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Title: PyMDA : microcrystal data assembly using Python

Abstract

The recent developments at microdiffraction X-ray beamlines are making microcrystals of macromolecules appealing subjects for routine structural analysis. Microcrystal diffraction data collected at synchrotron microdiffraction beamlines may be radiation-damaged with incomplete data per microcrystal and with unit cell variations. We have previously devised a multi-stage data assembly method for microcrystal synchrotron crystallography. Here we have implemented the strategy as a Python program for microcrystal data assembly (PyMDA). PyMDA optimizes microcrystal data quality including weak anomalous signals through iterative crystal and frame rejections. Beyond microcrystals, PyMDA may be applicable for assembling data sets from larger crystals for improved data quality.

Authors:
; ; ; ; ORCiD logo; ORCiD logo
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Laboratory Directed Research and Development (LDRD) Program; National Institutes of Health (NIH); USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Workforce Development for Teachers and Scientists (WDTS)
OSTI Identifier:
1617953
Alternate Identifier(s):
OSTI ID: 1579902; OSTI ID: 1777433
Report Number(s):
BNL-212432-2019-JAAM; BNL-221258-2021-JAAM
Journal ID: ISSN 1600-5767; JACGAR; PII: S160057671901673X
Grant/Contract Number:  
SC0012704; GM107462; P30GM133893; KP1607010
Resource Type:
Published Article
Journal Name:
Journal of Applied Crystallography (Online)
Additional Journal Information:
Journal Name: Journal of Applied Crystallography (Online) Journal Volume: 53 Journal Issue: 1; Journal ID: ISSN 1600-5767
Publisher:
International Union of Crystallography (IUCr)
Country of Publication:
Denmark
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; X-ray crystallography; microcrystal; data assembly; radiation damage; multi-crystal; python; 36 MATERIALS SCIENCE; microcrystals

Citation Formats

Takemaru, Lina, Guo, Gongrui, Zhu, Ping, Hendrickson, Wayne A., McSweeney, Sean, and Liu, Qun. PyMDA : microcrystal data assembly using Python. Denmark: N. p., 2020. Web. https://doi.org/10.1107/S160057671901673X.
Takemaru, Lina, Guo, Gongrui, Zhu, Ping, Hendrickson, Wayne A., McSweeney, Sean, & Liu, Qun. PyMDA : microcrystal data assembly using Python. Denmark. https://doi.org/10.1107/S160057671901673X
Takemaru, Lina, Guo, Gongrui, Zhu, Ping, Hendrickson, Wayne A., McSweeney, Sean, and Liu, Qun. Sat . "PyMDA : microcrystal data assembly using Python". Denmark. https://doi.org/10.1107/S160057671901673X.
@article{osti_1617953,
title = {PyMDA : microcrystal data assembly using Python},
author = {Takemaru, Lina and Guo, Gongrui and Zhu, Ping and Hendrickson, Wayne A. and McSweeney, Sean and Liu, Qun},
abstractNote = {The recent developments at microdiffraction X-ray beamlines are making microcrystals of macromolecules appealing subjects for routine structural analysis. Microcrystal diffraction data collected at synchrotron microdiffraction beamlines may be radiation-damaged with incomplete data per microcrystal and with unit cell variations. We have previously devised a multi-stage data assembly method for microcrystal synchrotron crystallography. Here we have implemented the strategy as a Python program for microcrystal data assembly (PyMDA). PyMDA optimizes microcrystal data quality including weak anomalous signals through iterative crystal and frame rejections. Beyond microcrystals, PyMDA may be applicable for assembling data sets from larger crystals for improved data quality.},
doi = {10.1107/S160057671901673X},
journal = {Journal of Applied Crystallography (Online)},
number = 1,
volume = 53,
place = {Denmark},
year = {2020},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
https://doi.org/10.1107/S160057671901673X

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