The following is an adaptation of Martyn Winn's article in the CCP4 Newsletter Implementation of Data Harvesting in the CCP4 Suite.
The Data Harvesting paradigm, pioneered by Kim Henrick at the European Bioinformatics Institute (EBI) has been under development for a number of years and is in operation in users' labs. Background information can be found in two earlier Newsletter articles: an overview of data harvesting, and a report on the September 1998 Joint CCP4/EBI software developers and Data Harvesting Workshop. The concept of data harvesting, within CCP4, implies the collection of various relevant pieces of information obtained during the different steps from structure solution, into a collection of files. These files are then deposited into a database, for example the PDB, along with the final model coordinates to provide a full characterisation of the methods used and results and statistics obtained from the experiment.
Data harvesting uses the concept of "datasets". A particular dataset is identified by a Project Name / Dataset Name pair. The Project Name specifies the structure solution project, and is equivalent to what will become a PDB ID code (or in mmCIF terms the _entry.id ). The Dataset Name identifies the particular dataset within the project (either X-ray diffraction structure factors or NMR experimentally determined data) that is being used ( _diffrn.id in mmCIF). Thus, a particular structure solution may involve several datasets with the same Project Name but distinguished by different Dataset Names (e.g. for native and heavy atom derivatives, or for different wavelengths in a MAD experiment). Alternatively, one may have several datasets for an apoprotein and its complexes, and these would be distinguished by different Project Names since they correspond to different structure solutions.
Every harvest deposition file should have associated in-house tags that identify the "Project Name" and "Dataset Name". For each program that writes out a deposition file, it is possible to specify the Project and Dataset names using the program keywords PNAME and DNAME. In principle, however, the Project and Dataset names should be considered attributes of the dataset being used, and be specified once only for that dataset. The Project and Dataset names would then be inherited from the dataset by each program in turn.
This has been implemented in CCP4 by adding information on Project and Dataset names to the header of the MTZ file. In a merged MTZ file, datasets are held as one or more data columns. In addition to the label and type attributes, each column now has an extra attribute specifying to which dataset it belongs. A list of all datasets included in the file, with the corresponding Project and Dataset names, is held separately in the MTZ header.
Since CCP4 release 5.0, datasets are grouped according to the crystal they were obtained from. The MTZ header now holds various information about the crystals and the datasets represented in the file, see the MTZ format document. This structure continues to support the Project Name / Dataset Name for harvesting, but is also used in a wider context.
The code changes necessary to manipulate this information were included in CCP4 release 3.5. Ideally, dataset information should be added to the MTZ file at the beginning, e.g. in MOSFLM, but this information can be added at any time, most conveniently with the program CAD. Once the information is in the MTZ file, it can be checked by running mtzdmp which shows all the MTZ header information (go on, try it!), including the list of datasets:
* Number of Datasets = 4 * Dataset ID, project/crystal/dataset names, cell dimensions, wavelength: 1 RNASE RNASE NATIVE 64.8970 78.3230 38.7920 90.0000 90.0000 90.0000 1.54200 2 RNASE RNASE3 DERIV_HG 64.9000 78.3200 38.7900 90.0000 90.0000 90.0000 1.54200 3 RNASE RNASE4 DERIV_PT 64.8500 78.5600 39.5100 90.0000 90.0000 90.0000 1.54200 4 RNASE RNASE5 DERIV_I 65.0000 78.6600 38.8100 90.0000 90.0000 90.0000 1.54200and the datasets which each column corresponds to:
* Column Labels : H K L FNAT SIGFNAT FreeR_flag FHG2 SDFHG2 FHG2DEL SDFHG2DEL FPTNCD25 SIGFPTNCD25 DELFPTNCD25 SIGDELFPTNCD25 FIOD25 SIGFIOD25 DELFIOD25 SIGDELFIOD25 * Column Types : H H H F Q I F Q D Q F Q D Q F Q D Q * Associated datasets : 0 0 0 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4
In CCP4, columns to be used are selected from the MTZ file by the LABIN keyword; for example, the command
LABIN FP=FIOD25 SIGFP=SIGFIOD25tells the program to use the 15th and 16th columns. In addition, the program now also knows that these columns are from the 4th dataset, with Project Name RNASE and Dataset Name DERIV_I.
Unmerged or multi-record MTZ files are treated slightly differently. In this case, a particular column may correspond to several datasets, distinguished by different batch numbers. Datasets are therefore attached to batches rather than columns, and a pointer to the relevant dataset is held in the batch header.
