<i>De novo</i> clustering of long-read transcriptome data using a greedy, quality-value based algorithm

Sahlin, Kristoffer; Medvedev, Paul

doi:10.1101/463463

Cited by 22 publications

(20 citation statements)

References 44 publications

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“…Furthermore, there are applications where the k-mer set is not related to sequence read data at all, e.g. a universal hitting set [26], a chromosome-specific reference dictionary [27], or a winnowed min-hash sketch (for example as in [28], or see [29,30] for a survey).…”

Section: Related Workmentioning

confidence: 99%

Representation ofk-mer sets using spectrum-preserving string sets

Rahman

Medvedev

2020

Preprint

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Given the popularity and elegance of k-mer based tools, finding a space-efficient way to represent a set of k-mers is important for improving the scalability of bioinformatics analyses. One popular approach is to convert the set of k-mers into the more compact set of unitigs. We generalize this approach and formulate it as the problem of finding a smallest spectrum-preserving string set (SPSS) representation. We show that this problem is equivalent to finding a smallest path cover in a compacted de Bruijn graph. Using this reduction, we prove a lower bound on the size of the optimal SPSS and propose a greedy method called UST that results in a smaller representation than unitigs and is nearly optimal with respect to our lower bound. We demonstrate the usefulness of the SPSS formulation with two applications of UST. The first one is a compression algorithm, UST-Compress, which we show can store a set of k-mers using an order-of-magnitude less disk space than other lossless compression tools. The second one is an exact static k-mer membership index, UST-FM, which we show improves index size by 10-44% compared to other state-of-the-art low memory indices. Our tool is publicly available at: https://github.com/medvedevgroup/UST/.

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Section: Related Workmentioning

confidence: 99%

Representation ofk-mer sets using spectrum-preserving string sets

Rahman

Medvedev

2020

Preprint

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“…To generate the consensus sequence for each sample, all reads were first clustered using isONclust v0.0.4 (Sahlin & Medvedev, 2018). We chose isONclust over other clustering tools previously used in nanopore-based DNA barcoding pipelines, such as VSEARCH (implemented in ONTrack, Maestri et al, 2019), as it was specifically designed to work with error-prone longread data and thus should be less affected by read errors and more efficient in cluster formation.…”

Section: Read Clustering and Consensus Sequence Generationmentioning

confidence: 99%

MinION-based DNA barcoding of preserved and non-invasively collected wildlife samples

Seah

Lim

McAloose

et al. 2020

Preprint

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AbstractThe ability to sequence a variety of wildlife samples with portable, field-friendly equipment will have significant impacts on wildlife conservation and health applications. However, the only currently available field-friendly DNA sequencer, the MinION by Oxford Nanopore Technologies, has a high error rate compared to standard laboratory-based sequencing platforms and has not been systematically validated for DNA barcoding accuracy for preserved and non-invasively collected tissue samples.We tested whether various wildlife sample types, field-friendly methods, and our clustering-based bioinformatics pipeline, SAIGA, can be used to generate consistent and accurate consensus sequences for species identification. Here, we systematically evaluate variation in cytochrome b sequences amplified from scat, hair, feather, fresh frozen liver, and formalin-fixed paraffin-embedded (FFPE) liver. Each sample was processed by three DNA extraction protocols.For all sample types tested, the MinION consensus sequences matched the Sanger references with 99.29-100% sequence similarity, even for samples that were difficult to amplify, such as scat and FFPE tissue extracted with Chelex resin. Sequencing errors occurred primarily in homopolymer regions, as identified in previous MinION studies.We demonstrate that it is possible to generate accurate DNA barcode sequences from preserved and non-invasively collected wildlife samples using portable MinION sequencing, creating more opportunities to apply portable sequencing technology for species identification.

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“…While these algorithms continue to be widely leveraged in bioinformatics, they are even more prevalent for long-read (PacBio/Oxford Nanopore) analyses because longer strings are more amenable to compaction. As such, several long-read based mappers (Li, 2016;Popic and Batzoglou, 2017;Jain et al, 2018;Li, 2018), genome assemblers (Berlin et al, 2015;Koren et al, 2017;Chin and Khalak, 2019;Shafin et al, 2019;Kundu et al, 2019), metagenomic read classifiers (Dilthey et al, 2019), transcriptomic tools (Sahlin and Medvedev, 2019;Sahlin et al, 2020) employ either minimizer-or MinHash-based sequence comparison.…”

Section: Introductionmentioning

confidence: 99%

Weighted minimizer sampling improves long read mapping

Jain

Rhie

Zhang

et al. 2020

Preprint

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Motivation:In this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence comparison. This technique yields a sub-linear representation of sequences, enabling their comparison in reduced space and time. A key property of the minimizer technique is that if two sequences share a substring of a specified length, then they can be guaranteed to have a matching minimizer. However, because the k-mer distribution in eukaryotic genomes is highly uneven, minimizer-based tools (e.g., Minimap2, Mashmap) opt to discard the most frequently occurring minimizers from the genome in order to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions. Results: We introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while taking into account a weight for each k-mer; i.e, the higher the weight of a k-mer, the more likely it is to be selected. By down-weighting frequently occurring k-mers, we are able to meet both objectives: (i) avoid excessive false-positive matches, and (ii) maintain the minimizer match guarantee. We tested our algorithm, Winnowmap, using both simulated and real long-read data and compared it to a state-of-the-art long read mapper, Minimap2. Our results demonstrate a reduction in the mapping error-rate from 0.14% to 0.06% in the recently finished human X chromosome (154.3 Mbp), and from 3.6% to 0% within the highly repetitive X centromere (3.1 Mbp). Winnowmap improves mapping accuracy within repeats and achieves these results with sparser sampling, leading to better index compression and competitive runtimes. Contact: adam.phillippy@nih.gov Availability: Winnowmap is built on top of the Minimap2 codebase (Li, 2018) and is available at https://github.com/marbl/winnowmap.

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De novo clustering of long-read transcriptome data using a greedy, quality-value based algorithm

Cited by 22 publications

References 44 publications

Representation ofk-mer sets using spectrum-preserving string sets

Representation ofk-mer sets using spectrum-preserving string sets

MinION-based DNA barcoding of preserved and non-invasively collected wildlife samples

Weighted minimizer sampling improves long read mapping

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