KAMO: towards automated data processing for microcrystals
In protein microcrystallography, radiation damage often hampers complete and high‐resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5–10°) separately from multiple crystals. The data from these crystals can t...
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| Vydáno v: | Acta crystallographica. Section D, Structural biology Ročník 74; číslo 5; s. 441 - 449 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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5 Abbey Square, Chester, Cheshire CH1 2HU, England
International Union of Crystallography
01.05.2018
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| Témata: | |
| ISSN: | 2059-7983, 2059-7983 |
| On-line přístup: | Získat plný text |
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| Abstract | In protein microcrystallography, radiation damage often hampers complete and high‐resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5–10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection‐intensity set. However, data processing of multiple small‐wedge data sets is challenging. Here, a new open‐source data‐processing pipeline, KAMO, which utilizes existing programs, including the XDS and CCP4 packages, has been developed to automate whole data‐processing tasks in the case of multiple small‐wedge data sets. Firstly, KAMO processes individual data sets and collates those indexed with equivalent unit‐cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real‐world data sets collected from hundreds of crystals, it was demonstrated that merged structure‐factor amplitudes can be obtained in a largely automated manner using KAMO, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals.
An automated data‐processing pipeline for protein microcrystals is presented. The processing of multiple small‐wedge data sets was made dramatically easier by this pipeline. |
|---|---|
| AbstractList | In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5–10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection-intensity set. However, data processing of multiple small-wedge data sets is challenging. Here, a new open-source data-processing pipeline,
KAMO
, which utilizes existing programs, including the
XDS
and
CCP
4 packages, has been developed to automate whole data-processing tasks in the case of multiple small-wedge data sets. Firstly,
KAMO
processes individual data sets and collates those indexed with equivalent unit-cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real-world data sets collected from hundreds of crystals, it was demonstrated that merged structure-factor amplitudes can be obtained in a largely automated manner using
KAMO
, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals. An automated data-processing pipeline for protein microcrystals is presented. The processing of multiple small-wedge data sets was made dramatically easier by this pipeline. In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5–10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection-intensity set. However, data processing of multiple small-wedge data sets is challenging. Here, a new open-source data-processing pipeline, KAMO, which utilizes existing programs, including the XDS and CCP4 packages, has been developed to automate whole data-processing tasks in the case of multiple small-wedge data sets. Firstly, KAMO processes individual data sets and collates those indexed with equivalent unit-cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real-world data sets collected from hundreds of crystals, it was demonstrated that merged structure-factor amplitudes can be obtained in a largely automated manner using KAMO, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals. In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5-10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection-intensity set. However, data processing of multiple small-wedge data sets is challenging. Here, a new open-source data-processing pipeline, KAMO, which utilizes existing programs, including the XDS and CCP4 packages, has been developed to automate whole data-processing tasks in the case of multiple small-wedge data sets. Firstly, KAMO processes individual data sets and collates those indexed with equivalent unit-cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real-world data sets collected from hundreds of crystals, it was demonstrated that merged structure-factor amplitudes can be obtained in a largely automated manner using KAMO, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals.In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5-10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection-intensity set. However, data processing of multiple small-wedge data sets is challenging. Here, a new open-source data-processing pipeline, KAMO, which utilizes existing programs, including the XDS and CCP4 packages, has been developed to automate whole data-processing tasks in the case of multiple small-wedge data sets. Firstly, KAMO processes individual data sets and collates those indexed with equivalent unit-cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real-world data sets collected from hundreds of crystals, it was demonstrated that merged structure-factor amplitudes can be obtained in a largely automated manner using KAMO, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals. In protein microcrystallography, radiation damage often hampers complete and high‐resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5–10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection‐intensity set. However, data processing of multiple small‐wedge data sets is challenging. Here, a new open‐source data‐processing pipeline, KAMO, which utilizes existing programs, including the XDS and CCP4 packages, has been developed to automate whole data‐processing tasks in the case of multiple small‐wedge data sets. Firstly, KAMO processes individual data sets and collates those indexed with equivalent unit‐cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real‐world data sets collected from hundreds of crystals, it was demonstrated that merged structure‐factor amplitudes can be obtained in a largely automated manner using KAMO, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals. An automated data‐processing pipeline for protein microcrystals is presented. The processing of multiple small‐wedge data sets was made dramatically easier by this pipeline. In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic conditions. One promising solution is to collect small wedges of data (5-10°) separately from multiple crystals. The data from these crystals can then be merged into a complete reflection-intensity set. However, data processing of multiple small-wedge data sets is challenging. Here, a new open-source data-processing pipeline, KAMO, which utilizes existing programs, including the XDS and CCP4 packages, has been developed to automate whole data-processing tasks in the case of multiple small-wedge data sets. Firstly, KAMO processes individual data sets and collates those indexed with equivalent unit-cell parameters. The space group is then chosen and any indexing ambiguity is resolved. Finally, clustering is performed, followed by merging with outlier rejections, and a report is subsequently created. Using synthetic and several real-world data sets collected from hundreds of crystals, it was demonstrated that merged structure-factor amplitudes can be obtained in a largely automated manner using KAMO, which greatly facilitated the structure analyses of challenging targets that only produced microcrystals. |
| Author | Yamashita, Keitaro Hirata, Kunio Yamamoto, Masaki |
| Author_xml | – sequence: 1 givenname: Keitaro surname: Yamashita fullname: Yamashita, Keitaro – sequence: 2 givenname: Kunio surname: Hirata fullname: Hirata, Kunio – sequence: 3 givenname: Masaki surname: Yamamoto fullname: Yamamoto, Masaki email: yamamoto@riken.jp |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29717715$$D View this record in MEDLINE/PubMed |
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| Keywords | small-wedge data sets microcrystals KAMO automatic data processing |
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| PublicationTitle | Acta crystallographica. Section D, Structural biology |
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| Publisher | International Union of Crystallography |
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| Snippet | In protein microcrystallography, radiation damage often hampers complete and high‐resolution data collection from a single crystal, even under cryogenic... In protein microcrystallography, radiation damage often hampers complete and high-resolution data collection from a single crystal, even under cryogenic... An automated data-processing pipeline for protein microcrystals is presented. The processing of multiple small-wedge data sets was made dramatically easier by... |
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| SubjectTerms | automatic data processing Cluster Analysis Crystallography, X-Ray - methods Data Collection - methods Datasets as Topic Electronic Data Processing - methods Humans KAMO microcrystals Proteins - chemistry Research Papers small‐wedge data sets Software Viral Proteins - chemistry |
| Title | KAMO: towards automated data processing for microcrystals |
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