Biology subjects

Jetzinger, F.

Publications and source records attributed to Jetzinger, F..

3 recordsLinked to original sources

bioRxiv2026

Systematic evaluation of transcript normalization methods and quantification pipelines across long-read sequencing platforms

Long-read RNA sequencing (lrRNA-seq) has transformed transcriptomics by enabling full-length isoform resolution, yet transcript quantification remains confounded by platform-dependent length biases and tool choices. While a quantification normalization step is common in short-read RNA-seq, this has not yet been extended to lrRNA-seq and the field lacks a systematic analysis of normalization strategies applied to long reads data. Here, we systematically benchmarked quantification pipelines and downstream normalization strategies across Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) platforms. Using synthetic spike-in controls (SIRVs and ERCC) alongside matched Illumina short-read benchmarks across mouse tissues and human cell models, we evaluated five normalization methods (CPM, TPM, CQN, EDASeq, and Scaled Median Ratio) across five quantification workflows (Bambu, FLAIR, IsoQuant, Oarfish, and Kallisto). Our framework assessed PacBio Iso-Seq and Kinnex protocols together with ONT cDNA and direct RNA (dRNA) sequencing datasets. We demonstrate that standard Transcripts Per Million (TPM) scaling counteracts short-molecule depletion in PacBio datasets, but severely distorts Nanopore expression estimates. For Nanopore libraries, covariate-aware normalizations (CQN and EDASeq) mitigate length bias without TPM's over-correction penalty. Conversely, relative median ratio normalization (SMR) degrades accuracy and introduces noise in low-variability biological samples. Across computational tools, FLAIR minimized absolute error on short SIRVs, while reference-restricted quantifiers (Oarfish and Kallisto) consistently outperformed transcript reconstruction tools in short-read validation. These findings show that quantification normalization should be applied to lrRNA-seq data and establish platform-specific guidelines for long-read transcriptomics.

bioinformatics↗

bioRxiv2025

To join or not to join: handling biological replicates in long-read RNA sequencing data

Long-read RNA sequencing (lrRNA-seq) has revolutionized transcriptomics facilitating the study of alternative splicing and resulting in identification of thousands of novel transcripts. While isoform identification has received significant attention, the handling of biologically replicated lrRNA-seq datasets remains less explored. However, how multiple samples are combined in a lrRNA-seq study may strongly impact transcript identification. This study defines and evaluates two strategies for obtaining consensus transcriptomes from multi-sample lrRNA-seq data: "Join & Call", where reads from all samples are combined before transcript identification, and "Call & Join", where transcript identification is performed on individual samples before combining the resulting annotations. We applied these strategies to a highly replicated dataset of mouse brain and kidney tissues, using both PacBio and ONT technologies, across six widely used transcript reconstruction tools. Our results indicate that the optimal strategy depends on the chosen computational tool and research objective. We found that Join & Call is generally more suitable for discovering rarely occurring, novel isoforms, as pooling evidence increases confidence in calling lowly-expressed transcripts. Conversely, Call & Join is computationally more efficient and often preferable for highly replicated datasets when the investigation of rare novel transcripts is not the primary objective. Our findings provide a conceptual and practical framework for multi-sample transcriptome reconstruction, guiding best practices in the context of increasingly large-scale lrRNA-seq studies.

bioinformatics↗

bioRxiv2025

Transcriptome Universal Single-isoform COntrol: A Framework for Evaluating Transcriptome reconstruction Quality

Long-read sequencing (LRS) platforms, such as Oxford Nanopore and Pacific Biosciences, enable comprehensive transcriptome analysis but face challenges such as sequencing errors, sample quality variability, and library preparation biases. Current benchmarking approaches address these issues insufficiently: BUSCO assesses transcriptome completeness using conserved single-copy orthologs but can misinterpret alternative splicing as gene duplications, while spike-ins (SIRVs, ERCCs) oversimplify real- sample complexity, neglecting RNA degradation and RNA extraction artifacts, thus inflating performance metrics. Simulation algorithms are limited to recapitulate this complexity. To overcome these limitations, we introduce the Transcriptome Universal Single-isoform Control (TUSCO), a curated internal reference set of genes lacking alternative isoforms. TUSCO evaluates precision by identifying transcripts deviating from reference annotations and assesses sensitivity by verifying detection completeness in human and mouse samples. Masking TUSCO transcripts--and optionally inserting decoy splice variants--creates a novel- isoform challenge that assesses recovery of the true, now-unannotated isoforms. Our validation demonstrates that TUSCO provides accurate and reliable benchmarking without external controls, significantly improving quality control standards for transcriptome reconstruction using LRS.

bioinformatics↗