Sipros5 Setup Guide

1. Create Conda Environment

conda install bioconda::sipros

2. Download Raw Files

mkdir raw
# Download raw file with 1% 13C
wget ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2024/06/PXD041414/Pan_062822_X1iso5.raw -P raw
# Download raw file with 50% 13C  
wget ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2024/06/PXD041414/Pan_052322_X13.raw -P raw

3. Download E. coli Protein FASTA Sequence

wget https://ftp.uniprot.org/pub/databases/uniprot/knowledgebase/reference_proteomes/Bacteria/UP000000625/UP000000625_83333.fasta.gz
gunzip UP000000625_83333.fasta.gz -c > Ecoli.fasta

4. Example Commands

Regular Search

siproswf -i raw/Pan_062822_X1iso5.raw -f Ecoli.fasta -o regular_output

Extract protein sequences identified in Regular search

• This step is particularly useful when your protein FASTA is large (for example, several GB in metaproteomics studies). The regular_output/protein.tsv file can be replaced with results from other proteomics search engines (e.g., FragPipe, MaxQuant, or Proteome Discoverer) as long as the first column contains the protein identifier.
• If you are working with a small FASTA, you can skip this extraction step and use the original FASTA for the label search.

extractPro Ecoli.fasta regular_output/protein.tsv db.faa

Label Search

siproswf -i raw -f db.faa -e C13 -o sip_output

Label Search with negative control using unlabeled sample

siproswf -i raw -f db.faa -e C13 --negative_control Pan_062822_X1iso5 -o sip2_output

5. Output Files

• SIP_filtered_psms.tsv: PSMs from all samples that pass the unlabeled negative-control filter (1% FDR), with SIP element labeling percentages (MS1IsotopicAbundances, MS2IsotopicAbundances). MS1IsotopicAbundances are more sensitive; MS2IsotopicAbundances are more accurate.
• protein_with_SIP_filtered_PSM.tsv: maps unlabeled negative-control filtered PSMs to the proteins identified in each sample.
• For each raw-file subdirectory:
  • peptide.tsv, protein.tsv: peptides, and proteins passing 1% FDR decoy filtering.
  • *_filtered_psms.tsv: PSMs passing 1% FDR decoy filtering with MS1IsotopicAbundances, MS2IsotopicAbundances.

Sipros5 Setup Guide (set the python and binary by yourself)

1. Create Conda Environment

conda create -n sipros5 lxml pandas seqkit python=3.12 -c bioconda -c conda-forge
conda activate sipros5

2. Download Sipros5 Release

wget https://github.com/xyz1396/sipros5/releases/download/5.0.1/siprosRelease.zip
unzip siprosRelease.zip
chmod +x sipros/tools/* sipros/script33/extractPro.sh

3. Example Commands

Regular Search

python sipros/script33/main.py -i raw/Pan_062822_X1iso5.raw -f Ecoli.fasta -o regular_output

Extract protein sequences identified in Regular search

sipros/script33/extractPro.sh Ecoli.fasta regular_output/protein.tsv db.faa

Label Search

python sipros/script33/main.py -i raw -f db.faa -e C13 -o sip_output

Label Search with negative control using unlabeled sample

python sipros/script33/main.py -i raw -f db.faa -e C13 --negative_control Pan_062822_X1iso5 -o sip2_output

6. Citation

1. Xiong, Y., Mueller, R.S., Feng, S., Guo, X. and Pan, C., 2024. Proteomic stable isotope probing with an upgraded Sipros algorithm for improved identification and quantification of isotopically labeled proteins. Microbiome, 12.
2. Li, J., Xiong, Y., Feng, S., Pan, C., & Guo, X. (2024). CloudProteoAnalyzer: scalable processing of big data from proteomics using cloud computing. Bioinformatics Advances, vbae024.
3. Guo, X., Li, Z., Yao, Q., Mueller, R.S., Eng, J.K., Tabb, D.L., Hervey IV, W.J. and Pan, C., 2018. Sipros ensemble improves database searching and filtering for complex metaproteomics. Bioinformatics, 34(5), pp.795-802.
4. Wang, Y., Ahn, T.H., Li, Z. and Pan, C., 2013. Sipros/ProRata: a versatile informatics system for quantitative community proteomics. Bioinformatics, 29(16), pp.2064-2065.
5. Pan, C., Kora, G., McDonald, W.H., Tabb, D.L., VerBerkmoes, N.C., Hurst, G.B., Pelletier, D.A., Samatova, N.F. and Hettich, R.L., 2006. ProRata: a quantitative proteomics program for accurate protein abundance ratio estimation with confidence interval evaluation. Analytical Chemistry, 78(20), pp.7121-