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1. SpudDB: a database for accessing potato genomic data

   https://doi.org/10.1093/genetics/iyae205  

   Hamilton, John P; Brose, Julia; Buell, C Robin (December 2024, GENETICS)

   Harris, T (Ed.)

   Abstract Potato is a key food crop with a complex, polyploid genome. Advancements in sequencing technologies coupled with improvements in genome assembly algorithms have enabled generation of phased, chromosome-scale genome assemblies for cultivated tetraploid potato. The SpudDB database houses potato genome sequence and annotation, with the doubled monoploid DM 1–3 516 R44 (hereafter DM) genome serving as the reference genome and haplotype. Diverse annotation data types for DM genes are provided through a suite of Gene Report Pages including gene expression profiles across 438 potato samples. To further annotate potato genes based on expression, 65 gene co-expression modules were constructed that permit the identification of tightly co-regulated genes within DM across development and responses to wounding, abiotic stress, and biotic stress. Genome browser views of DM and 28 other potato genomes are provided along with a download page for genome sequence and annotation. To link syntenic genes within and between haplotypes, syntelogs were identified across 25 cultivated potato genomes. Through access to potato genome sequences and associated annotations, SpudDB can enable potato biologists, geneticists, and breeders to continue to improve this key food crop. 

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2. k -mer approaches for biodiversity genomics

   https://doi.org/10.1101/gr.279452.124  

   Jenike, Katharine M; Campos-Domínguez, Lucía; Boddé, Marilou; Cerca, José; Hodson, Christina N; Schatz, Michael C; Jaron, Kamil S (January 2025, Genome Research)

   The wide array of currently available genomes displays a wonderful diversity in size, composition, and structure and is quickly expanding thanks to several global biodiversity genomics initiatives. However, sequencing of genomes, even with the latest technologies, can still be challenging for both technical (e.g., small physical size, contaminated samples, or access to appropriate sequencing platforms) and biological reasons (e.g., germline-restricted DNA, variable ploidy levels, sex chromosomes, or very large genomes). In recent years,k-mer-based techniques have become popular to overcome some of these challenges. They are based on the simple process of dividing the analyzed sequences (e.g., raw reads or genomes) into a set of subsequences of lengthk, calledk-mers, and then analyzing the frequency or sequences of thosek-mers. Analyses based onk-mers allow for a rapid and intuitive assessment of complex sequencing data sets. Here, we provide a comprehensive review to the theoretical properties and practical applications ofk-mers in biodiversity genomics with a special focus on genome modeling. 

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3. A haplotype-resolved chromatin landscape connects cis-regulatory variants to trait variation in Citrus

   https://doi.org/10.1186/s12864-025-12137-0  

   Diaz, Isaac A; Ostovar, Talieh; Chen, Jinfeng; de_Dios, Emmanuel Avila; Piscatella, Ryan; Perez-Alfaro, Ruth S; Zayed, Omar; Saddoris, Sarah; Schmitz, Robert J; Wessler, Susan R; et al (December 2025, BMC Genomics)

   Abstract BackgroundGenetic and epigenetic perturbation of cis-regulatory sequences can shift patterns of gene expression and result in novel phenotypes. Phased genome assemblies now enable the local dissection of linkages between cis-regulatory sequences, including their epigenetic state, and allele-specific gene expression to further characterize gene regulation and resulting phenotypes in heterozygous genomes. ResultsWe assembled a locally phased genome for a mandarin hybrid named ‘Fairchild’ to explore the molecular signatures of allele-specific gene expression. With local genome phasing, genes with allele-specific expression were paired with haplotype-specific chromatin states, including levels of chromatin accessibility, histone modifications, and DNA methylation. We found that 30% of variation in allele-specific expression could be attributed to haplotype associated factors, with allelic levels of chromatin accessibility and three histone modifications in gene bodies having the most influence. Structural variants in promoter regions were also associated with allele-specific expression, including specific enrichments of hAT and MULE-MuDR DNA transposon sequences. Integration of haplotype-resolved genetic and epigenetic landscapes with high-throughput phenotypic analysis of fruit traits in a panel of 154 accessions with mandarin and pummelo ancestry revealed that trait-associated variants were enriched in regions of open chromatin. Mining of trait-associated variants uncovered a Gypsy retrotransposon insertion in a gene that regulates potassium transport and may contribute to the reduction in fruit size that is observed in mandarins. Conclusions​​Using a locally phased assembly of a heterozygous cultivar of citrus, we dissected the interplay between genetic variants and molecular phenotypes to reveal cis-regulatory sequences with potential functional effects on phenotypes relevant for genetic improvement. 

