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1. Gramene 2025: expanded comparative genomics and pathway resources, integrated search, and pan-genome portals for crop research

   https://doi.org/10.1093/nar/gkaf1260  

   Olson, Andrew; Kumari, Sunita; Wei, Xuehong; Chougule, Kapeel; Lu, Zhenyuan; Tello-Ruiz, Marcela Karey; Kumar, Vivek; Van Buren, Peter; Olson, Audra; Kim, Catherine; et al (January 2026, Nucleic Acids Research)

   Abstract Gramene (gramene.org) is a comprehensive reference database for comparative plant genomics and pathway analysis, integrating functional annotations, evidence-based curated pathways and their projections, and multi-omics datasets. Since our last report, Gramene has added crop-specific pan-genome portals for maize, sorghum, rice, and grapevine. These pan-genome portals host population-scale datasets and multiple assembled genomes per species, all anchored by shared reference genomes. Importantly, these portals now adopt standardized rsIDs for genetic variants, advancing FAIR data principles and enabling cross-database interoperability. The main site is now Gramene Plants, emphasizing its broad genome coverage. Release 69 features 233 reference genomes, curated pathways for 139 species, expression data from 1026 studies across 27 species, and genetic variation data mapped to 27 genomes from 19 species. Key updates to the integrated search functionality include embedded expression viewers from the Bio-Analytic Resource for Plant Biology and EMBL-EBI Expression Atlas, a literature-curated catalog of gene functions, and a new Germplasm tab linking accessions with loss-of-function alleles to seed repositories. These advances reinforce Gramene as a comprehensive platform for exploring plant genomic diversity, gene function, and evolutionary conservation across the Green Tree of Life and within key agricultural species. 

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2. Cuttlefish: fast, parallel and low-memory compaction of de Bruijn graphs from large-scale genome collections

   https://doi.org/10.1093/bioinformatics/btab309  

   Khan, Jamshed; Patro, Rob (July 2021, Bioinformatics)

   null (Ed.)

   Abstract Motivation The construction of the compacted de Bruijn graph from collections of reference genomes is a task of increasing interest in genomic analyses. These graphs are increasingly used as sequence indices for short- and long-read alignment. Also, as we sequence and assemble a greater diversity of genomes, the colored compacted de Bruijn graph is being used more and more as the basis for efficient methods to perform comparative genomic analyses on these genomes. Therefore, time- and memory-efficient construction of the graph from reference sequences is an important problem. Results We introduce a new algorithm, implemented in the tool Cuttlefish, to construct the (colored) compacted de Bruijn graph from a collection of one or more genome references. Cuttlefish introduces a novel approach of modeling de Bruijn graph vertices as finite-state automata, and constrains these automata’s state-space to enable tracking their transitioning states with very low memory usage. Cuttlefish is also fast and highly parallelizable. Experimental results demonstrate that it scales much better than existing approaches, especially as the number and the scale of the input references grow. On a typical shared-memory machine, Cuttlefish constructed the graph for 100 human genomes in under 9 h, using ∼29 GB of memory. On 11 diverse conifer plant genomes, the compacted graph was constructed by Cuttlefish in under 9 h, using ∼84 GB of memory. The only other tool completing these tasks on the hardware took over 23 h using ∼126 GB of memory, and over 16 h using ∼289 GB of memory, respectively. Availability and implementation Cuttlefish is implemented in C++14, and is available under an open source license at https://github.com/COMBINE-lab/cuttlefish. Supplementary information Supplementary data are available at Bioinformatics online. 

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3. Plant Metabolic Network 16: expansion of underrepresented plant groups and experimentally supported enzyme data

   https://doi.org/10.1093/nar/gkae991  

   Hawkins, Charles; Xue, Bo; Yasmin, Farida; Wyatt, Gabrielle; Zerbe, Philipp; Rhee, Seung Y (January 2025, Nucleic Acids Research)

   Abstract The Plant Metabolic Network (PMN) is a free online database of plant metabolism available at https://plantcyc.org. The latest release, PMN 16, provides metabolic databases representing >1200 metabolic pathways, 1.3 million enzymes, >8000 metabolites, >10 000 reactions and >15 000 citations for 155 plant and green algal genomes, as well as a pan-plant reference database called PlantCyc. This release contains 29 additional genomes compared with PMN 15, including species listed by the African Orphan Crop Consortium and nonflowering plant species. Furthermore, 52 new enzymes with experimentally supported function information have been included in this release. The single-species databases contain a combination of experimental information from the literature and computationally predicted information obtained through PMN’s database generation pipeline for a single species, while PlantCyc contains only experimental information but for any species within Viridiplantae. PMN is a comprehensive resource for querying, visualizing, analyzing and interpreting omics data with metabolic knowledge. It also serves as a useful and interactive tool for teaching plant metabolism. 

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4. The Naïve Bayes classifier++ for metagenomic taxonomic classification—query evaluation

   https://doi.org/10.1093/bioinformatics/btae743  

   Duan, Haozhe Neil; Hearne, Gavin; Polikar, Robi; Rosen, Gail L (December 2024, Bioinformatics)

   Kendziorski, Christina (Ed.)

   Abstract MotivationThis study examines the query performance of the NBC++ (Incremental Naive Bayes Classifier) program for variations in canonicality, k-mer size, databases, and input sample data size. We demonstrate that both NBC++ and Kraken2 are influenced by database depth, with macro measures improving as depth increases. However, fully capturing the diversity of life, especially viruses, remains a challenge. ResultsNBC++ can competitively profile the superkingdom content of metagenomic samples using a small training database. NBC++ spends less time training and can use a fraction of the memory than Kraken2 but at the cost of long querying time. Major NBC++ enhancements include accommodating canonical k-mer storage (leading to significant storage savings) and adaptable and optimized memory allocation that accelerates query analysis and enables the software to be run on nearly any system. Additionally, the output now includes log-likelihood values for each training genome, providing users with valuable confidence information. Availability and implementationSource code and Dockerfile are available at http://github.com/EESI/Naive_Bayes. 

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5. Sigmoni: classification of nanopore signal with a compressed pangenome index

   https://doi.org/10.1093/bioinformatics/btae213  

   Shivakumar, Vikram S; Ahmed, Omar Y; Kovaka, Sam; Zakeri, Mohsen; Langmead, Ben (June 2024, Bioinformatics)

   Summary: Improvements in nanopore sequencing necessitate efficient classification methods, including pre-filtering and adaptive sampling algorithms that enrich for reads of interest. Signal-based approaches circumvent the computational bottleneck of basecalling. But past methods for signal-based classification do not scale efficiently to large, repetitive references like pangenomes, limiting their utility to partial references or individual genomes. We introduce Sigmoni: a rapid, multiclass classification method based on the r-index that scales to references of hundreds of Gbps. Sigmoni quantizes nanopore signal into a discrete alphabet of picoamp ranges. It performs rapid, approximate matching using matching statistics, classifying reads based on distributions of picoamp matching statistics and co-linearity statistics, all in linear query time without the need for seed-chain-extend. Sigmoni is 10–100× faster than previous methods for adaptive sampling in host depletion experiments with improved accuracy, and can query reads against large microbial or human pangenomes. Sigmoni is the first signal-based tool to scale to a complete human genome and pangenome while remaining fast enough for adaptive sampling applications. Availability and implementation: Sigmoni is implemented in Python, and is available open-source at https://github.com/vshiv18/sigmoni 

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