https://sourceforge.net/p/rop2/wiki/Get%2520started/Recent changes to Get startedenSun, 26 Jun 2016 07:26:29 -0000https://sourceforge.net/p/rop2/wiki/Get%2520started/<div class="markdown_content"><pre>--- v50 +++ v51 @@ -37,40 +37,39 @@ You expect the following output of the ROP pipeline: ********************************************* - ROP is a computational protocol aimed to discover the source of all reads, originated from complex RNA molecules, recombinant antibodies and microbial communities. Written by Serghei Mangul (smangul@ucla.edu) and Harry Taegyun Yang (harry2416@gmail.com), University of California, Los Angeles (UCLA). (c) 2016. Released under the terms of the General Public License version 3.0 (GPLv3) + ROP (version 1.0.1) is a computational protocol aimed to discover the source of all reads, originated from complex RNA molecules, recombinant antibodies and microbial communities. Written by Serghei Mangul (smangul@ucla.edu) and Harry Taegyun Yang (harry2416@gmail.com), University of California, Los Angeles (UCLA). (c) 2016. Released under the terms of the General Public License version 3.0 (GPLv3) For more details see: - http://serghei.bioinformatics.ucla.edu/rop/ - https://github.com/smangul1/rop/wiki + https://sergheimangul.wordpress.com/rop/ ********************************************* - /u/home/s/serghei/code2/rop/example/rop6/NCL//unmappedExample_circRNA.csv - Processing 2508 unmapped reads + Processing 2510 unmapped reads 1. Quality Control... --filtered 2193 low quality reads --filtered 2 low complexity reads (e.g. ACACACAC...) --filtered 22 rRNA reads In toto : 2217 reads failed QC and are filtered out 2. Remaping to human references... - --identified 6 lost human reads from unmapped reads + --identified 6 lost human reads from unmapped reads. Among those: 4 reads with 0 mistmathes; 2 reads with 1 mistmath; 0 reads with 2 mistmathes + ***Note: Complete list of lost human reads is available from sam files: /u/home/s/serghei/code2/rop/example/test12/lostHumanReads/unmappedExample_genome.sam,/u/home/s/serghei/code2/rop/example/test12/lostHumanReads/unmappedExample_transcriptome.sam 3. Maping to repeat sequences... -Identify 1 lost repeat sequences from unmapped reads ***Note : Repeat sequences classification into classes (e.g. LINE) and families (e.g. Alu) will be available in next release - 3. Non-co-linear RNA profiling + 4. Non-co-linear RNA profiling ***Note : Trans-spicing and gene fusions are currently not supported, but will be in the next release. --identified 2 reads from circRNA - 4a. B lymphocytes profiling... - /u/home/s/serghei/code2/rop/example/rop6/BCR/IGH/unmappedExample_IGH_igblast.csv + ***Note: circRNAs detected by CIRI are available here: unmappedExample_circRNA.csv + 5a. B lymphocytes profiling... --identified 1 reads mapped to immunoglobulin heavy (IGH) locus - --identified 2 reads mapped to immunoglobulin kappa (IGK) locus + --identified 0 reads mapped to immunoglobulin kappa (IGK) locus --identified 1 reads mapped to immunoglobulin lambda (IGL) locus - 4b. T lymphocytes profiling... + 5b. T lymphocytes profiling... --identified 0 reads mapped to T cell receptor alpha (TCRA) locus --identified 2 reads mapped to T cell receptor beta (TCRB) locus --identified 1 reads mapped to T cell receptor delta (TCRD) locus --identified 0 reads mapped to T cell receptor gamma locus (TCRG) locus - In toto : 7 reads mapped to antibody repertoire loci + In toto : 5 reads mapped to antibody repertoire loci ***Note : Combinatorial diversity of the antibody repertoire (recombinations of the of VJ gene segments) will be available in the next release. - 5. Microbiome profiling... + 6. Microbiome profiling... --identified 0 reads mapped bacterial genomes --identified 0 reads mapped viral genomes --identified 4 reads mapped ameoba genomes @@ -84,7 +83,7 @@ --identified 0 reads mapped tritryp genomes In toto : 7 reads mapped to microbial genomes Summary: The ROP protocol is able to account for 2238 reads - ***Unaccounted reads (not explained by ROP) are saved to /u/home/s/serghei/code2/rop/example/rop6/unmappedExample_unaccountedReads.fasta + ***Unaccounted reads (not explained by ROP) are saved to /u/home/s/serghei/code2/rop/example/test12/unmappedExample_unaccountedReads.fasta The ropOut directory now contains the output of ROP. The structure of the output is explained [here](https://github.com/smangul1/rop/wiki/ROP-output-details). For example it contains `/antibodyProfile/` directory with reads spanning antigen receptor gene rearrangement in the variable domain being identified by [IgBLAST](http://mirrors.vbi.vt.edu/mirrors/ftp.ncbi.nih.gov/blast/executables/igblast/release/1.4.0/). </pre> </div>Serghei MangulSun, 26 Jun 2016 07:26:29 -0000https://sourceforge.neta3dcba37b91625c7d2d137bc7769bce83ded312ahttps://sourceforge.net/p/rop2/wiki/Get%2520started/<div class="markdown_content"><pre>--- v49 +++ v50 @@ -1,5 +1,5 @@ -For a quick start a toy example with 2508 unmapped reads is distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis:--one-RNA-Seq-sampe). +For a quick start, a toy example with 2508 unmapped reads is distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis:--one-RNA-Seq-sampe). /rop/example/unmappedExample.fastq @@ -8,7 +8,7 @@ Please make sure that the basic unix commands (wget, python, perl) are available on the cluster. Please install ROP first. The instructions how to install ROP are provided [here](https://github.com/smangul1/rop/wiki/How-to-install-ROP%3F) -The first operation consists in navigating to ROP directory and creating a subdirectory for storing the training data. +The first operation consists of navigating to ROP directory and creating a subdirectory for storing the training data. cd rop mkdir tutorial @@ -17,7 +17,8 @@ Now, download the mapped and unmapped reads from RNA-Seq - wget (to fix) + wget https://googledrive.com/host/0B_NUyiE86yDwaUxoVjhlSjN5SkE/skinExample.tar + tar -xvf skinExample.tar ROP is an intensive pipeline requiring substantial amount of computations resources. Thus we don't recommend to run ROP from login nodes. Please check the policy of you