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1. A super-pangenome of the North American wild grape species

   https://doi.org/10.1186/s13059-023-03133-2  

   Cochetel, Noé ; Minio, Andrea ; Guarracino, Andrea ; Garcia, Jadran F. ; Figueroa-Balderas, Rosa ; Massonnet, Mélanie ; Kasuga, Takao ; Londo, Jason P. ; Garrison, Erik ; Gaut, Brandon S. ; et al ( December 2023 , Genome Biology)

   Capturing the genetic diversity of wild relatives is crucial for improving crops because wild species are valuable sources of agronomic traits that are essential to enhance the sustainability and adaptability of domesticated cultivars. Genetic diversity across a genus can be captured in super-pangenomes, which provide a framework for interpreting genomic variations.

   Here we report the sequencing, assembly, and annotation of nine wild North American grape genomes, which are phased and scaffolded at chromosome scale. We generate a reference-unbiased super-pangenome using pairwise whole-genome alignment methods, revealing the extent of the genomic diversity among wild grape species from sequence to gene level. The pangenome graph captures genomic variation between haplotypes within a species and across the different species, and it accurately assesses the similarity of hybrids to their parents. The species selected to build the pangenome are a great representation of the genus, as illustrated by capturing known allelic variants in the sex-determining region and for Pierce’s disease resistance loci. Using pangenome-wide association analysis, we demonstrate the utility of the super-pangenome by effectively mapping short reads from genus-wide samples and identifying loci associated with salt tolerance in natural populations of grapes.

   This study highlights how a reference-unbiased super-pangenome can reveal the genetic basis of adaptive traits from wild relatives and accelerate crop breeding research.

    

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2. A variant selection framework for genome graphs

   https://doi.org/10.1093/bioinformatics/btab302  

   Jain, Chirag ; Tavakoli, Neda ; Aluru, Srinivas ( July 2021 , Bioinformatics)

   Abstract Motivation Variation graph representations are projected to either replace or supplement conventional single genome references due to their ability to capture population genetic diversity and reduce reference bias. Vast catalogues of genetic variants for many species now exist, and it is natural to ask which among these are crucial to circumvent reference bias during read mapping. Results In this work, we propose a novel mathematical framework for variant selection, by casting it in terms of minimizing variation graph size subject to preserving paths of length α with at most δ differences. This framework leads to a rich set of problems based on the types of variants [e.g. single nucleotide polymorphisms (SNPs), indels or structural variants (SVs)], and whether the goal is to minimize the number of positions at which variants are listed or to minimize the total number of variants listed. We classify the computational complexity of these problems and provide efficient algorithms along with their software implementation when feasible. We empirically evaluate the magnitude of graph reduction achieved in human chromosome variation graphs using multiple α and δ parameter values corresponding to short and long-read resequencing characteristics. When our algorithm is run with parameter settings amenable to long-read mapping (α = 10 kbp, δ = 1000), 99.99% SNPs and 73% SVs can be safely excluded from human chromosome 1 variation graph. The graph size reduction can benefit downstream pan-genome analysis. Availability and implementation https://github.com/AT-CG/VF. Supplementary information Supplementary data are available at Bioinformatics online. 

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3. Validating Paired-end Read Alignments in Sequence Graphs

   https://doi.org/10.1101/682799  

   Jain, Chirag ; Zhang, Haowen ; Dilthey, Alexander ; Aluru, Srinivas ( September 2019 , Leibniz international proceedings in informatics)

   Graph based non-linear reference structures such as variation graphs and colored de Bruijn graphs enable incorporation of full genomic diversity within a population. However, transitioning from a simple string-based reference to graphs requires addressing many computational challenges, one of which concerns accurately mapping sequencing read sets to graphs. Paired-end Illumina sequencing is a commonly used sequencing platform in genomics, where the paired-end distance constraints allow disambiguation of repeats. Many recent works have explored provably good index-based and alignment-based strategies for mapping individual reads to graphs. However, validating distance constraints efficiently over graphs is not trivial, and existing sequence to graph mappers rely on heuristics. We introduce a mathematical formulation of the problem, and provide a new algorithm to solve it exactly. We take advantage of the high sparsity of reference graphs, and use sparse matrix-matrix multiplications (SpGEMM) to build an index which can be queried efficiently by a mapping algorithm for validating the distance constraints. Effectiveness of the algorithm is demonstrated using real reference graphs, including a human MHC variation graph, and a pan-genome de-Bruijn graph built using genomes of 20 B. anthracis strains. While the one-time indexing time can vary from a few minutes to a few hours using our algorithm, answering a million distance queries takes less than a second. 

