RESUMEN

Next-generation sequencing methods provide comprehensive data for the analysis of structural and functional analysis of the genome. The draft genomes with low contig number and high N50 value can give insight into the structure of the genome as well as provide information on the annotation of the genome. In this study, we designed a pipeline that can be used to assemble prokaryotic draft genomes with low number of contigs and high N50 value. We aimed to use combination of two de novo assembly tools (SPAdes and IDBA-Hybrid) and evaluate the impact of this approach on the quality metrics of the assemblies. The followed pipeline was tested with the raw sequence data with short reads (< 300) for a total of 10 species from four different genera. To obtain the final draft genomes, we firstly assembled the sequences using SPAdes to find closely related organism using the extracted 16 s rRNA from it. IDBA-Hybrid assembler was used to obtain the second assembly data using the closely related organism genome. SPAdes assembler tool was implemented using the second assembly, produced by IDBA-hybrid as a hint. The results were evaluated using QUAST and BUSCO. The pipeline was successful for the reduction of the contig numbers and increasing the N50 statistical values in the draft genome assemblies while preserving the coverage of the draft genomes.

Asunto(s)

RESUMEN

Gliomas develop and grow in the brain and central nervous system. Examining glioma grading processes is valuable for improving therapeutic challenges. One of the most extensive repositories storing transcriptomics data for gliomas is The Cancer Genome Atlas (TCGA). However, such big cohorts should be processed with caution and evaluated thoroughly as they can contain batch and other effects. Furthermore, biological mechanisms of cancer contain interactions among biomarkers. Thus, we applied an interpretable machine learning approach to discover such relationships. This type of transparent learning provides not only good predictability, but also reveals co-predictive mechanisms among features. In this study, we corrected the strong and confounded batch effect in the TCGA glioma data. We further used the corrected datasets to perform comprehensive machine learning analysis applied on single-sample gene set enrichment scores using collections from the Molecular Signature Database. Furthermore, using rule-based classifiers, we displayed networks of co-enrichment related to glioma grades. Moreover, we validated our results using the external glioma cohorts. We believe that utilizing corrected glioma cohorts from TCGA may improve the application and validation of any future studies. Finally, the co-enrichment and survival analysis provided detailed explanations for glioma progression and consequently, it should support the targeted treatment.