratio start ath miR156a AT1G69170 1 1 38 2 0 9340 1296 ath miR156b AT1G69170 1 1 38 2 0 9340 1296 ath miR156e AT1G69170 1 1 38 2 0 9340 1296 ath miR156f AT1G69170 1 1 38 2 0 9340 1296 9 13 6 predicted_sample1 result go After running prioritization pl all the predicted target were scored based on GO similarity to the targets which could be detected from degradome sequencing file
7. User Manual for MTide Last updated 31 August 2014 Author ZhaoZhang From Zhejiang University Email Zhangzhao87 zju edu cn 1 13 Introduction This is a brief tutorial to describe the usage of MTide MTide is an integrated pipeline designed to parse SRNA seq and degradome data for miRNA target identification in plant It can quantify the known miRNA expression and identify novel miRNA from sRNA seq data identify the target of miRNA from degradome data signature predict target of miRNA precisely prioritize predicted target according to GO similarity to known or validated target and identify the expressed miRNA between two samples Mtide includes four modules and eight steps for an overall survey of miRNA and its target It is suitable for different experiment design a just one sample with sRNA seq data b just one sample with degradome data c just one sample with sRNA seq and degradome data d paired samples with sRNA seq or and degradome data The core algorithm consists of a modified miRDeep2 and a modified CleaveLand4 We delete some fat scripts modify some parts of scripts affecting overall speed add support for multiple threads add support for plant and report more information compared to the original ones After refining these scripts MTide can run very fast and precisely and as some other tools have been added such as TAPIR a precise miRNA target prediction tool DESeq a nice R package for differential expression ana
8. _13_06_2014_t_10_22_06 csv This file includes four parts the first is the overview performance of miRDeep2 the second is the detail information of novel miRNAs predicted by miRDeep2 the third is the detail information of mature miRBase miRNAs detected by miRDeep2 and the last is the information of miRBase miRNAs not detected by miRDeep For CleaveLand4 1 cleaveland_sample1 result This is the final running result of CleaveLand4 It contains 19 columns as below SiteID A general ID of miRNA and target transcript Query Query miRNA name Transcript Transcript name TStart Start miRNA binding site in transcript Tstop Stop miRNA binding site in transcript Tslice The site complementary to the 10 miRNA site TrueTSlice The true cleave site SliceType Type of cleave It could be 9 10 or 11 MFEperfect Perfect MFE value MFEsite Actual miRNA transcript MFE value MFEratio MFE ration of MFEsite to MFEperfect 8 13 AllenScore Allen score of miRNA transcript Paired Paired sites Unpaired Unpaired sites Structure Base pair structure of miRNA and transcript Sequence Complementary sequence of miRNA and transcript DegradomeCategory Category of signal It could be 0 1 2 3 4 DegradomePval P value Tplot_file_path The T plot file path of each miRNA target pairs 2 all_mirna_sample1 fa_GSTAr txt The miRNA and predicted target file The explanation of each column
9. gnments m int Number of threads to launch default 1 q Quiet mode no log progress information to STDERR h Print help message and quit 4 Mapping the collapsed reads to genome and parsing to arf format map_parse pl Description The script processes the reads and or maps them to the reference genome as designated by the options given The mapped file is then converted to an arf file which used in miRDeep2 Usage map_parse pl collapse_reads fa g genome fa 1 18 n 2 q p 8 gt reads_vs_genome arf Options g genome The genome file where input reads file will be mapped to int The seed length default 18 n int Mismatches allowed in the seed default 2 m int Suppress all alignments for a particular read or pair if more than lt int gt reportable alignments exist for it default 15 q Quiet mode no log progress information to STDERR p int Number of threads to use for bowtie h Print help message and quit 5 Identifying known miRNA and predicting new miRNA miRDeep2 pl Description This script is modified from miRDeep2 We adjust some parameters for used in plant and add the support for multiple threads to speed up the overall procedure Usage miRDeep2 pl Atshoot fa TAIR10_genome fa reads_vs_genome arf ath_mature fa none ath_hairpin fa d m 20 v P l 250 6 Target identification CleaveLand4 pl Description This script is modified from CleaveLand4 We add the support for m
