Integrative Computational Methods for Second-and Third-Generation Sequencing Data

Dear Colleagues,

The advent of second- and third-generation sequencing technologies has revolutionized our capacity to investigate cellular systems at unprecedented resolutions. While short-read RNA-Seq and single-cell RNA-Seq offer high accuracy and throughput, long-read technologies such as Iso-Seq provide full-length transcript coverage, isoform-level resolution, and direct detection of epigenetic modifications. However, the integration of these heterogeneous data types presents significant algorithmic and computational challenges. This Special Issue is dedicated to highlighting novel algorithms, workflows, and frameworks designed for the integration of multi-platform sequencing data. Interdisciplinary contributions that leverage bioinformatics, machine learning, and systems biology to tackle fundamental challenges in transcriptomics and cellular regulation are especially encouraged. Papers may report on original research, discuss methodological aspects, review the current state of the art, or offer perspectives on future prospects.

Specific methods and fields of applications include, but are not limited to, the following:

• Hybrid modeling of short- and long-read transcriptomic data;
• Machine learning approaches for integrative omics;
• Noise reduction, normalization, and batch effect correction;
• Annotation pipelines leveraging long-read accuracy;
• Reference-based and reference-free cell identification;
• Cell typing in rare or heterogeneous cell populations;
• Isoform quantification and novel transcript discovery;
• Detection of tissue- or disease-specific splicing events;
• Functional implications of splicing diversity;
• Tumor-specific isoform identification and fusion detection;
• Splicing-based biomarkers and therapeutic targets;
• Single-cell multi-omics integration for tumor heterogeneity;
• Integration of gene expression, splicing, and epigenetic data;
• Open-source tools for integrative sequencing analysis;
• Longitudinal and spatial transcriptomics using multi-omics sequencing.

Prof. Dr. Hao Lin
Dr. Guojun Liu
Dr. Alexey Sarapultsev
Guest Editors

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• transcriptomics
• single-cell RNA sequencing
• long-read sequencing
• multi-omics integration
• alternative splicing
• isoform discovery
• cell type annotation
• regulatory networks
• cancer genomics
• computational biology