Update README.md

README.md

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 The main program `CIRIdeep.py` can be used to predict differentially spliced circRNAs with CIRIdeep or CIRIdeep(A) or train your own model.
 
 ## Predict
-CIRIdeep 
+
+**Prediction with CIRIdeep using total RNA-seq data**
+
+CIRIdeep provides probability of given circRNAs being differentially spliced between any of two samples. When predict with CIRIdeep, expression value of 1499 RBPs (listed in `./demo/RBPmax.tsv`) and splicing amount (derived from SAM alignment files) in both samples are needed. We recommend to process raw total RNA-seq raw fastq files with `CIRIquant`, which provides junction ratio of each circRNA and expression value of each gene in a one-stop manual. SAM files generated with BWA is recommended when producing splicing amount values.
+
 ```
 python CIRIdeep.py predict -geneExp_absmax ./demo/RBPmax.tsv -seqFeature ./demo/cisFeature.tsv -splicing_max ./demo/splicingamountmax.tsv -predict_list ./demo/sample.txt -model_path ./model/CIRIdeep.h5 -outdir ./outdir -RBP_dir ./demo/RBPexp_total -splicing_dir ./demo/splicingamount
 ```
+
+Several files are needed for prediction.
+
+`-geneExp_absmax` This file contains maximum value of 1499 RBP expression value (TPM) across the training datasets used for normalization. 
+
+`-seqFeature` This file contains normalized cis features of circRNAs to be predicted. A table containing cis features of 71459 circRNAs has been constructed.
+
+`-splicing_max` This file contains maximum value of splicing amount of each circRNA across the training datasets used for normalization.
+
+`-predict_list` This file is comprised of two columns. The first column contains the name of sample pairs seperated by `_`. The second column contains the path to files containing circRNA to be predicted.
+CircRNAs are given as coodination on `hg19` genome, like `chr10:102683732|102685776`.
+
+`-model_path` We have provided fully trained CIRIdeep model for using.
+
+`-outdir` Directory to output prediction result.
+
+`-RBP_dir` Directory containing the RBP expression value in TPM of samples to be predicted.
+
+`-splicing_dir` Directory containing the splicing amount of circRNAs to be predicted in each sample. We have provided a basic script `splicing_amount.py` to produce splicing amount in samples.
+
+**Prediction with CIRIdeep(A) using poly(A) selected RNA-seq data**
+
+CIRIdeep(A) gives three probabilities indicating the circRNA being unchanged, having higher junction ratio in sample A or having higher junction ratio in sample B, which sum to one.
+As in some cases, like in scRNA-seq or spatial transcriptomics data, only gene expression matrix is provided, splicing amount is not needed in CIRIdeep(A) any more.
+
+```
+python CIRIdeep.py predict -geneExp_absmax ./demo/RBPmax.tsv -seqFeature ./demo/cisFeature.tsv -predict_list ./demo/sample.txt -model_path ./model/CIRIdeepA.h5 -outdir ./outdir -RBP_dir ./demo/RBPexp_total --CIRIdeepA
+```
+`--CIRIdeepA` When predict using CIRIdeepA, this parameter is needed.
+
+Basically, the input files are similar to CIRIdeep, excluding splicing amount related files. **Notably**, the `RBPmax` file is different from that used in CIRIdeep and all the expression values should be derived from poly(A) selected RNA-seq data.
+