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4. K-mer-based Approaches to Bridging Pangenomics and Population Genetics

   https://doi.org/10.1093/molbev/msaf047  

   Roberts, Miles D; Davis, Olivia; Josephs, Emily B; Williamson, Robert J (March 2025, Molecular Biology and Evolution)

   Harris, Kelley (Ed.)

   Abstract Many commonly studied species now have more than one chromosome-scale genome assembly, revealing a large amount of genetic diversity previously missed by approaches that map short reads to a single reference. However, many species still lack multiple reference genomes and correctly aligning references to build pangenomes can be challenging for many species, limiting our ability to study this missing genomic variation in population genetics. Here, we argue that k-mers are a very useful but underutilized tool for bridging the reference-focused paradigms of population genetics with the reference-free paradigms of pangenomics. We review current literature on the uses of k-mers for performing three core components of most population genetics analyses: identifying, measuring, and explaining patterns of genetic variation. We also demonstrate how different k-mer-based measures of genetic variation behave in population genetic simulations according to the choice of k, depth of sequencing coverage, and degree of data compression. Overall, we find that k-mer-based measures of genetic diversity scale consistently with pairwise nucleotide diversity (π) up to values of about π=0.025 (R2=0.97) for neutrally evolving populations. For populations with even more variation, using shorter k-mers will maintain the scalability up to at least π=0.1. Furthermore, in our simulated populations, k-mer dissimilarity values can be reliably approximated from counting bloom filters, highlighting a potential avenue to decreasing the memory burden of k-mer-based genomic dissimilarity analyses. For future studies, there is a great opportunity to further develop methods to identifying selected loci using k-mers. 

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5. Assessing the Resilience of Machine Learning Classification Algorithms on SARS-CoV-2 Genome Sequences Generated with Long-Read Specific Errors

   https://doi.org/10.3390/biom13060934  

   Sahoo, Bikram; Ali, Sarwan; Chen, Pin-Yu; Patterson, Murray; Zelikovsky, Alexander (June 2023, Biomolecules)

   The emergence of third-generation single-molecule sequencing (TGS) technology has revolutionized the generation of long reads, which are essential for genome assembly and have been widely employed in sequencing the SARS-CoV-2 virus during the COVID-19 pandemic. Although long-read sequencing has been crucial in understanding the evolution and transmission of the virus, the high error rate associated with these reads can lead to inadequate genome assembly and downstream biological interpretation. In this study, we evaluate the accuracy and robustness of machine learning (ML) models using six different embedding techniques on SARS-CoV-2 error-incorporated genome sequences. Our analysis includes two types of error-incorporated genome sequences: those generated using simulation tools to emulate error profiles of long-read sequencing platforms and those generated by introducing random errors. We show that the spaced k-mers embedding method achieves high accuracy in classifying error-free SARS-CoV-2 genome sequences, and the spaced k-mers and weighted k-mers embedding methods are highly accurate in predicting error-incorporated sequences. The fixed-length vectors generated by these methods contribute to the high accuracy achieved. Our study provides valuable insights for researchers to effectively evaluate ML models and gain a better understanding of the approach for accurate identification of critical SARS-CoV-2 genome sequences. 

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