A better scoring model for de novo peptide sequencing: the symmetric difference between explained and measured masses.

Background: Given a peptide as a string of amino acids, the masses of all its prefixes and suffixes can be found by a trivial linear scan through the amino acid masses. The inverse problem is the : Given all prefix and suffix masses, determine the string of amino acids. In biological reality, the given masses are measured in a lab experiment, and measurements by necessity are noisy.

Genevestigator v3: a reference expression database for the meta-analysis of transcriptomes.

The Web-based software tool Genevestigator provides powerful tools for biologists to explore gene expression across a wide variety of biological contexts. Its first releases, however, were limited by the scaling ability of the system architecture, multiorganism data storage and analysis capability, and availability of computationally intensive analysis methods. Genevestigator V3 is a novel meta-analysis system resulting from new algorithmic and software development using a client/server architecture, large-scale manual curation and quality control of microarray data for several organisms, and curation of pathway data for mouse and Arabidopsis.

PepSplice: cache-efficient search algorithms for comprehensive identification of tandem mass spectra.

Motivation: Tandem mass spectrometry allows for high-throughput identification of complex protein samples. Searching tandem mass spectra against sequence databases is the main analysis method nowadays. Since many peptide variations are possible, including them in the search space seems only logical.

NovoHMM: a hidden Markov model for de novo peptide sequencing.

De novo sequencing of peptides poses one of the most challenging tasks in data analysis for proteome research. In this paper, a generative hidden Markov model (HMM) of mass spectra for de novo peptide sequencing which constitutes a novel view on how to solve this problem in a Bayesian framework is proposed. Further extensions of the model structure to a graphical model and a factorial HMM to substantially improve the peptide identification results are demonstrated.