A spectral library, or simply a library in Next Generation Proteomics, is a collection or database of peptide fragment ions assigned to a precursor. The most common way of obtaining the information to build a library is through a shotgun approach where data dependent acquisition (DDA) is combined with database searches. The libraries are then used for peptide and protein identification in data independent acquisition (DIA) workflows. This is done by targeted extraction of the ions in the library from the DIA data. The better the match, the higher the identification confidence.
Which information should a library contain?
Strictly, the minimum library information needed for Spectronaut™ Pulsar is the m/z of the fragment ions and the m/z of the precursor each fragment ion is assigned to. However, adding some particular extra information improves dramatically the result of the identification algorithm. Thus, it is highly recommended to add the normalized retention time of the precursor (iRT) and the intensities of the fragment ions to the library. Other important information a library should contained is annotation related, such as fragment ion charge and type, precursor charge, peptide sequence, modified peptide sequence, protein ID, etc. When generating a library within Spectronaut Pulsar, the software will automatically add all the important information to the library.
Does the library need to contain iRT values?
High precision iRT values greatly increase the performance of your library during DIA data extraction. We recommend spiking the iRT Kit in every sample intended to generate a library. By doing this, you will assure the usability of the library to a broader spectrum of projects, including those involving samples of low complexity or from uncommon organisms. Furthermore, adding the iRT Kit to your samples allows for easy quality control monitoring. Missing or unexpected iRT values may for instance indicate problems with your LC.
How can I generate a library?
Both Spectronaut Pulsar and SpectroDive™ software contain perspectives dedicated to generating libraries from DDA data. Most search engines (MaxQuant, Proteome Discoverer™, Protein Pilot™ and Mascot™) are supported. Furthermore, Spectronaut Pulsar allows library building from both DDA and DIA data searched directly (using a FASTA file) with Biognosys own search engine, Pulsar. To know more on how to generate a library on Sectronaut Pulsar, visit this article here.
Do I need to generate a library for every new experiment I do?
Comprehensive project specific libraries show the best performance in terms of number of identifications and data completeness. However, generating these libraries can result in mass spectrometer overhead especially for small projects. Therefore, two alternative workflows are possible in Spectronaut™ Pulsar. First, publicly available libraries such as the H. sapiens library generated in the Aebersold group (Rosenberger et al. 2014), can be used more confidently than before thanks to the implementation of protein FDR estimation in Spectronaut Pulsar.
Second, with Spectronaut Pulsar you can perform a DIA workflow without the need of building a library. This is possible with our directDIA™ approach. In directDIA, the same DIA runs used for quantification (your experimental runs) are directly searched against a protein database for protein identification, using Biognosys own search engine, Pulsar. The directDIA workflow is the fastest and least resource-intensive way of analyzing DIA data.
To know more about libraries and Biognosys solutions, please go to other related articles here, here, and here. If you want to read more about the different combinations Spectronaut Pulsar allows you to do, visit our ScienceHub here. Or contact us here!
Rosenberger G, Koh CC, Guo T, Röst HL, Kouvonen P, Collins BC, Heusel M, Liu Y, Caron E, Vichalkovski A, Faini M, Schubert OT, Faridi P, Ebhardt HA, Matondo M, Lam H, Bader SL, Campbell DS, Deutsch EW, Moritz RL, Tate S, and Aebersold R (2014) A repository of assays to quantify 10,000 human proteins by SWATH-MS. Sci Data 1:140031.
Created by SEZ. Last update 2018-03-15 by SEZ