A profile likelihood algorithm is proposed for quantitative shotgun proteomics to infer the abundance ratios of proteins from the abundance ratios of isotopically labeled peptides derived from proteolysis. Previously, we have shown that the estimation variability and bias of peptide abundance ratios can be predicted from their profile signal-to-noise ratios. Given multiple quantified peptides for a protein, the profile likelihood algorithm probabilistically weighs the peptide abundance ratios by their inferred estimation variability, accounts for their expected estimation bias, and suppresses contribution from outliers. This algorithm yields maximum likelihood point estimation and profile likelihood confidence interval estimation of protein abundance ratios. This point estimator is more accurate than an estimator based on the average of peptide abundance ratios. The confidence interval estimation provides an "error bar" for each protein abundance ratio that reflects its estimation precision and statistical uncertainty. The accuracy of the point estimation and the precision and confidence level of the interval estimation were benchmarked with standard mixtures of isotopically labeled proteomes. The profile likelihood algorithm was integrated into a quantitative proteomics program, called ProRata, freely available at www.MSProRata.org.

A database searching approach can be used for metabolite identification in metabolomics by matching measured tandem mass spectra (MS/MS) against the predicted fragments of metabolites in a database. Here, we present the open-source MIDAS algorithm (Metabolite Identification via Database Searching). To evaluate a metabolite-spectrum match (MSM), MIDAS first enumerates possible fragments from a metabolite by systematic bond dissociation, then calculates the plausibility of the fragments based on their fragmentation pathways, and finally scores the MSM to assess how well the experimental MS/MS spectrum from collision-induced dissociation (CID) is explained by the metabolite's predicted CID MS/MS spectrum. MIDAS was designed to search high-resolution tandem mass spectra acquired on time-of-flight or Orbitrap mass spectrometer against a metabolite database in an automated and high-throughput manner. The accuracy of metabolite identification by MIDAS was benchmarked using four sets of standard tandem mass spectra from MassBank. On average, for 77% of original spectra and 84% of composite spectra, MIDAS correctly ranked the true compounds as the first MSMs out of all MetaCyc metabolites as decoys. MIDAS correctly identified 46% more original spectra and 59% more composite spectra at the first MSMs than an existing database-searching algorithm, MetFrag. MIDAS was showcased by searching a published real-world measurement of a metabolome from Synechococcus sp. PCC 7002 against the MetaCyc metabolite database. MIDAS identified many metabolites missed in the previous study. MIDAS identifications should be considered only as candidate metabolites, which need to be confirmed using standard compounds. To facilitate manual validation, MIDAS provides annotated spectra for MSMs and labels observed mass spectral peaks with predicted fragments. The database searching and manual validation can be performed online at http://midas.omicsbio.org.

The abundance ratio between the light and heavy isotopologues of an isotopically labeled peptide can be estimated from their selected ion chromatograms. However, quantitative shotgun proteomics measurements yield selected ion chromatograms at highly variable signal-to-noise ratios for tens of thousands of peptides. This challenge calls for algorithms that not only robustly estimate the abundance ratios of different peptides but also rigorously score each abundance ratio for the expected estimation bias and variability. Scoring of the abundance ratios, much like scoring of sequence assignment for tandem mass spectra by peptide identification algorithms, enables filtering of unreliable peptide quantification and use of formal statistical inference in the subsequent protein abundance ratio estimation. In this study, a parallel paired covariance algorithm is used for robust peak detection in selected ion chromatograms. A peak profile is generated for each peptide, which is a scatterplot of ion intensities measured for the two isotopologues within their chromatographic peaks. Principal component analysis of the peak profile is proposed to estimate the peptide abundance ratio and to score the estimation with the signal-to-noise ratio of the peak profile (profile signal-to-noise ratio). We demonstrate that the profile signal-to-noise ratio is inversely correlated with the variability and bias of peptide abundance ratio estimation.

BackgroundMore than 80% of the microbial genomes in GenBank are of ‘draft’ quality (12,553 draft vs. 2,679 finished, as of October, 2013). We have examined all the microbial DNA sequences available for complete, draft, and Sequence Read Archive genomes in GenBank as well as three other major public databases, and assigned quality scores for more than 30,000 prokaryotic genome sequences.ResultsScores were assigned using four categories: the completeness of the assembly, the presence of full-length rRNA genes, tRNA composition and the presence of a set of 102 conserved genes in prokaryotes. Most (~88%) of the genomes had quality scores of 0.8 or better and can be safely used for standard comparative genomics analysis. We compared genomes across factors that may influence the score. We found that although sequencing depth coverage of over 100x did not ensure a better score, sequencing read length was a better indicator of sequencing quality. With few exceptions, most of the 30,000 genomes have nearly all the 102 essential genes.ConclusionsThe score can be used to set thresholds for screening data when analyzing “all published genomes” and reference data is either not available or not applicable. The scores highlighted organisms for which commonly used tools do not perform well. This information can be used to improve tools and to serve a broad group of users as more diverse organisms are sequenced. Unexpectedly, the comparison of predicted tRNAs across 15,000 high quality genomes showed that anticodons beginning with an ‘A’ (codons ending with a ‘U’) are almost non-existent, with the exception of one arginine codon (CGU); this has been noted previously in the literature for a few genomes, but not with the depth found here.