Pipasic

Pipasic corrects identification and spectral counting–based quantification in metaproteomic datasets by estimating peptide similarity, weighting by expression level, and applying a non-negative lasso (Least Absolute Shrinkage and Selection Operator) framework to mitigate biases from protein conservation across related organisms.

Key Features:

• Peptide Similarity Estimation: Estimates peptide similarity to distinguish closely related peptides from different organisms in metaproteomic samples.
• Expression Level Weighting: Incorporates expression level weighting to adjust quantification for biases correlated with protein abundance and conservation.
• Non-Negative Lasso Framework: Uses a non-negative lasso (Least Absolute Shrinkage and Selection Operator) framework to compute corrections while enforcing non-negativity constraints.

Scientific Applications:

• Viral Diagnostics: Improves identification and quantification of viral proteins in contexts with high sequence similarity.
• Environmental Studies: Has been applied to environmental metaproteomic datasets such as acid mine drainage to improve microbial community quantification.

Methodology:

Estimates peptide similarity, applies expression level weighting, and integrates these within a non-negative lasso (Least Absolute Shrinkage and Selection Operator) framework.