Jiayi Wang

Tagmentation-based methods such as ATAC-seq and Cut&Tag have provided easy ways to profile the epigenome in low-input samples and even single cells. In this contribution, we discuss forms of bias (i.e. technical variations) in tagmentation-based data, in particular ATAC-seq, and introduce three R/bioconductor packages to facilitate bulk and single-cell epigenomic data analysis, with a special focus on motif accessibility analysis. The weightedMotifAccess package uses weight models to enable motif accessibility analysis, including transcription factor footprint information. The betterChromVAR package provides a novel, analytical re-implementation of the popular chromVAR method that offers substantial speed improvements, eliminates stochasticity, and offers additional features. Based on this, we also propose a method, CVnorm, that outperforms alternatives in normalizing technical bias in peak count data. The computational efficiency of these tools further enables a new framework for systematically investigating synergistic and antagonistic interactions between transcription factor motifs. Finally, the epiwraps package streamlines the visualization, normalization, and summarization of epigenomic data.

Background: Long-read RNA sequencing (lrRNA-seq) enables transcript-resolved variant detection, but systematic and neutral evaluations of small variants calling pipelines remain limited. The performance of existing tools across sequencing technologies, alignment strategy, variant caller choice, genomic contexts and downstream haplotype phasing is not fully understood. Results: Here, we systematically benchmark four lrRNA-seq variant callers (Clair3-RNA, DeepVariant, longcallR, and longcallR-nn), along with a widely used short-read RNA-seq variant caller (GATK HaplotypeCaller) as a baseline, using Genome in a Bottle (GIAB) datasets comprising three cell lines sequenced with four Oxford Nanopore Technologies (ONT) and two PacBio library preparation protocols. We further evaluate the impact of upstream alignment strategies, including aligner choice and alignment transformation, on variant-calling performance. Accuracy is assessed across sequencing depths and genomic contexts. Additionally, we compare haplotype phasing tools (WhatsHap, LongPhase, HapCUT2, HiPhase and longcallR) using variant calls generated by different callers to identify optimal pipeline combinations. Finally, we extend our evaluation of variant-calling performance to more recent LongBench datasets. Conclusions: Our benchmark shows that sequencing quality is the primary determinant of lrRNA-seq variant-calling performance, followed by variant caller and alignment strategy, with additional effects from genomic context. In GIAB datasets, all lrRNA-seq-specific callers performed reasonably well, with Clair3-RNA (across both ONT and PacBio) and DeepVariant (for PacBio) ranking among the top-performing methods. In more recent LongBench datasets of cancer cell lines, DeepVariant and longcallR showed higher sensitivity, whereas Clair3-RNA and longcallR-nn were more conservative, yielding fewer variant calls. For downstream haplotype phasing, we recommend WhatsHap or HapCUT2 for most libraries, owing to their high phasing coverage and accuracy, respectively, while longcallR performs better on ONT dRNA004 datasets across both metrics.

BD Rhapsody is a widely used single-cell RNA-seq (scRNA-seq) platform to profile gene expression. Based on barcoded beads, processing BD Rhapsody experiments is demanding because of the multiple barcode versions and layouts, which makes it difficult to locate barcodes by scanning reads positionally. We introduce rhapsodist , a reproducible and scalable Snakemake workflow to process BD Rhapsody whole transcriptome analysis (WTA) data from raw FASTQ reads. As a result, rhapsodist generates count tables in a HDF5-backed SingleCellExperiment format. It supports all generations of BD Rhapsody beads, regardless of the barcode allowlist version and the presence of a variable-length diversity inset. rhapsodist orchestrates STARsolo, kallisto/bustools, salmon/alevin, and the official BD Rhapsody CWL pipeline, enabling direct cross-aligner comparison. Optionally, sample tag demultiplexing, read downsampling, tunable linker-mismatch tolerance, and per-sample reports are provided. We showcase a remarkable consistency across aligner results, and highlight their differences in performance (e.g., quantification, clustering, or speed). On simulated data all four aligners recovered true barcodes with perfect precision and recall and equivalent UMI counts. On HeLa cells (a cell line; enhanced beads), both per-cell correlations across aligners and pseudobulk correlations were remarkably high on every comparison (Pearson r of 0.89–0.96 and 0.94–0.98 per cell and pseudobulk, respectively). On a mouse epidermis dataset with a more complex transcriptional profile (e.g., with celltypes; on legacy beads), cell type pseudobulk correlations showed a similar consistency (Pearson r 0.92–0.98), pointing to calling robustness. As expected, pseudoaligners (kallisto/bustools and salmon/alevin) were best performers speed-wise, and BD’s workflow recovered a higher number of barcodes and UMIs. rhapsodist is available at https://github.com/imallona/rhapsodist under GPLv3, tried on Linux with conda, and includes unit and integration tests.

