In collaboration with the International Conference on Research in Computational Molecular Biology (RECOMB), Genome Research publishes a collection of 20 computational methods and their applications in genomics including spatial, single-cell, and long-read sequencing. These include algorithmic innovations in genomic variation analysis, privacy-preserving algorithms, DNA structural properties, cancer genomics, transcriptomic studies, gene regulatory networks, biomolecular representation learning, and metagenomic data analysis. Several of these studies are highlighted below.
PRiMeR (Sens et al. 2024) is a method that leverages genetic information to learn disease risk predictors across cohorts, circumventing the need for traditional longitudinal studies. With training on risk factors and genetic data from a healthy cohort, along with results from genome-wide association studies (GWAS), PRiMeR can assess risk for new patients. This method was validated on simulations of type 2 diabetes and Alzheimer's and Parkinson's disease onset. This method could facilitate more timely and targeted preventive strategies.
In another study, Hong et al. (2024) developed SF-Relate, a practical and secure federated algorithm for identifying genetic relatives across distributed genomic datasets. Using novel hashing and bucketing strategies, SF-Relate distinguishes relatives from nonrelatives and securely estimates kinship using encrypted data. This method allows for the exclusion of close relatives that can introduce bias in study results while providing privacy protection.
Circular extrachromosomal DNA (ecDNA) is a form of oncogene amplification found across cancer types and is associated with poor outcome in patients. EcDNAs drive tumor formation, evolution, and drug resistance by modulating oncogene copy-number and rewiring gene-regulatory networks. Two methods CoRAL (Zue et al. 2024) and Decoil (Giurgiu et al. 2024) resolve ecDNA structure using long-read sequencing data, profiling the landscape and evolution of focal amplifications in tumors.
Another method, DIISCO (Park et al. 2024), characterizes the temporal dynamics of cell–cell interactions in complex biological systems using single-cell RNA sequencing data, elucidating mechanisms underlying normal biological processes and disease progression. This method was demonstrated on simulated and experimental lymphoma–immune interaction data and revealed immune interactions of a cytotoxic T cell subtype that expands with therapy. This method can guide the design of improved treatments to promote cell states and crosstalk crucial for therapeutic response.
Schrod et al. (2024) present SpaCeNet, a method for analyzing patterns of correlation in spatial transcriptomics data, facilitating reconstruction of both the intracellular and the intercellular interaction networks with single-cell spatial resolution. SpaCeNet was validated on several datasets including mouse visual cortex, mouse organoids, and the Drosophila blastoderm revealing insights into the spatial organization of cell populations capturing complex patterns of interactions related to cellular growth, development, and disease.
Finally, repetitive DNA poses significant challenges for accurate and efficient genome assembly and sequence alignment. This is particularly true for metagenomic data, where genome dynamics such as horizontal gene transfer, gene duplication, and gene loss/gain complicate accurate genome assembly from microbial communities. Detecting repeats is a crucial first step in overcoming these challenges. Azizpour et al. (2024) presents GraSSRep, a novel approach that detects and classifies DNA sequences into repetitive and non-repetitive categories in metagenomics data.
Additional computational methods that advance studies in genomic variation, genome structure, cancer genomics, transcriptomics, gene regulation, data privacy preservation, and metagenomic data analysis are also included in this Special Issue.
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