Alexey Nesvizhskii

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Video: “Peptide ID and Protein Inference..” by Prof. Alexey Nesvizhskii (U. Michigan)

September 16, 2009 in Download, Interesting, News, tips and hints, Webcasts by techteam | No comments


Prof. Alexey Nesvizhskii (left) of University of Michigan receives a thank-you gift from David Chiang after his talk.

If you really want to understand how peptide and protein identification is done, this video talk is a must-see!

Professor Alexey Nesvizhskii of the University of Michigan is one of the co-inventors (with Dr. Andy Keller) of the popular PeptideProphet/ProteinProphet algorithm for turning search engine results into statistically consistent peptide and protein identifications. (This algorithm is also the basis for the popular Scaffold software.)

At the “Translational Proteomics 2.0″ meeting, we were privileged to have Alexey give his insightful talk that reviews the various steps involved in inferring peptide and protein identifications from large spectra datasets.

In this talk, you will learn why False Discovery Rates are preferred over P-values, why you probably should not run more than 4 replicates of a MudPIT experiment, how FDR estimations from decoy differ from Peptide/ProteinProphet, how “The Two Prophets” compute probabilities by curve-fitting the score distributions, how sensitivity and FDR are computed, and the what and why of some advanced TPP options.

The talk is available at: http://www.scivee.tv/node/12671 (45 minutes).

I recommend using the “full screen” mode so you can view the slides, which are also available as a download from the site. (Please be aware that the slideset order is different from that in the presentation.)

(Note: Both Trans-Proteomic Pipeline and Scaffold Batch software are integrated into the SORCERER platforms.)

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Translational Proteomics Meeting: Secrets from the Masters

September 2, 2009 in Interesting, News by david | No comments


“Translational Proteomics 2.0″ 2009 Users Meeting in Philadelphia.
Guest speakers Jimmy Eng (UWashington), Alexey Nesvizhskii (UMichigan), Josh Elias (Stanford), along with SAB member John Yates (Scripps) are in the middle row.


Stanford’s Dr. Chris Adams (left) must be feeling pretty lucky!
He gets to use a SORCERER 2 for his research (as part of Allis Chien’s mass spec core facility), AND wins an Acer One netbook door prize from David Chiang!

Translational proteomics — aka Proteomics 2.0 — is high-sensitivity proteomics for translational research, whose mastery is your key to unimaginable fame and fortune in biology and medicine!

Whether you need to catch up or to keep up, you need to hear the leading proteomics technologists reveal their secrets!

We were fortunate to have three of most accomplished technologists (Mr. Jimmy Eng, Prof Josh Elias, and Prof Alexey Nesvizhskii) at our “Translational Proteomics 2.0 Meeting” give their insider insights on high-sensitivity data analysis.

In addition, we were privileged to have Sage-N Research SAB advisor Prof John Yates, one of the fathers of proteomics, attend our meeting and join in our lively panel discussions regarding the present and future of translational proteomics.

From the talks, these are tips for best sensitivity and specificity:

* There are several equivalent ways to calculate precursor mass, all of which can result in several AMUs of mass error due to incorrect isotope assignment.
* Semi-tryptic settings for database searching gives the best performance
* Use a wider mass tolerance than your experiments will yield
* However, you don’t need a wide mass tolerance for searching if (a) you use isotope shift check and (b) you have a decent source of noisy peptide, e.g. with semi-enzyme search
* Post-process peptide IDs with proper statistical tools (e.g. PeptideProphet, DTASelect or target-decoy analysis)
* Key is to monitor the false discovery rates (FDR) with different filtering criteria
* Use monoisotopic mass for fragment ions, and for precursor ions if using high-resolution instrument
* P-values or E-values are not good for large-scale proteomics, because they don’t give you estimated data rates for a given score cut-off, and they ignore other relevant factors (e.g. retention time, mass accuracy, etc.)
* The target-decoy method is a simple and effective means of FDR estimation. It gives scores more discriminatory power by improving signal-to-noise ratio.
* Can use search scores in combination with other characteristics to get more good IDs at a particular FDR than by using score alone

We will be publishing the meeting talks online. Watch this space for details!

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