With the widespread uptake of 2D and 3D single molecule localization microscopy, a large set of different data analysis packages have been developed to generate super-resolution images. In a large community effort we designed a competition to extensively characterise and rank the performance of 2D and 3D single molecule localization microscopy software packages. We generated realistic simulated datasets for popular imaging modalities-2D, astigmatic 3D, biplane 3D, and double helix 3D-and evaluated 36 participant packages against these data. This provides the first broad assessment of 3D single molecule localization microscopy software and provides a holistic view of how the latest 2D and 3D single molecule localization software perform in realistic conditions. This resource allows researchers to identify optimal analytical software for their experiments, allows 3D SMLM software developers to benchmark new software against current state of the art, and provides insight into the current limits of the field. RESULTS Competition design We established a broad committee from the SMLM community, including experimentalists and software developers, to define the scope of the challenge, ensure realism of the datasets and define analysis metrics. We opened this discussion to all interested parties in an online discussion forum 17. In 2016, we ran a first round of the 3D SMLM competition with explicit submission deadlines, culminating in a special session at the 6th annual Single Molecule Localization Microscopy Symposium (SMLMS 2016). Since then, the challenge has been opened to continuously accept new entries. Thirtysix software packages have been entered in the competition thus far, including four packages used in commercial software (Table S1, Supplementary Note 1). Participation in the competition actually led at least eight teams to modify their software to support additional 3D SMLM modalities, showing how competition can foster microscopy software development. Realistic 3D simulations Testing super-resolution software on experimental data lacks the ground truth information required for rigorous quantification of software performance. Therefore, realistic simulated datasets are required. A critical challenge to in simulating 3D SMLM data was accurate modeling of the

Structural and functional annotation of the large and growing database of genomic sequences is a major problem in modern biology. Protein structure prediction by detecting remote homology to known structures is a well-established and successful annotation technique. However, the broad spectrum of evolutionary change that accompanies the divergence of close homologues to become remote homologues cannot easily be captured with a single algorithm. Recent advances to tackle this problem have involved the use of multiple predictive algorithms available on the Internet. Here we demonstrate how such ensembles of predictors can be designed in-house under controlled conditions and permit significant improvements in recognition by using a concept taken from protein loop energetics and applying it to the general problem of 3D clustering. We have developed a stringent test that simulates the situation where a protein sequence of interest is submitted to multiple different algorithms and not one of these algorithms can make a confident (95%) correct assignment. A method of meta-server prediction (Phyre) that exploits the benefits of a controlled environment for the component methods was implemented. At 95% precision or higher, Phyre identified 64.0% of all correct homologous query-template relationships, and 84.0% of the individual test query proteins could be accurately annotated. In comparison to the improvement that the single best fold recognition algorithm (according to training) has over PSI-Blast, this represents a 29.6% increase in the number of correct homologous query-template relationships, and a 46.2% increase in the number of accurately annotated queries. It has been well recognised in fold prediction, other bioinformatics applications, and in many other areas, that ensemble predictions generally are superior in accuracy to any of the component individual methods. However there is a paucity of information as to why the ensemble methods are superior and indeed this has never been systematically addressed in fold recognition. Here we show that the source of ensemble power stems from noise reduction in filtering out false positive matches. The results indicate greater coverage of sequence space and improved model quality, which can consequently lead to a reduction in the experimental workload of structural genomics initiatives.

SummaryActively transcribed regions of the genome are vulnerable to genomic instability. Recently, it was discovered that transcription is repressed in response to neighboring DNA double-strand breaks (DSBs). It is not known whether a failure to silence transcription flanking DSBs has any impact on DNA repair efficiency or whether chromatin remodelers contribute to the process. Here, we show that the PBAF remodeling complex is important for DSB-induced transcriptional silencing and promotes repair of a subset of DNA DSBs at early time points, which can be rescued by inhibiting transcription globally. An ATM phosphorylation site on BAF180, a PBAF subunit, is required for both processes. Furthermore, we find that subunits of the PRC1 and PRC2 polycomb group complexes are similarly required for DSB-induced silencing and promoting repair. Cancer-associated BAF180 mutants are unable to restore these functions, suggesting PBAF's role in repressing transcription near DSBs may contribute to its tumor suppressor activity.

Crossover recombination reshuffles genes and prevents errors in segregation that lead to extra or missing chromosomes (aneuploidy) in human eggs, a major cause of pregnancy failure and congenital disorders. Here, we generate genome-wide maps of crossovers and chromosome segregation patterns by recovering all three products of single female meioses. Genotyping > 4 million informative single-nucleotide polymorphisms (SNPs) from 23 complete meioses allowed us to map 2,032 maternal and 1,342 paternal crossovers and to infer the segregation patterns of 529 chromosome pairs. We uncover a novel reverse chromosome segregation pattern in which both homologs separate their sister chromatids at meiosis I; detect selection for higher recombination rates in the female germline by the elimination of aneuploid embryos; and report chromosomal drive against non-recombinant chromatids at meiosis II. Collectively, our findings reveal that recombination not only affects homolog segregation at meiosis I but also the fate of sister chromatids at meiosis II.

Accurate and reproducible quantification of the accumulation of proteins into foci in cells is essential for data interpretation and for biological inferences. To improve reproducibility, much emphasis has been placed on the preparation of samples, but less attention has been given to reporting and standardizing the quantification of foci. The current standard to quantitate foci in open-source software is to manually determine a range of parameters based on the outcome of one or a few representative images and then apply the parameter combination to the analysis of a larger dataset. Here, we demonstrate the power and utility of using machine learning to train a new algorithm (FindFoci) to determine optimal parameters. FindFoci closely matches human assignments and allows rapid automated exploration of parameter space. Thus, individuals can train the algorithm to mirror their own assignments and then automate focus counting using the same parameters across a large number of images. Using the training algorithm to match human assignments of foci, we demonstrate that applying an optimal parameter combination from a single image is not broadly applicable to analysis of other images scored by the same experimenter or by other experimenters. Our analysis thus reveals wide variation in human assignment of foci and their quantification. To overcome this, we developed training on multiple images, which reduces the inconsistency of using a single or a few images to set parameters for focus detection. FindFoci is provided as an open-source plugin for ImageJ.