Package: ezLncPred
ezLncPred: An integrated python package for LncRNA identificationezLncPredis an comprehensive python package for LncRNA identification which integrates9state-of-the-art lncRNA prediction models.ezLncPredpython package provides a convenient command line method for researchers who intends to identify lncRNAs.IntegrationezLncPredcurrently provides 9 LncRNA prediction models, which are listed as follows.CNCICPC2lgcPLEKCPATCPPredlongdistPredLnc-GFStackLncADeepPython package installationPrerequisitepython 3.0 version (or above)linux operating systemtkinter/python-tk supportC/C++ compiler(for PLEK)DownloadezLncPredbypipinstallezLncPredHelpFor detailed message ofezLncPred, runezLncPred--manualFor detailed message of each model and their parameters, runezLncPred--manual[model]{CNCI,CPC2,CPAT,lgc,CPPred,GFStack,longdist,PLEK,LncADeep}UsageezLncPredoffers a total of 9 LncRNA prediction models, each with a different variety of parameter choices, users can refer to the list below to customarize your prediction procedure.
First,ezLncPredmustreceive at least three parameters to specify theinput fileoutput directoryandprediction model-i --input		fasta format input files

-o --output		the output directory to store the identification results

-m --manual		show manuals

-v --version		show program's version number and exitFor example, runezLncPred-iyour_fasta_file-ooutput_directorymodel[parameters]Individual model parametersCNCI-h --help		show this help message and exit

--parallel		assign the running CPU numbers

-p {ve,pl} --species {ve,pl}
			assign the classification models ("ve" for vertebrate species, "pl" for plat species)exampleezLncPredCNCI-hezLncPred-iexample.fa-oresultsCNCIezLncPred-iexample.fa-oresultsCNCI--parallelezLncPred-iexample.fa-oresultsCNCI-pveCPC2-h --help		show this help message and exit

-r REVERSE --reverse
			REVERSE also check the reverse strandexampleezLncPredCPC2-hezLncPred-iexample.fa-oresultsCPC2ezLncPred-iexample.fa-oresultsCPC2-rREVERSElgc-h --help		show this help message and exitexampleezLncPredlgc-hezLncPred-iexample.fa-oresultslgcPLEK-h --help		show this help message and exit

--thread		the number of threads to run the PLEK programme

--isoutmsg		Output messages to stdout(screen) or not. "0" means 
			that PLEK be run quietly. "1" means that PLEK outputs
			the details of processing. Default value: 0

--isrmtempfile		Remove temporary files or not. "0" means that PLEK 
			retains temporary files. "1" means that PLEK remove 
			temporary files. Default value: 1exampleezLncPredPLEK-hezLncPred-iexample.fa-oresultsPLEKezLncPred-iexample.fa-oresultsPLEK--thread4ezLncPred-iexample.fa-oresultsPLEK--isoutmsg1ezLncPred-iexample.fa-oresultsPLEK--isrmtempfile0CPAT-h --help		show this help message and exit

-p --species    	{Human,Mouse,Fly,Zebrafish}
			specify the species of the LncRNAs choose from Human 
			Mouse Fly Zebrafish (note that the first character 
			is upper case)

-s --start		Start codon (DNA sequence, so use 'T' instead of 'U')
			used to define open reading frame (ORF), default is ATG

-t --stop		Stop codon (DNA sequence, so use 'T' instead of 'U')
			used to define open reading frame (ORF). Multiple stop
			codons should be separated by ',' default is TAG,TAA,TGAexampleezLncPredCPAT-hezLncPred-iexample.fa-oresultsCPATezLncPred-iexample.fa-oresultsCPAT-pHumanezLncPred-iexample.fa-oresultsCPAT-sTAGezLncPred-iexample.fa-oresultsCPAT-tATG,TGA,TTACPPred-h --help		show this help message and exit

-p --species		{Human,Integrated}
			the model of the species to choose (Human,Integrated).exampleezLncPredCPPred-hezLncPred-iexample.fa-oresultsCPPredezLncPred-iexample.fa-oresultsCPPred-pIntegratedlongdist-h --help		show this help message and exit

-z <200>, --size <200>
			Minimun sequence size to consider. Default is 200.

-p --species	{Human,Mouse}
			the model of the species to choose (human,mouse).exampleezLncPredlongdist-hezLncPred-iexample.fa-oresultslongdistezLncPred-iexample.fa-oresultslongdist-z150ezLncPred-iexample.fa-oresultslongdist-pHumanPredLnc-GFStack-h --help		show this help message and exit

-p --species		{human,mouse}
			choose a species type from Human and MouseexampleezLncPredGFStack-hezLncPred-iexample.fa-oresultsGFStackezLncPred-iexample.fa-oresultsGFStack-phumanLncADeep-h --help		show this help message and exit

-mt --modeltype	{full,partial}
			the model used for lncRNA identification,
			choose from partial full default is partial
			default is "partial".

-HMM --HMMthread
			the thread number of using HMMER, default is 8

-p --species	{human,mouse}
			the model of the species to choose (human,mouse).
			default is "human".

-t --thread	THREAD
                		The number of threads for running the LncADeep program.default is 8.exampleezLncPredLncADeep-hezLncPred-iexample.fa-oresultsLncADeepezLncPred-iexample.fa-oresultsLncADeep-mtfullezLncPred-iexample.fa-oresultsLncADeep-HMM4ezLncPred-iexample.fa-oresultsLncADeep-phumanezLncPred-iexample.fa-oresultsLncADeep-t4Test caseBased on ezLncPred, we generate a dataset to predict lncRNAs and evaluate the state-of-art lncRNA prediction methods.
To test the models, we collect two types of transcripts: human lncRNAs and human protein-coding RNAs, each type comprises 100 transcripts.
The dataset is collected from CPPred human test set, and can be downloaded athttps://github.com/LittleHannah/ezLncPred.
Then, we test the cost time and accuracy of prediction by each model.
The test set is run in the default command without any additional parameters onIntel(R) Xeon(R) Gold 6146 CPU @ 3.20GHz.
The result is shown in Table.ModelCost TimeAccuracyCNCI47.415s0.935PLEK4.026s0.950CPC20.261s0.980CPPred5.741s0.980lgc0.217s0.945longdist0.697s0.895CPAT0.377s0.990LncADeep180.950s0.890GFStack23.278s0.980PapersCNCI :“Utilizing sequence intrinsic composition to classify protein-coding and long non-coding transcripts”, Sun et al. (2013).CPC2 :“CPC2: a fast and accurate coding potential calculator based on sequence intrinsic features”, Kang, J., et al. (2017).CPAT :“CPAT: Coding-Potential Assessment Tool using an alignment-free logistic regression model”, Wang et al. (2013).lgc :“Characterization and identification of long noncoding RNAs based on feature relationship”, Wang et al. (2019).CPPred :“CPPred: coding potential prediction based on the global description of RNA sequence”, Tong et al. (2019).GFStack :“PredLnc-GFStack: A Global Sequence Feature Based on a Stacked Ensemble Learning Method for Predicting lncRNAs from Transcripts”, Liu et al. (2019).longdist :“A Support Vector Machine based method to distinguish long non-coding RNAs from protein coding transcripts”, W.Schneider, H., et al. (2017).PLEK :“PLEK: a tool for predicting long non-coding RNAs and messenger RNAs based on an improved k-mer scheme”, Li, A., et al. (2014).LncADeep :“LncADeep: An ab initio lncRNA identification and annotation tool based on deep learning”, Yang et al. (2017).