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4. OGUs enable effective, phylogeny-aware analysis of even shallow metagenome community structures

   https://doi.org/2021.04.04.438427  

   Qiyun Zhu ; Shi Huang ; Antonio Gonzalez ; Imran McGrath ; Daniel McDonald ; Niina Haiminen ; George Armstrong ; Yoshiki Vázquez-Baeza ; Julian Yu ; Justin Kuczynski ; et al ( April 2021 , bioRxiv)

   We introduce Operational Genomic Unit (OGU), a metagenome analysis strategy that directly exploits sequence alignment hits to individual reference genomes as the minimum unit for assessing the diversity of microbial communities and their relevance to environmental factors. This approach is independent from taxonomic classification, granting the possibility of maximal resolution of community composition, and organizes features into an accurate hierarchy using a phylogenomic tree. The outputs are suitable for contemporary analytical protocols for community ecology, differential abundance and supervised learning while supporting phylogenetic methods, such as UniFrac and phylofactorization, that are seldomly applied to shotgun metagenomics despite being prevalent in 16S rRNA gene amplicon studies. As demonstrated in one synthetic and two real-world case studies, the OGU method produces biologically meaningful patterns from microbiome datasets. Such patterns further remain detectable at very low metagenomic sequencing depths. Compared with taxonomic unit-based analyses implemented in currently adopted metagenomics tools, and the analysis of 16S rRNA gene amplicon sequence variants, this method shows superiority in informing biologically relevant insights, including stronger correlation with body environment and host sex on the Human Microbiome Project dataset, and more accurate prediction of human age by the gut microbiomes in the Finnish population. We provide Woltka, a bioinformatics tool to implement this method, with full integration with the QIIME 2 package and the Qiita web platform, to facilitate OGU adoption in future metagenomics studies. Importance Shotgun metagenomics is a powerful, yet computationally challenging, technique compared to 16S rRNA gene amplicon sequencing for decoding the composition and structure of microbial communities. However, current analyses of metagenomic data are primarily based on taxonomic classification, which is limited in feature resolution compared to 16S rRNA amplicon sequence variant analysis. To solve these challenges, we introduce Operational Genomic Units (OGUs), which are the individual reference genomes derived from sequence alignment results, without further assigning them taxonomy. The OGU method advances current read-based metagenomics in two dimensions: (i) providing maximal resolution of community composition while (ii) permitting use of phylogeny-aware tools. Our analysis of real-world datasets shows several advantages over currently adopted metagenomic analysis methods and the finest-grained 16S rRNA analysis methods in predicting biological traits. We thus propose the adoption of OGU as standard practice in metagenomic studies. 

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5. Computing Maximal Unique Matches with the r-index

   https://doi.org/10.4230/LIPIcs.SEA.2022.22  

   Giuliani, Sara ; Romana, Giuseppe ; Rossi, Massimiliano ( January 2022 , SEA (International Symposium on Experimental Algorithms))

   In recent years, pangenomes received increasing attention from the scientific community for their ability to incorporate population variation information and alleviate reference genome bias. Maximal Exact Matches (MEMs) and Maximal Unique Matches (MUMs) have proven themselves to be useful in multiple bioinformatic contexts, for example short-read alignment and multiple-genome alignment. However, standard techniques using suffix trees and FM-indexes do not scale to a pangenomic level. Recently, Gagie et al. [JACM 20] introduced the r-index that is a Burrows-Wheeler Transform (BWT)-based index able to handle hundreds of human genomes. Later, Rossi et al. [JCB 22] enabled the computation of MEMs using the r-index, and Boucher et al. [DCC 21] showed how to compute them in a streaming fashion. In this paper, we show how to augment Boucher et al.'s approach to enable the computation of MUMs on the r-index, while preserving the space and time bounds. We add additional O(r) samples of the longest common prefix (LCP) array, where r is the number of equal-letter runs of the BWT, that permits the computation of the second longest match of the pattern suffix with respect to the input text, which in turn allows the computation of candidate MUMs. We implemented a proof-of-concept of our approach, that we call mum-phinder, and tested on real-world datasets. We compared our approach with competing methods that are able to compute MUMs. We observe that our method is up to 8 times smaller, while up to 19 times slower when the dataset is not highly repetitive, while on highly repetitive data, our method is up to 6.5 times slower and uses up to 25 times less memory. 

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