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Original Project site: PPR Code Prediction Server - From PPR to RNA
This original website is Down.
Please switch to:
- Colab release
- Docker release
- Biolib release
- WebServer from BioLib; the original webserver provided by Yin Lab is down and will be no longer maintained.
- Colab Reimplementation
- Local run: Docker image or BioLib cloud scripts
- install required BioLib package
pip3 install -U pybiolib
- run PPRCODE via Shell commands
wget -qnc https://raw.githubusercontent.com/YaoYinYing/PPRCODE_Guideline/main/ppr_example.fasta biolib run YaoYinYing/pprcode --fasta ppr_example.fasta
the run results will be located at $PWD/biolib_results
PS:
Due to the I/O issue of Biolib as docker container wrapper, the customized --save_dir
option will produce no results.
-
Install docker daemon by following the official getting-started page instruction.
-
Clone this repo
git clone https://github.com/YaoYinYing/PPRCODE_Guideline
-
PPRCODE docker image.
fetch the latest image
docker pull yaoyinying/pprcode:latest
You may also build it from scratch:
cd PPRCODE_Guideline docker build -f docker/Dockerfile -t pprcode .
Alternatively, if you wish to run PPRCODE with your own version of
docker/run_pprcode.py
, you may build a patched image for local usage by the following:docker build -f docker/Dockerfile_patch -t pprcode .
-
Create Conda environment for run this docker image in an instance container
conda create -y -n pprcode python pip conda activate pprcode cd <repo>/PPRCODE_Guideline pip install -r docker/requirements.txt
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Run
run_docker.py
to an example dataconda activate pprcode mkdir test # fetch an example dataset wget -qnc https://raw.githubusercontent.com/YaoYinYing/PPRCODE_Guideline/main/ppr_example.fasta -P test # use PS_Scan as default program python /repo/PPRCODE_Guideline/docker/run_docker.py --fasta test/ppr_example.fasta --save_dir ./save-ps_scan --plot_item=bar,score,edge,ppr,rna # or use pprfinder provided by Small's Lab python /repo/PPRCODE_Guideline/docker/run_docker.py --fasta test/ppr_example.fasta --save_dir ./save-pprfinder --plot_item=bar,score,edge,ppr,rna --program=pprfinder
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Advance options
python /repo/PPRCODE_Guideline/docker/run_docker.py --help
Pentatricopeptide repeat (PPR) proteins constitute a large family whose members serve as single-stranded RNA (ssRNA)-binding proteins; these proteins are particularly abundant in terrestrial plants, as more than 400 members have been identified in Arabidopsis and rice.
PPR proteins are typically characterized by tandem degenerate repeats of a 35-amino acid motif. Within a given repeat, the combinatorial di-residues at the 5th and 35th positions are responsible for specific RNA base recognition. These di-residues are referred to as the PPR code.
PPRCODE prediction server is aimed to provide services to the PPR community to facilitate PPR code and target RNA prediction. Once a PPR protein sequence is submitted, the server firstly identifies the PPR motifs using the PScan algorithm provided by Prosite, and then outputs the individual PPR motifs that is demarcated based on the PPR structure. PPR code is generally extracted from the 5th and 35th amino acids of each PPR motif, and the best matched RNA base for the PPR code is provided. As a result, the potential RNA target for the PPR sequence is available.
Go to the PPRCODE prediction server in BioLib submission form directly and do the following:
- Paste your FASTA sequence in the upper text area.
- Modify the options if needed.
- Click the Run button. After the submission, the webpage will be automatically run for several second until the job is finished.
Less than three second for each sequence.
As many as you want.
The result page contains a table like the following:
This is a demo sequence of PPR10 from Zea mays.