cluster, from where to run the ROP pipeline. For hoffman2 (UCLA cluster) read the policy [here] (http://ccn.ucla.edu/wiki/index.php/Hoffman2:Interactive_Sessions). </pre> </div>Serghei MangulSun, 26 Jun 2016 07:26:28 -0000https://sourceforge.net7f5437f70832905e37bf525d3c11d9d481020e23https://sourceforge.net/p/rop2/wiki/Get%2520started/<div class="markdown_content"><pre>--- v48 +++ v49 @@ -1,5 +1,5 @@ -For a quick start a toy example with 2508 unmapped reads is distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). +For a quick start a toy example with 2508 unmapped reads is distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis:--one-RNA-Seq-sampe). /rop/example/unmappedExample.fastq </pre> </div>Serghei MangulSun, 26 Jun 2016 07:26:27 -0000https://sourceforge.netfc9db8a43c22cb579a9d6510eb8676d94cd19a41https://sourceforge.net/p/rop2/wiki/Get%2520started/<div class="markdown_content"><pre>--- v47 +++ v48 @@ -1,5 +1,5 @@ -For a quick start a toy example, 2508 unmapped reads saved in .fastq format are distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). +For a quick start a toy example with 2508 unmapped reads is distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). /rop/example/unmappedExample.fastq </pre> </div>Serghei MangulSun, 26 Jun 2016 07:26:27 -0000https://sourceforge.net60f95635aba820413e6ef4d0687b6c200400ebabhttps://sourceforge.net/p/rop2/wiki/Get%2520started/<div class="markdown_content"><pre>--- v46 +++ v47 @@ -1,5 +1,5 @@ -For a quick start a toy example (2508 unmapped reads saved in .fastq format) is distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). +For a quick start a toy example, 2508 unmapped reads saved in .fastq format are distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). /rop/example/unmappedExample.fastq </pre> </div>Serghei MangulSun, 26 Jun 2016 07:26:27 -0000https://sourceforge.net021359a7f32835ed27c38af1be6d32922dca698fhttps://sourceforge.net/p/rop2/wiki/Get%2520started/<div class="markdown_content"><pre>--- v45 +++ v46 @@ -1,9 +1,5 @@ - -We provide the toy example - -Small training data (2508 unmapped reads saved in .fastq format) is distributed with the ROP package. Please note -that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). +For a quick start a toy example (2508 unmapped reads saved in .fastq format) is distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). /rop/example/unmappedExample.fastq </pre> </div>Serghei MangulSun, 26 Jun 2016 07:26:27 -0000https://sourceforge.netf4c5c2a2e2c13c5054d18b165a5578358456cb5ehttps://sourceforge.net/p/rop2/wiki/Get%2520started/<div class="markdown_content"><pre>--- v44 +++ v45 @@ -1,18 +1,6 @@ -##About the ROP tutorial -This tutorial focuses on performing a comprehensive analysis analysis of unmapped reads to profile repeats, circRNAs, gene fusions, trans-splicing events, recombined B/T-cell receptor sequences and microbial communities. This tutorial is a step-by-step description of the ROP (Read Origin Protocol) pipeline to explore the unmapped reads left from you study. -We assume you have a basic knowledge of sequence analysis and of Unix-based operating systems (although you should be able to run the pipeline on MacOS, some commands may require modification). If you have limited knowledge of UNIX, we encourage you to follow the online [video tutorial](http://qcb.ucla.edu/collaboratory/workshops/collaboratory-workshop-1/) for UNIX . The UNIX tutorial is 9 hour video tutorial covering the basic concepts of UNIX operating system. After completing the tutorial you should be able to confidently use the command line interface on either a local (laptop) or remote (cluster) Unix system and to navigate around the Unix file system from the command line and use a number of basic, common Unix commands. The UNIX tutorial is supplemented with many hands-on exercises. - -Alternatively you can use slides from the UNIX tutorial : [Day1](https://www.dropbox.com/s/ggv7ijwateim7zt/day1_Unix.pdf?dl=0), [Day2](https://www.dropbox.com/s/xorsuvk1cugiyw8/day2_Unix.pdf?dl=0), [Day3] (https://www.dropbox.com/s/88wu7svvfur8upw/day3_Unix.pdf?dl=0) - -Please do not hesitate to contact us (smangul@ucla.edu) if you have any comments, suggestions, or clarification requests regarding the tutorial or if you would like to contribute to this resource. - -#How to install ROP? - -Details are [here](https://github.com/smangul1/rop/wiki/How-to-install-ROP%3F) - -#Toy example +We provide the toy example Small training data (2508 unmapped reads saved in .fastq format) is distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). </pre> </div>Serghei MangulSun, 26 Jun 2016 07:26:26 -0000https://sourceforge.net61361a00d2ef917a9ef6f29b67659f09790856f9https://sourceforge.net/p/rop2/wiki/Get%2520started/<div class="markdown_content"><pre>--- v43 +++ v44 @@ -15,12 +15,12 @@ #Toy example Small training data (2508 unmapped reads saved in .fastq format) is distributed with the ROP package. Please note -that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory: +that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory. Instruction for the analysis of the full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). /rop/example/unmappedExample.fastq -Instruction for the analysis of full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). + Please make sure that the basic unix commands (wget, python, perl) are available on the cluster. Please install ROP first. The instructions how to install ROP are provided [here](https://github.com/smangul1/rop/wiki/How-to-install-ROP%3F) </pre> </div>Serghei MangulSun, 26 Jun 2016 07:26:26 -0000https://sourceforge.netb68104f2bed696a74e4406a2f7f3a20d7f0a4f64https://sourceforge.net/p/rop2/wiki/Get%2520started/<div class="markdown_content"><pre>--- v42 +++ v43 @@ -17,165 +17,154 @@ Small training data (2508 unmapped reads saved in .fastq format) is distributed with the ROP package. Please note that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory: - :::/rop/example/unmappedExample.fastq``` + /rop/example/unmappedExample.fastq + + +Instruction for the analysis of full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). + +Please