The integration of single-cell multi-omics data provides a powerful approach for understanding the complex interplay between different molecular modalities, such as RNA expression, chromatin accessibility and protein abundance, measured through assays like scRNA-seq, scATAC-seq and CITE-seq, at single-cell resolution. However, most existing single-cell technologies focus on individual modalities, limiting a comprehensive understanding of their interconnections. Integrating such diverse and often unpaired datasets remains a challenging task due to unknown cell correspondences across distinct feature spaces and limited insights into cell-type-specific activities in non-scRNA-seq modalities. In this work, we propose BiCLUM, a Bilateral Contrastive Learning approach for Unpaired single-cell Multi-omics integration, which simultaneously enforces cell-level and feature-level alignment across modalities. BiCLUM first transforms one modality, such as scATAC-seq, into the data space of another modality, such as scRNA-seq, using prior genomic knowledge. It then learns cell and gene embeddings simultaneously through a bilateral contrastive learning framework, incorporating both cell-level and feature-level contrastive losses. Across multiple RNA+ATAC and RNA+protein datasets, BiCLUM consistently outperforms or matches existing integration methods in both visualization and quantitative benchmarks. Importantly, BiCLUM embeddings preserve biologically meaningful regulatory relationships between chromatin accessibility and gene expression, as evidenced by significantly higher gene-peak correlations than random controls. Downstream analyses further demonstrate that BiCLUM-derived embeddings facilitate transcription factor activity inference, identification of cell-type-specific marker genes, functional enrichment, and cell-cell interaction mapping. Comprehensive hyperparameter sensitivity and ablation analyses further establish BiCLUM as a robust and interpretable framework that not only achieves effective cross-modal alignment but also retains the underlying regulatory and functional landscape across single-cell modalities.

Benchmarking, which involves collecting reference datasets and demonstrating method performance, is a requirement for the development of new computational tools, but also becomes a domain of its own to achieve neutral comparisons of methods. Although a lot has been written about how to design and conduct benchmark studies, this Perspective sheds light on a wish list for a computational platform to orchestrate benchmark studies. We discuss various ideas for organizing reproducible software environments, formally defining benchmarks, orchestrating standardized workflows, and how they interface with computing infrastructure.

Computational biologists are frequently engaged in collaborative data analysis with wet lab researchers. These interdisciplinary projects, as necessary as they are to the scientific endeavor, can be surprisingly challenging due to cultural differences in operations and values. In this Ten Simple Rules guide, we aim to help dry lab researchers identify sources of friction and provide actionable tools to facilitate respectful, open, transparent, and rewarding collaborations.

Summary: Chainy is a cross-platform web tool providing systematic pipelines and steady criteria to process real-time PCR data, including the calculation of efficiencies from raw data by kinetic methods, evaluation of the suitability of multiple references, standardized normalization using one or more references, and group-wise relative quantification statistical testing. We illustrate the utility of Chainy for differential expression and chromatin immunoprecipitation enrichment (ChIP-QPCR) analysis. Availability and Implementation: Chainy is open source and freely available at http://maplab.cat/chainy. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

Bioinformatics (2017) doi: 10.1093/bioinformatics/btw839 The authors note the R/Bioconductor libraries sourced at Chainy were described at the supplementary material, section ‘Availability and runtime’ owing to the application note length constraints. To acknowledge the qpcR, NormqPCR and RDML developers the main manuscript should have contained the following section (last paragraph, before Acknowledgements): Availability and runtime Chainy is written in R/shiny and freely accessible at http://maplab.cat/chainy without registration. As the calculations are performed at the server-side, the application can be accessed from commodity computers or mobile devices, as far as they have a recent Web browser installed. Chainy can be run locally by downloading its source code from http://bitbucket.com/imallona/chainy (under the GNU Public License terms); an R script to check and install dependencies as well to launch the application is included. Chainy imports the qpcR (Ritz and Spiess, 2008), NormqPCR (Perkins et al., 2012) and RDML (Blagodatskikh et al., 2016) R packages. The user manual is available at the main page and covers results interpretation. A zip-compressed verbose report conveying the outputs and the user parameters can be downloaded at the final step. The final report includes barplots for both the scaled and the differential quantifications (for the sake of simplicity, the dynamic interface only renders tables to summarize these results).

BACKGROUND: Relative calculation of differential gene expression in quantitative PCR reactions requires comparison between amplification experiments that include reference genes and genes under study. Ignoring the differences between their efficiencies may lead to miscalculation of gene expression even with the same starting amount of template. Although there are several tools performing PCR primer design, there is no tool available that predicts PCR efficiency for a given amplicon and primer pair. RESULTS: We have used a statistical approach based on 90 primer pair combinations amplifying templates from bacteria, yeast, plants and humans, ranging in size between 74 and 907 bp to identify the parameters that affect PCR efficiency. We developed a generalized additive model fitting the data and constructed an open source Web interface that allows the obtention of oligonucleotides optimized for PCR with predicted amplification efficiencies starting from a given sequence. CONCLUSIONS: pcrEfficiency provides an easy-to-use web interface allowing the prediction of PCR efficiencies prior to web lab experiments thus easing quantitative real-time PCR set-up. A web-based service as well the source code are provided freely at http://srvgen.upct.es/efficiency.html under the GPL v2 license.