Journal: bioRxiv

Article Title: Benchmarking circRNA Detection Tools from Long-Read Sequencing Using Data-Driven and Flexible Simulation Framework

doi: 10.1101/2025.04.17.649290

Figure Lengend Snippet: Overview of the benchmarking study. Schematic representation of the benchmarking framework for circular RNA (circRNA) detection tools, using an available real dataset of circRNA long read sequencing from Nanopore from (mouse brain cell lines) as training model. The workflow progresses through five stages: (I) input FASTQ sequences from circRNA databases (see Methods section) and real wet lab protocol as training model; (II) ground truth preparation; (III) in silico read simulation with NanoSim version 3.1.0; (IV) computational analysis using CIRI-long, isoCirc, and circNICK-lrs tools; (V) comprehensive performance evaluation through multiple metrics including sensitivity, precision, specificity, accuracy, and F1 score, calculated across different overlap thresholds. Figure created in https://BioRender.com .

Article Snippet: NanoSim ( ): is a fast and scalable read simulator that captures the technology-specific features of Oxford Nanopore Technologies (ONT) data.

Techniques: Sequencing, In Silico

Journal: bioRxiv

Article Title: Benchmarking circRNA Detection Tools from Long-Read Sequencing Using Data-Driven and Flexible Simulation Framework

doi: 10.1101/2025.04.17.649290

Figure Lengend Snippet: Computational Workflow for Nanopore Sequencing circRNA Simulation. The workflow integrates wet-lab Oxford Nanopore Technologies (ONT) read characteristics with computational simulation techniques. Panels A-C depict sequential stages of data processing and simulation: (A) initial feature extraction from circRNA databases and experimental reads, (B) generation of protocol-specific reference circRNA sequences, and (C) NanoSim-based simulation generating detailed computational read models.

Article Snippet: NanoSim ( ): is a fast and scalable read simulator that captures the technology-specific features of Oxford Nanopore Technologies (ONT) data.

Techniques: Nanopore Sequencing, Extraction

Journal: iScience

Article Title: Mapping-friendly sequence reductions: Going beyond homopolymer compression

doi: 10.1016/j.isci.2022.105305

Figure Lengend Snippet: Performance of our 58 selected mapping-friendly sequence reductions across genomes on reads simulated by nanosim (Panel A) shows the whole human genome assembly, (B and C) the subset of mapped reads from panel B that originate from repetitive regions, and C) the “TandemTools” synthetic centromeric reference sequence. We highlighted the best-performing mapping-friendly sequence reductions as MSR E, F, and P, respectively, in terms of cumulative mapeval mapping error rate, fraction of reads mapped, and percentage of better thresholds than HPC. Each point on a line represents, from left to right, the mapping quality thresholds 60, 50, 40, 30, 20, 10, and 0. For the first point of each line, only reads of mapping quality 60 are considered, and the y value represents the rate of these reads that are not correctly mapped, the x value represents the fraction of reads that are mapped at this threshold. The next point is computed for all reads of mapping quality ≥ 50 , etc. The rightmost point on any curve represents the mapping error rate and the fraction of mapped reads for all primary alignments. The x-axes are clipped for lower mapped read fractions to better differentiate HPC, raw and MSRs E, F, and P. See Also Figure S7 .

Article Snippet: Given a reference sequence, simulated reads were obtained using nanosim ( ) with the human_NA12878_DNA_FAB49712_guppy_flipflop pre-trained model, mimicking sequencing with an Oxford Nanopore instrument.

Techniques: Sequencing

Journal: iScience

Article Title: Mapping-friendly sequence reductions: Going beyond homopolymer compression

doi: 10.1016/j.isci.2022.105305

Figure Lengend Snippet:

Article Snippet: Given a reference sequence, simulated reads were obtained using nanosim ( ) with the human_NA12878_DNA_FAB49712_guppy_flipflop pre-trained model, mimicking sequencing with an Oxford Nanopore instrument.

Techniques: Sequencing, Software