Alevin-fry-atac enables rapid and memory frugal mapping of single-cell ATAC-seq data using virtual colors for accurate genomic pseudoalignment.

Ultrafast mapping of short reads to transcriptomic and metagenomic references via lightweight mapping techniques such as pseudoalignment has demonstrated success in substantially accelerating several types of analyses without much loss in accuracy compared to alignment-based approaches. The application of pseudoalignment to large reference sequences - like the genome - is, however, not trivial, due to the large size of the references or "targets" (i.e.

Collapsible tree: interactive web app to present collapsible hierarchies.

Motivation: A crucial component of intuitive data visualization is presenting a hierarchical tree structure with interactive functions. For example, single-cell transcriptomics studies may generate gene expression values with developmental trajectories or cell lineage structures. Two common visualization methods, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP), require two separate figures to depict the distribution of cell types and gene expression data, with low-dimension projections that may not capture the hierarchical structures among cells.

Where the Patterns Are: Repetition-Aware Compression for Colored de Bruijn Graphs.

We describe lossless compressed data structures for the de Bruijn graph (or c-dBG). Given a collection of reference sequences, a c-dBG can be essentially regarded as a map from -mers to their . The color set of a -mer is the set of all identifiers, or , of the references that contain the -mer.

A novel selective NLRP3 inhibitor shows disease-modifying potential in animal models of Parkinson's disease.

Pathological activation of the Nod-like receptor family pyrin domain containing protein 3 (NLRP3) inflammasome signaling underlies many autoimmune and neuroinflammatory conditions. Here we report that, a rationally designed, novel, orally active, selective NLRP3 inflammasome inhibitor, Usnoflast (ZYIL1), showed potent inhibition of ATP, Nigericin and monosodium urate-mediated interleukin (IL)-1β release in THP-1 cells and human PBMC. In isolated microglia cells, the IC of ZYIL1 mediated inhibition of IL-1β was 43 nM.

Where the patterns are: repetition-aware compression for colored de Bruijn graphs .

Unlabelled: We describe lossless compressed data structures for the de Bruijn graph (or, c-dBG). Given a collection of reference sequences, a c-dBG can be essentially regarded as a map from -mers to their . The color set of a -mer is the set of all identifiers, or , of the references that contain the -mer.

Identification of intracellular bacteria from multiple single-cell RNA-seq platforms using CSI-Microbes.

The study of the tumor microbiome has been garnering increased attention. We developed a computational pipeline (CSI-Microbes) for identifying microbial reads from single-cell RNA sequencing (scRNA-seq) data and for analyzing differential abundance of taxa. Using a series of controlled experiments and analyses, we performed the first systematic evaluation of the efficacy of recovering microbial unique molecular identifiers by multiple scRNA-seq technologies, which identified the newer 10x chemistries (3' v3 and 5') as the best suited approach.

Forseti: a mechanistic and predictive model of the splicing status of scRNA-seq reads.

Motivation: Short-read single-cell RNA-sequencing (scRNA-seq) has been used to study cellular heterogeneity, cellular fate, and transcriptional dynamics. Modeling splicing dynamics in scRNA-seq data is challenging, with inherent difficulty in even the seemingly straightforward task of elucidating the splicing status of the molecules from which sequenced fragments are drawn. This difficulty arises, in part, from the limited read length and positional biases, which substantially reduce the specificity of the sequenced fragments.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g.

Fast, parallel, and cache-friendly suffix array construction.

Purpose: String indexes such as the suffix array (SA) and the closely related longest common prefix (LCP) array are fundamental objects in bioinformatics and have a wide variety of applications. Despite their importance in practice, few scalable parallel algorithms for constructing these are known, and the existing algorithms can be highly non-trivial to implement and parallelize.

Methods: In this paper we present CAPS-SA, a simple and scalable parallel algorithm for constructing these string indexes inspired by samplesort and utilizing an LCP-informed mergesort.

Designing efficient randstrobes for sequence similarity analyses.

Motivation: Substrings of length k, commonly referred to as k-mers, play a vital role in sequence analysis. However, k-mers are limited to exact matches between sequences leading to alternative constructs. We recently introduced a class of new constructs, strobemers, that can match across substitutions and smaller insertions and deletions.

Oarfish: Enhanced probabilistic modeling leads to improved accuracy in long read transcriptome quantification.

Motivation: Long read sequencing technology is becoming an increasingly indispensable tool in genomic and transcriptomic analysis. In transcriptomics in particular, long reads offer the possibility of sequencing full-length isoforms, which can vastly simplify the identification of novel transcripts and transcript quantification. However, despite this promise, the focus of much long read method development to date has been on transcript identification, with comparatively little attention paid to quantification.

Forseti: A mechanistic and predictive model of the splicing status of scRNA-seq reads.

Motivation: Short-read single-cell RNA-sequencing (scRNA-seq) has been used to study cellular heterogeneity, cellular fate, and transcriptional dynamics. Modeling splicing dynamics in scRNA-seq data is challenging, with inherent difficulty in even the seemingly straightforward task of elucidating the splicing status of the molecules from which sequenced fragments are drawn. This difficulty arises, in part, from the limited read length and positional biases, which substantially reduce the specificity of the sequenced fragments.

Fulgor: a fast and compact k-mer index for large-scale matching and color queries.

