ArticleScience progress
SCA-NGS: Secure compression algorithm for next generation sequencing data using genetic operators and block sorting.
Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Data Compression in LoRa Networks: Performance and Energy Trade-Offs of Classical and Cutting-Edge Compression Algorithms.Sensors (Basel, Switzerland) · 2026Article
- An efficient and secure compression technique for data protection using burrows-wheeler transform algorithm.Heliyon · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in sequencing methods have led to significant increase in sequencing data. Increase in sequencing data leads to research challenges such as storage, transfer, processing, etc. data compression techniques have been opted to cope with the storage of these data. There have been good achievements in compression ratio and execution time. This fast-paced advancement has raised major concerns about the security of data. Confidentiality, integrity, authenticity of data needs to be ensured. This paper presents a novel lossless reference-free algorithm that focuses on data compression along with encryption to achieve security in addition to other parameters. The proposed algorithm uses preprocessing of data before applying general-purpose compression library. Genetic algorithm is used to encrypt the data. The technique is validated with experimental results on benchmark datasets. Comparative analysis with state-of-the-art techniques is presented. The results show that the proposed method achieves better results in comparison to existing methods.
Indexed as
Identifiers
What OpenQuestion holds
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.