make sure that the basic unix commands (wget, python, perl) are available on the cluster. Please install ROP first. The instructions how to install ROP are provided [here](https://github.com/smangul1/rop/wiki/How-to-install-ROP%3F) + +The first operation consists in navigating to ROP directory and creating a subdirectory for storing the training data. + + cd rop + mkdir tutorial + cd tutorial + mkdir data + +Now, download the mapped and unmapped reads from RNA-Seq + + wget (to fix) + +ROP is an intensive pipeline requiring substantial amount of computations resources. Thus we don't recommend to run ROP from login nodes. Please check the policy of you cluster, from where to run the ROP pipeline. For hoffman2 (UCLA cluster) read the policy [here] (http://ccn.ucla.edu/wiki/index.php/Hoffman2:Interactive_Sessions). + + +ROP requires two mandatory command line arguments, i.e. (1) the unmapped reads and (2) the directory to save the results of ROP. + + usage: python rop.py [-h] [--qsub] [--qsubArray] [--b] [--skipLowq] [--skipQC] + [--circRNA] [--immune] [--gzip] [--quiet] [--dev] + [--license] + unmappedReads dir + +To test ROP for the small training data use the following command under the ROP directory, where results will be saved to example/ropOut/ directory + + python rop.py example/unmappedExample.fastq example/ropOut/ + +You expect the following output of the ROP pipeline: + + ********************************************* + ROP is a computational protocol aimed to discover the source of all reads, originated from complex RNA molecules, recombinant antibodies and microbial communities. Written by Serghei Mangul (smangul@ucla.edu) and Harry Taegyun Yang (harry2416@gmail.com), University of California, Los Angeles (UCLA). (c) 2016. Released under the terms of the General Public License version 3.0 (GPLv3) - - Instruction for the analysis of full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). - - Please make sure that the basic unix commands (wget, python, perl) are available on the cluster. Please install ROP first. The instructions how to install ROP are provided [here](https://github.com/smangul1/rop/wiki/How-to-install-ROP%3F) - - The first operation consists in navigating to ROP directory and creating a subdirectory for storing the training data. - -cd rop -mkdir tutorial -cd tutorial -mkdir data + For more details see: + http://serghei.bioinformatics.ucla.edu/rop/ + https://github.com/smangul1/rop/wiki + ********************************************* + /u/home/s/serghei/code2/rop/example/rop6/NCL//unmappedExample_circRNA.csv + Processing 2508 unmapped reads + 1. Quality Control... + --filtered 2193 low quality reads + --filtered 2 low complexity reads (e.g. ACACACAC...) + --filtered 22 rRNA reads + In toto : 2217 reads failed QC and are filtered out + 2. Remaping to human references... + --identified 6 lost human reads from unmapped reads + 3. Maping to repeat sequences... + -Identify 1 lost repeat sequences from unmapped reads + ***Note : Repeat sequences classification into classes (e.g. LINE) and families (e.g. Alu) will be available in next release + 3. Non-co-linear RNA profiling + ***Note : Trans-spicing and gene fusions are currently not supported, but will be in the next release. + --identified 2 reads from circRNA + 4a. B lymphocytes profiling... + /u/home/s/serghei/code2/rop/example/rop6/BCR/IGH/unmappedExample_IGH_igblast.csv + --identified 1 reads mapped to immunoglobulin heavy (IGH) locus + --identified 2 reads mapped to immunoglobulin kappa (IGK) locus + --identified 1 reads mapped to immunoglobulin lambda (IGL) locus + 4b. T lymphocytes profiling... + --identified 0 reads mapped to T cell receptor alpha (TCRA) locus + --identified 2 reads mapped to T cell receptor beta (TCRB) locus + --identified 1 reads mapped to T cell receptor delta (TCRD) locus + --identified 0 reads mapped to T cell receptor gamma locus (TCRG) locus + In toto : 7 reads mapped to antibody repertoire loci + ***Note : Combinatorial diversity of the antibody repertoire (recombinations of the of VJ gene segments) will be available in the next release. + 5. Microbiome profiling... + --identified 0 reads mapped bacterial genomes + --identified 0 reads mapped viral genomes + --identified 4 reads mapped ameoba genomes + --identified 1 reads mapped crypto genomes + --identified 0 reads mapped giardia genomes + --identified 0 reads mapped microsporidia genomes + --identified 0 reads mapped piroplasma genomes + --identified 1 reads mapped plasmo genomes + --identified 1 reads mapped toxo genomes + --identified 0 reads mapped trich genomes + --identified 0 reads mapped tritryp genomes + In toto : 7 reads mapped to microbial genomes + Summary: The ROP protocol is able to account for 2238 reads + ***Unaccounted reads (not explained by ROP) are saved to /u/home/s/serghei/code2/rop/example/rop6/unmappedExample_unaccountedReads.fasta - - Now, download the mapped and unmapped reads from RNA-Seq - -wget (to fix) +The ropOut directory now contains the output of ROP. The structure of the output is explained [here](https://github.com/smangul1/rop/wiki/ROP-output-details). For example it contains `/antibodyProfile/` directory with reads spanning antigen receptor gene rearrangement in the variable domain being identified by [IgBLAST](http://mirrors.vbi.vt.edu/mirrors/ftp.ncbi.nih.gov/blast/executables/igblast/release/1.4.0/). - - ROP is an intensive pipeline requiring substantial amount of computations resources. Thus we don't recommend to run ROP from login nodes. Please check the policy of you cluster, from where to run the ROP pipeline. For hoffman2 (UCLA cluster) read the policy [here] (http://ccn.ucla.edu/wiki/index.php/Hoffman2:Interactive_Sessions). - - - ROP requires two mandatory command line arguments, i.e. (1) the unmapped reads and (2) the directory to save the results of ROP. - -usage: python rop.py [-h] [--qsub] [--qsubArray] [--b] [--skipLowq] [--skipQC] - [--circRNA] [--immune] [--gzip] [--quiet] [--dev] - [--license] - unmappedReads dir +##Genomic profile of RNA-Seq - - To test ROP for the small training data