The BSI team hosted live demos, user meetings and introductory lessons into PEAKS from 10am - 5pm on the Sunday in the Marriott Downtown (across the street from the Convention Center) to help kick-start ASMS 2010.

ASMS 2010 User's Meeting Slideshow

ASMS 2010 Posters

Han X, Shan P, Ma B. Precursor Mono-Isotopic Mass and Charge Determination with Almost 100% Accuracy. ASMS 2010: WP001.

Design an algorithm to determine the correct charge state and mono-isotopic mass of the precursor ion from high-resolution MS data. By generating all the potential isotopic envelope candidates in the given m/z range, we compare their theoretical isotopic distributions with the observed distribution, and pick up the most similar one. This greatly increases the accuracy of the mono-isotopic mass and charge state of the precursor ion.

	He L, Ma B. No News is Good News: de novo Determination of Amino Acids when Peaks are Missing. ASMS 2010: MP018.
	To improve the accuracy of de novo sequencing through the determination of amino acids when peaks are missing. Using the probability that the fragment ions between a pair of amino acids are missing (learned from the NIST database), this probability is used to determine the local sequence when peaks are missing. The end result is an increase in the prediction correctness of de novo sequencing.

Xin L, Shan P, Ma B. Determining the False Discovery Rate for Peptide Identification without a Decoy Database. ASMS 2010: MP025.

The ability to estimate the FDR (false discovery rate) of MS/MS results are of crucial importance. Instead of the timely (although popular) method for FDR estimation by running a search on both the target and decoy databases, we propose to use the 'second best matches' of the spectra on the target database to learn the distribution of the false matches, and use that distribution to estimate the FDR of the first matches. This method showed excellent performance without any penalty on searching speed.

Shan P, Chen W, Ma B. Systematic Assessment of the Reproducibility of Relative Quantification Based on LC-MS with Replicates. ASMS 2010: ThP016.

Knowing the reproducibility of the system provides more confident expression ratios, which are used as criteria for differential analysis. An understanding of the system of reproducibility can be achieved through a reproducibility assessment, which will minimize the number of false positives and negatives in differential analyses. This approach ensures greater experimental success via the incorporation of replicates.