Public Comment on OMB Draft Risk Assessment Bulletin

The OIRA draft Risk Assessment Bulletin has worthy intentions and has stimulated useful review and discussion.; Most previous major documents in the development of the risk assessment field have been cited and used appropriately. In general, the formulation is too broad. The Revision should clarify the place of risk assessment as distinguished from hazard identification and from risk management.The category of "influential risk assessment" is unnecessary and confusing, and should be deleted. A single set of six standards would suffice, without the additional nine special standards for "influential risk assessments". Greater transparency within the EOP is desirable to give this process credibility and meet one of the explicit aims of the Bulletin. Finally, several omissions should be addressed: proactive engagement of stakeholders, public health context, deceptive use of quantitation, exclusion for research agencies, interagency steering committee and symmetry of risk assessment guidance for manufacturers as well as regulatory agencies.

The human eye proteome project

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/99592/1/pmic7517.pd

Prognostic Factors in Cancer, 3 rd edition. By M. K. Gospodarowicz, B. O'Sullivan, L. H. Sobin (Eds.)

No abstracts.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/55917/1/6385_ftp.pd

Revisiting the identification of canonical splice isoforms through integration of functional genomics and proteomics evidence

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/109787/1/pmic7911.pd

Highlights of B/D‐HPP and HPP Resource Pillar Workshops at 12th Annual HUPO World Congress of Proteomics

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106985/1/pmic7698.pd

WaveletQuant, an improved quantification software based on wavelet signal threshold de-noising for labeled quantitative proteomic analysis

<p>Abstract</p> <p>Background</p> <p>Quantitative proteomics technologies have been developed to comprehensively identify and quantify proteins in two or more complex samples. Quantitative proteomics based on differential stable isotope labeling is one of the proteomics quantification technologies. Mass spectrometric data generated for peptide quantification are often noisy, and peak detection and definition require various smoothing filters to remove noise in order to achieve accurate peptide quantification. Many traditional smoothing filters, such as the moving average filter, Savitzky-Golay filter and Gaussian filter, have been used to reduce noise in MS peaks. However, limitations of these filtering approaches often result in inaccurate peptide quantification. Here we present the WaveletQuant program, based on wavelet theory, for better or alternative MS-based proteomic quantification.</p> <p>Results</p> <p>We developed a novel discrete wavelet transform (DWT) and a 'Spatial Adaptive Algorithm' to remove noise and to identify true peaks. We programmed and compiled WaveletQuant using Visual C++ 2005 Express Edition. We then incorporated the WaveletQuant program in the <b>Trans-Proteomic Pipeline (TPP)</b>, a commonly used open source proteomics analysis pipeline.</p> <p>Conclusions</p> <p>We showed that WaveletQuant was able to quantify more proteins and to quantify them more accurately than the ASAPRatio, a program that performs quantification in the TPP pipeline, first using known mixed ratios of yeast extracts and then using a data set from ovarian cancer cell lysates. The program and its documentation can be downloaded from our website at <url>http://systemsbiozju.org/data/WaveletQuant</url>.</p