use the following command under the ROP directory, where results will be saved to example/ropOut/ directory - -python rop.py example/unmappedExample.fastq example/ropOut/ +To get the genomic profile of the mapped reads use `gprofile.py`. To get the genomic profile of the toy bam file (reads from chr22) use the following command: - - You expect the following output of the ROP pipeline: - -********************************************* -ROP is a computational protocol aimed to discover the source of all reads, originated from complex RNA molecules, recombinant antibodies and microbial communities. Written by Serghei Mangul (smangul@ucla.edu) and Harry Taegyun Yang (harry2416@gmail.com), University of California, Los Angeles (UCLA). (c) 2016. Released under the terms of the General Public License version 3.0 (GPLv3) + python gprofile.py example/mappedReads_chr22.bam /example/mappedReads_chr22.csv -For more details see: -http://serghei.bioinformatics.ucla.edu/rop/ -https://github.com/smangul1/rop/wiki -********************************************* -/u/home/s/serghei/code2/rop/example/rop6/NCL//unmappedExample_circRNA.csv -Processing 2508 unmapped reads -1. Quality Control... ---filtered 2193 low quality reads ---filtered 2 low complexity reads (e.g. ACACACAC...) ---filtered 22 rRNA reads -In toto : 2217 reads failed QC and are filtered out -2. Remaping to human references... ---identified 6 lost human reads from unmapped reads -3. Maping to repeat sequences... --Identify 1 lost repeat sequences from unmapped reads -***Note : Repeat sequences classification into classes (e.g. LINE) and families (e.g. Alu) will be available in next release -3. Non-co-linear RNA profiling -***Note : Trans-spicing and gene fusions are currently not supported, but will be in the next release. ---identified 2 reads from circRNA -4a. B lymphocytes profiling... -/u/home/s/serghei/code2/rop/example/rop6/BCR/IGH/unmappedExample_IGH_igblast.csv ---identified 1 reads mapped to immunoglobulin heavy (IGH) locus ---identified 2 reads mapped to immunoglobulin kappa (IGK) locus ---identified 1 reads mapped to immunoglobulin lambda (IGL) locus -4b. T lymphocytes profiling... ---identified 0 reads mapped to T cell receptor alpha (TCRA) locus ---identified 2 reads mapped to T cell receptor beta (TCRB) locus ---identified 1 reads mapped to T cell receptor delta (TCRD) locus ---identified 0 reads mapped to T cell receptor gamma locus (TCRG) locus -In toto : 7 reads mapped to antibody repertoire loci -***Note : Combinatorial diversity of the antibody repertoire (recombinations of the of VJ gene segments) will be available in the next release. -5. Microbiome profiling... ---identified 0 reads mapped bacterial genomes ---identified 0 reads mapped viral genomes ---identified 4 reads mapped ameoba genomes ---identified 1 reads mapped crypto genomes ---identified 0 reads mapped giardia genomes ---identified 0 reads mapped microsporidia genomes ---identified 0 reads mapped piroplasma genomes ---identified 1 reads mapped plasmo genomes ---identified 1 reads mapped toxo genomes ---identified 0 reads mapped trich genomes ---identified 0 reads mapped tritryp genomes -In toto : 7 reads mapped to microbial genomes -Summary: The ROP protocol is able to account for 2238 reads -***Unaccounted reads (not explained by ROP) are saved to /u/home/s/serghei/code2/rop/example/rop6/unmappedExample_unaccountedReads.fasta +The output of the module is number of reads assigned to each genomic category saved into the `/example/mappedReads_chr22.csv` - - The ropOut directory now contains the output of ROP. The structure of the output is explained [here](https://github.com/smangul1/rop/wiki/ROP-output-details). For example it contains `/antibodyProfile/` directory with reads spanning antigen receptor gene rearrangement in the variable domain being identified by [IgBLAST](http://mirrors.vbi.vt.edu/mirrors/ftp.ncbi.nih.gov/blast/executables/igblast/release/1.4.0/). - - ##Genomic profile of RNA-Seq - - To get the genomic profile of the mapped reads use `gprofile.py`. To get the genomic profile of the toy bam file (reads from chr22) use the following command: - -python gprofile.py example/mappedReads_chr22.bam /example/mappedReads_chr22.csv + sampleName,nTotalMapped,nJunction,nCDS,nUTR3,nUTR5,nUTR_,nIntron,nIntergenic,nDeep,nMT,nMultiMapped + mappedReads,397134,129580,101541,96210,7457,22473,19420,3084,649,0,16720 - - The output of the module is number of reads assigned to each genomic category saved into the `/example/mappedReads_chr22.csv` - -sampleName,nTotalMapped,nJunction,nCDS,nUTR3,nUTR5,nUTR_,nIntron,nIntergenic,nDeep,nMT,nMultiMapped -mappedReads,397134,129580,101541,96210,7457,22473,19420,3084,649,0,16720 +You can use `/example/mappedReads_chr22.csv` to create pie chart. The pie chart corresponding to `/example/mappedReads_chr22.csv` is presented bellow: - - You can use `/example/mappedReads_chr22.csv` to create pie chart. The pie chart corresponding to `/example/mappedReads_chr22.csv` is presented bellow: - -  - - Read more about Genomic Profile of RNA-Seq [here](https://github.com/smangul1/rop/wiki/ROP-output-details). - - - ##Profile of repeat elements -python rprofile.py example/mappedReads_chr22.bam example/mappedReads_chr22_repeatProfile + - - The ROP provided three levels of repeat profile, i.e class level (e.g SINE, LINE); family level (e.g. Alu, L1); gene level (e.g. L1P4c). The files contain relative proportions of repeat categories based on the number of reads from the category. More details about the repeat classes used by ROP are [here](https://github.com/smangul1/rop/wiki/What-is-ROP%3F). - - Those the output files created for each level - - - - Class level of classification (`mappedReads_chr22_repeatProfilerepeatClass.csv`): - - -sample,LINE?,LTR,Satellite,Retroposon,DNA,SINE?,RNA,DNA?,RC,LINE,SINE,LTR? -mappedReads_chr22,0,2445,3,6,568,0,0,0,0,2588,5356,0 +Read more about Genomic Profile of RNA-Seq [here](https://github.com/smangul1/rop/wiki/ROP-output-details). - - The classes with the `?` are provided by [RepeatMasker](http://www.repeatmasker.org/). For example [MER129](http://www.repeatmasker.org/cgi-bin/ViewRepeat?id=MER129) is classified as `LTR?