DNA sequencing has become widely popular, evidenced by genomic databases that have been doubling in size every two years. However, on the downside, the tools to search the data have not kept pace with the advancements in the DNA sequencing sector. Researchers involved with comparing DNA across genomes or studying the evolution viruses responsible for coronavirus have to wait for long weeks for software to index large metagenomics databases. In addition, these databases get bigger and bigger every month, so much so that they are now measured in petabytes. Thus, it is necessary to have a better research tools for DNA Sequencing.
To tackle this problem, researchers have managed to create RAMBO (Repeated and Merged Bloom Filter). It is a new method with the ability to cut indexing time for huge databases from weeks to hours and their search time from hours to within mere seconds. The innovation is a ground-shattering breakthrough for DNA Sequencing Market as it could potentially democratize genomic search by enabling every lab to quickly and cost-efficiently search huge genomic archives with just off-the-shelf computer systems.
The team revealed that the study wanted to address the problem of querying millions of DNA sequences against a large database, with conventional approaches taking hours upon hours on a compute cluster. Their primary aim was to reduce indexing times and query times, as they are critical to any genomic research.
RAMBO makes use of a data structure with significantly quicker query time in contrast to the current state-of-the-art genome indexing methods. Further, it also has several other advantages, such as a low false-positive rate, a zero false-negative rate, and eases of parallelization zero false-negative rates. Not only is this, but the search time of RAMBO ahead by 35 times to any other existing methods.
The new tool, RAMBO, has helped in improving the performance of Blood filters – a five decades-old search approach that was used for search purposes in genomic sequencing for a number of previous studies. The technology builds on earlier Bloom filter methods for genomic search through the employment of probabilistic data structure referred to as a “count-min sketch.” The structure helps to lead a better query time and memory trade-off in comparison to earlier methods and also managed to beat the present baseline by acquiring a thoroughly robust, ultrafast, low-memory indexing data structure.
The advancement is huge for DNA Sequencing as now the wait time for tons of investigations in bioinformatics can be reduced. For instance, looking for the presence of SARS-CoV-2 within wastewater metagenomes worldwide. The tool could even become a critical instrument in the study of bacterial genome evolution, cancer genomics, and many others.
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