ROP output details
Here we describe the output structure of the ROP protocol. The details of the ROP protocol are explain [here] (https://github.com/smangul1/rop/wiki/What-is-ROP). The ROP output consists of six directories corresponding to six steps of the ROP analysis of the unmapped reads. Additionally, it contains two directories corresponding to optional modules to characterize the mapped reads.
This is the typical output of the ROP pipeline
-rw-r--r-- 1 serghei eeskin 11516 May 5 14:28 dev.log
drwxr-xr-x 4 serghei eeskin 4096 May 5 14:24 lostHumanReads
drwxr-xr-x 4 serghei eeskin 4096 May 5 14:24 lostRepeatSequences
drwxr-xr-x 5 serghei eeskin 4096 May 5 14:24 microbiomeProfile
drwxr-xr-x 2 serghei eeskin 4096 May 5 14:24 NCL
-rw-r--r-- 1 serghei eeskin 39 May 5 14:28 numberReads_unmappedExample.log
drwxr-xr-x 2 serghei eeskin 4096 May 5 14:28 QC
drwxr-xr-x 6 serghei eeskin 4096 May 5 14:24 antibodyProfile
-rw-r--r-- 1 serghei eeskin 2050 May 5 14:28 unmappedExample.log
drwxr-xr-x 4 serghei eeskin 4096 May 5 14:24 genomicProfile
drwxr-xr-x 4 serghei eeskin 4096 May 5 14:24 repeatProfile
The directory contains the output of Step 1 (Quality Control) . It contains details about the number of low quality, low complexity, and rRNA reads detected.
The information about number of low quality reads in _QC.log logfile obtained using [FASTX](http://hannonlab.cshl.edu/fastx_toolkit/commandline.html
Quality cut-off: 20
Minimum percentage: 75
Input: 2505 reads.
Output: 312 reads.
discarded 2193 (87%) low-quality reads.
The information about number of low complexity reads in _QC.log logfile obtained using SEQCLEAN
Sequences analyzed: 312
-----------------------------------
valid: 310 (1 trimmed)
trashed: 2
**************************************************
----= Trashing summary =------
by 'shortq': 1
by 'dust': 1
------------------------------
The information about number of rRNA reads in _QC.log logfile obtained using Megablast
Identified 22 reads mapped to rRNA repeat sequence
The reads failed QC are filtered out and the remaining reads are passed to the next step of the ROP analysis.
The directory contains the output of [Step 2. Remap to human sequences] (https://github.com/smangul1/rop/wiki/What-is-ROP%3F). The lost human reads mapped to the human reference genome are saved into the _genome.sam and reads mapped to the transcriptome reference are saved into the _transcriptome.sam.
The directory contains the output of Step 3 (Map to repeat sequences). Reads mapped to the repeat sequences are saved into the _blastFormat6.csv in tabular output format 6.
| n | id | What does it mean? |
|---|---|---|
| 1 | qseqid | read name |
| 2 | sseqid | reference genome |
| 3 | pident | percentage of identical matches |
| 4 | length | alignment length |
| 5 | mismatch | number of mismatches |
| 6 | gapopen | number of gap openings |
| 7 | qstart | start of alignment in query |
| 8 | qend | end of alignment in query |
| 9 | sstart | start of alignment in subject |
| 10 | send | end of alignment in subject |
| 11 | evalue | expect value |
| 12 | bitscore | bit score |
An example of the unmapped read mapped to L1 retrotransposon
SRR1146076.9992259 L1PA4-L1-Homo 97.44 78 2 0 2 79 795 872 3e-33 134
This directory contains the output of Step 4 (Non-co-linear (NCL) RNA profiling). More details will be posted soon
This directory contains the output of Step 5. B and T lymphocytes profiling.
Starting from release ROP v1.0.6 we are using in-house tool to detect reads spanning V(D)J recombination to quantify individual immune response.
