Analyses

A machine learning competition results in tools for labeling protein patterns of single cells in images with population labels. The winners improve the state of the art and provide strategies to deal with weak classification challenges.

A comparison of nine computational methods for identification of reprogramming factors for cell differentiation.

This work presents a comprehensive benchmarking analysis of computational methods that integrates spatial and single-cell transcriptomics data for transcript distribution prediction and cell type deconvolution.

This study presents the results of the second round of the Critical Assessment of Metagenome Interpretation challenges (CAMI II), which is a community-driven effort for comprehensively benchmarking tools for metagenomics data analysis.

This benchmarking study compares 16 methods for integrating complex single-cell RNA and ATAC datasets and provides a guide to method choice.

This analysis presents the results of a community-based evaluation of existing software for large-scale glycopeptide data analysis.

This analysis systematically evaluates cross-linking chemistry and chromatin fragmentation strategies commonly used in 3C assays and introduces an improved Hi-C protocol for detecting loops and compartments.

Many computational tools for metagenomic profiling have been developed, with different algorithms and features. This analysis shows that, when comparing these tools, the distinction of different types of relative sequence abundance should be taken into consideration.

This Analysis reports a computational approach to implement Hi-C, SPRITE and GAM, which allows researchers to assess the performances of the three technologies to capture DNA contacts in chromatin three-dimensional models.

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.

This Analysis compares microfluidics-based methods for assessing mechanical properties of cells in high throughput.

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.