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Biology subjects

Oliveira, L. S.

Publications and source records attributed to Oliveira, L. S..

2 recordsLinked to original sources

The soybean rust pathogen Phakopsora pachyrhizi displays transposable element proliferation that correlates with broad host-range adaptation on legumes

Asian soybean rust, caused by Phakopsora pachyrhizi, is one of the worlds most economically damaging agricultural diseases. Despite P. pachyrhizis impact, the exceptional size and complexity of its genome prevented generation of an accurate genome assembly. We simultaneously sequenced three P. pachyrhizi genomes uncovering a genome up to 1.25 Gb comprising two haplotypes with a transposable element (TE) content of ~93%. The proliferation of TEs within the genome occurred in several bursts and correlates with the radiation and speciation of the legumes. We present data of clear de-repression of TEs that mirrors expression of virulence-related candidate effectors. We can see a unique expansion in amino acid metabolism for this fungus. Our data shows that TEs play a dominant role in P. pachyrhizis genome and have a key impact on various processes such as host range adaptation, stress responses and genetic plasticity of the genome.

microbiology↗

Impact of sequencing technologies on long non-coding RNA computational identification

The correct annotation of non-coding RNAs, especially long non-coding RNAs (lncRNAs), is still an important critial challenge in genome analyses. One crucial issue in lncRNA transcript annotation is the transcriptome resource that supports lncRNA loci. Long-read technologies now bring the potential to improve the quality of transcriptome annotation. Consequently, long non-coding RNAs (lncRNA) are probably the most benefited class of transcripts that would have improved annotation using this novel technology. However, there is a gap regarding benchmarking studies that highlighted if the direct use of lncRNA predictors in long-reads makes more precise identification of these transcripts. Considering that these lncRNA tools were not trained with these reads, we want to address: how is the performance of these tools? Are they also able to efficiently identify lncRNAs? We could provide evidence of where and how to make potential better approaches for the lncRNA annotation by understanding these issues. Keywords: Non-coding RNAs, high-throughput sequencing technologies, coding, methods, benchmarking, tools, NGS, transcripts

bioinformatics↗