`. You may merge LTR? with LTR or ignore those. - - Those are the repeat classes with sufficient number of reads supporting the class. The repeat profile is presented as a table: -sample LTR DNA LINE SINE -mappedReads_chr22 2445 568 2588 5356 - - Alternatively you can visualize the repeat profile as a pie chart - -  - - Family level of classification (`mappedReads_chr22_repeatProfilerepeatFamily.csv`) is presented bellow. Each family is represented in the following format `Class_Family` allowing to retrieve the particular class the family belongs to. For example `LINE____L1` corresponds to family element L1 from LINE class. - - -sample,SINE____Alu,DNA____TcMar-Tigger,LTR?____LTR,DNA____PiggyBac,DNA____hAT,Retroposon____SVA_E,LTR____ERV1,LINE____L1,LINE____L2,DNA____MuDR,DNA____TcMar,LINE?____Penelope,LTR____ERVL-MaLR,DNA____DNA,LTR____ERV,DNA____hAT-Charlie,RC____Helitron,DNA____hAT-Blackjack,SINE____MIR,Satellite____centr,DNA____Merlin,LTR____LTR,DNA____hAT-Tip100,RNA____RNA,SINE____Deu,Retroposon____SVA_D,LTR____Gypsy,Retroposon____SVA_F,Retroposon____SVA_A,LINE____CR1,Retroposon____SVA_C,Retroposon____SVA_B,DNA____TcMar-Mariner,LINE____RTE,LINE____RTE-BovB,DNA____TcMar-Tc2,LTR____ERVK,Satellite____acro,Satellite____telo,LTR____ERVL,Satellite____Satellite,SINE?____SINE,DNA?____DNA,SINE____SINE,LINE____Dong-R4 -mappedReads_chr22,4502,123,0,1,16,0,859,1476,1009,0,2,0,943,2,0,349,0,4,849,2,0,0,50,0,1,2,3,0,3,93,0,1,21,10,0,0,151,0,0,489,1,0,0,4,0 +##Profile of repeat elements - - Those are the repeat families with sufficient number of reads supporting the family. The repeat profile is presented as a table: - -sample LTR____ERV1 LTR____ERVL-MaLR LTR____ERVK DNA____hAT-Charlie DNA____TcMar-Tigger DNA____hAT-Tip100 LINE____L1 LINE____L2 SINE____Alu SINE____MIR -mappedReads_chr22 859 943 151 349 123 50 1476 1009 4502 849 + python rprofile.py example/mappedReads_chr22.bam example/mappedReads_chr22_repeatProfile - - - Alternatively you can visualize the repeat profile as a pie chart - -  - - - ROP also provides gene level resolution (`mappedReads_chr22_repeatProfilerepeatGene.csv`) , where the number of reads assigned to each repeat instance is reported. Each instance is represented in the following format `Family__Class__Instance` allowing to retrieve the particular class and family the repeat instance belongs to. For example `L1____LINE____L1MD` corresponds to element L1MD from L1 gamily of LINE class. - - - +The ROP provided three levels of repeat profile, i.e class level (e.g SINE, LINE); family level (e.g. Alu, L1); gene level (e.g. L1P4c). The files contain relative proportions of repeat categories based on the number of reads from the category. More details about the repeat classes used by ROP are [here](https://github.com/smangul1/rop/wiki/What-is-ROP%3F). + +Those the output files created for each level + + + +Class level of classification (`mappedReads_chr22_repeatProfilerepeatClass.csv`): + + + sample,LINE?,LTR,Satellite,Retroposon,DNA,SINE?,RNA,DNA?,RC,LINE,SINE,LTR? + mappedReads_chr22,0,2445,3,6,568,0,0,0,0,2588,5356,0 + +The classes with the `?` are provided by [RepeatMasker](http://www.repeatmasker.org/). For example [MER129](http://www.repeatmasker.org/cgi-bin/ViewRepeat?id=MER129) is classified as `LTR?`. You may merge LTR? with LTR or ignore those. + +Those are the repeat classes with sufficient number of reads supporting the class. The repeat profile is presented as a table: + + sample LTR DNA LINE SINE + mappedReads_chr22 2445 568 2588 5356 + +Alternatively you can visualize the repeat profile as a pie chart + + + +Family level of classification (`mappedReads_chr22_repeatProfilerepeatFamily.csv`) is presented bellow. Each family is represented in the following format `Class_Family` allowing to retrieve the particular class the family belongs to. For example `LINE____L1` corresponds to family element L1 from LINE class. + + + sample,SINE____Alu,DNA____TcMar-Tigger,LTR?____LTR,DNA____PiggyBac,DNA____hAT,Retroposon____SVA_E,LTR____ERV1,LINE____L1,LINE____L2,DNA____MuDR,DNA____TcMar,LINE?____Penelope,LTR____ERVL-MaLR,DNA____DNA,LTR____ERV,DNA____hAT-Charlie,RC____Helitron,DNA____hAT-Blackjack,SINE____MIR,Satellite____centr,DNA____Merlin,LTR____LTR,DNA____hAT-Tip100,RNA____RNA,SINE____Deu,Retroposon____SVA_D,LTR____Gypsy,Retroposon____SVA_F,Retroposon____SVA_A,LINE____CR1,Retroposon____SVA_C,Retroposon____SVA_B,DNA____TcMar-Mariner,LINE____RTE,LINE____RTE-BovB,DNA____TcMar-Tc2,LTR____ERVK,Satellite____acro,Satellite____telo,LTR____ERVL,Satellite____Satellite,SINE?____SINE,DNA?____DNA,SINE____SINE,LINE____Dong-R4 + mappedReads_chr22,4502,123,0,1,16,0,859,1476,1009,0,2,0,943,2,0,349,0,4,849,2,0,0,50,0,1,2,3,0,3,93,0,1,21,10,0,0,151,0,0,489,1,0,0,4,0 + +Those are the repeat families with sufficient number of reads supporting the family. The repeat profile is presented as a table: + + sample LTR____ERV1 LTR____ERVL-MaLR LTR____ERVK DNA____hAT-Charlie DNA____TcMar-Tigger DNA____hAT-Tip100 LINE____L1 LINE____L2 SINE____Alu SINE____MIR + mappedReads_chr22 859 943 151 349 123 50 1476 1009 4502 849 + + +Alternatively you can visualize the repeat profile as a pie chart + + + + +ROP also provides gene level resolution (`mappedReads_chr22_repeatProfilerepeatGene.csv`) , where the number of reads assigned to each repeat instance is reported. Each instance is represented in the following format `Family__Class__Instance` allowing to retrieve the particular class and family the repeat instance belongs to. For example `L1____LINE____L1MD` corresponds to element L1MD from L1 gamily of LINE class. + + </pre> </div>Serghei MangulSun, 26 Jun 2016 07:26:25 -0000https://sourceforge.netc035e65bf83a8e9d33ba20db265526eabecf860ahttps://sourceforge.net/p/rop2/wiki/Get%2520started/<div class="markdown_content"><pre>--- v41 +++ v42 @@ -15,154 +15,167 @@ #Toy example Small training data (2508 unmapped reads saved in .fastq format) is distributed with the ROP package. Please note -that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory: `/rop/example/unmappedExample.fastq`. +that