The additional mutations and matchings introduced in UdeACompress seemed to have a positive impact in the final compression, but this still needs to be explored as well. At compression, throughput was shown to be similar to most other programs when including the parallel estimation, despite being lower for the sequential version. Although memory requirements of UdeACompress were high, it can be considered as acceptable in comparison to the other evaluated software. Furthermore, current results of UdeACompress show that it can be competitive when compared to the most relevant tools in the state of the art for FASTQ compression. As many of the other algorithms in the state of the art, UdeACompress changes the order of the input reads to improve compression, which should be considered by the user. The amount and importance of genomic data will continue to increase in the near future, hence the need to create strategies for its efficient compression and later storage or transmission. A multi-technique scheme for referential compression of FASTQ files has been proposed here.

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The work presented in this article aims at advancing the state of the art of referential compression for FASTQ files considering the aforementioned challenges. Technically, any file manager app has basic archiving abilities.

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To measure the peak memory consumption , all programs were executed using their default configuration, both during compression and decompression. In terms of memory usage, UdeACompress is not as thrifty as the other compressors for the whole FASTQ file. However, in the field of read sequences compression, it is common to expect much higher memory requirements, due to the complexity of the techniques and data structures involved. Both during compression and decompression, UdeACompress could be classified among the most memory demanding tools, along with Kpath and Assembltrie. We have built a FASTQ compressor that uses multiple techniques to deal with each of the three data streams as shown in Figure 3. Since quality scores play a role in the compression of reads, they are also fed to UdeACompress. After reordering the reads, UdeACompress sends the sorted position of each read to the blocks in charge of compressing the quality scores and the identifiers; to keep the compressed file consistent.

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Most of the current tools have been designed for the compression of how to download LG PC Suite FASTQ files with short reads, a common property of the dataset used for the tests presented here. Nevertheless, in UdeACompress, we envisioned a scenario where reads are longer, which is a clear trend in sequencing technology. Using simulated data, we evaluated many scenarios of long reads compression. In all of such tests, UdeACompress achieved high compression ratios, even under unfavorable conditions.

When it comes to creating archival/master files, there are a few competing codecs to consider, the most popular of which are ProRes, DNxHR, and Cineform. Codecs come and go over time and H.264, while dominant, won’t be king of the hill forever. H.265, also known as HEVC, is the successor to H.264, but comes with some pros and cons at the moment. One major pro is that HEVC is able to compress an image at around half the bit rate as H.264 with no discernible quality difference.