A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet.

PeptideProphet is a post-processing algorithm designed to evaluate the confidence in identifications of MS/MS spectra returned by a database search. In this manuscript we describe the "what and how" of PeptideProphet in a manner aimed at statisticians and life scientists who would like to gain a more in-depth understanding of the underlying statistical modeling. The theory and rationale behind the mixture-modeling approach taken by PeptideProphet is discussed from a statistical model-building perspective followed by a description of how a model can be used to express confidence in the identification of individual peptides or sets of peptides.

Cross-kingdom comparison of transcriptomic adjustments to low-oxygen stress highlights conserved and plant-specific responses.

High-throughput technology has facilitated genome-scale analyses of transcriptomic adjustments in response to environmental perturbations with an oxygen deprivation component, such as transient hypoxia or anoxia, root waterlogging, or complete submergence. We showed previously that Arabidopsis (Arabidopsis thaliana) seedlings elevate the levels of hundreds of transcripts, including a core group of 49 genes that are prioritized for translation across cell types of both shoots and roots. To recognize low-oxygen responses that are evolutionarily conserved versus species specific, we compared the transcriptomic reconfiguration in 21 organisms from four kingdoms (Plantae, Animalia, Fungi, and Bacteria).