This webserver is provided by the author of the Hong-Bin Shen‘s group^[1].

Background

RNA-binding proteins (RBPs) are involved in many biological processes, their binding sites on RNAs can give insights into mechanisms behind diseases involving RBPs. Thus, how to identify the RBP binding sites on RNAs is very crucial for follow-up analysis, like the impact of mutations on binding sites. With high-throughput sequencing developing, there is an explosion in the amount of experimentally verified RBP binding sites, e.g. eCLIP in ENCODE. However,CLIP-seq to detect RBP binding sites relies on gene expression which can be highly variable between experiments, and cannot provide a complete picture of the RBP binding landscape. Computational methods are in urgent needed to predict missing binding sites for individual RBPs.
RBPsuite mainly contain two deep learning-based approaches, iDeepS and iDeepC for linear RNAs and circular RNAs. iDeepS is deveoped for predicting RBP binding sites on linear RNAs. iDeepC is developed for predicting RBP binding sites on circular RNAs.

Benchmark Dataset

This benchmark dataset consists of 353 RBPs and their binding sites (RNAs) are derived from POSTAR3 database. Click here (722M) to download the benchmark dataset for RBP binding linear RNAs of RBPSuite.

Workflow

Figure. The flowchart of updated iDeepS for predicting RBP binding sites on linear RNAs

If you use RBPsuite, please cite the following paper:

[1] Xiaoyong Pan, Yi Fang, XianFeng Li, Yang Yang, and Hong-Bin Shen. RBPsuite: RNA-protein binding sites prediction suite based on deep learning. BMC Genomics, 2020, 21:884.

Input RNA sequence

uc009vjk.2_3UTR
GCCATTTCCAGGCCTAGTGCCTGCCTCGTGGCTGACTCTTGAAGCCCAAAACTTCCTCAAATCAGGCTTTTGCCCAACTTCTGTCTACTGTCGGACTCTACAGGTCAGCCTCTGCCTCACAGTGGACCCTCCAGACCCAGATGGTGTCTCACTGTGGCATCCTCAGGCGAAGCTCCTGCCTTTCGGCAGCCTCTCCAGGCCCAGCTCCTCCTGCCTCCCAGTGGCCTCTTTCGGCCCAGCCCAGCTCATGCCTCCCGGCGGCCTTCCCAAGCCCCGCTTTTGACTTTCGGTGGCCTCTGCAGGCCTCGACAAGGCCCAGCCTCCTGCCTCCCGAAGGCCTGCACAGGCCCAGCCTCTGCCTCACAGCGGACTCTCCACGCCCAGCTAGCTGTTGCTTCACTGCGGCCTCCCGAGTCCAAAGCTCCTGCCTCTCGGCCGCTTCGGCAGGCCCAGCTCCCGCCTGCCAGTGGCCTCTTCAGGCCCATGGGGCTCATTCCTGACAACGGCCTTTCCAGGCCCAGTTTTTCCCTTCCGGCGGCCTCTCCGGGCCCAGAACCTCCTCAAGTCGGCCTCTCCAGACCCACTTGCACCCTCCGGGCGTTCTCTCCGGGCCCAGCTCTTCTTCCTGGTTGGGTCTCCAGGCCCGATTCCTGCCTCTCAACAACCTCTTTGGACTCAGTGCCTACCCATCTCCTGGCGGCCTTGGTCGGCCCACAGCTTCCTCAAGCCAAGCTCCCCAGGCCCAGGTCAGGCCTCACGGTGGCCTCTCCAGGATGAGCTCCTGCCCTCCGATGGCATCTCCAGGCCCCAAATGGTCTCCGGTCGGTGGGCTCCTCCACGCCAAGGTTGGGCCTCCCGGCGACCGCCGCAGGCCCAAGTTGTCCTGAAGTCGGGCTCTCCCGGCCCTGCCTCCCAGCAAGTAAGCAAGCTCTTTTGGCTCAACTCCTGCCCAGCTCCCAACCGCCTTTGTAGGCCCCGAACTTTCTCCAGCCAAGCTCTGAGGGCCCACCTCCTGCCTCCTGGTGGCCTGTAC

Results

v1.0 The RBPsuite mainly contain two deep learning-based approaches, iDeepS and iDeepC for linear RNAs and circular RNAs.

