COME --- calculate COding potential from Multiple fEatures.

0. About COME

COME (coding potential calculator based on multiple features) is a computational tool that predicts the coding potential for a given transcript. It integrates multiple sequence-derived and experiment-based features using a decompose-compose method, which makes COME’s performance more accurate and robust than other well-known tools. First, COME compose the feature matrix for the given transcripts using the pre-calculated features vectors. Second, COME predict the coding potential by the pre-trained models, using the feature matrix generated in the first step.

COME is currently pre-trained for five model species: human (hg19), mouse (mm10), fly (dm3), worm (ce10) and plant (TAIR10). The pre-trained models were avaible in the folder of [bin/models] (https://github.com/lulab/COME/tree/master/bin/models)

COME integreated features including GC content, DNA sequence conservation, protein conservation and RNA secondary structure conservation, expression abundance from poly(A)+, poly(A)- and small RNA sequencing, H3K36me3 and H3K4me3 modification. These input features were pre-calculated and avaiable in folder of [bin/HDF5] (https://github.com/lulab/COME/tree/master/bin/HDF5)

For users who are not familiar with Linux, we also provide a webserver, which is still in a beta version.

1. Installation

Pre-requisite

1. Download HDF5 and model files from onedrive or Tsinghua Cloud

2. Linux

3. R (>=2.15.2)

4. R packages ("randomForest" and "rhdf5"); You can install these packages by entering R and typing these:

    ## Install package "randomForest"
    install.packages("randomForest"); 
    ## Install package "rhdf5"
    source("http://bioconductor.org/biocLite.R");biocLite("rhdf5");
   

Download files into sepcific folders.

1. First, change directory to your working directory, download the source codes from https://github.com/lulab/COME/archive/master.zip and decompress it. Enter the subfolder "COME-master/bin" and define the path as the variable Bin_dir

    $ unzip		./COME-master.zip;
    $ cd 		./COME-master/bin;
    $ Bin_dir=`pwd|awk '{print $1}'`;
   

2. Second, download your species'(Let's say, human) feature vector files from onedrive or Tsinghua Cloud ( see download links above ). These (nine) files need to be placed in the subfolder "COME-master/bin/HDF5".

    $ unzip	./human.feature_vector.HDF5.zip;
    $ mv	./human/human.HDF5.*	$Bin_dir/HDF5;
   

3. Third, download your species' model file from onedrive or Tsinghua Cloud ( see download links above ). The (one) model file need to be placed in the subfolder "COME-master/bin/models".

    $ mv	./human.model	$Bin_dir/models;
   

2. Usage and Examples

bash /path/to/bin_subfolder/COME_main.sh /path/to/your/transcripts.gtf	/path/to/your/output_folder/	/path/to/bin_subfolder/	species	model;

---

• /path/to/bin_subfolder/ is the path where you kept downloaded COME's "bin" subfolder, i.e., the $Bin_dir

• /path/to/bin_subfolder/COME_main.sh is COME's main program script.

• /path/to/your/transcripts.gtf is your input gtf file. The input gtf file should be as the description of ucsc's [gtf format] (http://genome.ucsc.edu/FAQ/FAQformat.html#format4). In summary, the first field should be chormosome in lower and abbreviate case (e.g., chr1, chrX); the third field should be exactly "exon"; the seventh field should be strand (i.e., + or -). The subsequent attribute list must begin with the two mandatory attributes: gene_id "value"; transcript_id "value". In addition, transcript length should be longer than 50 nucleotides. Any lines of your input file don't match the criteria aboved will be skipped.

• /path/to/your/output_folder/ is a folder that will be created (if the user didn't create it already) to save your output file "result.txt"

• species is one of these five names: "human", "mouse", "fly", "worm" and "plant". It specifies which species' feature vector files should be applied to your calculation

• model is one of these ten names: "human.model", "human.NoExpHis.model", "mouse.model", "mouse.NoExpHis.model", "fly.model", "fly.NoExpHis.model", "worm.model", "worm.NoExpHis.model", "plant.model" and "plant.NoExpHis.model". It specifies which model should be applied to your calculation. *.model, e.g., human.model, is the default model trained by multiple sequence-derived and experiment-based features. We also provided *.NoExpHis.model, e.g., human.NoExpHis.model, which is the model trained by multiple sequence-derived features only.

---

An example:

Assuming I want to predict the human test transcripts from the [examples] (https://github.com/lulab/COME/tree/master/examples) folder, human.test.gtf. I would work on my home directory ~/ and I want the output of COME stored in a folder named ~/COME_out/.

1. ~/COME-master.zip was downloaded to my working directory ~/ from [github] (https://github.com/lulab/COME/archive/master.zip) by clicking the link or wget:

    $ cd ~;
    $ wget -c --content-disposition   http://github.com/lulab/COME/archive/master.zip;
   

2. ~/human.feature_vector.HDF5.zip was downloaded to my working directory ~/ from onedrive or Tsinghua Cloud ( see download links above ) or wget:

    $ cd ~;
    $ wget -c --content-disposition http://lulab.life.tsinghua.edu.cn/RNAfinder/download_files_for_COME/HDF5/human.feature_vector.HDF5.zip
   

3. ~/human.model was downloaded to my working directory ~/ from onedrive or Tsinghua Cloud ( see download links above ) or wget:

    $ cd ~;
    $ wget -c --content-disposition   http://lulab.life.tsinghua.edu.cn/RNAfinder/download_files_for_COME/models/human.model
   

4. Then run COME by the following commands:

    ## Installation and preparison
    $ cd ~/;		
    $ unzip	./COME-master.zip;
    $ cd 	./COME-master/bin;
    ## Save the path of "bin" subfolder to the variable "$Bin_dir"
    $ Bin_dir=`pwd|awk '{print $1}'`;
    $ cd ~/;
    $ unzip	./human.feature_vector.HDF5.zip;
    $ mv	./human/human.HDF5.*	$Bin_dir/HDF5;
    $ rm -rf	./human;
    $ mv	./human.model	$Bin_dir/models;
    ## Running COME
    $ bash $Bin_dir/COME_main.sh	$Bin_dir/../examples/human.test.gtf	~/COME_out	$Bin_dir	human	human.model;
   

5. The final output will be stored in ~/COME_out/result.txt. We can compare it with the example output file ~/human.test.result.txt. (Notice: the subclass number may be different, because the K-means algorithm used random seed.)

6. Users are recommended to use the absolute path (/dir1/dir2/file1) instead of the relative path (../../file2).

3. Citing COME

=================

Hu L., Xu Z., Hu B. and Lu ZJ, COME: a robust coding potential calculation tool for lncRNA identification and characterization based on multiple features, 2016

4. Contact

==========

Long Hu hulongptp@gmail.com