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Search · four archives
27 papers · ranked by Valyu relevance
Saloni Bhatia, Matt A Field, Lionel Hebbard, Ulf Schmitz
Alternative splicing (AS) plays a key role in regulating gene expression, and its dysregulation is implicated in numerous human diseases, including cancer. While bulk RNA sequencing has advanced our understanding of AS, it cannot capture cellular heterogeneity or reliably reconstruct full-length isoforms, both of which…
Maruša Koderman, Julia Pilarski, Erica Bianco, Danilo González + 2 more
The transition toward “atlas-scale” single cell research has resulted in datasets comprising millions of cells across hundreds of samples, creating significant challenges for data management, computational efficiency, and reproducibility. While numerous methods are available for individual steps in single cell data…
Noam Teyssier, Alexander Dobin
Single-cell genomics is rapidly scaling toward billion-cell atlases, but computational analysis has become a critical bottleneck. Processing multiplexed datasets with existing tools requires substantial computational resources and runtime that become prohibitive at scale. Here we present cyto, an ultra highthroughput…
Michael Bowman, Shreeya Gounder, Varsha Singh, Olga Shestova + 9 more
Advances in single cell multi-omics technologies have allowed for investigation into genotype-immunophenotype relationships and dynamic clonal changes in cancer patient samples. These technologies provide rich insights into genetic profiling, drivers of disease progression, and subclonal dynamics. Increased adoption…
Xiang Xiang Wang, Sean Cottrell, Guo-Wei Wei
Single-cell data analysis seeks to characterize cellular heterogeneity based on high-dimensional gene expression profiles. Conventional approaches represent each cell as a vector in Euclidean space, which limits their ability to capture intrinsic correlations and multiscale geometric structures. We propose a multiscale…
Tim Stohn, Nadine D. van de Brug, Anastasia Theodosiadou, Bram Thijssen + 3 more
Single-cell sequencing technologies increasingly rely on complex nucleotide barcoding schemes to encode cellular identities, experimental conditions, and multiple molecular modalities within a single experiment. While demultiplexing, alignment, and UMI-based quantification form the core preprocessing steps that…
Yilong Yang, Saurabh Parikh, Chieh-Yuan Li, Lindsey Murphy + 11 more
Chimeric antigen receptor T cell (CAR T) immunotherapies have transformed cancer treatment. These therapies typically involve lentiviral vector-mediated CAR gene integration, followed by reinfusion into patients, necessitating rigorous quality control. Critical parameters, including transduction efficiency and vector…
Hongfei Li, Jiechen Wang, Qing Liu, Quan Zou + 1 more
Joint profiling technologies combining single-cell chromatin accessibility (CA) and transcriptome sequencing enable cellular heterogeneity analysis from both gene and cis-regulatory element perspectives, greatly advancing molecular biology at a cellular resolution. These techniques have been used to construct gene…
Meier, Dominik, Yu, Shixing + 6 more
Single-cell RNA sequencing (scRNA-seq) enables the study of cellular heterogeneity. Yet, clustering accuracy, and with it downstream analyses based on cell labels, remain challenging due to measurement noise and biological variability. In standard latent spaces (e.g., obtained through PCA), data from different cell…
Gabriele Malagoli, Patrick Hanel, Anna Danese, Guy Wolf + 1 more
High-throughput single-cell sequencing is widely used to study cell identity. We present SEAGALL (Single-cell Explainable Geometry-Aware Graph Attention Learning pipeLine), a deep learning method to quantify the impact of molecular features on cellular phenotype, based on geometry-regularised autoencoders (GRAE) and…
Ali Anaissi, Seid Miad Zandavi, Weidong Huang, Junaid Akram + 3 more
Single-cell data analysis has the potential to revolutionize personalized medicine by characterizing disease-associated molecular changes at the single-cell level. Advanced single-cell multimodal assays can now simultaneously measure various molecules (e.g., DNA, RNA, Protein) across hundreds of thousands of individual…
Ping Xu, Z. Wang, Zhirui Wang, Pengjiang Li + 4 more
Cell clustering is crucial for uncovering cellular heterogeneity in single-cell RNA sequencing (scRNA-seq) data by identifying cell types and marker genes. Despite its importance, benchmarks for scRNA-seq clustering methods remain fragmented, often lacking standardized protocols and failing to incorporate recent…
Wei Zhao, Fei Yang, Wenting Zong, Xinchang Zheng + 1 more
Tumor heterogeneity represents a critical determinant affecting both cancer diagnosis and therapeutic efficacy. Traditional bulk sequencing approaches are limited in their ability to resolve genomic alterations at the resolution of individual cells. In this study, we present scVar, an integrated analytical framework…
