Interesting

We have developed an new flow for processing Thermo RAW files that works both with the most recent XCalibur V2.1, as well as with earlier versions. This flow has been giving good results in internal testing, and we are now releasing it for beta testing to any interested, actively supported Sorcerer customer.

Thermo LTQ Velos users will have noticed the major changes to the XCalibur software that were introduced at version 2.1. The installation process is different, and requires a new component called Thermo Foundation, and some of the file names and locations have changed. All of these changes are no longer compatible with the ReAdW program that is used within the CrossOver environment by Sorcerer. One workaround which has been commonly suggested in the Thermo field is to down-rev the XCalibur used on the instrument to V2.0 and to continue using the old software for analysis. This remains a viable option, but with our newly developed solution, it is now also possible to use 2.1 RAW files on Sorcerer.

We are moving to a new spectrum extractor called msconvert (part of the ProteoWizard suite)  which works with a different version of the Thermo libraries, and for which we have developed a new integration in the CrossOver environment. We are offering this as a beta release to our in-warranty customers. This solution  entails a few Linux operations to reinstall CrossOver with the latest release, to configure the required libraries and to install a new Sorcerer workflow script; it is fairly straightforward for people comfortable with the Linux environment, or alternatively, we can do it for you if you give us remote access to your system. Please contact us at support@sagenresearch.com for more information.

Prof. Josh Elias (left) of Stanford University receives a thank-you gift from David Chiang after his talk.

Ever wondered about target-decoy searching? Want to gain a better understanding and realistic expectation of this effective tool? SageNResearch’s video “Addressing Peptide Identification Signal-to-noise With Target-Decoy Searching”, given by Professor Josh Elias of Stanford University at our “Translational Proteomics 2.0″ meeting, can help. Dr. Elias is an Assistant Professor in Chemical and Systems Biology at Stanford University, and was part of the Steven Gygi Lab at Harvard Medical School before that. His lab is keenly interested in developing and applying methods to meet the current challenges facing scientists engaged in large scale proteome characterization.

Josh kicked off his talk with a stunning and very powerful visual to hit home the concept of what target-decoy database searching can do — you’ll never look at coffee beans in quite the same way. With this talk, you’ll know how to better find a happy medium for thresholds, smarter ways of designing your filtering criteria, when not to even consider using the method, how to get the most out of (really easy) decoy searching in SORCERER, and what’s so good about partial tryptic searches.

The 30-minute presentation is available at: http://www.scivee.tv/node/15544
To view slides, I recommend using the “full screen” mode. The slide set can also be downloaded as a Powerpoint file.

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.)

by David.Chiang@SageNResearch.com

Proteomics mass spectrometry is finally sensitive and specific enough for robust translational medicine (at least in capable hands), and holds tremendous promise to revolutionize biology and medicine. For some, it holds the key to incredible research power for decades to come.

However, there is a chasm that continues to grow between the productive and unproductive labs, because too many proteomics practitioners focus too early on low-level issues (i.e. cost, automation, ease-of-use) without first resolving high-level ones (i.e. sensitivity in presence of noise, quality of results, algorithmic suitability).

For many researchers experimenting with a new high-resolution instrument, the most common scenario is to select a workflow based on running a simple protein solution, usually a purified BSA solution or a commercial protein mixture.

Since different workflows will give basically identical protein IDs results for these simple test cases, they may conclude that all search engines are equivalent. While true when there is almost no signal noise, it is largely irrelevant in translational research. In fact, the exact same test will likely show that low-resolution and high-resolution mass specs are equivalent, the lowest quality reagents will suffice, or maybe you don’t have to clean your glassware as often. These are also true when there is little or no signal noise, but again, that is irrelevant for real-world research.

Seeing that there is little difference in protein IDs, some focus on using protein coverage as the sole metric for evaluating search engines. However, this is actually the opposite of what is needed for sensitive discovery proteomics. For example, if you are hunting for new protein biomarkers (especially a “one-hit wonder”), you do not want the protein inference engine tuned to assigning any ambiguous peptides to already found proteins, thereby hiding them from further study.

Not surprisingly, a workflow selected based on low-noise experiments and focused on protein coverage will excel for simple mixtures, but is not sensitive enough to analyze complex mixtures with wide dynamic range, such as in translational research. Scientists will be able to see the abundant peptides and proteins, but probably little else. That is roughly what most proteomics researchers find today, nothing meaningful, but enough of the obvious to not change their methodologies.

The result is that most labs are not getting the value commensurate with their investments in proteomics mass spectrometry. Under the current economic environment, this is both wasteful and dangerous.

Within the academic world, while many proteomics researchers have trouble getting any interest, a select few are swamped and have to turn away collaborators. Within drug discovery firms, while many are staring at their mostly idle mass spectrometers, a select few are running multiple mass spectrometers 24/7 sieving productively through millions of peptides.

So why are the majority of the proteomics research not producing high-value results?

With our access into the world’s top academic and drug discovery proteomics labs, we have a unique bird’s eye view into the answer. (However, like attorneys, we never give out client-specific information.)

Please allow me to share some secrets to your future success.

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“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!

Hear Khatereh discuss her work and her success with the SORCERER 2 system!

Dr. Khatereh Motamedchaboki is currently the Manager of the Proteomics Facility at the Burnham Institute for Medical Research.

She is one of our increasing number of two-time SORCERER success stories, as a previous user at the Ebrahim Zandi Lab at the University of Southern California.

Reference: Laurence M. Brill, Khatereh Motamedchabokia, Shuangding Wu, and Dieter A. Wolf, “Comprehensive proteomic analysis of Schizosaccharomyces pombe by two-dimensional HPLC-tandem mass spectrometry”, Methods (2009), doi:10.1016/j.ymeth.2009.02.023.

Click Here to See Video

Our R&D team is busy working on the next major version of the Sorcerer-PE software, and expects to release it to then-in-warranty customers in the next few weeks.  Early previews and beta tests of some of the components will be made available by arrangement to qualified customer sites.

Highlights of the upcoming release include:

• ETD fragmentation support and analysis
• MUSE scripting modules for rescoring peptide matches with Olsen-Mann and Sadygov-Coon scores
• Interoperation with major components of the Yates lab Sequest suite, including the DTASelect filtering and statistical analysis tool, and the Census quantitation application
• Enhancements to the SEQUEST engine which provide first-pass cross-correlation scoring and E-values for greater accuracy and sensitivity

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Three of the world’s leading experts on MS-MS protein identification came together recently at Sage-N Research’s annual user group meeting, and presented methods and results for the techniques and tools with which they are associated:

• Jimmy Eng, co-inventor of Sequest and developer of many proteomics tools, presented tips for Sequest analysis
• Josh Elias, who pioneered the systematic use of decoy databases for FDR estimation, gave a talk on how to use that technique to address Peptide ID signal-to-noise.
• Alexey Nesvizhskii spoke about the tools he co-authored, in “Peptide identification and protein inference using PeptideProphet and ProteinProphet”

Their talks were very wide-ranging and full of practical insights for the proteomics user community, and they explored the different research interests, data sets, analysis methods and workflows in the individual labs.  However, they all had this in common: they had kept a careful eye on their search settings, monitored sensitivity and error rates, and come to a common, if perhaps not entirely intuitive, conclusion: the most sensitive search and the lowest error rates for shotgun proteomics are achieved when using semi-enzymatic searches — that is, when one end, but not both, of the peptide is allowed to diverge from the expected cleavage site.

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