Pulldown techniques like IP-MS and Turbo-ID enable the identification of protein-protein interactions and proximal protein networks associated with specific chromatin regulators or histone modifications. In cancer epigenetics, these methods reveal how altered chromatin complexes or epigenetic enzymes recruit oncogenic cofactors, uncovering mechanistic insights into aberrant transcriptional control and potential therapeutic targets. However, there are no standard analysis pipelines or workflows to date; and analysis of such datasets is challenging due to the high prevalence of missing values arising from the stochastic nature of peptide detection by mass spectrometry, low-abundance interactors, and variable enrichment efficiency, which can obscure true biological interactions and lead to biased interpretations. To this end, we introduce a statistically robust pipeline for analysis of pulldown data powered by the limpa R package. The limpa package implements statistical methods for quantification and differential expression analysis of MS proteomics data, including probabilistic information recovery from missing values. Applications of the limpa pipeline on SCAF4/8 IP-MS and Turbo-ID data helped elucidate biological processes underpinning the transition between transcription elongation and termination regulated by RNA polymerase II (RNAPII) in human cancers.