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

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 333

Special Issue Editors


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Department of Bioinformatics, School of Life Science and Technology, Inner Mongolia University of Science and Technology, Baotou 014000, China
Interests: transcriptomics integration; alternative splicing; cancer transcriptomics; biomarker discovery; gene regulatory network; multi-omics

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Institute of Immunology and Physiology of the Ural Branch of the RAS, 620000 Ekaterinburg, Russia
Interests: psychosomatic disorders; PTSD; immunology; cardiology; stress; pediatric dentistry; oncology; inflammation
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Special Issue Information

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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Keywords

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

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Published Papers (1 paper)

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Research

24 pages, 15627 KiB  
Article
Construction and Evaluation of a Domain-Related Risk Model for Prognosis Prediction in Colorectal Cancer
by Xiangjun Cui, Yongqiang Xing, Guoqing Liu, Hongyu Zhao and Zhenhua Yang
Computation 2025, 13(7), 171; https://doi.org/10.3390/computation13070171 - 17 Jul 2025
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Abstract
Background: Epigenomic instability accelerates mutations in tumor suppressor genes and oncogenes, contributing to malignant transformation. Histone modifications, particularly methylation and acetylation, significantly influence tumor biology, with chromo-, bromo-, and Tudor domain-containing proteins mediating these changes. This study investigates how genes encoding these domain-containing [...] Read more.
Background: Epigenomic instability accelerates mutations in tumor suppressor genes and oncogenes, contributing to malignant transformation. Histone modifications, particularly methylation and acetylation, significantly influence tumor biology, with chromo-, bromo-, and Tudor domain-containing proteins mediating these changes. This study investigates how genes encoding these domain-containing proteins affect colorectal cancer (CRC) prognosis. Methods: Using CRC data from the GSE39582 and TCGA datasets, we identified domain-related genes via GeneCards and developed a prognostic signature using LASSO-COX regression. Patients were classified into high- and low-risk groups, and comparisons were made across survival, clinical features, immune cell infiltration, immunotherapy responses, and drug sensitivity predictions. Single-cell analysis assessed gene expression in different cell subsets. Results: Four domain-related genes (AKAP1, ORC1, CHAF1A, and UHRF2) were identified as a prognostic signature. Validation confirmed their prognostic value, with significant differences in survival, clinical features, immune patterns, and immunotherapy responses between the high- and low-risk groups. Drug sensitivity analysis revealed top candidates for CRC treatment. Single-cell analysis showed varied expression of these genes across cell subsets. Conclusions: This study presents a novel prognostic signature based on domain-related genes that can predict CRC severity and offer insights into immune dynamics, providing a promising tool for personalized risk assessment in CRC. Full article
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