Phaser is a program for phasing macromolecular crystal structures by both molecular replacement and experimental phasing methods. The novel phasing algorithms implemented in Phaser have been developed using maximum likelihood and multivariate statistics. For molecular replacement, the new algorithms have proved to be significantly better than traditional methods in discriminating correct solutions from noise, and for single-wavelength anomalous dispersion experimental phasing, the new algorithms, which account for correlations between F + and F À , give better phases (lower mean phase error with respect to the phases given by the refined structure) than those that use mean F and anomalous differences ÁF. One of the design concepts of Phaser was that it be capable of a high degree of automation. To this end, Phaser (written in C++) can be called directly from Python, although it can also be called using traditional CCP4 keyword-style input. Phaser is a platform for future development of improved phasing methods and their release, including source code, to the crystallographic community.

This paper is a companion to a recent paper on fast rotation functions [Storoni et al. (2004), Acta Cryst. D60, 432-438], which showed how a Taylor-series expansion of the maximumlikelihood rotation function leads to improved likelihoodenhanced fast rotation functions. In a similar manner, it is shown here how linear and quadratic Taylor-series expansions and least-squares approximations of the maximum-likelihood translation function lead to likelihood-enhanced translation functions, which can be calculated by FFT and which are more sensitive to the correct translation than the traditional correlation-coefficient fast translation function. These likelihood-enhanced translation targets for molecularreplacement searches have been implemented in the program Phaser using the Computational Crystallography Toolbox (cctbx).

Experiences with the molecular-replacement program Beast have shown that maximum-likelihood rotation targets are more sensitive to the correct orientation than traditional targets. However, this comes at a high computational cost: brute-force rotation searches can take hours or even days of computation time on current desktop computers. Series approximations to the full likelihood target have been developed that can be computed by fast Fourier transforms in minutes. These likelihood-enhanced targets are more sensitive to the correct orientation than the Crowther fast rotation function and they take advantage of information from partial solutions. The likelihood-enhanced rotation targets have been implemented in the program Phaser.