Phasertng: Directed acyclic graphs for crystallographic phasing

Airlie J. McCoy (Lead / Corresponding author), Duncan H. Stockwell, Massimo D. Sammito, Robert D. Oeffner, Kaushik S. Hatti, Tristan I. Croll, Randy J. Read

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)
114 Downloads (Pure)


Crystallographic phasing strategies increasingly require the exploration and ranking of many hypotheses about the number, types and positions of atoms, molecules and/or molecular fragments in the unit cell, each with only a small chance of being correct. Accelerating this move has been improvements in phasing methods, which are now able to extract phase information from the placement of very small fragments of structure, from weak experimental phasing signal or from combinations of molecular replacement and experimental phasing information. Describing phasing in terms of a directed acyclic graph allows graph-management software to track and manage the path to structure solution. The crystallographic software supporting the graph data structure must be strictly modular so that nodes in the graph are efficiently generated by the encapsulated functionality. To this end, the development of new software, Phasertng, which uses directed acyclic graphs natively for input/output, has been initiated. In Phasertng, the codebase of Phaser has been rebuilt, with an emphasis on modularity, on scripting, on speed and on continuing algorithm development. As a first application of phasertng, its advantages are demonstrated in the context of phasertng.xtricorder, a tool to analyse and triage merged data in preparation for molecular replacement or experimental phasing. The description of the phasing strategy with directed acyclic graphs is a generalization that extends beyond the functionality of Phasertng, as it can incorporate results from bioinformatics and other crystallographic tools, and will facilitate multifaceted search strategies, dynamic ranking of alternative search pathways and the exploitation of machine learning to further improve phasing strategies.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalActa Crystallographica Section D: Structural Biology
Issue numberPart 1
Publication statusPublished - 1 Jan 2021


  • directed acyclic graphs
  • machine learning
  • maximum likelihood
  • molecular replacement
  • Phaser
  • Phasertng
  • SAD phasing

ASJC Scopus subject areas

  • Structural Biology


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