LC-MSsim – a simulation software for liquid chromatography mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms.</p> <p>Results</p> <p>We present <it>LC-MSsim</it>, a simulation software for LC-ESI-MS experiments. It simulates ESI spectra on the MS level. It reads a list of proteins from a FASTA file and digests the protein mixture using a user-defined enzyme. The software creates an LC-MS data set using a predictor for the retention time of the peptides and a model for peak shapes and elution profiles of the mass spectral peaks. Our software also offers the possibility to add contaminants, to change the background noise level and includes a model for the detectability of peptides in mass spectra. After the simulation, <it>LC-MSsim </it>writes the simulated data to mzData, a public XML format. The software also stores the positions (monoisotopic m/z and retention time) and ion counts of the simulated ions in separate files.</p> <p>Conclusion</p> <p><it>LC-MSsim </it>generates simulated LC-MS data sets and incorporates models for peak shapes and contaminations. Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed. We anticipate that <it>LC-MSsim </it>will be useful to the wider community to perform benchmark studies and comparisons between computational tools.</p

Adrenergic-beta(2) receptor polymorphism and athletic performance

The focus of this review is to evaluate the influence of beta(2)-adrenergic receptor (ADRB2) polymorphism on human physiological function and in turn on athletic performance. A narrative review is conducted on available literature using MedLine, Pubmed and the Cochrane Library to document the location and function of ADRB2 receptors, and specifically to address the influence of genetic polymorphisms on cardiovascular, respiratory, metabolic and musculoskeletal systems and athletic performance. Search terms included ADRB2, endurance and polymorphism. Previous literature exploring the genetic composition of athletes has proposed that alterations in the genetic structure result in an enhancement in their capacity to achieve successful aerobic phenotypes such as a higher VO(2max) and increased fat oxidation. Polymorphism of the Gly16Glu27 haplotype is believed to promote positive aerobic phenotypes and regulate optimal lipolysis. Greater knowledge of the ADRB2 polymorphism can aid in understanding the specific phenotypes that are altered, which may influence performance. Until the interaction between fatigue and athletic performance is better understood, the development of appropriate training principles to enhance genetically polymorphic aerobic phenotypes remains complicated. Following the review, there is still no distinctive evidence for the predictive ability of the polymorphism of ADRB2 genotype for the purpose of identifying potential elite athletes.Vishnu Sarpeshkar and David J Bentle