Due to the costs of single-cell sequencing, sample sizes are often relatively limited, sometimes leading to poorly reproducible results. In many contexts, however, larger bulk RNAseq data is available for the same conditions or experimental paradigm, which can be used as additional evidence of a generalizable differential expression pattern. Here, we show how such data can be used, via bulk-based hypothesis weighing (bbhw), to increase the power and robustness of single-cell differential state analysis. We find that all methods improve performance, with the best results obtained by applying a grouped Benjamini-Hochberg procedure on bins based on proportion-adjusted significance (PAS). These methods are implemented in the muscat package, and should be applicable to a broader range of scenarios.

Long-read scRNA-seq enables simultaneous, unbiased identification of transcriptomic variants and gene expression but is often limited by low read coverage, restricting genotype–phenotype analyses at single-cell resolution. We systematically evaluated short-read whole-transcriptome amplification (SR-WTA) via Illumina, long-read whole-transcriptome amplification (LR-WTA), and long-read targeted sequencing (LR-Twist) via PacBio for cell typing and genotyping performance. Based on these comparisons, we propose a hybrid strategy and accompanying Snakemake pipeline that integrates SR-WTA with LR-Twist to leverage the strengths of both approaches. SR-WTA provides broad transcriptome coverage, while LR-Twist enriches a 50-gene panel for deeper variant detection. This hybrid approach improves power to link mutational profiles with transcriptional programs at single-cell resolution.

Due to the costs of single-cell sequencing, sample sizes are often relatively limited, sometimes leading to poorly reproducible results. In many contexts, however, larger bulk RNAseq data is available for the same conditions or experimental paradigm, which can be used as additional evidence of a generalizable differential expression pattern. Here, we show how such data can be used, via bulk-based hypothesis weighing (bbhw), to increase the power and robustness of single-cell differential state analysis. We find that all methods improve performance, with the best results obtained by applying a grouped Benjamini-Hochberg procedure on bins based on proportion-adjusted significance (PAS). These methods are implemented in the muscat package, and should be applicable to a broader range of scenarios.

ATAC-seq has emerged as a rich epigenome profiling technique, and is commonly used to identify Transcription Factors (TFs) underlying given phenomena. A number of methods can be used to identify differentially-active TFs through the accessibility of their DNA-binding motif, however little is known on the best approaches for doing so. Here we benchmark several such methods using a combination of curated datasets with various forms of short-term perturbations on known TFs, as well as semi-simulations. We include both methods specifically designed for this type of data as well as some that can be repurposed for it. We also investigate variations to these methods, and identify three particularly promising approaches (a chromVAR-limma workflow with critical adjustments, monaLisa and a combination of GC smooth quantile normalization and multivariate modeling). We further investigate the specific use of nucleosome-free fragments, the combination of top methods, and the impact of technical variation. Finally, we illustrate the use of the top methods on a novel dataset to characterize the impact on DNA accessibility of TRAnscription Factor TArgeting Chimeras (TRAFTAC), which can deplete TFs—in our case NFkB—at the protein level.

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.