{"id":3261,"date":"2014-10-27T06:00:22","date_gmt":"2014-10-27T10:00:22","guid":{"rendered":"http:\/\/admin.acceleratingscience.com\/?p=3261"},"modified":"2016-04-29T16:52:20","modified_gmt":"2016-04-29T16:52:20","slug":"number-crunching-to-generate-smaller-ms-data-files","status":"publish","type":"post","link":"https:\/\/www.thermofisher.com\/blog\/proteomics\/number-crunching-to-generate-smaller-ms-data-files\/","title":{"rendered":"Number Crunching to Generate Smaller MS Data Files"},"content":{"rendered":"<p><img loading=\"lazy\" decoding=\"async\" alt=\"proteomics data\" style=\"float: left;margin: 10px\" src=\"http:\/\/admin.acceleratingscience.com\/wp-content\/uploads\/2014\/10\/protein_algorithm.jpg\" height=\"178\" width=\"250\" \/>Sharing is good, and this includes proteomics data.&nbsp;Proteomics data sharing is constrained by file type and size, however,&nbsp;which hinders both translation of results across systems and physical portability&nbsp;among users.&nbsp;The merging of the two initial open XML (Extensible Markup Language) file formats into <a href=\"http:\/\/www.psidev.info\/index.php?q=node\/257\" target=\"_blank\">mzML<\/a> has freed researchers from their equipment choices and thus&nbsp;enabled platform-independent analysis.&nbsp;Unfortunately, with&nbsp;the rise in high-resolution, high-frequency mass spectrometry (MS) spectral data, file sizes are once again overloading the system. Compared to vendor-specific platform files, mzML files can be 4 to 18 times larger, requiring larger data storage repositories, with concomitantly longer processing times. In other words, big data need big storage and improved processing power.&nbsp;<\/p>\n<p><span>Teleman and colleagues (2014) recently exhibited&nbsp;a solution to the problem.<sup>1<\/sup> They noted that although file conversion into mzML from vendor formats is possible, it is less efficient for the reasons explained in the previous paragraph. Their response was to generate a series of near-lossless numerical compression algorithms, which they call MS-Numpress,<sup><span style=\"font-size: xx-small\">2<\/span><\/sup> that wrangle the mzML file sizes and read speeds into more manageable packages without compromising primary data.<\/span><\/p>\n<p><span>The authors managed this by writing three different algorithms that concentrated on the following data essentials: <em>m\/z<\/em> ratios, ion counts and retention times, all considered fundamental to mass spectral data representation. They wrote their algorithms specifically to compress the binary data contained in the mzML files, optimizing the&nbsp;performance for each fundamental and allowing for reconstruction of original data. <\/span><\/p>\n<p><span>Once constructed, the team applied their work using a test set of&nbsp;10 mass spectrometric data files obtained from different instruments, vendors and experiment types, testing the process on different computers. They included MS1 and MS2 spectrum data from experimental runs in data-dependent acquisition (DDA), selected reaction monitoring (SRM) and data-independent acquisition (DIA) SWATH modes.<\/span><\/p>\n<p><span>Comparing their method alone and in combination with existing compression schemes such as&nbsp;ZLIB and GZIP, the researchers found that MS-Numpress successfully compressed file sizes. Using traditional compression tools in conjunction with MS-Numpress reduced file sizes by 87%. This was, however, accompanied by 138% longer write times, which the researchers noted could be offset by faster (21%) read times. <\/span><\/p>\n<p><span>Furthermore, when Teleman et al. converted MS-Numpress files back to their original forms, they found that data loss was minimal. Using two Orbitrap (Thermo Scientific) DDA liquid chromatography&ndash;tandem mass spectrometry (LC-MS\/MS) mzML files, the team compressed and then uncompressed the data. They compared LC-MS\/MS data from the original files with the twice-converted files<\/span>&mdash;<span>identifying peptides using Mascot<\/span>&mdash;<span>and found that the lists were extremely similar.<\/span><\/p>\n<p><span>Overall, Teleman et al. found that their algorithms, in combination with other compression tools, could successfully compress files by 90%, leading to read time decreases of 50%, with minimal loss of data. They see these algorithms as useful &ldquo;simple and robust solutions&rdquo; for data handling within the proteomics community. To this end, they have enabled support within existing tools and have submitted MS-Numpress for further evaluation through the Proteomics Standards Initiative. &nbsp;<\/span><\/p>\n<p><span>&nbsp;<\/span><\/p>\n<p><strong>Reference and Note&nbsp;<\/strong><\/p>\n<p><span>1. Teleman, J., et al. (2014, June) &#8220;<\/span><a href=\"http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/24677029\" target=\"_blank\">Numerical compression schemes for&nbsp;proteomics mass spectrometry data<\/a>,&#8221; Molecular and Cellular Proteomics,&nbsp;<span>13 (pp. 1537&ndash;42), doi: 10.1074\/mcp.O114.037879 (e-pub ahead of print).&nbsp;<\/span><\/p>\n<p>2. Read more about MS-Numpress here,&nbsp;<a href=\"https:\/\/github.com\/ms-numpress\/ms-numpress\">https:\/\/github.com\/ms-numpress\/ms-numpress<\/a>, under the Apache 2.0 license.<\/p>\n<p><span>&nbsp;<\/span><\/p>\n<p><i>Post Author: Amanda Maxwell. Mixed media artist; blogger and social media communicator; clinical scientist and writer.<\/p>\n<p>A digital space explorer, engaging readers by translating complex theories and subjects creatively into everyday language.<\/i><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Sharing is good, and this includes proteomics data.&nbsp;Proteomics data sharing is constrained by file type and size, however,&nbsp;which hinders both translation of results across systems and physical portability&nbsp;among users.&nbsp;The merging of the two initial open XML (Extensible Markup Language) file formats into mzML has freed researchers from their equipment choices and thus&nbsp;enabled platform-independent analysis.&nbsp;Unfortunately, with&nbsp;the<\/p>\n","protected":false},"author":21,"featured_media":3260,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_genesis_hide_title":false,"_genesis_hide_breadcrumbs":false,"_genesis_hide_singular_image":false,"_genesis_hide_footer_widgets":false,"_genesis_custom_body_class":"","_genesis_custom_post_class":"","_genesis_layout":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[12],"tags":[74],"division":[],"class_list":{"0":"post-3261","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-methods","8":"tag-bioinformatics","9":"entry"},"_selected_authors":"","_selected_reviewers":"","acf":[],"yoast_head":"<!-- This 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