More Like this

1. Constructing small genome graphs via string compression

   https://doi.org/10.1093/bioinformatics/btab281  

   Qiu, Yutong; Kingsford, Carl (July 2021, Bioinformatics)

   Abstract MotivationThe size of a genome graph—the space required to store the nodes, node labels and edges—affects the efficiency of operations performed on it. For example, the time complexity to align a sequence to a graph without a graph index depends on the total number of characters in the node labels and the number of edges in the graph. This raises the need for approaches to construct space-efficient genome graphs. ResultsWe point out similarities in the string encoding mechanisms of genome graphs and the external pointer macro (EPM) compression model. We present a pair of linear-time algorithms that transform between genome graphs and EPM-compressed forms. The algorithms result in an upper bound on the size of the genome graph constructed in terms of an optimal EPM compression. To further reduce the size of the genome graph, we propose the source assignment problem that optimizes over the equivalent choices during compression and introduce an ILP formulation that solves that problem optimally. As a proof-of-concept, we introduce RLZ-Graph, a genome graph constructed based on the relative Lempel–Ziv algorithm. Using RLZ-Graph, across all human chromosomes, we are able to reduce the disk space to store a genome graph on average by 40.7% compared to colored compacted de Bruijn graphs constructed by Bifrost under the default settings. The RLZ-Graph scales well in terms of running time and graph sizes with an increasing number of human genome sequences compared to Bifrost and variation graphs produced by VGtoolkit. AvailabilityThe RLZ-Graph software is available at: https://github.com/Kingsford-Group/rlzgraph. Supplementary informationSupplementary data are available at Bioinformatics online. 

   more » « less
2. A map of cis-regulatory modules and constituent transcription factor binding sites in 80% of the mouse genome

   https://doi.org/10.1186/s12864-022-08933-7  

   Ni, Pengyu; Wilson, David; Su, Zhengchang (October 2022, BMC Genomics)

   Abstract BackgroundMouse is probably the most important model organism to study mammal biology and human diseases. A better understanding of the mouse genome will help understand the human genome, biology and diseases. However, despite the recent progress, the characterization of the regulatory sequences in the mouse genome is still far from complete, limiting its use to understand the regulatory sequences in the human genome. ResultsHere, by integrating binding peaks in ~ 9,000 transcription factor (TF) ChIP-seq datasets that cover 79.9% of the mouse mappable genome using an efficient pipeline, we were able to partition these binding peak-covered genome regions into acis-regulatory module (CRM) candidate (CRMC) set and a non-CRMC set. The CRMCs contain 912,197 putative CRMs and 38,554,729 TF binding sites (TFBSs) islands, covering 55.5% and 24.4% of the mappable genome, respectively. The CRMCs tend to be under strong evolutionary constraints, indicating that they are likelycis-regulatory; while the non-CRMCs are largely selectively neutral, indicating that they are unlikelycis-regulatory. Based on evolutionary profiles of the genome positions, we further estimated that 63.8% and 27.4% of the mouse genome might code for CRMs and TFBSs, respectively. ConclusionsValidation using experimental data suggests that at least most of the CRMCs are authentic. Thus, this unprecedentedly comprehensive map of CRMs and TFBSs can be a good resource to guide experimental studies of regulatory genomes in mice and humans. 

   more » « less
3. The Genome Explorer genome browser

   https://doi.org/10.1128/msystems.00267-24  

   Herson, James; Krummenacker, Markus; Spaulding, Aaron; O'Maille, Paul; Karp, Peter D (July 2024, mSystems)

   Fodor, Anthony (Ed.)

   ABSTRACT Are two adjacent genes in the same operon? What are the order and spacing between several transcription factor binding sites? Genome browsers are software data visualization and exploration tools that enable biologists to answer questions such as these. In this paper, we report on a major update to our browser, Genome Explorer, that provides nearly instantaneous scaling and traversing of a genome, enabling users to quickly and easily zoom into an area of interest. The user can rapidly move between scales that depict the entire genome, individual genes, and the sequence; Genome Explorer presents the most relevant detail and context for each scale. By downloading the data for the entire genome to the user’s web browser and dynamically generating visualizations locally, we enable fine control of zoom and pan functions and real-time redrawing of the visualization, resulting in smoother and more intuitive exploration of a genome than is possible with other browsers. Further, genome features are presented together, in-line, using familiar graphical depictions. In contrast, many other browsers depict genome features using data tracks, which have low information density and can visually obscure the relative positions of features. Genome Explorer diagrams have a high information density that provides larger amounts of genome context and sequence information to be presented in a given-sized monitor than for tracks-based browsers. Genome Explorer provides optional data tracks for the analysis of large-scale data sets and a unique comparative mode that aligns genomes at orthologous genes with synchronized zooming. IMPORTANCEGenome browsers provide graphical depictions of genome information to speed the uptake of complex genome data by scientists. They provide search operations to help scientists find information and zoom operations to enable scientists to view genome features at different resolutions. We introduce the Genome Explorer browser, which provides extremely fast zooming and panning of genome visualizations and displays with high information density. 

