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Posted by Ville Koskinen (July 29, 2019)

Identify proteins by more than ‘gut’ feeling

Last month, we discussed benchmarking protein inference and the role of shared peptide matches. Excluding shared matches may be beneficial to protein identification accuracy if the sequence database contains perfect representations of all proteins in the sample. Many real-life data sets don’t meet this condition. Metaproteomics and environmental samples, such as the various human body sites, peat bog and ocean [...]

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Posted by John Cottrell (June 14, 2019)

What are you inferring?

Benchmarking protein inference is notoriously difficult. Artificial samples of known content tend to be too simple while real samples lack ground truth. An interesting approach was adopted for the ABRF iPRG 2016 study, and has been the subject of a publication from The et al. A collection of human Protein Epitope Signature Tags (PrESTs) were expressed in E. coli and [...]

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Posted by John Cottrell (December 13, 2016)

Protein inference for spectral library searches

The major new feature of Mascot Server 2.6, now running on this web site, is that searches of spectral libraries have been fully integrated with ‘conventional’ Mascot searches of Fasta files. The search engine for spectral library searches is MSPepSearch from Steve Stein and colleagues at NIST. We didn’t have any revolutionary ideas for improving spectral library scoring so, rather [...]

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Posted by John Cottrell (January 19, 2015)

Creating a list of confidently identified proteins

This can be done very easily using Report Builder: Select the Decoy checkbox when submitting the search Open the result report as a Protein Family Summary Switch to the Report Builder tab Expand the decoy search section and set the peptide FDR to 1% Expand the filters section and set ‘Num of significant unique sequences’ > 1 Optionally, expand the [...]

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Posted by John Cottrell (November 22, 2013)

Does protein FDR have any meaning?

Its easy to grasp the concept of using a target/decoy search to estimate peptide false discovery rate. You search against a decoy database where there are no true matches available, so the number of observed matches provides a good estimate of the number of false matches in the results from the target. People debate implementation details, such as whether the [...]

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