selected reads are randomly selected from a normal skin (SRR1146076) RNA-Seq sample and might not represent the typical reads of RNA-Seq experiment. The reads are provided for demonstration purposes and can be accessed under ROP directory: + :::/rop/example/unmappedExample.fastq``` + + + Instruction for the analysis of full RNA-Seq sample (SRR1146076) are provided [here](https://github.com/smangul1/rop/wiki/ROP-analysis-of-one-sample). + + Please make sure that the basic unix commands (wget, python, perl) are available on the cluster. Please install ROP first. The instructions how to install ROP are provided [here](https://github.com/smangul1/rop/wiki/How-to-install-ROP%3F) + + The first operation consists in navigating to ROP directory and creating a subdirectory for storing the training data. + +cd rop +mkdir tutorial +cd tutorial +mkdir data + + Now, download the mapped and unmapped reads from RNA-Seq + +wget (to fix) + + ROP is an intensive pipeline requiring substantial amount of computations resources. Thus we don't recommend to run ROP from login nodes. Please check the policy of you cluster, from where to run the ROP pipeline. For hoffman2 (UCLA cluster) read the policy [here] (http://ccn.ucla.edu/wiki/index.php/Hoffman2:Interactive_Sessions). + + + ROP requires two mandatory command line arguments, i.e. (1) the unmapped reads and (2) the directory to save the results of ROP. + +usage: python rop.py [-h] [--qsub] [--qsubArray] [--b] [--skipLowq] [--skipQC] + [--circRNA] [--immune] [--gzip] [--quiet] [--dev] + [--license] + unmappedReads dir -Please make sure that the basic unix commands (wget, python, perl) are available on the cluster. Please install ROP first. The instructions how to install ROP are provided [here](https://github.com/smangul1/rop/wiki/How-to-install-ROP%3F) + + To test ROP for the small training data use the following command under the ROP directory, where results will be saved to example/ropOut/ directory + +python rop.py example/unmappedExample.fastq example/ropOut/ -The first operation consists in navigating to ROP directory and creating a subdirectory for storing the training data. + + You expect the following output of the ROP pipeline: + +********************************************* +ROP is a computational protocol aimed to discover the source of all reads, originated from complex RNA molecules, recombinant antibodies and microbial communities. Written by Serghei Mangul (smangul@ucla.edu) and Harry Taegyun Yang (harry2416@gmail.com), University of California, Los Angeles (UCLA). (c) 2016. Released under the terms of the General Public License version 3.0 (GPLv3) - cd rop - mkdir tutorial - cd tutorial - mkdir data +For more details see: +http://serghei.bioinformatics.ucla.edu/rop/ +https://github.com/smangul1/rop/wiki +********************************************* +/u/home/s/serghei/code2/rop/example/rop6/NCL//unmappedExample_circRNA.csv +Processing 2508 unmapped reads +1. Quality Control... +--filtered 2193 low quality reads +--filtered 2 low complexity reads (e.g. ACACACAC...) +--filtered 22 rRNA reads +In toto : 2217 reads failed QC and are filtered out +2. Remaping to human references... +--identified 6 lost human reads from unmapped reads +3. Maping to repeat sequences... +-Identify 1 lost repeat sequences from unmapped reads +***Note : Repeat sequences classification into classes (e.g. LINE) and families (e.g. Alu) will be available in next release +3. Non-co-linear RNA profiling +***Note : Trans-spicing and gene fusions are currently not supported, but will be in the next release. +--identified 2 reads from circRNA +4a. B lymphocytes profiling... +/u/home/s/serghei/code2/rop/example/rop6/BCR/IGH/unmappedExample_IGH_igblast.csv +--identified 1 reads mapped to immunoglobulin heavy (IGH) locus +--identified 2 reads mapped to immunoglobulin kappa (IGK) locus +--identified 1 reads mapped to immunoglobulin lambda (IGL) locus +4b. T lymphocytes profiling... +--identified 0 reads mapped to T cell receptor alpha (TCRA) locus +--identified 2 reads mapped to T cell receptor beta (TCRB) locus +--identified 1 reads mapped to T cell receptor delta (TCRD) locus +--identified 0 reads mapped to T cell receptor gamma locus (TCRG) locus +In toto : 7 reads mapped to antibody repertoire loci +***Note : Combinatorial diversity of the antibody repertoire (recombinations of the of VJ gene segments) will be available in the next release. +5. Microbiome profiling... +--identified 0 reads mapped bacterial genomes +--identified 0 reads mapped viral genomes +--identified 4 reads mapped ameoba genomes +--identified 1 reads mapped crypto genomes +--identified 0 reads mapped giardia genomes +--identified 0 reads mapped microsporidia genomes +--identified 0 reads mapped piroplasma genomes +--identified 1 reads mapped plasmo genomes +--identified 1 reads mapped toxo genomes +--identified 0 reads mapped trich genomes +--identified 0 reads mapped tritryp genomes +In toto : 7 reads mapped to microbial genomes +Summary: The ROP protocol is able to account for 2238 reads +***Unaccounted reads (not explained by ROP) are saved to /u/home/s/serghei/code2/rop/example/rop6/unmappedExample_unaccountedReads.fasta -Now, download the mapped and unmapped reads from RNA-Seq + + The ropOut directory now contains the output of ROP. The structure of the output is explained [here](https://github.com/smangul1/rop/wiki/ROP-output-details). For example it contains `/antibodyProfile/` directory with reads spanning antigen receptor gene rearrangement in the variable domain being identified by [IgBLAST](http://mirrors.vbi.vt.edu/mirrors/ftp.ncbi.nih.gov/blast/executables/igblast/release/1.4.0/). + + ##Genomic profile of RNA-Seq + + To get the genomic profile of the mapped reads use `gprofile.py`. To get the genomic profile of the toy bam file (reads from chr22) use the following command: + +python gprofile.py example/mappedReads_chr22.bam /example/mappedReads_chr22.csv - wget (to fix) + + The output of the module is number of reads assigned to each genomic category saved into the `/example/mappedReads_chr22.csv` + +sampleName,nTotalMapped,nJunction,nCDS,nUTR3,nUTR5,nUTR_,nIntron,nIntergenic,nDeep,nMT,nMultiMapped +mappedReads,397134,129580,101541,96210,7457,22473,19420,3084,649,0,16720 -ROP is an intensive pipeline requiring substantial amount of computations resources. Thus we don't recommend to run ROP from login nodes. Please check the policy of you cluster, from where to run the ROP pipeline. For hoffman2 (UCLA cluster) read the policy [here] (http://ccn.ucla.edu/wiki/index.php/Hoffman2:Interactive_Sessions). + + You can use `/example/mappedReads_chr22.csv` to create pie chart. The pie chart corresponding to `/example/mappedReads_chr22.csv` is presented bellow: + +  + + Read more about Genomic Profile of RNA-Seq [here](https://github.com/smangul1/rop/wiki/ROP-output-details). + + + ##Profile of repeat elements +python rprofile.py example/mappedReads_chr22.bam example/mappedReads_chr22_repeatProfile + + The ROP provided three levels of repeat profile, i.e class level (e.g SINE, LINE); family level (e.g. Alu, L1); gene level (e.g. L1P4c). The files contain relative proportions of repeat categories based on the number of reads from the category. More details about the repeat classes used by ROP are [here](https://github.com/smangul1/rop/wiki/What-is-ROP%3F). + + Those the output files created for each level + + + + Class level of classification (`mappedReads_chr22_repeatProfilerepeatClass.csv`): + + +sample,LINE?,LTR,Satellite,Retroposon,DNA,SINE?,RNA,DNA?,RC,LINE,SINE,LTR? +mappedReads_chr22,0,2445,3,6,568,0,0,0,0,2588,5356,0 -ROP requires two mandatory command line arguments, i.e. (1) the unmapped reads and (2) the directory to save the results of ROP. + + The classes with the `?` are provided by [RepeatMasker](http://www.repeatmasker.org/). For example [MER129](http://www.repeatmasker.org/cgi-bin/ViewRepeat?id=MER129) is classified as `LTR?`. You may merge LTR? with LTR or ignore those. + + Those are the repeat classes with sufficient number of reads supporting the class. The repeat profile is presented as a table: +sample LTR DNA LINE SINE +mappedReads_chr22 2445 568 2588 5356 - usage: python rop.py [-h] [--qsub] [--qsubArray] [--b] [--skipLowq] [--skipQC] - [--circRNA] [--immune] [--gzip] [--quiet] [--dev] - [--license] - unmappedReads dir + + Alternatively you can visualize the repeat profile as a pie chart + +  + + Family level of classification (`mappedReads_chr22_repeatProfilerepeatFamily.csv`) is presented bellow. Each family is represented in the following format `Class_Family` allowing to retrieve the particular class the family belongs to. For example `LINE____L1` corresponds to family element L1 from LINE class. + + +sample,SINE____Alu,DNA____TcMar-Tigger,LTR?____LTR,DNA____PiggyBac,DNA____hAT,Retroposon____SVA_E,LTR____ERV1,LINE____L1,LINE____L2,DNA____MuDR,DNA____TcMar,LINE?____Penelope,LTR____ERVL-MaLR,DNA____DNA,LTR____ERV,DNA____hAT-Charlie,RC____Helitron,DNA____hAT-Blackjack,SINE____MIR,Satellite____centr,DNA____Merlin,LTR____LTR,DNA____hAT-Tip100,RNA____RNA,SINE____Deu,Retroposon____SVA_D,LTR____Gypsy,Retroposon____SVA_F,Retroposon____SVA_A,LINE____CR1,Retroposon____SVA_C,Retroposon____SVA_B,DNA____TcMar-Mariner,LINE____RTE,LINE____RTE-BovB,DNA____TcMar-Tc2,LTR____ERVK,Satellite____acro,Satellite____telo,LTR____ERVL,Satellite____Satellite,SINE?____SINE,DNA?____DNA,SINE____SINE,LINE____Dong-R4 +mappedReads_chr22,4502,123,0,1,16,0,859,1476,1009,0,2,0,943,2,0,349,0,4,849,2,0,0,50,0,1,2,3,0,3,93,0,1,21,10,0,0,151,0,0,489,1,0,0,4,0 -To test ROP for the small training data use the following command under the ROP directory, where results will be saved to example/ropOut/ directory + + Those are the repeat families with sufficient number of reads supporting the family. The repeat profile is presented as a table: + +sample LTR____ERV1 LTR____ERVL-MaLR LTR____ERVK DNA____hAT-Charlie DNA____TcMar-Tigger DNA____hAT-Tip100 LINE____L1 LINE____L2 SINE____Alu SINE____MIR +mappedReads_chr22 859 943 151 349 123 50 1476 1009 4502 849 - python rop.py example/unmappedExample.fastq example/ropOut/ - -You expect the following output of the ROP pipeline: - - ********************************************* - ROP is a computational protocol aimed to discover the source of all reads, originated from complex RNA molecules, recombinant antibodies and microbial communities. Written by Serghei Mangul (smangul@ucla.edu) and Harry Taegyun Yang (harry2416@gmail.com), University of California, Los Angeles (UCLA). (c) 2016. Released under the terms of the General Public License version 3.0 (GPLv3) - For more details see: - http://serghei.bioinformatics.ucla.edu/rop/ - https://github.com/smangul1/rop/wiki - ********************************************* - /u/home/s/serghei/code2/rop/example/rop6/NCL//unmappedExample_circRNA.csv - Processing 2508 unmapped reads - 1. Quality Control... - --filtered 2193 low quality reads - --filtered 2 low complexity reads (e.g. ACACACAC...) - --filtered 22 rRNA reads - In toto : 2217 reads failed QC and are filtered out - 2. Remaping to human references... - --identified 6 lost human reads from unmapped reads - 3. Maping to repeat sequences... - -Identify 1 lost repeat sequences from unmapped reads - ***Note : Repeat sequences classification into classes (e.g. LINE) and families (e.g. Alu) will be available in next release - 3. Non-co-linear RNA profiling - ***Note : Trans-spicing and gene fusions are currently not supported, but will be in the next release. - --identified 2 reads from circRNA - 4a. B lymphocytes profiling... - /u/home/s/serghei/code2/rop/example/rop6/BCR/IGH/unmappedExample_IGH_igblast.csv - --identified 1 reads mapped to immunoglobulin heavy (IGH) locus - --identified 2 reads mapped to immunoglobulin kappa (IGK) locus - --identified 1 reads mapped to immunoglobulin lambda (IGL) locus - 4b. T lymphocytes profiling... - --identified 0 reads mapped to T cell receptor alpha (TCRA) locus - --identified 2 reads mapped to T cell receptor beta (TCRB) locus - --identified 1 reads mapped to T cell receptor delta (TCRD) locus - --identified 0 reads mapped to T cell receptor gamma locus (TCRG) locus - In toto : 7 reads mapped