10. lysis MTide can be a good tool for comprehensive miRNA and target analysis in plants Each step of Mtide can run separately for a custom design or it can run in an integrated way by MTide pl which is a wrapper script for all of these eight steps Overview of MTide scripts MTide pl A wrapper for all of these eight steps 1 Module 1 reads processing 1 1 Step 1 CleanReads pl Clean and collapse reads 1 2 Step 2 filter pl Filter reads mapped to other non coding RNA 1 3 Step 3 map_parse pl Map reads to genome and parsed to arf format 1 4 Step 4 miRDeep2 pl Identification of known and novel miRNA 1 5 Step 5 de_for_miRNA pl Differential expression of miRNA for two samples 2 Module 2 target identification 2 1 Step 6 CleaveLand4 pl Target identification from degradome sequencing data 3 Module 3 target prediction 3 1 Step 7 tapir_wrapper pl Target prediction using TAPIR 4 Module 4 target prioritization 4 1 Step 8 prioritization pl Prioritization of predicted target 2 13 Installation Dependencies Several dependencies are required to run MTide Please make sure perl 5 10 or later version python 2 7 or later version and R 2 5 or later version have been installed in your compute 1 Math CDF CleaveLand4 pl will not compile unless this required perl module is installed PDF API2 For pdf file generating in miRDeep 2 pl cutadapt See http code google com p cutadapt samtools See http samtools sourceforge net
11. nge line with INCLUDE I to INCLUDE I I home test soft squid1 9g L home test soft squid 1 9 make gt cd tar zxvf _ download PDF API2 2 021 tar gz cd PDF API2 2 021 gt perl Makefile pl gt make gt sudo make install gt cd tar jxvf download samtools 0 1 19 tar gz2 cd samtools 0 1 19 gt make gt cd enter R shell source http bioconductor org biocLite R biocLite DESeq biocLite Biobase biocLite annotate biocLite GO db install _packages csbl go_1 4 1 tar gz repos NULL install _packages plyr tar zxvf download tapir 1 1 tar gz if you have cpan command in your computer just run cpan Math CDFP Third install MTide tar zxvf download MTide tar gz Fourth attach the executable path to your PATH echo SOFT homef test soft gt gt bashrce echo export PATH SOFT cutadapt 1 4 1 bin SOFT bowtie 1 0 1 SOFT ViennaRNA 2 1 7 Progs SOFT s amtools 0 1 19 SOFT randfold 2 0 SOFT tapir 1 1 SOFT MTide scripts PATH gt gt bashre source bashrc 4 13 Use Example The tutorial data can be downloaded from http bis zju edu cn MTide tutorial_data tar gz We tested in a computer containing two Intel Xeon E5 2620 CPUs It took about 8 hours to finish all the analysis If your computer is more powerful we suggest you use more threads while running MTide 1 One sample experiment 1 1 Step
12. q Options h Print this usage g string File containing GO annotation v string validated miRNA target file in degradome data p string Predicted miRNA target file using TAPIR or other tools q Quiet mode no log progress information to STDERR 9 Differential expression analysis of known miRNA de_for_miRNA pl Description This script does differential expression analysis of known miRNA quantified by miRDeep2 It invokes DESeq package Usage de_for_miRNA pl s samplel_expressed_miRNA sample1 s sample2_expressed_miRNA sample2 Options s string The format must be like lt expressed_miRNA_file gt lt sample gt And the sample denotes the experiment condition which can be treated untreated or other name The option can exist many times for experiment with replicates 13 13
