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1. Stacks 2: Analytical methods for paired‐end sequencing improve RADseq‐based population genomics

   https://doi.org/10.1111/mec.15253  

   Rochette, Nicolas C.; Rivera‐Colón, Angel G.; Catchen, Julian M. (October 2019, Molecular Ecology)

   Abstract For half a century population genetics studies have put type II restriction endonucleases to work. Now, coupled with massively‐parallel, short‐read sequencing, the family of RAD protocols that wields these enzymes has generated vast genetic knowledge from the natural world. Here, we describe the first software natively capable of using paired‐end sequencing to derive short contigs from de novo RAD data. Stacks version 2 employs a de Bruijn graph assembler to build and connect contigs from forward and reverse reads for each de novo RAD locus, which it then uses as a reference for read alignments. The new architecture allows all the individuals in a metapopulation to be considered at the same time as each RAD locus is processed. This enables a Bayesian genotype caller to provide precise SNPs, and a robust algorithm to phase those SNPs into long haplotypes, generating RAD loci that are 400–800 bp in length. To prove its recall and precision, we tested the software with simulated data and compared reference‐aligned and de novo analyses of three empirical data sets. Our study shows that the latest version of Stacks is highly accurate and outperforms other software in assembling and genotyping paired‐end de novo data sets. 

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2. On de novo Bridging Paired-end RNA-seq Data

   https://doi.org/10.1145/3584371.3612987  

   Li, Xiang; Shao, Mingfu (September 2023, Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics)

   The high-throughput short-reads RNA-seq protocols often produce paired-end reads, with the middle portion of the fragments being unsequenced. We explore if the full-length fragments can be com- putationally reconstructed from the sequenced two ends in the absence of the reference genome—a problem here we refer to as de novo bridging. Solving this problem provides longer, more infor- mative RNA-seq reads, and benefits downstream RNA-seq analysis such as transcript assembly, expression quantification, and splic- ing differential analysis. However, de novo bridging is a challeng- ing and complicated task owing to alternative splicing, transcript noises, and sequencing errors. It remains unclear if the data pro- vides sufficient information for accurate bridging, let alone efficient algorithms that determine the true bridges. Methods have been proposed to bridge paired-end reads in the presence of reference genome (called reference-based bridging), but the algorithms are far away from scaling for de novo bridging as the underlying com- pacted de Bruijn graph (cdBG) used in the latter task often contains millions of vertices and edges. We designed a new truncated Dijk- stra’s algorithm for this problem, and proposed a novel algorithm that reuses the shortest path tree to avoid running the truncated Di- jkstra’s algorithm from scratch for all vertices for further speeding up. These innovative techniques result in scalable algorithms that can bridge all paired-end reads in a cdBG with millions of vertices. Our experiments showed that paired-end RNA-seq reads can be accurately bridged to a large extent. The resulting tool is freely available at https://github.com/Shao-Group/rnabridge-denovo. 

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3. Unbiased pangenome graphs

   https://doi.org/10.1093/bioinformatics/btac743  

   Garrison, Erik; Guarracino, Andrea (November 2022, Bioinformatics)

   Alkan, Can (Ed.)

   Abstract Motivation 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. Results 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. Availability and implementation 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. 

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4. Compression algorithm for colored de Bruijn graphs

   https://doi.org/10.1186/s13015-024-00254-6  

   Rahman, Amatur; Dufresne, Yoann; Medvedev, Paul (December 2024, Algorithms for Molecular Biology)

   Abstract A colored de Bruijn graph (also called a set of k-mer sets), is a set of k-mers with every k-mer assigned a set of colors. Colored de Bruijn graphs are used in a variety of applications, including variant calling, genome assembly, and database search. However, their size has posed a scalability challenge to algorithm developers and users. There have been numerous indexing data structures proposed that allow to store the graph compactly while supporting fast query operations. However, disk compression algorithms, which do not need to support queries on the compressed data and can thus be more space-efficient, have received little attention. The dearth of specialized compression tools has been a detriment to tool developers, tool users, and reproducibility efforts. In this paper, we develop a new tool that compresses colored de Bruijn graphs to disk, building on previous ideas for compression of k-mer sets and indexing colored de Bruijn graphs. We test our tool, called ESS-color, on various datasets, including both sequencing data and whole genomes. ESS-color achieves better compression than all evaluated tools and all datasets, with no other tool able to consistently achieve less than 44% space overhead. The software is available athttp://github.com/medvedevgroup/ESSColor. 

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5. 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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