Step-by-Step Bioinformatics Analysis of Schistosoma mansoni Long Non-coding RNA Sequences

Maciel, Lucas F.; Verjovski-Almeida, Sérgio

doi:10.1007/978-1-0716-0635-3_10

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…Since 2017, many efforts in terms of RNA-seq data have been reported in the context of Schistosoma (85,(269)(270)(271)(272). A thorough protocol of how to gather, process, reconstruct the transcripts, and identify novel long non-coding RNA (lncRNAs), as well as their expression levels (273). In this protocol, Maciel and Verjovski-Almeida use a set of open-source Unixbased tools combined with several R ( 274) packages to support the analysis of differential expression of some lncRNAs.…”

Section: Genes and Proteins Functional Annotationmentioning

confidence: 99%

Schistosomiasis Drug Discovery in the Era of Automation and Artificial Intelligence

et al. 2021

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Schistosomiasis is a parasitic disease caused by trematode worms of the genus Schistosoma and affects over 200 million people worldwide. The control and treatment of this neglected tropical disease is based on a single drug, praziquantel, which raises concerns about the development of drug resistance. This, and the lack of efficacy of praziquantel against juvenile worms, highlights the urgency for new antischistosomal therapies. In this review we focus on innovative approaches to the identification of antischistosomal drug candidates, including the use of automated assays, fragment-based screening, computer-aided and artificial intelligence-based computational methods. We highlight the current developments that may contribute to optimizing research outputs and lead to more effective drugs for this highly prevalent disease, in a more cost-effective drug discovery endeavor.

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