A reference genome is a high quality individual genome that is used as a coordinate system for the genomes of a population, or genomes of closely related subspecies. Given a set of genomes partitioned by homology into alignment blocks we formalize the problem of ordering and orienting the blocks such that the resulting ordering maximally agrees with the underlying genomes' ordering and orientation, creating a pan-genome reference ordering. We show this problem is NP-hard, but also demonstrate, empirically and within simulations, the performance of heuristic algorithms based upon a cactus graph decomposition to find locally maximal solutions. We describe an extension of our Cactus software to create a pan-genome reference for whole genome alignments, and demonstrate how it can be used to create novel genome browser visualizations using human variation data as a test. In addition, we test the use of a pan-genome for describing variations and as a reference for read mapping.

Motivation

Researchers now have access to large volumes of genome sequences for comparative analysis, some generated by the plethora of public sequencing projects and, increasingly, from individual efforts. It is not possible, or necessarily desirable, that the public genome browsers attempt to curate all these data. Instead, a wealth of powerful tools is emerging to empower users to create their own visualizations and browsers.

Results

We introduce a pipeline to easily generate collections of Web-accessible UCSC Genome Browsers interrelated by an alignment. It is intended to democratize our comparative genomic browser resources, serving the broad and growing community of evolutionary genomicists and facilitating easy public sharing via the Internet. Using the alignment, all annotations and the alignment itself can be efficiently viewed with reference to any genome in the collection, symmetrically. A new, intelligently scaled alignment display makes it simple to view all changes between the genomes at all levels of resolution, from substitutions to complex structural rearrangements, including duplications. To demonstrate this work, we create a comparative assembly hub containing 57 Escherichia coli and 9 Shigella genomes and show examples that highlight their unique biology.

Availability and implementation

The source code is available as open source at: https://github.com/glennhickey/progressiveCactus The E.coli and Shigella genome hub is now a public hub listed on the UCSC browser public hubs Web page.

High-throughput experimental platforms have revolutionized the ability to profile biochemical and functional properties of biological sequences such as DNA, RNA and proteins. By collating several data modalities with customizable tracks rendered using intuitive visualizations, genome browsers enable an interactive and interpretable exploration of diverse types of genome profiling experiments and derived annotations. However, existing genome browser tracks are not well suited for intuitive visualization of high-resolution DNA sequence features such as transcription factor motifs. Typically, motif instances in regulatory DNA sequences are visualized as BED-based annotation tracks, which highlight the genomic coordinates of the motif instances but do not expose their specific sequences. Instead, a genome sequence track needs to be cross-referenced with the BED track to identify sequences of motif hits. Even so, quantitative information about the motif instances such as affinity or conservation as well as differences in base resolution from the consensus motif are not immediately apparent. This makes interpretation slow and challenging. This problem is compounded when analyzing several cellular states and/or molecular readouts (such as ATAC-seq and ChIP–seq) simultaneously, as coordinates of enriched regions (peaks) and the set of active transcription factor motifs vary across cell states.

Despite the rapid development of sequencing technologies, the assembly of mammalian-scale genomes into complete chromosomes remains one of the most challenging problems in bioinformatics. To help address this difficulty, we developed Ragout 2, a reference-assisted assembly tool that works for large and complex genomes. By taking one or more target assemblies (generated from an NGS assembler) and one or multiple related reference genomes, Ragout 2 infers the evolutionary relationships between the genomes and builds the final assemblies using a genome rearrangement approach. By using Ragout 2, we transformed NGS assemblies of 16 laboratory mouse strains into sets of complete chromosomes, leaving <5% of sequence unlocalized per set. Various benchmarks, including PCR testing and realigning of long Pacific Biosciences (PacBio) reads, suggest only a small number of structural errors in the final assemblies, comparable with direct assembly approaches. We applied Ragout 2 to the Mus caroli and Mus pahari genomes, which exhibit karyotype-scale variations compared with other genomes from the Muridae family. Chromosome painting maps confirmed most large-scale rearrangements that Ragout 2 detected. We applied Ragout 2 to improve draft sequences of three ape genomes that have recently been published. Ragout 2 transformed three sets of contigs (generated using PacBio reads only) into chromosome-scale assemblies with accuracy comparable to chromosome assemblies generated in the original study using BioNano maps, Hi-C, BAC clones, and FISH.