As an aside, classifying MTZ columns according to dataset has other uses. Previously, it was assumed that columns existed as independent entities, but this is clearly not the case, for example F(+) and F(-) columns, or F and sigmaF columns. Some programs now use dataset information to check for certain dependencies, for example the program REINDEX may need to swap F(+) and F(-) columns and therefore needs to identify which F(+) column goes with which F(-) column.
From CCP4 release 4.0, dataset information will be used to write out deposition files. The CCP4 programs affected are SCALA, TRUNCATE, MLPHARE, REFMAC and RESTRAIN. Provided a Project Name and a Dataset Name are specified (either explicitly or from the MTZ file) and provided the NOHARVEST keyword is not given, these programs will automatically produce a deposition file. This file will be written to
The environment variable $HARVESTHOME defaults to the user's home directory, but could be changed, for example, to a group project directory.
At the end of a project, the entire contents of the directory $HARVESTHOME/DepositFiles/<projectname> can be sent to the deposition centre for processing. Note that, because of the file-naming scheme, only the last run of a particular program with a particular dataset will be preserved, and it is the user's responsibility to ensure that this is the authoritative version. The USECWD keyword can be used to send deposit files from speculative runs to the local directory rather than the official project directory. This keyword can also be used when the program is being run on a machine without access to the directory $HARVESTHOME, in which case the user must transfer the deposition file afterwards.
In summary, the extra keywords associated with harvesting that will be included in most programs are:
Deposition files are written in mmCIF format. The possible contents of an mmCIF file are described in a continually-evolving dictionary of allowed data items. Harvesting requires additional data items to those in the current standard dictionary, and an extended dictionary is distributed by CCP4 as $CLIBD/cif_mm.dic
The distributed TOXD example dataset contains 4 datasets, all assigned to the Project Name "TOXD", and having the Dataset Names "NATIVE", "DERIV_AU", "DERIV_MM" and "DERIV_I" (see above). Running mlphare to phase the native dataset produces a file $HARVESTHOME/DepositFiles/TOXD/NATIVE.mlphare. This file starts with information on when and how the file was created:
data_TOXD[NATIVE] _entry.id TOXD _diffrn.id NATIVE _audit.creation_date 1999-07-08T11:19:51+01:00 _software.classification phasing _software.contact_author 'Z.Otwinowski or E.Dodson' _software.contact_author_email 'email@example.com, firstname.lastname@example.org' _software.description 'maximum likelihood heavy atom refinement & phase calculation' _software.name mlphare _software.version CCP4_3.5This is followed by details such as the cell dimensions and symmetry information, and then by a summary of the results, for example the figures of merit for the phases obtained:
loop_ _phasing_MIR_shell.d_res_high _phasing_MIR_shell.d_res_low _phasing_MIR_shell.reflns _phasing_MIR_shell.fom _phasing_MIR_shell.reflns_centric _phasing_MIR_shell.fom_centric _phasing_MIR_shell.reflns_acentric _phasing_MIR_shell.fom_acentric 9.56 15.00 61 0.484 41 0.553 20 0.343 7.01 9.56 80 0.315 36 0.423 44 0.227 5.54 7.01 120 0.351 45 0.502 75 0.261 4.58 5.54 186 0.338 61 0.506 125 0.256 3.90 4.58 255 0.327 68 0.484 187 0.270 3.40 3.90 345 0.276 86 0.417 259 0.230 3.01 3.40 430 0.271 90 0.446 340 0.225 2.70 3.01 536 0.287 108 0.454 428 0.245The deposit files should be easily readable, but they should not be altered - they represent an authentic record of the structure solution process.
Details on the implementation of data harvesting in CCP4I (mainly for programmers only) can be found elsewhere.
The process of deposition involves sending a collection of files containing all the information about the structure of the molecule, and how it was solved, to a deposition centre. The deposition centre will then run a series of programs to check the validity of the structure before accepting it into the PDB. There are three main deposition sites:
1. European Bioinformatics Institute, UK
2. Rutgers, USA
3. Osaka, Japan
The current version of Autodep, version 3.1, supports the upload of CCP4 Harvest files. Autodep recommends the following information be available when depositing a structure:
Harvest Files from CCP4 or CNS PDB file of atomic coordinates Sequence reference Description of heterogens Manuscript describing structure Data collection results, unit cell and structure refinement statistics
For information regarding the deposition of Structure Factors into the Protein Data Bank, click here.
The current version of ADIT! (AutoDep Input Tool) involves 3 steps: a data format check, which checks the file format of the PDB model and CIF structure factor files; a validation precheck, which creates a validation report for the structure, and finally the deposition of the structure into the database. ADIT requires the same information as the AutoDep tool.