mehdiforoozandeh / MCIC

MCIC is a software developed with the primary purpose of identifying cellulose-degrading enzymes in large metagenomic data followed by the characterization of their pH and temperature dependence using trained machine learning models.

MCIC

Using a sequence similarity based annotation and an ensemble machine learning approach, MCIC (metagenome cellulase identification and classification) aims to identify and classify cellulolytic enzymes from a given metagenomic data as well as any other amino-acid sequence on the basis of optimum temperature and pH.

In the "dist" directory, 4 builds of this tool can be found as standalone toolkit and python packages for Windows and Linux-based operating systems. The "src" directory contains MCIC's core modules and models.

***USAGE: (arguments are separated with a space)

-firts argument : MCIC 

-second argument options: [-h] [--help] [csp] [CelScreenPred] [cs] [CelScreen] [fp] [FastaPred] [sp] [SinglePred]

-third argument: [query input file]

-forth+ argument options: [-bs] [--bitscore] [-out] [-noexport]

***FUNCTIONS:

1. [csp] or [CelScreenPred]: Accepts nucleotide sequences and screens the cellulolytic enzymes and predicts pH and temperature dependence.

2. [cs] or [CelScreen]: Accepts nucleotide sequences and screens the cellulolytic enzymes. (without prediction)

3. [fp] or [FastaPred]: Accepts a fasta file containing protein sequence of cellulolytic enzymes and predicts pH and temperature dependence.

4. [sp] or [SinglePred]: Accepts a single protein sequence or entry or accession number of cellulolytic enzymes and predicts pH and temperature dependence.

***HOW TO USE EACH FUNCTION:

1. [csp] or [CelScreenPred]:

   options:

   • [-bs]/[--bitscore]: choose your bitscore limit for filteration during the screening process. ==> "default: 50"
   • [-out]: choose your output file's name/address (*.csv). ==> "default: inputname.csv"
   • [-noexport]: if chosen, the results will get printed on screen and no output file gets exported. "default: False"

   << By default, all results will be written in "results" folder.>>

   usage examples:

   • MCIC csp input.format
   • MCIC CelScreenPred input.format -bs 100 -out John
   • MCIC csp input.format --bitscore 500 -noexport
   • MCIC CelScreenPred input.format -out home/John
   • MCIC csp input.format -noexport

2. [cs] or [CelScreen]:

   options:

   • [-bs]/[--bitscore]: choose your bitscore limit for filteration during the screening process. ==> "default: 50"
   • [-out]: choose your output file's name/address (*.csv). ==> "default: inputname.csv"
   • [-noexport]: if chosen, the results will get printed on screen and no output file gets exported. "default: False"

   << By default, all results will be written in "results" folder.>>

   usage examples:

   • MCIC cs input.format
   • MCIC CelScreen input.format -bs 300 -out John
   • MCIC cs input.format --bitscore 50 -noexport
   • MCIC CelScreen input.format -out home/John
   • MCIC cs input.format -noexport

3. [fp] or [FastaPred]:

   options:

   • [-out]: choose your output file's name/address (*.csv). ==> "default: inputname.csv"
   • [-noexport]: if chosen, the results will get printed on screen and no output file gets exported. "default: False"

   << By default, all results will be written in "results" folder.>>

   usage examples:

   • MCIC fp input.fasta
   • MCIC FastaPred input.fasta -out John
   • MCIC fp input.fasta -noexport

4. [sp] or [SinglePred]:

   This function does not have any options. Two types of inputs are possible (1) amin acid sequence (2) protein entry/accession

   usage examples:

   • MCIC sp MKSCAILAALGCLA....
   • MCIC SinglePred Q7Z9M7