Identificação e caracterização de novos lncRNAS no genoma de lúpulo (Humulus lupulus), usando ferramentas de bioinformática

Abstract

Hops (Humulus lupulus) have been used in beer production for over 1,200 years and also have pharmaceutical applications, exhibiting considerable benefits for human health. Although Brazil is the third largest beer producer in the world, approximately 98% of the hops used in the country are imported, which significantly increases the final cost of the product. Knowing these lncRNAs sequences is essential for breeding programs, with the aim of adapting hops to different climates, increasing their productivity and improving their resistance to pathogens. In this context, long non-coding RNAs (lncRNAs) plays important roles in the plant genome, including the regulation of gene expression, epigenetic regulation and response to abiotic and biotic stresses. During the analysis of the AlnC database, we identified that the largest lncRNA sequence, registered as AlnC 91911, had 2.405 base pairs. This data was crucial to define the process of preparing the genome for subsequent analyses. Based on the largest lncRNA fragment size found, we developed a Python script to cut the genome into overlapping sequences, defining the size of 2.500 bp with overlaps of 2.499 bp. After assembling the genomic library, composed of 8,713 files of approximately 1,1 GB each, we used the CPC2 and RNAplonc software to predict lncRNAs. CPC2 identified 3.582.809 transcripts as lncRNAs, while RNAplonc cataloged 2.263.355 transcripts. To remove redundant sequences generated by overlaps and extract only those classified as lncRNAs or non-coding, we used the Pentaho Business Intelligence tool. After eliminating redundancies, the sequences were analyzed in other prediction programs to increase the reliability of the results. The non-coding sequences identified by CPC2 and RNAplonc were validated using other prediction programs. Thus, we classified the sequences according to the number of programs that confirmed the prediction. The lncRNA sequences identified by the predictors were compared with those in the AlnC database to eliminate duplicates and ensure the originality of the results. This approach was optimized due to the large volume of data (approximately 8.7 TB) and the processing time required for each 1,1 GB file. In addition, in order to validate and compare the effectiveness of the different tools, we re-evaluated the sequences found in the AlnC database with the same tools RNAplonc (99.69%), CPC2 (99.62%), CNCI (98%) and PLEK (99%). We conclude that the ‘genomic slicing’ method used in this study is as efficient as conventional methods based on RNA-seq, which present a lower number of identifications when compared to the method proposed in this study. Furthermore, through cross-validation and comparison of already known sequences, we ensure reliability and innovation in the new predicted sequences. The lncRNAsidentified in this study represent a rich resource for future research, opening new perspectives for the genetic improvement of hops. Understanding the function of these regulatory elements can lead to the development of more productive varieties, resistant to diseases and with superior agronomic characteristics.