Biology subjects

Zang, X. C.

Publications and source records attributed to Zang, X. C..

4 recordsLinked to original sources

bioRxiv2025

Amaranth: Enhanced Single-Cell Transcript Assembly via Discriminative Modeling of UMI Reads and Internal Reads

MotivationSingle-cell RNA sequencing has transformed transcriptome profiling at cellular resolution, yet accurate reconstruction of full-length transcripts for individual cells remains a central challenge. Emerging scRNA-seq protocols can produce reads that span entire transcripts, enabling isoform-level expression analysis. For example, Smart-seq protocols combine UMI-linked reads that index and stitch together multiple reads from the same molecule, with internal reads filling coverage gaps. We demonstrate that these read types exhibit markedly different biological and statistical properties in strandness, 5/3 coverage bias, and genomic locality. Existing assemblers fail to leverage these distinctions, yielding suboptimal assembly. ResultsWe developed Amaranth, a novel single-cell assembler that discriminatively models UMI and internal reads. Amaranth implements heuristics specifically designed to address the distinct biases of UMI-linked and internal reads, enabling accurate strandness assignment for internal reads, reliable splicing graph refinement, and precise transcript start site determination. We also developed Amaranth-meta, which integrates information across cells to enhance individual cell assemblies. Benchmarked on Smart-seq3 datasets from human HEK293T and mouse fibroblast cells, Amaranth outperformed other state-of-the-art assemblers in assembling individual cells and in meta-assembly. Amaranth advances isoform-level analysis in single-cell transcriptomics, facilitating detailed studies at cellular resolution. Availability and ImplementationAmaranth is implemented in C++ and is freely available at https://github.com/Shao-Group/amaranth under the BSD-3-Clause license. Scripts, documentation, and data for reproducing experiments in this manuscript are available at https://github.com/Shao-Group/amaranth-test.

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bioRxiv2025

Boosting Transcript Assembly via Delineating Transcript Start and End Sites

Transcript assembly remains a challenging task despite the development of numerous methods. A major contributor to low assembly accuracy is the difficulty in accurately determining transcript start sites (TSSs) and end sites (TESs), due to the weak and noisy signals typically found in RNA-seq data. We present Telos, a two-stage machine learning framework for precise detection of TSSs and TESs and for transcript ranking. The method takes as input any assembly, typically generated by an existing assembler. In the first stage, Telos scores the TSSs and TESs in the input assembly using a machine learning model trained on a rich set of engineered features. These site-level scores will be passed to the second stage for transcript-level evaluation. In its second stage, Telos scores the entire transcripts by training another model that integrates features of their TSS and TES (including the inferred probabilities from the first stage), along with transcript abundance estimated by the assembler and statistics about exon lengths. We extensively evaluated Telos on ONT (cDNA and direct RNA), PacBio, and Illumina short-read RNA-seq datasets. In all cases, it consistently outperformed baseline methods. Telos is agile, but achieves substantial improvements, demonstrating the value of explicitly modeling TSS and TES, a gap in current transcript assembly tools. Telos can be paired with any assembler to accurately score the assembled transcripts. It is modular, easily extensible to emerging sequencing technologies, and hence we anticipate its broad adoption in transcriptomic studies.

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bioRxiv2024

Augmenting Transcriptome Annotations through the Lens of Splicing Evolution

Alternative splicing (AS) is a ubiquitous mechanism in eukaryotes. It is estimated that 90% of human genes are alternatively spliced. Despite enormous efforts, transcriptome annotations remain, nevertheless, incomplete. Conventional means of annotation were largely driven by experimental data such as RNA-seq and protein sequences, while little insight was shed on understanding transcriptomes and alternative splicings from the perspective of evolution. This study addresses this critical gap by presenting TENNIS (Transcript EvolutioN for New Isoform Splicing), an evolution-based model to predict unannotated isoforms and refine existing annotations without requiring additional data. The model of TENNIS is based on two minimal premises-AS isoforms evolve sequentially from existing isoforms, and each evolutionary step involves a single AS event. We formulate the identification of missing transcripts as an optimization problem and parsimoniously find the minimal number of novel transcripts. Our analysis showed approximately 80% of multi-transcript groups from six transcriptome annotations satisfy our evolutionary model. At a high confidence level, 40% of isoforms predicted by TENNIS were validated by deep long-read RNA-seq. In a simulated incomplete annotation scenario, TENNIS dramatically outperforms two randomized baseline approaches by a 2.25-3 fold-change in precision or a 3.5-3.9 fold-change in recall, after controlling the same level of recall or precision of the baseline methods. These results demonstrate that TENNIS effectively identifies missing transcripts by complying with minimal propositions, offering a powerful approach for transcriptome augmentations through the lens of alternative splicing evolutions. TENNIS is freely available at https://github.com/Shao-Group/tennis.

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bioRxiv2024

Accurate Assembly of Circular RNAs with TERRACE

Circular RNA (circRNA) is a class of RNA molecules that forms a closed loop with its 5 and 3 ends covalently bonded. Due to this specific structure circRNAs are more stable than linear RNAs, admit distinct biological properties and functions, and have been proven to be promising biomarkers. Circular RNAs were severely overlooked previously owing to the biases in the RNA-seq protocols and in the detection algorithms, but recently gained tremendous attentions in both aspects. However, most existing methods for assembling circRNAs heavily rely on the annotated transcriptomes, and hence exhibit unsatisfactory accuracy when a high-quality transcriptome is unavailable. Here we present TERRACE, a new algorithm for full-length assembly of circRNAs from paired-end total RNA-seq data. TERRACE uses the splice graph as the underlying data structure to organize the splicing and coverage information. We transform the problem of assembling circRNAs into finding two paths that "bridge" the three fragments in the splice graph induced by back-spliced reads. To solve this formulation, we adopted a definition for optimal bridging paths and a dynamic programming algorithm to calculate such paths, an approach that was proven useful for assembling linear RNAs. TERRACE features an efficient algorithm to detect back-spliced reads that are missed by RNA-seq aligners, contributing to its much improved sensitivity. It also incorporates a new machine-learning approach that is trained to assign a confidence score to each assembled circRNA, which is shown superior to using abundance for scoring. TERRACE is compared with leading circRNA detection methods on both simulations and biological datasets. Our method consistently outperforms by a large margin in sensitivity while maintaining better or comparable precision. In particular, when the annotations are not provided, TERRACE can assemble 123%-412% more correct circRNAs than state-of-the-art methods on human tissues. TERRACE presents a major leap on assembling full-length circRNAs from RNA-seq data, and we expect it to be widely used in the downstream research on circRNAs.

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