abPOA: an SIMD-based C library for fast partial order alignment using adaptive band

Gao, Yan; Liu, Yongzhuang; Ma, Yinghe; Liu, Bo; Wang, Yadong; Xing, Yi

doi:10.1101/2020.05.07.083196

Cited by 8 publications

(2 citation statements)

References 12 publications

(7 reference statements)

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“…When set very high, this filter can be used to generate a coarse, high-confidence graph built only from very long exact matches which will tend to be unique in the genome. Although the application of the

filter can result in a graph that is relatively ‘under-aligned’, we can further refine it through the application of local multiple sequence alignment ( Gao et al , 2020 ) or graph normalization ( https://github.com/marschall-lab/GFAffix ). In a pangenomic context, underalignment caused by

match filtering can be mitigated by transitive relationships present in the pangenome.…”

Section: Methodsmentioning

confidence: 99%

Unbiased pangenome graphs

Garrison

2022

Bioinformatics

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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 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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match filtering can be mitigated by transitive relationships present in the pangenome.…”

Section: Methodsmentioning

confidence: 99%

Unbiased pangenome graphs

Garrison

2022

Bioinformatics

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“…To identify and quantify isoforms we combined data from all samples and used the 3.5 version of Mandalorion (-O 0,40,0,40 -r 0.01 -i 1 -w 1 -n 2 -R 5) (https://github.com/rvolden/Mandalorion) which uses the minimap2 (Li, 2018) , racon (Vaser et al, 2017) , Medaka (https://github.com/nanoporetech/medaka), and abpoa (Gao et al, 2020) tools. Isoforms were categorized using the sqanti_qc.py script of the SQANTI (Tardaguila et al, 2018) .…”

Section: R2c2mentioning

confidence: 99%

Isoform-level transcriptome Atlas of Macrophage Activation

Vollmers

Mekonen

Campos

et al. 2020

Preprint

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RNA-seq is routinely used to measure gene expression changes in response to cell perturbation. Genes that are up or down-regulated following perturbation in RNA-seq studies are designated as target genes for follow-up. However, RNA-seq is limited in its ability to capture the complexity of gene isoforms, defined by the exact composition of exons and transcription start sites (TSS) and poly(A) sites they contain, as well as the expression of these isoforms. Without knowing the composition of the most dominant isoform(s) of a target gene, a minority or non-existent isoform could be selected for follow-up solely based on available annotations for that target gene from databases that are incomplete, or by their nature not tissue specific, or do not provide key information on expression levels. In all, this can lead to loss in valuable resources and time. As the vast majority of genes in the human genome express more than one isoform, there is a great need to identify the complete range of isoforms present for each gene along with their corresponding levels of expression.Here, using the long-read nanopore-based R2C2 method, we generated an Isoform-level transcriptome Atlas of Macrophage Activation (IAMA) that identifies full-length isoforms in primary human monocyte-derived macrophages (MDMs). Macrophages are critical innate immune cells important for recognizing pathogens through use of Toll-like receptors (TLRs), culminating in the initiation of host defense pathways. We characterized isoforms for most moderate to highly expressed genes in resting and TLR-activated MDMs and generated a user-friendly portal built into the UCSC Genome Browser to explore the data (https://genome.ucsc.edu/s/vollmers/IAMA). Our atlas represents a valuable resource for innate immune research as it provides unprecedented isoform information for primary human macrophages.

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Optimal Sequence Alignment to ED-Strings

Mwaniki

Pisanti

2022

Bioinformatics Research and Applications

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abPOA: an SIMD-based C library for fast partial order alignment using adaptive band

Cited by 8 publications

References 12 publications

Unbiased pangenome graphs

Unbiased pangenome graphs

Isoform-level transcriptome Atlas of Macrophage Activation

Optimal Sequence Alignment to ED-Strings

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