Spectral Library Download Free
Sharmaine Kass
Version 1.0 of the ECOSTRESS spectral library was released on February 2, 2018. This release added over 1100 new vegetation and non-photosynthetic vegetation spectra. The ECOSTRESS spectral library is a compilation of over 3400 spectra of natural and man made materials. These libraries were developed as part of the ASTER and ECOSTRESS projects.
The USGS publication cited above describes the instruments used, measurement procedures, contents of metadata descriptions of spectra and samples, and possible artifacts in the spectra. Please reference any use of these data with the above citation. The 2,457 spectra in version 7 of the library cover wavelengths from the ultraviolet to the far infrared regions of the electromagnetic spectrum (0.2 to 200 µm). The library has grown from its initial release in 1993 to now include 1,497 spectra of mineral samples, grain size fractions, coatings, and mixtures. The library has expanded to include spectra of other substances, including vegetation, plant biochemical constituents, organic compounds, and manmade materials.
In addition to the original measured spectra, the library also includes convolved and resampled versions of spectra for selected laboratory spectrometers (ASD), imaging spectrometers (AVIRIS-Classic, HyMap, Hyperion, CRISM, M3, and VIMS), and broad band multispectral sensors (ASTER, Landsat8 OLI, Sentinel-2 MSI, and WorldView-3). See the links and drop-down selections in the black menu bar at the top of the page.
Spectra were divided into sub-categories (described as library chapters) by material type:
Explore our wide range of databases for broad coverage or specific applications.
At the helm of our collections are the renowned Wiley Registry, KnowItAll Spectral Libraries, Sadtler Libraries, and many other important databases used in spectral analysis.
Wiley is the authoritative source for spectral data. Our renowned databases are processed according to rigorous protocols to ensure they are of the highest quality. Qualification procedures start at data acquisition and continue throughout the database development process. Any data acquired from trusted partners is thoroughly vetted before inclusion in our collections.
The Wiley Registry/NIST Mass Spectral Library 2023 is the most comprehensive combined mass spectral library commercially available, with over 3 million spectra (includes EI and tandem MS data) making it the clear choice for general unknown compound identification. Broad, up-to-date compound coverage ensures identification across multiple use cases and a wide variety of analytes.
Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics studies require high-quality spectral libraries for reliable metabolite identification. We have constructed EMBL-MCF (European Molecular Biology Laboratory-Metabolomics Core Facility), an open LC-MS/MS spectral library that currently contains over 1600 fragmentation spectra from 435 authentic standards of endogenous metabolites and lipids. The unique features of the library include the presence of chromatographic profiles acquired with different LC-MS methods and coverage of different adduct ions. The library covers many biologically important metabolites with some unique metabolites and lipids as compared with other public libraries. The EMBL-MCF spectral library is created and shared using an in-house-developed web application at The library is freely available online and also integrated with other mass spectral repositories.
You can import these signatures (after a bit preparation) in the spectral unmixing tool of SNAP. It allows the fuzzy classification of materials in an image. Have a look at the help menu. It gives an example how the data should look.
Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.
Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics.
With the ability to identify and precisely quantify thousands of proteins from complex samples, liquid chromatography (LC)-tandem mass spectrometry (MS/MS) has been the most widely used tool for proteomic studies over the past decades1,2. Recent advances in the data-independent acquisition (DIA) technique allow systematic and unbiased proteomic measurement. In DIA experiments, the mass spectrometer performs a sequence of MS/MS scans within defined isolation windows in each acquisition cycle, recording fragmentation information of all peptides present in a sample3. Nevertheless, data analysis for DIA is extremely difficult since the fragments of various precursor ions can present on one MS/MS spectrum. For the past few years, a wide variety of strategies have been developed to analyze DIA data, including spectrum-centric and peptide-centric strategies4. Spectrum-centric workflows, such as DIA-Umpire5 and Group-DIA6, generate pseudo-MS/MS spectra for each precursor from DIA data for routine data-dependent acquisition (DDA) database search by assembling precursor-fragment groups based on the elution profiles of precursor and fragment ions. In peptide-centric methods, target peptides are queried against DIA data to extract the best candidate chromatogram signals using prebuilt spectral libraries, also known as peptide query parameters, or peptide assays, containing the information of retention time (RT) and fragment ions7. As an alternative, peptide query can also be applied to individual DIA MS/MS spectra by spectral matching tools, such as MSPLIT-DIA8. It has been reported that tools that rely on prior knowledge in the form of spectral libraries deal better with low selectivity data than library-free tools9, and peptide-centric approaches perform better to exploit highly comprehensive DIA data than spectrum-centric methods10. To date, a sample-specific spectral library, which is typically generated from DDA data acquired a priori from fractionated or enriched samples on the same instrument, is necessary in most studies using DIA. The method is not only time-consuming but also limits the identification/quantification by DIA to the peptides identified by DDA, which hinders the inherent advantages of DIA of unbiased measurement. In this regard, it is of great value to generate in silico spectral libraries containing predicted RT and fragment ions with quality comparable to that of experimental spectral libraries for DIA analysis.
Herein, we present DeepDIA, a deep learning-based method to generate in silico spectral libraries to support DIA analysis (Fig. 1). In contrast to Prosit27, another recently reported tool that pursues a general deep learning model for MS/MS and RT prediction by taking collision energy (CE) into consideration, DeepDIA aims at training instrument-specific models for more accurate MS/MS spectrum and RT prediction. In addition, DeepDIA can select a list of target peptides to be included in in silico spectral libraries from protein sequence databases, e.g. SwissProt, by predicting the MS detectability of candidate proteotypic peptides. The in silico spectral libraries are readily applicable to data analysis using state-of-the-art DIA analysis software, e.g. Spectronaut30. We benchmark the performance of DeepDIA on datasets of HeLa cells and mixed proteome samples, and the results are comparable to those obtained with DDA-based sample-specific spectral libraries. Instrument-specific libraries by DeepDIA outperform Prosit in terms of detectable peptides and proteins as well as reproducibility among technical replicates.
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