Paws mass spec software

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The protein database complexity and apparent size can vary considerably among the choices, although larger is probably not better [13]. Picking a search engine program to identify likely peptide sequences associated with tandem mass spectra, known as peptide spectrum matches PSMs , can be even more challenging.

There are commercial products like Mascot, Proteome Discoverer, and Byonic that can be quite expensive. Alternatively, there are widely used freely available options such as X! Tandem and MaxQuant. There are also many less commonly used open source options, and opinions run high on the relative merits of the various search engines.

Comet is free, fast, sensitive, and a little simpler to configure than other options; this is what the PAW pipeline [1] uses. The basic method of identifying peptides from tandem mass spectra MS2 scans has not really changed too much in 25 years [14]. It basically involves noise filtering and normalizing an MS2 scan, then comparing that to mass-filtered candidate peptide spectra from a theoretical digest of a protein database.

Despite this clever leveraging of genomic sequencing the protein sequences , the challenge has always been in deciding if a particular PSM is correct or not.

Early heuristic approaches [15] led to basic classifiers [9], and even machine learning methods [16]. A big step forward came when decoy databases were used to eavesdrop on random noise scores, as popularized by the Gygi lab [6, 7]. More recent instrumental advances have added high-resolution and accurate mass to the equation [8]. With more robust statistical methods for controlling PSM errors, confident lists of identified peptides present in digests of complex protein mixtures can be reliably determined.

The next issue is to determine a confident list of the proteins present in the samples from the identified peptides. This is known as the protein inference problem [11] and it persists despite significant advances in genomic sequencing maybe that has made the problem even worse. Guidelines for parsimonious protein identifications [17] have now been widely used for many years.

The basic parsimony rules are outlined in [11], but may need to be extended [12] for large datasets now routinely acquired. As proteomics has matured, there have been many analysis ideas that have come and gone. Only a few have really passed the test of time. Here is a summary of a modern proteomics analysis pipeline as implemented in the PAW pipeline:.

Identification of the proteins present in a sample is almost never the end goal of a modern proteomics experiment. Estimating the relative expression levels of the proteins is often required. The above discussion and list has not mentioned anything about quantification.

Quantitative processing is really more of a parallel set of analysis steps. Quantitative information can take many forms. There are label free approaches and stable isotope labeling approaches. The support for TMT is even more limited to high resolution instruments.

In a similar inference process, protein expression values are inferred from quantitative data acquired in individual instrument scans. These lists demonstrate that even a basic processing pipeline will involve several steps. A robust pipeline will keep these step separate to allow greater flexibility and to allow inspection of the data in between steps for quality control.

Each step is listed below by the name of the Python script. This program has yet to be reviewed by IonSource. Drahos and K. Mass Spectrom. Theory and Windows-based program to calculate mass spectra.