Murong Zhou, Xin Lu, Yingjian Liang, Alfred Wei Chieh Kow + 4 more
As fundamental units of life, cells perform a variety of biological functions depending on their tissue context and physiological state. Although traditional bulk RNA sequencing offers high sequencing depth, it captures only the average transcriptional profile of large cell populations, making it challenging to uncover…
Rawin Poonperm, Taiki Yoneda, Taito Imada, Saori Takahashi + 10 more
Single-cell sequencing has advanced our understanding of cell-type diversity and heterogeneity. However, existing single-cell multi-omics methods lack the ability to monitor gene expression dynamics during S-phase progression. Here, we introduce single-cell (sc)Repli-RamDA-seq (scRR-seq), a multi-omics method that…
Ali Ranjbaran, Francesca Luca, Roger Pique-Regi
Sample multiplexing reduces cost and batch effects in population based large-scale single-cell genomics studies but requires accurate and scalable computational demultiplexing. Existing genotype-based methods, such as demuxlet, provide high accuracy but can be computationally slow and memory intensive as the number of…
Umair Seemab, Katri Vainionpaa, Ziaurrehman Tanoli, Henri Leinonen
Bulk and single-cell RNA sequencing (scRNA-seq) have become essential for investigating disease mechanisms and identifying diagnostic biomarkers. However, the growing volume of transcriptomic data remains difficult to reuse efficiently for many researchers. Downstream analysis often requires multiple statistical…
MV Cannon, MJ Gust, AC Gross, M Cam + 7 more
Single cell RNAseq (scRNAseq) is an ideal tool to characterize the heterogeneity within the tumor microenvironment, however, accurate identification of tumor cells can be a challenge. Reference-based methods can be inaccurate, if reference datasets are even available. Current purpose-built methods can be inaccurate…
Anna E Elz, Derrik Gratz, Annalyssa Long, David Sowerby + 2 more
The rapid development of updated and new commercially available single-cell transcriptomics platforms provides users with a range of experimental options. Cost, sensitivity, throughput, flexibility, and ease of use all influence the selection of an optimal workflow. We performed a comprehensive comparison of…
Huan Souza, Pankaj Mehta
Single-cell RNA sequencing (scRNA-seq) data exhibit strong and reproducible statistical structure. This has motivated the development of large-scale foundation models, such as TranscriptFormer, that use transformer-based architectures to learn a generative model for gene expression by embedding genes into a latent…
Shuyu Chen, Chen Zhu, Ye Zhang, Yang Li + 2 more
Identifying therapeutic target genes from single-cell RNA sequencing (scRNA-seq) data remains a fundamental challenge in translational biology. Unlike bulk assays, scRNA-seq captures heterogeneous cellular states and rare subpopulations, but this same heterogeneity makes target discovery highly sensitive to analytical…
Yiyang Zhang, Bokai Zhao, Xiaoru Zhang, Zongchang Du + 2 more
Single-cell-resolution spatial transcriptomics profiles gene expression at cellular locations in native tissues, yet accurate cell-type annotation remains challenging: imaging-based platforms are constrained by targeted gene panels, whereas sequencing-based platforms often suffer from sparse molecular capture and…
Junha Shin, Spencer Halberg, Yuda Liu, Suvojit Hazra + 2 more
- 1 Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA, 53715 - 2 Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA, 53726 - 3 email: sroy@biostat.wisc.edu - \* These authors contributed equally to this article. - \* This article is…
Authors not listed
The new European (EU) regulation on water reuse explicitly incorporates antimicrobial resistance (AMR) into routine monitoring and risk management, creating an urgent need to define target antibiotic resistance genes (ARGs) for reclaimed irrigation water and agricultural sludge. However, existing global data largely…
Authors not listed
The WRN helicase has recently emerged as a promising therapeutic target for microsatellite instability (MSI)-high cancers. Here, we report LXW-P1, a potent WRN degrader derived from marine bromotyrosine alkaloids. Its molecular target was identified using an AI-guided, pathway-informed perturbation transcriptomics…
Authors not listed
DNA-encoded libraries (DELs) have emerged as a powerful platform for screening ultra-large chemical spaces by leveraging DNA barcodes to tag and track individual small molecules. Recent work has shown that machine learning can enhance DEL based hit discovery by denoising sequencing artifacts and improving binder…
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Quantitative analysis of small extracellular vesicles (sEVs) at single-particle resolution remains challenging due to their nanoscale dimensions and compositional heterogeneity. Existing methods often rely on specialized and costly instrumentation, limiting accessibility for many researchers and necessitates extensive…