13. rameters survey csv result_13_06_2014_t_10_22_06 csv The main result file of miRDeep2 including the novel miRNA and known miRNA result_13_06_2014_t_10_22_06 html predicted_samplel result predicted target file by TAPIR predicted_samplel result go predicted target file after prioritization sRNA_cleaned_vs_Rfam_for_miRDeep fa small RNA seq file which could be mapped to Rfam 7 13 Files Description Input For the input files genome_file transcriptome_file miRNA_mature_file miRNA_mature_other_file miRNA_precursor_file nonRNA_libs_file srna_file and degradome sequence file are standard fasta format files and can be set in MTide conf Apart from these files the go_annotation_file should be in format like Gene mRNA GOid with each line separated by tab Below is an example of AT1G01073 in Arabidopsis thaliana AT1G01073 G0O 0003674 GO 0008150 GO 0009507 Output Take the output files in tutorial data as an example For miRDeep The files in directory miRDeep_for_sample1 are as the same as the original miRDeep 2 result 1 miRNAs_expressed_all_samples_13_06_2014_t_10_22_06 csv The reads count of all the known mature miRNAs in miRBase miRNA read_count precursor total seq seq norm ath miR156a 9835 10 ath MIR156a 9835 10 9835 10 11936 97 ath miR156b 9855 74 ath MIR156b 9855 74 9855 74 11962 01 ath miR156c 9835 10 ath MIR156c 9835 10 9835 10 11936 97 2 result
14. sion 1 4 1 b bowtie short read aligner http bowtie bio sourceforge net index shtml Version 1 0 1 c Vienna package with RNAfold http www tbi univie ac at ivo RNA Version 2 1 17 d SQUID library http selab janelia org software html Version 1 9g e randfold http bioinformatics psb ugent be software details Randfold Version 2 0 f PDF API2 http search cpan org search query PDF API2 amp mode all Version 2 021 g samtools http samtools sourceforge net Version 0 1 19 h csbl go http csbi ltdk helsinki fi csbl go Version 1 4 1 i TAPIR http bioinformatics psb ugent be webtools tapir Version 1 1 Second install all the dependencies Suggest your home directory is home test and all the packages will be downloaded in 3 13 home test download and installed in home test soft a b c d e g h i j cd home test soft tar zxvf download cutadapt 1 4 1 tar gz cd cutadapt 1 4 1 gt python setup py build gt cd unzip download bowtie 1 0 1 src zip cd bowtie 1 0 1 gt make gt cd tar xvfz download ViennaRNA 2 1 7 tar gz cd ViennaRNA 2 1 7 gt configure prefix home test soft ViennaRNA 2 1 7 gt make gt make install gt cd tar zxvf download squid tar gz cd squid1 9g gt configure gt make gt cd tar zxvf download randfold 2 0 tar gz cd randfold 2 0 gt edit Makefile and cha
15. ultiple threads 1 running and report not only the 10 splice site but also the 9 and 11 splice site as multiple kinds of variant miRNA exists in plant Usage CleaveLand4 pl e GSM278370 fasta u ath_mature fa n TAIR10_cdna fasta m 4 t o tplot gt cleaveland result 7 Target prediction tapir_wrapper pl 12 13 Description This script predicts the target of miRNA using a modified TAPIR As plant transcriptome are mostly large in size the original TAPIR will take a few days for a precise prediction of target We add the support for multiple threads to speed up this step Usage tapir_wrapper pl i miRNA fa t TAIR10_cdna fasta m 4 r 0 65 b o predicted result Options i string miRNA file in fasta format t string Target transcriptome file in fasta format o string Output file s float Score cutoff in TAPIR default 4 r float mfe ratio cutoff default 0 65 b Tabular report of the result m int Number of threads to lanuch default 1 q Quiet mode no log progress information to STDERR h Print help message and quit 8 Prioritization of predicted target prioritization pl Description This script takes two files as input prioritizing the predicted miRNA target pairs according to GO similarity to identified miRNA either validated by experiment or identified from degradome data Usage prioritization pl g go_annotation_file v validated csv p predicted csv
Download Pdf Manuals
Related Search