Motif Start | Motif End | Motif Sequence | Fifth amino acid | Last amino acid | PPR Code | RNA base | Motif Length | ProSite Score |
---|---|---|---|---|---|---|---|---|
138 | 172 | ASALEMVVRALGREGQHDAVCALLDETPLPPGSRL | E | L | EL | ? | 35 | 5.031 |
174 | 208 | VRAYTTVLHALSRAGRYERALELFAELRRQGVAPT | T | T | TT | A>G | 35 | 12.989 |
209 | 244 | LVTYNVVLDVYGRMGRSWPRIVALLDEMRAAGVEPD | N | D | ND | U>C>G | 36 | 11.093 |
245 | 279 | GFTASTVIAACCRDGLVDEAVAFFEDLKARGHAPC | S | C | SC | ? | 35 | 11.411 |
280 | 314 | VVTYNALLQVFGKAGNYTEALRVLGEMEQNGCQPD | N | D | ND | U>C>G | 35 | 12.737 |
315 | 349 | AVTYNELAGTYARAGFFEEAARCLDTMASKGLLPN | N | N | NN | C>U | 35 | 11.477 |
350 | 384 | AFTYNTVMTAYGNVGKVDEALALFDQMKKTGFVPN | N | N | NN | C>U | 35 | 14.096 |
385 | 419 | VNTYNLVLGMLGKKSRFTVMLEMLGEMSRSGCTPN | N | N | NN | C>U | 35 | 10.358 |
420 | 454 | RVTWNTMLAVCGKRGMEDYVTRVLEGMRSCGVELS | N | S | NS | C>U>A | 35 | 9.887 |
455 | 489 | RDTYNTLIAAYGRCGSRTNAFKMYNEMTSAGFTPC | N | C | NC | U>C>>A | 35 | 11.674 |
490 | 524 | ITTYNALLNVLSRQGDWSTAQSIVSKMRTKGFKPN | N | N | NN | C>U | 35 | 11.542 |
525 | 560 | EQSYSLLLQCYAKGGNVAGIAAIENEVYGSGAVFPS | S | S | SS | A | 36 | 6.467 |
561 | 595 | WVILRTLVIANFKCRRLDGMETAFQEVKARGYNPD | R | D | RD | - | 35 | 6.445 |
596 | 630 | LVIFNSMLSIYAKNGMYSKATEVFDSIKRSGLSPD | N | D | ND | U>C>G | 35 | 12.419 |
631 | 666 | LITYNSLMDMYAKCSESWEAEKILNQLKCSQTMKPD | N | D | ND | U>C>G | 36 | 8.67 |
667 | 701 | VVSYNTVINGFCKQGLVKEAQRVLSEMVADGMAPC | N | C | NC | U>C>>A | 35 | 13.778 |
702 | 736 | AVTYHTLVGGYSSLEMFSEAREVIGYMVQHGLKPM | H | M | HM | ? | 35 | 10.348 |
737 | 771 | ELTYRRVVESYCRAKRFEEARGFLSEVSETDLDFD | R | D | RD | - | 35 | 8.089 |
and finally you will also get a predicted sequence like this:
(?) (A>G) (U>C>G) (?) (U>C>G) (C>U) (C>U) (C>U) (C>U>A) (U>C>>A) (C>U) (A) (-) (U>C>G) (U>C>G) (U>C>>A) (?) (-)
PS_Scan/PPRfinder identifies the sequence and motifs of a PPR protein by its similarity to the general P-type PPR. Sequences with low identity will hardly be predicted. In this circumstance, manual correction is strongly recommended.
If there is any problem and advice with the website, you are welcome to contact us via email.
- Yinying Yao: Main program development and further maintainance.
- Zeyuan Guan: Basic Framework of the original webserver.
- Junjie Yan: Writing and data collecting.
- Xiang Wang: Providing useful advices to the original webserver design.
Yan Junjie#, Yao Yinying#, Hong Sixing, Yang Yan, Shen Cuicui, Zhang Qunxia, Zhang Delin, Zou Tingting, Yin Ping*. Delineation of pentatricopeptide repeat codes for target RNA prediction, Nucleic Acids Research. 2019 February 11. doi: doi.org/10.1093/nar/gkz075