K-mer clustering algorithm using a MapReduce framework: application to the parallelization of the Inchworm module of Trinity

Kim, Chang Sik; Winn, Martyn; Sachdeva, Vipin; Jordan, Kirk E.

doi:10.1186/s12859-017-1881-8

Cited by 18 publications

(6 citation statements)

References 46 publications

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“…After obtaining high-quality sequencing data, Trinity was employed for sequence assembly. The k-mer library was constructed by interrupting the reads according to the short k-mer fragment (k-mer) [ 58 ]. We used HISAT2 v2.1.0 to align the quality-controlled transcriptome reads to the yellow mushroom ( Floccularia luteovirens ) genome ( (accessed on 15 October 2019)).…”

Section: Methodsmentioning

confidence: 99%

Chemical Constituents and Molecular Mechanism of the Yellow Phenotype of Yellow Mushroom (Floccularia luteovirens)

Gan

Bao

Liu

et al. 2022

JoF

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(1) Background: Yellow mushroom (Floccularia luteovirens) is a natural resource that is highly nutritional, has a high economic value, and is found in Northwest China. Despite its value, the chemical and molecular mechanisms of yellow phenotype formation are still unclear. (2) Methods: This study uses the combined analysis of transcriptome and metabolome to explain the molecular mechanism of the formation of yellow mushroom. Subcellular localization and transgene overexpression techniques were used to verify the function of the candidate gene. (3) Results: 112 compounds had a higher expression in yellow mushroom; riboflavin was the ninth most-expressed compound. HPLC showed that a key target peak at 23.128 min under visible light at 444 nm was Vb2. All proteins exhibited the closest relationship with Agaricus bisporus var. bisporus H97. One riboflavin transporter, CL911.Contig3_All (FlMCH5), was highly expressed in yellow mushrooms with a different value (log2 fold change) of −12.98, whereas it was not detected in white mushrooms. FlMCH5 was homologous to the riboflavin transporter MCH5 or MFS transporter in other strains, and the FlMCH5-GFP fusion protein was mainly located in the cell membrane. Overexpression of FlMCH5 in tobacco increased the content of riboflavin in three transgenic plants to 26 μg/g, 26.52 μg/g, and 36.94 μg/g, respectively. (4) Conclusions: In this study, it is clear that riboflavin is the main coloring compound of yellow mushrooms, and FlMCH5 is the key transport regulatory gene that produces the yellow phenotype.

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Section: Methodsmentioning

confidence: 99%

Chemical Constituents and Molecular Mechanism of the Yellow Phenotype of Yellow Mushroom (Floccularia luteovirens)

Gan

Bao

Liu

et al. 2022

JoF

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“…Kim et al . 7 proposed a modification to the Trinity assembler by pre-clustering the input k -mers using the MapReduce framework, which works if a suitable infrastructure is available. Techniques such as entropy based compression 8 can also be used to reduce the memory required to store k -mers.…”

Section: Introductionmentioning

confidence: 99%

Improving in-silico normalization using read weights

Durai

Schulz

2019

Sci Rep

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Specialized de novo assemblers for diverse datatypes have been developed and are in widespread use for the analyses of single-cell genomics, metagenomics and RNA-seq data. However, assembly of large sequencing datasets produced by modern technologies is challenging and computationally intensive. In-silico read normalization has been suggested as a computational strategy to reduce redundancy in read datasets, which leads to significant speedups and memory savings of assembly pipelines. Previously, we presented a set multi-cover optimization based approach, ORNA, where reads are reduced without losing important k-mer connectivity information, as used in assembly graphs. Here we propose extensions to ORNA, named ORNA-Q and ORNA-K, which consider a weighted set multi-cover optimization formulation for the in-silico read normalization problem. These novel formulations make use of the base quality scores obtained from sequencers (ORNA-Q) or k-mer abundances of reads (ORNA-K) to improve normalization further. We devise efficient heuristic algorithms for solving both formulations. In applications to human RNA-seq data, ORNA-Q and ORNA-K are shown to assemble more or equally many full length transcripts compared to other normalization methods at similar or higher read reduction values. The algorithm is implemented under the latest version of ORNA (v2.0, https://github.com/SchulzLab/ORNA).

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“…Regarding the practical applications of the constructed MISs, one potentiality is their usage in k-merbased reads clustering problems [6,7], where the choice of centers is crucial. Because an MIS contains a group of representatives for the k-mer space that are guaranteed to be a certain edit distance apart, it is ideal for generating even-sized clusters.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

On the Maximal Independent Sets of $k$-mers with the Edit Distance

Ma¹,

Chen²,

Shao³

2023

Preprint

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In computational biology, k-mers and edit distance are fundamental concepts. However, little is known about the metric space of all k-mers equipped with the edit distance. In this work, we explore the structure of the k-mer space by studying its maximal independent sets (MISs). An MIS is a sparse sketch of all k-mers with nice theoretical properties, and therefore admits critical applications in clustering, indexing, hashing, and sketching large-scale sequencing data, particularly those with high error-rates. Finding an MIS is a challenging problem, as the size of a k-mer space grows geometrically with respect to k. We propose three algorithms for this problem. The first and the most intuitive one uses a greedy strategy. The second method implements two techniques to avoid redundant comparisons by taking advantage of the locality-property of the k-mer space and the estimated bounds on the edit distance. The last algorithm avoids expensive calculations of the edit distance by translating the edit distance into the shortest path in a specifically designed graph. These algorithms are implemented and the calculated MISs of k-mer spaces and their statistical properties are reported and analyzed for k up to 15. Source code is freely available at https://github.com/Shao-Group/kmerspace.

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K-mer clustering algorithm using a MapReduce framework: application to the parallelization of the Inchworm module of Trinity

Cited by 18 publications

References 46 publications

Chemical Constituents and Molecular Mechanism of the Yellow Phenotype of Yellow Mushroom (Floccularia luteovirens)

Chemical Constituents and Molecular Mechanism of the Yellow Phenotype of Yellow Mushroom (Floccularia luteovirens)

Improving in-silico normalization using read weights

On the Maximal Independent Sets of $k$-mers with the Edit Distance

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