MetaProFi: A protein-based Bloom filter for storing and querying sequence data for accurate identification of functionally relevant genetic variants

Srikakulam, Sanjay Kumar; Keller, Sebastian; Dabbaghie, Fawaz; Bals, Robert; Kalinina, Olga V.

doi:10.1101/2021.08.12.456081

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…MetaProFi ( Srikakulam et al , 2021 ) is a recent indexing scheme for k -mers which supports both nucleotide and amino acid sequences. MetaProFi, like kmtricks, builds a Bloom filter matrix from large sequencing data collections.…”

Section: Related Workmentioning

confidence: 99%

kmtricks: efficient and flexible construction of Bloom filters for large sequencing data collections

Lemane

Medvedev

Chikhi

et al. 2022

Bioinformatics Advances

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When indexing large collections of short-read sequencing data, a common operation that has now been implemented in several tools (Sequence Bloom Trees and variants, BIGSI,.) is to construct a collection of Bloom filters, one per sample. Each Bloom filter is used to represent a set of k-mers which approximates the desired set of all the non-erroneous k-mers present in the sample. However, this approximation is imperfect, especially in the case of metagenomics data. Erroneous but abundant k-mers are wrongly included, and non-erroneous but low-abundant ones are wrongly discarded. We propose kmtricks, a novel approach for generating Bloom filters from terabase-sized collections of sequencing data. Our main contributions are 1/an efficient method for jointly counting k-mers across multiple samples, including a streamlined Bloom filter construction by directly counting, partitioning and sorting hashes instead of k-mers, which is approximately four times faster than state-of-the-art tools; 2/a novel technique that takes advantage of joint counting to preserve low-abundant k-mers present in several samples, improving the recovery of non-erroneous k-mers. Our experiments highlight that this technique preserves around 8x more k-mers than the usual yet crude filtering of low-abundance k-mers in a large metagenomics dataset. Availability https://github.com/tlemane/kmtricks Funding The work was funded by IPL Inria Neuromarkers, ANR Inception (ANR-16-CONV-0005), ANR Prairie (ANR-19-P3IA-0001), ANR SeqDigger (ANR-19-CE45-0008)

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Section: Related Workmentioning

confidence: 99%

kmtricks: efficient and flexible construction of Bloom filters for large sequencing data collections

Lemane

Medvedev

Chikhi

et al. 2022

Bioinformatics Advances

View full text Add to dashboard Cite

show abstract

“…MetaProFi [26] is a recent indexing scheme for k -mers which supports both nucleotide and amino acid sequences. , like , builds a Bloom filter matrix from large sequencing data collections.…”

Section: Related Workmentioning

confidence: 99%

kmtricks: Efficient and flexible construction of Bloom filters for large sequencing data collections

Lemane

Medvedev

Chikhi

et al. 2021

Preprint

View full text Add to dashboard Cite

When indexing large collection of sequencing data, a common operation that has now been implemented in several tools (Sequence Bloom Trees and variants, BIGSI, ..) is to construct a collection of Bloom filters, one per sample. Each Bloom filter is used to represent a set of k-mers which approximates the desired set of all the non-erroneous k-mers present in the sample. However, this approximation is imperfect, especially in the case of metagenomics data. Erroneous but abundant k-mers are wrongly included, and non-erroneous but low-abundant ones are wrongly discarded. We propose kmtricks, a novel approach for generating Bloom filters from terabase-sized collections of sequencing data. Our main contributions are 1/ an efficient method for jointly counting k-mers across multiple samples, including a streamlined Bloom filter construction by directly counting hashes instead of k-mers; 2/ a novel technique that takes advantage of joint counting to preserve low-abundant k-mers present in several samples, improving the recovery of non-erroneous k-mers. In addition, our experimental results highlight that the usual yet crude filtering of low-abundant k-mers is inappropriate for complex data such as metagenomes.

show abstract

MetaProFi: A protein-based Bloom filter for storing and querying sequence data for accurate identification of functionally relevant genetic variants

Cited by 2 publications

References 15 publications

kmtricks: efficient and flexible construction of Bloom filters for large sequencing data collections

kmtricks: efficient and flexible construction of Bloom filters for large sequencing data collections

kmtricks: Efficient and flexible construction of Bloom filters for large sequencing data collections

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