ArticleBioinformatics advances2026
Kaminari: a frugal colored index for approximate
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Identifying which documents in a large database contain a query string is a fundamental problem in Information Retrieval and Computational Biology. We focus on the approximate version of this problem for genomic sequences: the result set may contain false positive matches but no false negatives. State-of-the-art solutions rely on Bloom filters to index all Results: Here, we explore an alternative index design based on Availability and implementation: The software is freely available at github.com/yhhshb/kaminari under the MIT license. Reproducibility scripts are available at github.com/vicLeva/benchmarks_kaminari.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.