Biology subjects

Zahin, T.

Publications and source records attributed to Zahin, T..

6 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

Accurate Reconstruction of Circular RNAs from Complex Rolling Circular Long Reads with CircPlex

Rolling circle amplification (RCA) coupled with long-read sequencing has emerged as a powerful strategy for detecting full-length circular RNAs (circRNAs). Such protocols produce long reads that are normally composed of several tandemly repeated copies of the original circRNA. The circRNA sequence can be reconstructed through detecting the repeating unit of the long reads, which are aligned to the genome to validate and to identify back-splice junctions (BSJs). We revealed a previously unrecognized phenomenon: a substantial fraction of long reads contain complex repeat patterns in which the repeating unit consists of a sequence combined with its partial reverse complement. In these cases, only the original sequence corresponds to the true circRNA, while the concatenated pattern may produce false circRNAs and misidentify correct circRNAs. We present a new approach CircPlex that extracts the authentic circRNA sequence from these complex repeat units, overcoming the limitations of standard repeat-based consensus prediction. Comparison with isoCirc annotations and circRNA database demonstrates that a significant number of BSJs and full-length sequences, previously ignored, can be recovered. Our results suggest that leveraging partially repetitive reads from RCA-based sequencing can substantially increase circRNA detection sensitivity and uncover novel isoforms, providing a more comprehensive view of the circular transcriptome.

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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

Accurate Detection of Tandem Repeats from Error-Prone Sequences with EquiRep

A tandem repeat is a sequence of nucleotides that occurs as multiple contiguous and near-identical copies positioned next to each other. These repeats play critical roles in genetic diversity, gene regulation, and are strongly linked to various neurological and developmental disorders. While several methods exist for detecting tandem repeats, they often exhibit low accuracy when the repeat unit length increases or the number of copies is low. Furthermore, methods capable of handling highly mutated sequences remain scarce, highlighting a significant opportunity for improvement. We introduce EquiRep, a tool for accurate detection of tandem repeats from erroneous sequences. EquiRep estimates the likelihood of positions originating from the same position in the unit by self-alignment followed by a novel approach that refines the estimation. The built equivalent classes and the consecutive position information will be then used to build a weighted graph, and the cycle in this graph with maximum bottleneck weight while covering most nucleotide positions will be identified to reconstruct the repeat unit. We test EquiRep on simulated and real HOR and RCA datasets where it consistently outperforms or is comparable to state-of-the-art methods. EquiRep is robust to sequencing errors, and is able to make better predictions for long units and low frequencies which underscores its broad usability for studying tandem repeats.

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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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bioRxiv2019

An Alignment-free Method for Phylogeny Estimation using Maximum Likelihood

While alignment has traditionally been the primary approach for establishing homology prior to phylogenetic inference, alignment-free methods offer a simplified alternative, particularly beneficial when handling genome-wide data involving long sequences and complex events such as rearrangements. Moreover, alignment-free methods become crucial for data types like genome skims, where assembly is impractical. However, despite these benefits, alignment-free techniques have not gained widespread acceptance since they lack the accuracy of alignment-based techniques, primarily due to their reliance on simplified models of pairwise distance calculation. Here, we present a likelihood based alignment-free technique for phylogenetic tree construction. We encode the presence or absence of k-mers in genome sequences in a binary matrix, and estimate phylogenetic trees using a maximum likelihood approach. We analyze the performance of our method on seven real datasets and compare the results with the state of the art alignment-free methods. Results suggest that our method is competitive with existing alignment-free tools. This indicates that maximum likelihood based alignment-free methods may in the future be refined to outperform alignment-free methods relying on distance calculation as has been the case in the alignment-based setting. A likelihood based alignment-free method for phylogeny estimation is implemented for the first time in a software named PO_SCPLOWEAFOWLC_SCPLOW, which is available at: https://github.com/hasin-abrar/Peafowlrepo.

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