Recombination rates in admixed individuals identified by ancestry-based inference.

Studies of recombination and how it varies depend crucially on accurate recombination maps. We propose a new approach for constructing high-resolution maps of relative recombination rates based on the observation of ancestry switch points among admixed individuals. We show the utility of this approach using simulations and by applying it to SNP genotype data from a sample of 2,565 African Americans and 299 African Caribbeans and detecting several hundred thousand recombination events. Comparison of the inferred map with high-resolution maps from non-admixed populations provides evidence of fine-scale differentiation in recombination rates between populations. Overall, the admixed map is well predicted by the average proportion of admixture and the recombination rate estimates from the source populations. The exceptions to this are in areas surrounding known large chromosomal structural variants, specifically inversions. These results suggest that outside of structurally variable regions, admixture does not substantially disrupt the factors controlling recombination rates in humans.

Precursor-ion mass re-estimation improves peptide identification on hybrid instruments.

Mass spectrometry-based proteomics experiments have become an important tool for studying biological systems. Identifying the proteins in complex mixtures by assigning peptide fragmentation spectra to peptide sequences is an important step in the proteomics process. The 1-2 ppm mass-accuracy of hybrid instruments, like the LTQ-FT, has been cited as a key factor in their ability to identify a larger number of peptides with greater confidence than competing instruments. However, in replicate experiments of an 18-protein mixture, we note parent masses deviate 171 ppm, on average, for ion-trap data directed identifications and 8 ppm, on average, for preview Fourier transform (FT) data directed identifications. These deviations are neither caused by poor calibration nor by excessive ion-loading and are most likely due to errors in parent mass estimation. To improve these deviations, we introduce msPrefix, a program to re-estimate a peptide's parent mass from an associated high-accuracy full-scan survey spectrum. In 18-protein mixture experiments, msPrefix parent mass estimates deviate only 1 ppm, on average, from the identified peptides. In a cell lysate experiment searched with a tolerance of 50 ppm, 2295 peptides were confidently identified using native data and 4560 using msPrefixed data. Likewise, in a plasma experiment searched with a tolerance of 50 ppm, 326 peptides were identified using native data and 1216 using msPrefixed data. msPrefix is also able to determine which MS/MS spectra were possibly derived from multiple precursor ions. In complex mixture experiments, we demonstrate that more than 50% of triggered MS/MS may have had multiple precursor ions and note that spectra with multiple candidate ions are less likely to result in an identification using TANDEM. These results demonstrate integration of msPrefix into traditional shotgun proteomics workflows significantly improves identification results.