Tackling the challenges of FASTQ referential compression

Aníbal Guerra, Jaime Lotero, José Édinson Aedo, Sebastián Isaza

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

2 Citas (Scopus)


The exponential growth of genomic data has recently motivated the development of compression algorithms to tackle the storage capacity limitations in bioinformatics centers. Referential compressors could theoretically achieve a much higher compression than their non-referential counterparts; however, the latest tools have not been able to harness such potential yet. To reach such goal, an efficient encoding model to represent the differences between the input and the reference is needed. In this article, we introduce a novel approach for referential compression of FASTQ files. The core of our compression scheme consists of a referential compressor based on the combination of local alignments with binary encoding optimized for long reads. Here we present the algorithms and performance tests developed for our reads compression algorithm, named UdeACompress. Our compressor achieved the best results when compressing long reads and competitive compression ratios for shorter reads when compared to the best programs in the state of the art. As an added value, it also showed reasonable execution times and memory consumption, in comparison with similar tools.

Idioma originalInglés estadounidense
PublicaciónBioinformatics and Biology Insights
EstadoPublicada - 2019
Publicado de forma externa

Áreas temáticas de ASJC Scopus

  • Bioquímica
  • Biología molecular
  • Informática aplicada
  • Matemática computacional
  • Matemáticas aplicadas


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