Unbiased pangenome graphs.

Pangenome variation graphs model the mutual alignment of collections of DNA sequences. A set of pairwise alignments implies a variation graph, but there are no scalable methods to generate such a graph from these alignments. Existing related approaches depend on a single reference, a specific ordering of genomes or a de Bruijn model based on a fixed k-mer length. A scalable, self-contained method to build pangenome graphs without such limitations would be a key step in pangenome construction and manipulation pipelines. We design the seqwish algorithm, which builds a variation graph from a set of sequences and alignments between them. We first transform the alignment set into an implicit interval tree. To build up the variation graph, we query this tree-based representation of the alignments to reduce transitive matches into single DNA segments in a sequence graph. By recording the mapping from input sequence to output graph, we can trace the original paths through this graph, yielding a pangenome variation graph. We present an implementation that operates in external memory, using disk-backed data structures and lock-free parallel methods to drive the core graph induction step. We demonstrate that our method scales to very large graph induction problems by applying it to build pangenome graphs for several species. seqwish is published as free software under the MIT open source license. Source code and documentation are available at https://github.com/ekg/seqwish. seqwish can be installed via Bioconda https://bioconda.github.io/recipes/seqwish/README.html or GNU Guix https://github.com/ekg/guix-genomics/blob/master/seqwish.scm.

Garrison E ，Guarracino A 《-》

ODGI: understanding pangenome graphs.

Pangenome graphs provide a complete representation of the mutual alignment of collections of genomes. These models offer the opportunity to study the entire genomic diversity of a population, including structurally complex regions. Nevertheless, analyzing hundreds of gigabase-scale genomes using pangenome graphs is difficult as it is not well-supported by existing tools. Hence, fast and versatile software is required to ask advanced questions to such data in an efficient way. We wrote Optimized Dynamic Genome/Graph Implementation (ODGI), a novel suite of tools that implements scalable algorithms and has an efficient in-memory representation of DNA pangenome graphs in the form of variation graphs. ODGI supports pre-built graphs in the Graphical Fragment Assembly format. ODGI includes tools for detecting complex regions, extracting pangenomic loci, removing artifacts, exploratory analysis, manipulation, validation and visualization. Its fast parallel execution facilitates routine pangenomic tasks, as well as pipelines that can quickly answer complex biological questions of gigabase-scale pangenome graphs. ODGI is published as free software under the MIT open source license. Source code can be downloaded from https://github.com/pangenome/odgi and documentation is available at https://odgi.readthedocs.io. ODGI can be installed via Bioconda https://bioconda.github.io/recipes/odgi/README.html or GNU Guix https://github.com/pangenome/odgi/blob/master/guix.scm. Supplementary data are available at Bioinformatics online.

Guarracino A ，Heumos S ，Nahnsen S ，Prins P ，Garrison E ... -  《-》

Panacus: fast and exact pangenome growth and core size estimation.

Parmigiani L ，Garrison E ，Stoye J ，Marschall T ，Doerr D ... -  《-》

Pangenome graph layout by Path-Guided Stochastic Gradient Descent.

Heumos S ，Guarracino A ，Schmelzle JM ，Li J ，Zhang Z ，Hagmann J ，Nahnsen S ，Prins P ，Garrison E ... -  《-》

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

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. 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. Cuttlefish is implemented in C++14, and is available under an open source license at https://github.com/COMBINE-lab/cuttlefish. Supplementary data are available at Bioinformatics online.

Khan J ，Patro R 《-》