Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain
the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in
Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles
and JavaScript.
Results are presented from the first Critical Assessment of protein Intrinsic Disorder prediction (CAID) experiment, a community-based blind test to determine the state of the art in predicting intrinsically disordered regions in proteins.
A multi-laboratory study in the form of a community challenge assesses the quality of models that can be produced from cryo-EM maps using different software tools, the reproducibility of models generated by different users and the performance of metrics used for model validation.
Massively parallel reporter assays (MPRAs) enable high-throughput assessments of regulatory elements in single experiments. This work compares nine MPRA designs and reports how differences in reporter assays influence the results of MPRAs.
Comprehensive evaluation of algorithms for inferring gene regulatory networks using synthetic and experimental single-cell RNA-seq datasets finds heterogeneous performance and suggests recommendations to users.
The 2018 Human Protein Atlas Image Classification competition sought to improve automated classification of protein subcellular localizations from fluorescence images. The winning strategies involved innovative deep learning approaches for multi-label classification.
One third of verified gene knock outs with CRISPR still show residual protein expression owing to translation reinitiation or exon skipping. Several proteins are still functional. The authors call for a systematic analysis of protein levels after genome editing.
The 2018 Data Science Bowl challenged competitors to develop an accurate tool for segmenting stained nuclei from diverse light microscopy images. The winners deployed innovative deep-learning strategies to realize configuration-free segmentation.
In this DREAM challenge, 75 methods for the identification of disease-relevant modules from molecular networks are compared and validated with GWAS data. The authors provide practical guidelines for users and establish benchmarks for network analysis.
A dataset made up of single cancer cells or their mixtures serves as a benchmark for testing almost 4,000 combinations of scRNA-seq data analysis methods.
Among 17 measures of association tested, measures of proportionality consistently performed well for inference of gene and cellular networks, cell clusters and links to disease from scRNA-seq data. In contrast, several widely used measures of association performed well on only a subset of tasks.
This study reports results from the second community-wide single-molecule localization microscopy software challenge, which tested over 30 software packages on realistic simulated data for multiple popular 3D image acquisition modes, as well as 2D localization microscopy.
kBET informs attempts at single-cell RNA-seq data integration by quantifying batch effects and determining how well batch regression and normalization approaches remove technical variation while preserving biological variability.
A multi-laboratory study finds that single-molecule FRET is a reproducible and reliable approach for determining accurate distances in dye-labeled DNA duplexes.
Reanalysis of DNA-immunoprecipitation-based data shows that modification-specific antibodies bind unmodified short tandem repeats, and IgG controls are needed to avoid false positives.
This Analysis compares and contrasts methods for measuring the mechanical properties of cells by applying the different approaches to the same breast cancer cell line.
A direct comparison of 5′-end RNA-seq methods reveals strong performance by CAGE, and identifies differential transcriptional start site usage among brain-related samples.
An extensive evaluation of differential expression methods applied to single-cell expression data, using uniformly processed public data in the new conquer resource.
This analysis describes the results of three Cell Tracking Challenge editions for examining the performance of cell segmentation and tracking algorithms and provides practical feedback for users and developers.
The Critical Assessment of Metagenome Interpretation (CAMI) community initiative presents results from its first challenge, a rigorous benchmarking of software for metagenome assembly, binning and taxonomic profiling.