   more » « less
4. Chromosome‐scale reference genome of Pectocarya recurvata , the species with the smallest reported genome size in Boraginaceae

   https://doi.org/10.1002/aps3.70008  

   Northing, Poppy C; Pelosi, Jessie A; Venable, D Lawrence; Dlugosch, Katrina M (May 2025, Applications in Plant Sciences)

   Abstract PremisePectocarya recurvata(Boraginaceae, subfamily Cynoglossoideae), a species native to the Sonoran Desert (North America), has served as a model system for a suite of ecological and evolutionary studies. However, no reference genomes are currently available in Cynoglossoideae. A high‐quality reference genome forP. recurvatawould be valuable for addressing questions in this system and across broader taxonomic scales. MethodsUsing PacBio HiFi sequencing, we assembled a reference genome forP. recurvataand annotated coding regions with full‐length transcripts from an Iso‐Seq library. We assessed genome completeness with BUSCO andk‐mer analysis, and estimated the genome size of six individuals using flow cytometry. ResultsThe chromosome‐scale genome assembly forP. recurvatawas 216.0 Mbp long (N50 = 12.1 Mbp). Previous observations indicatedP. recurvatais 2n = 24. Our assembly included 12 primary contigs (158.3 Mbp) containing 30,655 genes with telomeres at 23 out of 24 ends. Flow cytometry measurements from the same population included two plants with 1C = 196.9 Mbp, the smallest measured for Boraginaceae, and four with 1C = 385.8 Mbp, which is consistent with tetraploidy in this population. DiscussionTheP. recurvatagenome assembly and annotation provide a high‐quality genomic resource in a sparsely represented area of the angiosperm phylogeny. This new reference genome will facilitate answering open questions in ecophysiology, biogeography, and systematics. 

   more » « less
5. Welcome to the big leaves: Best practices for improving genome annotation in non‐model plant genomes

   https://doi.org/10.1002/aps3.11533  

   Vuruputoor, Vidya S.; Monyak, Daniel; Fetter, Karl C.; Webster, Cynthia; Bhattarai, Akriti; Shrestha, Bikash; Zaman, Sumaira; Bennett, Jeremy; McEvoy, Susan L.; Caballero, Madison; et al (July 2023, Applications in Plant Sciences)

   Abstract PremiseRobust standards to evaluate quality and completeness are lacking in eukaryotic structural genome annotation, as genome annotation software is developed using model organisms and typically lacks benchmarking to comprehensively evaluate the quality and accuracy of the final predictions. The annotation of plant genomes is particularly challenging due to their large sizes, abundant transposable elements, and variable ploidies. This study investigates the impact of genome quality, complexity, sequence read input, and method on protein‐coding gene predictions. MethodsThe impact of repeat masking, long‐read and short‐read inputs, and de novo and genome‐guided protein evidence was examined in the context of the popular BRAKER and MAKER workflows for five plant genomes. The annotations were benchmarked for structural traits and sequence similarity. ResultsBenchmarks that reflect gene structures, reciprocal similarity search alignments, and mono‐exonic/multi‐exonic gene counts provide a more complete view of annotation accuracy. Transcripts derived from RNA‐read alignments alone are not sufficient for genome annotation. Gene prediction workflows that combine evidence‐based and ab initio approaches are recommended, and a combination of short and long reads can improve genome annotation. Adding protein evidence from de novo assemblies, genome‐guided transcriptome assemblies, or full‐length proteins from OrthoDB generates more putative false positives as implemented in the current workflows. Post‐processing with functional and structural filters is highly recommended. DiscussionWhile the annotation of non‐model plant genomes remains complex, this study provides recommendations for inputs and methodological approaches. We discuss a set of best practices to generate an optimal plant genome annotation and present a more robust set of metrics to evaluate the resulting predictions. 

   more » « less