Journal article Open Access
<?xml version='1.0' encoding='UTF-8'?> <record xmlns="http://www.loc.gov/MARC21/slim"> <leader>00000nam##2200000uu#4500</leader> <datafield tag="942" ind1=" " ind2=" "> <subfield code="a">2020-06-06</subfield> </datafield> <controlfield tag="005">20200606101819.0</controlfield> <controlfield tag="001">3612745</controlfield> <datafield tag="856" ind1="4" ind2=" "> <subfield code="s">206185</subfield> <subfield code="z">md5:835226ad06da8291ecc0458b23c99b7a</subfield> <subfield code="u">https://zenodo.org/record/3612745/files/Bittremieux2020.pdf</subfield> </datafield> <datafield tag="542" ind1=" " ind2=" "> <subfield code="l">open</subfield> </datafield> <datafield tag="260" ind1=" " ind2=" "> <subfield code="c">2020-01-07</subfield> </datafield> <datafield tag="909" ind1="C" ind2="O"> <subfield code="p">openaire</subfield> <subfield code="o">oai:zenodo.org:3612745</subfield> </datafield> <datafield tag="909" ind1="C" ind2="4"> <subfield code="c">659-661</subfield> <subfield code="n">1</subfield> <subfield code="p">Analytical Chemistry</subfield> <subfield code="v">92</subfield> </datafield> <datafield tag="100" ind1=" " ind2=" "> <subfield code="u">Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States</subfield> <subfield code="0">(orcid)0000-0002-3105-1359</subfield> <subfield code="a">Bittremieux, Wout</subfield> </datafield> <datafield tag="245" ind1=" " ind2=" "> <subfield code="a">spectrum_utils: A Python package for mass spectrometry data processing and visualization</subfield> </datafield> <datafield tag="540" ind1=" " ind2=" "> <subfield code="u">https://creativecommons.org/licenses/by/4.0/legalcode</subfield> <subfield code="a">Creative Commons Attribution 4.0 International</subfield> </datafield> <datafield tag="650" ind1="1" ind2="7"> <subfield code="a">cc-by</subfield> <subfield code="2">opendefinition.org</subfield> </datafield> <datafield tag="520" ind1=" " ind2=" "> <subfield code="a"><p>Given the wide diversity in applications of biological mass spectrometry, custom data analyses are often needed to fully interpret the results of an experiment. Such bioinformatics scripts necessarily include similar basic functionality to read mass spectral data from standard file formats, process it, and visualize it. Rather than having to reimplement this functionality, to facilitate this task, spectrum_utils is a Python package for mass spectrometry data processing and visualization. Its high-level functionality enables developers to quickly prototype ideas for computational mass spectrometry projects in only a few lines of code. Notably, the data processing functionality is highly optimized for computational efficiency to be able to deal with the large volumes of data that are generated during mass spectrometry experiments. The visualization functionality makes it possible to easily produce publication-quality figures as well as interactive spectrum plots for inclusion on web pages. spectrum_utils is available for Python 3.6+, includes extensive online documentation and examples, and can be easily installed using conda. It is freely available as open source under the Apache 2.0 license at https://github.com/bittremieux/spectrum_utils.</p></subfield> </datafield> <datafield tag="024" ind1=" " ind2=" "> <subfield code="a">10.1021/acs.analchem.9b04884</subfield> <subfield code="2">doi</subfield> </datafield> <datafield tag="980" ind1=" " ind2=" "> <subfield code="a">publication</subfield> <subfield code="b">article</subfield> </datafield> </record>
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