The problem of sequence identification or matching-determining the subset of reference sequences from a given collection that are likely to contain a short, queried nucleotide sequence-is relevant for many important tasks in Computational Biology, such as metagenomics and pangenome analysis. Due to the complex nature of such analyses and the large scale of the reference collections a resource-efficient solution to this problem is of utmost importance. This poses the threefold challenge of representing the reference collection with a data structure that is efficient to query, has light memory usage, and scales well to large collections.

Tree-based differential testing using inferential uncertainty for RNA-Seq.

Identifying differentially expressed transcripts poses a crucial yet challenging problem in transcriptomics. Substantial uncertainty is associated with the abundance estimates of certain transcripts which, if ignored, can lead to the exaggeration of false positives and, if included, may lead to reduced power. For a given set of RNA-Seq samples, TreeTerminus arranges transcripts in a hierarchical tree structure that encodes different layers of resolution for interpretation of the abundance of transcriptional groups, with uncertainty generally decreasing as one ascends the tree from the leaves.

simpleaf: a simple, flexible, and scalable framework for single-cell data processing using alevin-fry.

Summary: The alevin-fry ecosystem provides a robust and growing suite of programs for single-cell data processing. However, as new single-cell technologies are introduced, as the community continues to adjust best practices for data processing, and as the alevin-fry ecosystem itself expands and grows, it is becoming increasingly important to manage the complexity of alevin-fry's single-cell preprocessing workflows while retaining the performance and flexibility that make these tools enticing. We introduce simpleaf, a program that simplifies the processing of single-cell data using tools from the alevin-fry ecosystem, and adds new functionality and capabilities, while retaining the flexibility and performance of the underlying tools.

DifferentialRegulation: a Bayesian hierarchical approach to identify differentially regulated genes.

Motivation: Although transcriptomics data is typically used to analyse mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g.

Meta-colored compacted de Bruijn graphs.

Motivation: The colored compacted de Bruijn graph (c-dBG) has become a fundamental tool used across several areas of genomics and pangenomics. For example, it has been widely adopted by methods that perform read mapping or alignment, abundance estimation, and subsequent downstream analyses. These applications essentially regard the c-dBG as a map from k-mers to the set of references in which they appear.

SEESAW: detecting isoform-level allelic imbalance accounting for inferential uncertainty.

Detecting allelic imbalance at the isoform level requires accounting for inferential uncertainty, caused by multi-mapping of RNA-seq reads. Our proposed method, SEESAW, uses Salmon and Swish to offer analysis at various levels of resolution, including gene, isoform, and aggregating isoforms to groups by transcription start site. The aggregation strategies strengthen the signal for transcripts with high uncertainty.

TreeTerminus -creating transcript trees using inferential replicate counts.

A certain degree of uncertainty is always associated with the transcript abundance estimates. The uncertainty may make many downstream analyses, such as differential testing, difficult for certain transcripts. Conversely, gene-level analysis, though less ambiguous, is often too coarse-grained.

Fulgor: A fast and compact -mer index for large-scale matching and color queries.

The problem of sequence identification or matching - determining the subset of references from a given collection that are likely to contain a query nucleotide sequence - is relevant for many important tasks in Computational Biology, such as metagenomics and pan-genome analysis. Due to the complex nature of such analyses and the large scale of the reference collections a resourceefficient solution to this problem is of utmost importance. The reference collection should therefore be pre-processed into an for fast queries.

simpleaf: A simple, flexible, and scalable framework for single-cell transcriptomics data processing using alevin-fry.

Summary: The alevin-fry ecosystem provides a robust and growing suite of programs for single-cell data processing. However, as new single-cell technologies are introduced, as the community continues to adjust best practices for data processing, and as the alevin-fry ecosystem itself expands and grows, it is becoming increasingly important to manage the complexity of alevin-fry ’s single-cell preprocessing workflows while retaining the performance and flexibility that make these tools enticing. We introduce simpleaf , a program that simplifies the processing of single-cell data using tools from the alevin-fry ecosystem, and adds new functionality and capabilities, while retaining the flexibility and performance of the underlying tools.

Understanding and evaluating ambiguity in single-cell and single-nucleus RNA-sequencing.

Recently, a new modification has been proposed by Hjörleifsson and Sullivan . to the model used to classify the splicing status of reads (as spliced (mature), unspliced (nascent), or ambiguous) in single-cell and single-nucleus RNA-seq data. Here, we evaluate both the theoretical basis and practical implementation of the proposed method.

Scalable, ultra-fast, and low-memory construction of compacted de Bruijn graphs with Cuttlefish 2.

The de Bruijn graph is a key data structure in modern computational genomics, and construction of its compacted variant resides upstream of many genomic analyses. As the quantity of genomic data grows rapidly, this often forms a computational bottleneck. We present Cuttlefish 2, significantly advancing the state-of-the-art for this problem.

Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets.

Motivation: Allelic expression analysis aids in detection of cis-regulatory mechanisms of genetic variation, which produce allelic imbalance (AI) in heterozygotes. Measuring AI in bulk data lacking time or spatial resolution has the limitation that cell-type-specific (CTS), spatial- or time-dependent AI signals may be dampened or not detected.

Results: We introduce a statistical method airpart for identifying differential CTS AI from single-cell RNA-sequencing data, or dynamics AI from other spatially or time-resolved datasets.