dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts.
dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts.
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DOI：
10.1038/s41467-022-31492-0
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发表时间：
2022-07-08
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
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dia-PASEF技术使用离子迁移分离来减少信号干扰，提高蛋白质组学实验的灵敏度。本文提出了一种二维选峰算法和优化谱库的生成，并利用基于神经网络的dia-PASEF数据处理技术。与以前的工作相比，我们的计算平台将蛋白质组学深度提高了83%，特别有利于快速蛋白质组学实验和低样本量的实验。在timsTOF Pro质谱仪上使用evossep One色谱系统，在每天200个样品的单次注射中记录超过5300种蛋白质，在timsTOF Pro 2上使用93分钟的纳米流梯度记录200 ng HeLa肽的单次注射中记录近9000种蛋白质。通过将算法合并到DIA-NN软件中，并通过FragPipe工作流生成光谱库，提供了用户友好的实现。dia-PASEF技术使用离子迁移分离来减少信号干扰，提高基于质谱的蛋白质组学的灵敏度。作者提出了算法和软件解决方案，与以前的工作相比，dia-PASEF实验中的蛋白质组学深度提高了83%，并且特别有利于快速蛋白质组学实验和低样本量的实验。
The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation. The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity of mass spectrometry-based proteomics. The authors present algorithms and a software solution, which boost proteomic depth in dia-PASEF experiments by up to 83% compared to previous work, and are specifically beneficial for fast proteomic experiments and those with low sample amounts.
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DOI：
10.1111/1467-9868.00346
发表时间：
2002-01-01
影响因子：
5.8
作者：
Storey, JD
通讯作者：
Storey, JD
影响因子：
48
作者：
Reiter, Lukas;Rinner, Oliver;Aebersold, Ruedi
通讯作者：
Aebersold, Ruedi
影响因子：
2.9
作者：
Muntel, Jan;Gandhi, Tejas;Reiter, Lukas
通讯作者：
Reiter, Lukas
影响因子：
46.9
作者：
Messner CB;Demichev V;Bloomfield N;Yu JSL;White M;Kreidl M;Egger AS;Freiwald A;Ivosev G;Wasim F;Zelezniak A;Jürgens L;Suttorp N;Sander LE;Kurth F;Lilley KS;Mülleder M;Tate S;Ralser M
通讯作者：
Ralser M
影响因子：
4.4
作者：
Bern, Marshall W.;Kil, Yong J.
通讯作者：
Kil, Yong J.