Abstract
We present a versatile user-friendly software tool, PolyAlign, for the alignment of multiple LC-MS signal maps with the option of manual landmark setting or automated alignment. One of the spectral images is selected as a reference map, and after manually setting the landmarks, the program warps the images using either polynomial or Hermite transformation. The software provides an option for automated landmark finding. The software includes a very fast zoom-in function synchronized between the images, which facilitate detecting correspondences between the adjacent images. Such an interactive visual process enables the analyst to decide when the alignment is satisfactory and to correct known irregularities. We demonstrate that the software provides significant improvements in the alignment of LC-MALDI data, with 10–15 landmark pairs, and it is also applicable to correcting electrospray LC-MS data. The results with practical data show substantial improvement in peak alignment compared to MZmine, which was among the best analysis packages in a recent assessment. The PolyAlign software is freely available and easily accessible as an integrated component of the popular MZmine software, and also as a simpler stand-alone Perl implementation to preview data and apply landmark directed polynomial transformation.
Highlights
We demonstrate that the software provides significant improvements in the alignment of LC-MALDI data, with 10–15 landmark pairs, and it is applicable to correcting electrospray liquid chromatography coupled to mass spectrometry (LC-MS) data
With the emergence of liquid chromatography coupled to mass spectrometry (LC-MS) as a predominant method for bioanalysis, a number of software packages have been tailored for the alignment of data sets from multiple LCMS analyses
LC-MS profiling has a great potential in comparative proteomics [26, 27]
Summary
With the emergence of liquid chromatography coupled to mass spectrometry (LC-MS) as a predominant method for bioanalysis, a number of software packages have been tailored for the alignment of data sets from multiple LCMS analyses. In particular these and related algorithms have targeted comparative metabolomic and proteomics applications and have been widely demonstrated to provide an improved level of comparison for complex samples. Several of these methods have been recently reviewed by Katajamaa and Oresic [1], Vandenbogaert et al [2], America and Cordewener [3], and Lange et al [4]. There are those that use the whole data, which we refer to as profile-based methods
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