to antibody repertoire loci - ***Note : Combinatorial diversity of the antibody repertoire (recombinations of the of VJ gene segments) will be available in the next release. - 5. Microbiome profiling... - --identified 0 reads mapped bacterial genomes - --identified 0 reads mapped viral genomes - --identified 4 reads mapped ameoba genomes - --identified 1 reads mapped crypto genomes - --identified 0 reads mapped giardia genomes - --identified 0 reads mapped microsporidia genomes - --identified 0 reads mapped piroplasma genomes - --identified 1 reads mapped plasmo genomes - --identified 1 reads mapped toxo genomes - --identified 0 reads mapped trich genomes - --identified 0 reads mapped tritryp genomes - In toto : 7 reads mapped to microbial genomes - Summary: The ROP protocol is able to account for 2238 reads - ***Unaccounted reads (not explained by ROP) are saved to /u/home/s/serghei/code2/rop/example/rop6/unmappedExample_unaccountedReads.fasta - -The ropOut directory now contains the output of ROP. The structure of the output is explained [here](https://github.com/smangul1/rop/wiki/ROP-output-details). For example it contains `/antibodyProfile/` directory with reads spanning antigen receptor gene rearrangement in the variable domain being identified by [IgBLAST](http://mirrors.vbi.vt.edu/mirrors/ftp.ncbi.nih.gov/blast/executables/igblast/release/1.4.0/). - -##Genomic profile of RNA-Seq - -To get the genomic profile of the mapped reads use `gprofile.py`. To get the genomic profile of the toy bam file (reads from chr22) use the following command: - - python gprofile.py example/mappedReads_chr22.bam /example/mappedReads_chr22.csv - -The output of the module is number of reads assigned to each genomic category saved into the `/example/mappedReads_chr22.csv` - - sampleName,nTotalMapped,nJunction,nCDS,nUTR3,nUTR5,nUTR_,nIntron,nIntergenic,nDeep,nMT,nMultiMapped - mappedReads,397134,129580,101541,96210,7457,22473,19420,3084,649,0,16720 - -You can use `/example/mappedReads_chr22.csv` to create pie chart. The pie chart corresponding to `/example/mappedReads_chr22.csv` is presented bellow: - - - -Read more about Genomic Profile of RNA-Seq [here](https://github.com/smangul1/rop/wiki/ROP-output-details). - - -##Profile of repeat elements - - python rprofile.py example/mappedReads_chr22.bam example/mappedReads_chr22_repeatProfile - -The ROP provided three levels of repeat profile, i.e class level (e.g SINE, LINE); family level (e.g. Alu, L1); gene level (e.g. L1P4c). The files contain relative proportions of repeat categories based on the number of reads from the category. More details about the repeat classes used by ROP are [here](https://github.com/smangul1/rop/wiki/What-is-ROP%3F). - -Those the output files created for each level - - - -Class level of classification (`mappedReads_chr22_repeatProfilerepeatClass.csv`): - - - sample,LINE?,LTR,Satellite,Retroposon,DNA,SINE?,RNA,DNA?,RC,LINE,SINE,LTR? - mappedReads_chr22,0,2445,3,6,568,0,0,0,0,2588,5356,0 - -The classes with the `?` are provided by [RepeatMasker](http://www.repeatmasker.org/). For example [MER129](http://www.repeatmasker.org/cgi-bin/ViewRepeat?id=MER129) is classified as `LTR?`. You may merge LTR? with LTR or ignore those. - -Those are the repeat classes with sufficient number of reads supporting the class. The repeat profile is presented as a table: - - sample LTR DNA LINE SINE - mappedReads_chr22 2445 568 2588 5356 - -Alternatively you can visualize the repeat profile as a pie chart - - - -Family level of classification (`mappedReads_chr22_repeatProfilerepeatFamily.csv`) is presented bellow. Each family is represented in the following format `Class_Family` allowing to retrieve the particular class the family belongs to. For example `LINE____L1` corresponds to family element L1 from LINE class. - - - sample,SINE____Alu,DNA____TcMar-Tigger,LTR?____LTR,DNA____PiggyBac,DNA____hAT,Retroposon____SVA_E,LTR____ERV1,LINE____L1,LINE____L2,DNA____MuDR,DNA____TcMar,LINE?____Penelope,LTR____ERVL-MaLR,DNA____DNA,LTR____ERV,DNA____hAT-Charlie,RC____Helitron,DNA____hAT-Blackjack,SINE____MIR,Satellite____centr,DNA____Merlin,LTR____LTR,DNA____hAT-Tip100,RNA____RNA,SINE____Deu,Retroposon____SVA_D,LTR____Gypsy,Retroposon____SVA_F,Retroposon____SVA_A,LINE____CR1,Retroposon____SVA_C,Retroposon____SVA_B,DNA____TcMar-Mariner,LINE____RTE,LINE____RTE-BovB,DNA____TcMar-Tc2,LTR____ERVK,Satellite____acro,Satellite____telo,LTR____ERVL,Satellite____Satellite,SINE?____SINE,DNA?____DNA,SINE____SINE,LINE____Dong-R4 - mappedReads_chr22,4502,123,0,1,16,0,859,1476,1009,0,2,0,943,2,0,349,0,4,849,2,0,0,50,0,1,2,3,0,3,93,0,1,21,10,0,0,151,0,0,489,1,0,0,4,0 - -Those are the repeat families with sufficient number of reads supporting the family. The repeat profile is presented as a table: - - sample LTR____ERV1 LTR____ERVL-MaLR LTR____ERVK DNA____hAT-Charlie DNA____TcMar-Tigger DNA____hAT-Tip100 LINE____L1 LINE____L2 SINE____Alu SINE____MIR - mappedReads_chr22 859 943 151 349 123 50 1476 1009 4502 849 - - -Alternatively you can visualize the repeat profile as a pie chart - - - - -ROP also provides gene level resolution (`mappedReads_chr22_repeatProfilerepeatGene.csv`) , where the number of reads assigned to each repeat instance is reported. Each instance is represented in the following format `Family__Class__Instance` allowing to retrieve the particular class and family the repeat instance belongs to. For example `L1____LINE____L1MD` corresponds to element L1MD from L1 gamily of LINE class. - - + + Alternatively you can visualize the repeat profile as a pie chart + +  + + + ROP also provides gene level resolution (`mappedReads_chr22_repeatProfilerepeatGene.csv`) , where the number of reads assigned to each repeat instance is reported. Each instance is represented in the following format `Family__Class__Instance` allowing to retrieve the particular class and family the repeat instance belongs to. For example `L1____LINE____L1MD` corresponds to element L1MD from L1 gamily of LINE class. + + + </pre> </div>Serghei MangulSun, 26 Jun 2016 07:26:25 -0000https://sourceforge.netbf8c20e0f6992ee3729e5e655c94ce672e6892b2