1. Species

More species (Human, Mouse, Arabidopsis, Fly, Worm, Yeast, and Zebrafish) are available now for the RBPsuite linear RNA prediction model! There are 223 RBPs for human linear RNAs, 46 RBPs for mouse linear RNAs, 5 RBPs for arabidopsis linear RNAs, 7 RBPs for fly linear RNAs, 6 RBPs for worm linear RNAs, 65 RBPs for yeast linear RNAs, 1 RBP for zebrafish linear RNAs.   ```![step1.png#327px #114px](https://bmap.sjtu.edu.cn/editorfile/1078402282972000256.png) #### **2. RNA type** 

You need to choose the type of your RNA, whether it belongs to linear RNA or circRNA, and they will be predicted by different models. If you are not sure which RNA type your input sequence belongs to, you can first use our developed WebCircRNA to assess the circRNA potential, then choose the RNA type.

#### **3. Predition model** 

RBPsuite provides two types of prediction models: General model (all available RBPs) and the Specific model (one specific RBP). If you know exactly about your protein name, please use the Specific model. Otherwise, please choose the General model, which will predict interaction scores between all RBPs and this input RNA.

 There are 223 (human) specific models for linear RNA and 37 for circRNA in RBPsuite. Each model is trained by the known RNA targets of the RBP. Here we only predict the binding scores between the input RNA and the specified RBP. ![step3.png#320px #140px](https://bmap.sjtu.edu.cn/editorfile/1078398810813902848.png) ##### **3.2 All models**  The 'All models' option will use all the specific models(223 RBPs (human) for linear RNAs and 37 RBPs for circRNA) to predict binding site scores. If the input RNA is linear RNA, it predicts the binding scores between this input RNA and 223 RBPs using iDeepS. If the input RNA is circRNA, it predicts the binding scores between the input RNA and 37 RBPs using CRIP. ![step3-2.png#322px #103px](https://bmap.sjtu.edu.cn/editorfile/1078398898558742528.png) ##### **3.3.1 General model for unseen protein**  RBPsuite provides the prediction for unseen proteins. If your protein is not in the specific model list or you are not sure about the name of your protein, you can choose the option 'General model for unseen protein' in STEP 3 and then input your protein sequence. (single sequence ONLY) The general model of RBPsuite is an RBP-general binding prediction model enhanced by both RBP and RNA sequence information. It was trained on 134 eCLIP RBP datasets from ENCODE and can provide the prediction for any unseen protein. Currently, RBPsuite only provides this function for predicting the binding site of 'Human' proteins and 'Linear RNA'. ![step3-3-1.png#321px #145px](https://bmap.sjtu.edu.cn/editorfile/1078399081170350080.png) ##### **3.3.2 Specific model for unseen protein**  RBPsuite also provides the prediction for unseen proteins. If your protein is not in the specific model list or you are not sure about the name of your protein, you can choose the option 'Specific model for unseen protein' in STEP 3 and then input your protein sequence. (single sequence ONLY) RBPsuite will do alignment in the database concluding all the proteins in the specific model list and find the best match of your protein, then predict with that model. Currently, RBPsuite only provides this function for predicting the binding site of 'Yeast' proteins and 'Linear RNA'. ![step3-3-2.png#322px #146px](https://bmap.sjtu.edu.cn/editorfile/1078399191174361088.png) #### **4. RNA sequence** 

You can type in your RNA sequence or upload your sequence file in FASTA format. (You can also use the example sequence.)

#### **5. Email** 

Email address is optional. RBPsuite will send the results to the email address if it is provided.