A fast, lock-free approach for efficient parallel counting of occurrences of k-mers

By Guillaume Marçais, Carl Kingsford. Bioinform., 2011

4,185 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ff3afa71d4abae407b6ab277b96b61a5d7eb4b66

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Citations in the corpus

Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches (2022, positive): “To filter highly repetitive regions, the abundance of k-mers was obtained from the background transcriptome using Jellyfish (Marçais and Kingsford, 2011).”
The MaSuRCA genome assembler (2013, methodology): “We create a k-mer count look-up table, using a hash constructed by the Jellyfish program (Marçais and Kingsford, 2011), which allows us to determine quickly how many times each k-mer occurs in the reads.”
Improved transcriptome assembly using a hybrid of long and short reads with StringTie (2021, methodology): “For each set of long reads, we first counted all 21-mers in the short reads from the sample using Jellyfish [22].”
Sailfish enables alignment-free isoform quantification from RNA-seq reads using lightweight algorithms (2013, background): “We find that pairing the minimum perfect hash function with an atomically updateable array of k-mer counts allows us to count k-mers even faster than with existing advanced lock-free hashes such as that used in Jellyfish [6].”
Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads (2015, methodology): “To do so, Rcorrector uses Jellyfish2 [14] to build a Bloom counter that detects k -mers occurring multiple times, and then stores these in a hash table.”
plasmidSPAdes: Assembling Plasmids from Whole Genome Sequencing Data (2016, methodology): “Recently, Rozov et al. (2015) demonstrated how to use the de Bruijn graphs constructed by the SPADES assembler (Bankevich et al.”
Space-efficient and exact de Bruijn graph representation based on a Bloom filter (2012, background): “Classical methods that retrieve solid k-mers are based on hash tables [10], and their memory usage scale linearly with the number of distinct k-mers.”
Centrifuge: rapid and sensitive classification of metagenomic sequences (2016, methodology): “In order to facilitate this selection process, we used Jellyfish (Marcais and Kingsford 2011) to build atableindicating which k -mers belongtowhichgenomes.”
Comparative performance of the BGISEQ-500 vs Illumina HiSeq2500 sequencing platforms for palaeogenomic sequencing (2017, methodology): “From these subsampled reads, we computed the 6-mer frequencies using jellyfish (Jellyfish, RRID:SCR 005491) [97].”
Estimation of genomic characteristics by analyzing k-mer frequency in de novo genome projects (2013, background): “From our experience, the recently published jellyfish algorithm [1] performs better than the others, which has relatively faster running speed and smaller memory requirement, as well as broader accessible range of kmer size (up to 31 bp).”
De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity (2013, methodology): “Combine all ‘left’ reads into a single file, and combine all ‘right’ reads into a single file by using the following commands: Please note that the ‘--JM option’ enables the user to control the amount of RAM used during Jellyfish k -mer counting: 10 GB in this case.”
GenomeScope: Fast reference-free genome profiling from short reads (2016, methodology): “Either version has minimum user requirements, consisting of (i) a text file of the k-mer profile computed by Jellyfish or other tools, (ii) the value used for k and (iii) the length of the sequencing reads.”