Gazelle

Zhang, Xiaofei; Yu, Ye; Mok, Chan Hee; MacLeod, James N.; Liu, Jinze

doi:10.1145/3459930.3469548

Cited by 1 publication

(3 citation statements)

References 19 publications

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“…Furthermore, we confirmed the finding of Zhang et al (2021) that using a simple measure like the median or the interquartile mean [as done by Zhang et al (2021) ] of the k -mer occurrences results in meaningful expression values. Due to the similarity in robustness to outliers, we would not expect to see a significant difference between using the median or the interquartile mean, but further research would be necessary to confirm this hypothesis.…”

Section: Discussionsupporting

confidence: 85%

“…Recently, the tool Gazelle ( Zhang et al , 2021 ) was published. Similar to REINDEER, Gazelle stores k -mers and their counts, but unlike REINDEER, they always perform a log-transform of the count values to save space, claiming that storing the raw count values is not necessary as significant differences in gene expression data often follow a double log fold change ( Zhang et al , 2021 ). Furthermore, Gazelle offers an estimation of the quantification for a transcript by taking the interquartile mean of the stored count values.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the efficiency of the IBF, Needle can build the index

times faster than REINDEER and the count values can be obtained

times faster while only using

% of the space required by REINDEER, where the speed advantage depends on the chosen minimizer window size. A direct comparison to Gazelle was not possible as the tool is not publicly available yet, but based on their provided analysis of Gazelle ( Zhang et al , 2021 ), we are quite confident that Needle also outperforms Gazelle.…”

Section: Introductionmentioning

confidence: 99%

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Needle: a fast and space-efficient prefilter for estimating the quantification of very large collections of expression experiments

Darvish

Seiler

Mehringer³

et al. 2022

Bioinformatics

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Motivation The ever-growing size of sequencing data is a major bottleneck in bioinformatics as the advances of hardware development cannot keep up with the data growth. Therefore, an enormous amount of data is collected but rarely ever reused, because it is nearly impossible to find meaningful experiments in the stream of raw data. Results As a solution, we propose Needle, a fast and space-efficient index which can be built for thousands of experiments in less than two hours and can estimate the quantification of a transcript in these experiments in seconds, thereby outperforming its competitors. The basic idea of the Needle index is to create multiple interleaved Bloom filters that each store a set of representative k-mers depending on their multiplicity in the raw data. This is then used to quantify the query. Supplementary information Supplementary data are available at Bioinformatics online. Availability and implementation https://github.com/seqan/needle

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

“…Due to the efficiency of the IBF, Needle can build the index

times faster than REINDEER and the count values can be obtained

times faster while only using

Section: Introductionmentioning

confidence: 99%

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