An adaptive alignment algorithm for quality-controlled label-free LC-MS.

Sandin M, Ali A, Hansson K, Månsson O, Andreasson E, Resjö S, Levander F

Mol. Cell Proteomics 12 (5) 1407-1420 [2013-05-00; online 2013-01-12]

Label-free quantification using precursor-based intensities is a versatile workflow for large-scale proteomics studies. The method however requires extensive computational analysis and is therefore in need of robust quality control during the data mining stage. We present a new label-free data analysis workflow integrated into a multiuser software platform. A novel adaptive alignment algorithm has been developed to minimize the possible systematic bias introduced into the analysis. Parameters are estimated on the fly from the data at hand, producing a user-friendly analysis suite. Quality metrics are output in every step of the analysis as well as actively incorporated into the parameter estimation. We furthermore show the improvement of this system by comprehensive comparison to classical label-free analysis methodology as well as current state-of-the-art software.

Bioinformatics Support and Infrastructure

Bioinformatics Support, Infrastructure and Training

PubMed 23306530

DOI 10.1074/mcp.O112.021907

Crossref 10.1074/mcp.O112.021907

pii: O112.021907
pmc: PMC3650348

Publications 9.5.0