Details about ImReP output are available at: https://github.com/mandricigor/imrep/wiki/ImReP-output
An example of ImRep output:
-rw-r--r-- 1 serghei eeskin 0 Jan 10 15:33 full_cdr3.txt
-rw-r--r-- 1 serghei eeskin 229 Jan 10 15:33 immune_read_names.txt
-rw-r--r-- 1 serghei eeskin 0 Jan 10 15:33 output.txt
-rw-r--r-- 1 serghei eeskin 1181 Jan 10 15:33 partial_cdr3.txt
-rw-r--r-- 1 serghei eeskin 25248 Jan 10 15:33 unmapped_afterImmune.fasta
Where, full_cdr3.txt file contains full-length CDR3 sequences and corresponding V(D)J recombinations. Full-length CDR3 sequences are assembled from reads spanning V(D)J recombinations (Figure 1.b), i.e. a read with one end mapped to V gene and another end mapped to J gene. Full-length CDR3 are also assembled from reads overlapping only V or J gene. Such reads are matched based on the overlap of CDR3 sequence and assembled into full-length CDR3 sequences (Figure 1.c). In case, of no match between such reads, we report partial CDR3s, i.e. CDR3 inferred from read only overlapping V gene or read overlapping only J gene. Partial CDR3s are saved into partial_cdr3.txt.
CDR3s are defined as sequences of amino acids between the cysteine on the left and phenylalanine (for TCR and BCR(IG)) or tryptophan (for IGH) on the right of the junction. An example of CDR3 from IGH chain: CARQGFINGSSGRWYYGMDVW. An example of CDR3 from TCRA chain: CAASGNTGNQFYF
Figure 1. Overview of ImReP.
This directory contains the output of Step 6. Microbiome profiling. It contains a separate directory for each class of microbial organisms. Reads mapped to the microbial genomes are saved in _blastFormat6.csv. Microbial reads are further filter to save high confident alignments in Filtered_blastFormat6.csv
- viralProfile
- bacteriaProfile
- eukaryoticPathogenProfile
An example of the unmapped read aligned to the Cryptosporidium muris(DS989729), a species of coccidium
SRR1146076.9993022 DS989729 85.92 71 9 1 1 70 454894 454964 5e-13 75.0
###genomicProfile directory
This directory contains the output of optional module which determines genomic profile of RNA-Seq. It categorizes the mapped reads into genomic categories based on the compatibility of each read with the features defined by gene annotations.
It will estimate relative proportions of genomic categories (e.g. CDS, introns) based on the number of reads from the category. Those are the categories used by the ROP (more details are here)
- multi-mapped read
- CDS
- intron
- UTR3
- UTR5
- UTR
- junction read
- inter-genic read
- deep inter-genic read
- mitochondrial DNA
- fusion reads
An example of the genomic profile of a RNA-Seq sample
sampleName,nTotalMapped,nJunction,nCDS,nUTR3,nUTR5,nUTR_,nMixed,nIntron,nIntergenic,nDeep,nFussion,nrRNA,nMT,nMultiMapped
--,57012335,6698066,4632060,7223406,2342899,1298331,0,28870771,2382707,2336668,534336,7441,337688,1646293
This module also provides an option to get the detailed information about the reads assignment to the genomic categories. Choose the --perCategory option and get the genomic category for each mapped read. A separate file corresponding to reads assignment to each chromosome will be created (e.g. mappedReads.22.genomicFeature).
An example of a file with reads assigned to the genomic features from chr22 is presented bellow:
SRR1146076.3540543,22,junction
SRR1146076.17070789,22,UTR5
SRR1146076.21758998,22,UTR5
SRR1146076.20543213,22,UTR5
SRR1146076.9808865,22,DEEP
SRR1146076.16863731,22,INTERGENIC
SRR1146076.968340,22,UTR3
ROP has the functionality to output the genomic feature for each mapped read, which can be saved into a separate file. Be aware that it might take the significant space from the cluster. More details are here
It will estimate relative proportions of repeat categories (e.g. LINE. SINE, LTR) based on the number of reads from the category. More details about the repeat categories used by ROP are here
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). Those the output files created for each level
- Class level :
<sampleName>_repeat.repeatClass.csv - Family level :
<sampleName>_repeat.repeatFamily.csv - Gene level :
<sampleName>_repeat.repeatGene.csv
An example of the repeat profile from one GTEX sample:
sample,LINE?,LTR,Satellite,Retroposon,DNA,SINE?,RNA,DNA?,RC,LINE,SINE,LTR?
G42480.GTEX-QESD-0426.2,0,258615,2036,7691,163674,122,347,3115,439,570393,790630,32