KCMBT: a <i>k</i>-mer Counter based on Multiple Burst Trees

Mamun, Abdullah Al; Pal, Soumitra; Rajasekaran, Sanguthevar

doi:10.1093/bioinformatics/btw345

Cited by 25 publications

(7 citation statements)

References 19 publications

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“…KCMBT [ 43 ] is an in-memory approach using burst trie, a variant form of a trie [ 55 ]. Burst tries efficiently manage large string sets in the memory and maintain strings in a sorted or nearly sorted order.…”

Section: Overview Of K -Mer Counting Approachesmentioning

confidence: 99%

A benchmark study of k-mer counting methods for high-throughput sequencing

Manekar

Sathe

2018

GigaScience

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The rapid development of high-throughput sequencing technologies means that hundreds of gigabytes of sequencing data can be produced in a single study. Many bioinformatics tools require counts of substrings of length k in DNA/RNA sequencing reads obtained for applications such as genome and transcriptome assembly, error correction, multiple sequence alignment, and repeat detection. Recently, several techniques have been developed to count k-mers in large sequencing datasets, with a trade-off between the time and memory required to perform this function. We assessed several k-mer counting programs and evaluated their relative performance, primarily on the basis of runtime and memory usage. We also considered additional parameters such as disk usage, accuracy, parallelism, the impact of compressed input, performance in terms of counting large k values and the scalability of the application to larger datasets.We make specific recommendations for the setup of a current state-of-the-art program and suggestions for further development.

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Section: Overview Of K -Mer Counting Approachesmentioning

confidence: 99%

A benchmark study of k-mer counting methods for high-throughput sequencing

Manekar

Sathe

2018

GigaScience

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“…Other data structures used by k-mer counters include enhanced suffix trees used by Tallymer [20] and burst tries used by KCMBT [27].…”

Section: K-mer Countingmentioning

confidence: 99%

Extraction of Long k-mers Using Spaced Seeds

Leinonen¹,

Salmela²

2022

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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The extraction of k-mers from reads is an important task in many bioinformatics applications, such as all DNA sequence analysis methods based on de Bruijn graphs. These methods tend to be more accurate when the used k-mers are unique in the analyzed DNA, and thus the use of longer k-mers is preferred. When the read lengths of short read sequencing technologies increase, the error rate will become the determining factor for the largest possible value of k. Here we propose LOMEX which uses spaced seeds to extract long k-mers accurately even in the presence of sequencing errors. Our experiments show that LOMEX can extract long k-mers from current Illumina reads with a similar or higher recall than a standard k-mer counting tool. Furthermore, our experiments on simulated data show that when the read length further increases enabling even longer k-mers, the performance of standard k-mer counters declines, whereas LOMEX still extracts long k-mers successfully.

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“…Other data structures used by k-mer counters include enhanced suffix trees used by Tallymer [18] and burst tries used by KCMBT [24].…”

Section: K-mer Countingmentioning

confidence: 99%

Extraction of long k-mers using spaced seeds

Leinonen,

Salmela

2020

Preprint

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The extraction of k-mers from sequencing reads is an important task in many bioinformatics applications, such as all DNA sequence analysis methods based on de Bruijn graphs. These methods tend to be more accurate when the used k-mers are unique in the analyzed DNA, and thus the use of longer k-mers is preferred. When the read lengths of short read sequencing technologies increase, the error rate will become the determining factor for the largest possible value of k. Here we propose LoMeX which uses spaced seeds to extract long k-mers accurately even in the presence of sequencing errors. Our experiments show that LoMeX can extract long k-mers from current Illumina reads with a higher recall than a standard k-mer counting tool. Furthermore, our experiments on simulated data show that when the read length further increases, the performance of standard k-mer counters declines, whereas LoMeX still extracts long k-mers successfully.

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KCMBT: a k-mer Counter based on Multiple Burst Trees

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 25 publications

References 19 publications

A benchmark study of k-mer counting methods for high-throughput sequencing

A benchmark study of k-mer counting methods for high-throughput sequencing

Extraction of Long k-mers Using Spaced Seeds

Extraction of long k-mers using spaced seeds

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