Graph2Pro: A graph-centric approach for metagenome-guided peptide and protein identification in metaproteomics

Our method exploits the de Bruijn graph structure reported by metagenome assembly algorithms to generate a comprehensive database of protein sequences encoded.

In the following, we illustrated each step in the pipeline for metaproteomics identification:

Step1: 
The analysis pipeline starts with the step to remove the assembly has contigs shorter than 500bp for gene prediction FragGeneScan
In this step, the script remove_500bp.py can be used. 
Usage for remove_500bp is 
	python remove_500bp.py <input> > <output>
Example:
	python remove_500bp.py SRR1544596-SD6MG-s2-63mer-k31-d1.contig > MG_SD6.500.contig

Step2: 
Predict genes from contigs with length longer than 500 by FragGeneScan
Usage for FragGeneScan :
	FragGeneScan -s <contig> -o <output> -w 0/1 -t complete
Example:
	FragGeneScan -s MG_SD6.500.contig -o MG_SD6.500 -w 0 -t complete
Output is MG_SD6.500.faa

Step3:
Predict peptides by Graph2Pep (1st round)
Usage of Graph2Pep :
	DBGraph2Pro -d <depth> -e <edge> -s <contig> -o <output>
Example:
	DBGraph2Pro -d 5 -e SRR1046369-SD3MG-s2-63mer-k31-d1.updated.edge -s SRR1046369-SD3MG-s2-63mer-k31-d1.contig -o MG_SD3.contig-pep.fasta
Output is MG_SD3.contig-pep.fasta

Step4:
Create decoy database for DBGraph2Pro output by fixing the C-term residue K/R. The script is createFixedReverseKR.py. 
Usage of createFixedReverseKR.py :
	python createFixedReverseKR.py <input.fasta>
Example:
	python createFixedReverseKR.py MG_SD3.contig-pep.fasta
Output is MG_SD3.contig-pep.fixedKR.fasta


Step5:
Searching spectra against MG_SD3.500.faa and MG_SD3.contig-pep.fixedKR.fasta by MSGF+
Usage of MSGF+ :
	java -Xmx32g -jar MSGFPlus.jar -s SD3.mgf -o SD3.hybrid.bp.fixedKR.mzid -d hybrid_SD3-s2-63mer-k31-d1.contig-pep.fixedKR.fasta -inst 1 -t 15ppm -ti -1,2 -mod Mods_normal.txt -ntt 2 -tda 0 -maxCharge 7 -minCharge 1 -addFeatures 1 -n 1
	java -Xmx16g -cp MSGFPlus.jar edu.ucsd.msjava.ui.MzIDToTsv -i SD3.hybrid.bp.fixedKR.mzid -showDecoy 1
Output is SD3.hybrid.bp.fixedKR.tsv

Step6:
Parse the results to get the 5% FDR results for the 1st round by use parseFDR.py
Usage of parseFDR.py :
	python parseFDR.py <input> <FDRLevel>
Example:
	python parseFDR.py SD3.hybrid.bp.fixedKR.tsv 0.05
Output is SD3.hybrid.bp.fixedKR.tsv.0.05.tsv

Step7:
Combine the FGS and the Graph2Pro Database searching results by combineFragandDBGraph.py
Usage:
	python combineFragandDBGraph.py <MS result from FGS> <FGS NA sequence> <FGS AA sequence> <MSResult from DBGraph> <DBGraphDatabase> <contig> <output>
Output:
	python combineFragandDBGraph.py SD3.mg.frag.tsv.0.05.tsv MG_SD3.500.ffn MG_SD3.500.faa SD3.mg.bp.fixedKR.tsv.0.05.tsv MG_SD3.contig-pep.fasta MG_SD3.500.contig SD3.mg.combine.0.05.tsv

Step8:
Based on the combined peptides result, it use Graph2Pro to tranverse the proteins sequences. 
Usage:
        DBGraphPep2Pro -e <edge> -s <contig> -p <peptides> -o <protein.fasta>
Example:
	DBGraphPep2Pro -e SRR1046369-SD3MG-s2-63mer-k31-d1.updated.edge -s SRR1046369-SD3MG-s2-63mer-k31-d1.contig -p SD3.mg.combine.0.05.tsv -o SD3_mg_DBGraphPep2Pro_5.fasta

Step9:
Searching the spectra against the database outputed from DBGraphPep2Pro by MSGF+ and use false discovery rate control to get the final results.