Unlabelled

Two new tools on the UCSC Genome Browser web site provide improved ways of combining information from multiple datasets, optionally including the user's own custom track data and/or data from track hubs. The Data Integrator combines columns from multiple data tracks, showing all items from the first track along with overlapping items from the other tracks. The Variant Annotation Integrator is tailored to adding functional annotations to variant calls; it offers a more restricted set of underlying data tracks but adds predictions of each variant's consequences for any overlapping or nearby gene transcript. When available, it optionally adds additional annotations including effect prediction scores from dbNSFP for missense mutations, ENCODE regulatory summary tracks and conservation scores.

Availability and implementation

The web tools are freely available at http://genome.ucsc.edu/ and the underlying database is available for download at http://hgdownload.cse.ucsc.edu/ The software (written in C and Javascript) is available from https://genome-store.ucsc.edu/ and is freely available for academic and non-profit usage; commercial users must obtain a license.

Contact

angie@soe.ucsc.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

The UCSC Genome Browser has been an important tool for genomics and clinical genetics since the sequence of the human genome was first released in 2000. As it has grown in scope to display more types of data it has also grown more complicated. The data, which are dispersed at many locations worldwide, are collected into one view on the Browser, where the graphical interface presents the data in one location. This supports the expertise of the researcher to interpret variants in the genome. Because the analysis of single nucleotide variants and copy number variants require interpretation of data at very different genomic scales, different data resources are required. We present here several Recommended Track Sets designed to facilitate the interpretation of variants in the clinic, offering quick access to datasets relevant to the appropriate scale.

Unlabelled

Two new tools on the UCSC Genome Browser web site provide improved ways of combining information from multiple datasets, optionally including the user's own custom track data and/or data from track hubs. The Data Integrator combines columns from multiple data tracks, showing all items from the first track along with overlapping items from the other tracks. The Variant Annotation Integrator is tailored to adding functional annotations to variant calls; it offers a more restricted set of underlying data tracks but adds predictions of each variant's consequences for any overlapping or nearby gene transcript. When available, it optionally adds additional annotations including effect prediction scores from dbNSFP for missense mutations, ENCODE regulatory summary tracks and conservation scores.

Availability and implementation

The web tools are freely available at http://genome.ucsc.edu/ and the underlying database is available for download at http://hgdownload.cse.ucsc.edu/ The software (written in C and Javascript) is available from https://genome-store.ucsc.edu/ and is freely available for academic and non-profit usage; commercial users must obtain a license.

Contact

angie@soe.ucsc.edu

Supplementary information

Supplementary data are available at Bioinformatics online.

Summary

As the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets.

Availability and implementation

We provide the UCSC Cell Browser as a free website where scientists can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

In mammals, GPIHBP1 is absolutely essential for transporting lipoprotein lipase (LPL) to the lumen of capillaries, where it hydrolyzes the triglycerides in triglyceride-rich lipoproteins. In all lower vertebrate species (e.g., birds, amphibians, reptiles, fish), a gene for LPL can be found easily, but a gene for GPIHBP1 has never been found. The obvious question is whether the LPL in lower vertebrates is able to reach the capillary lumen. Using purified antibodies against chicken LPL, we showed that LPL is present on capillary endothelial cells of chicken heart and adipose tissue, colocalizing with von Willebrand factor. When the antibodies against chicken LPL were injected intravenously into chickens, they bound to LPL on the luminal surface of capillaries in heart and adipose tissue. LPL was released rapidly from chicken hearts with an infusion of heparin, consistent with LPL being located inside blood vessels. Remarkably, chicken LPL bound in a specific fashion to mammalian GPIHBP1. However, we could not identify a gene for GPIHBP1 in the chicken genome, nor could we identify a transcript for GPIHBP1 in a large chicken RNA-seq data set. We conclude that LPL reaches the capillary lumen in chickens - as it does in mammals - despite an apparent absence of GPIHBP1.