RT Journal Article
T1 Normalized Workflow to Optimize Hybrid De Novo Transcriptome Assembly for Non-Model Species: A Case Study in Lilium ledebourii (Baker) Boiss.
A1 Sheikh-Assadi, Morteza
A1 Naderi, Roohangiz
A1 Salami, Seyed Alireza
A1 Kafi, Mohsen
A1 Fatahi, Reza
A1 Shariati, Vahid
A1 Martinelli, Federico
A1 Cicatelli, Angela
A1 Triassi, Maria
A1 Guarino, Francesco
A1 Improta, Giovanni
A1 Claros, Manuel Gonzalo
K1 de novo assembly
K1 hybrid transcriptome
K1 non-model organisms
K1 normalized comparison
K1 optimization
K1 transcriptomics
AB A high-quality transcriptome is required to advance numerous bioinformatics workflows. Nevertheless, the effectuality of tools for de novo assembly and real precision assembled transcriptomes looks somewhat unexplored, particularly for non-model organisms with complicated (very long, heterozygous, polyploid) genomes. To disclose the performance of various transcriptome assembly programs, this study built 11 single assemblies and analyzed their performance on some significant reference-free and reference-based criteria. As well as to reconfirm the outputs of benchmarks, 55 BLAST were performed and compared using 11 constructed transcriptomes. Concisely, normalized benchmarking demonstrated that Velvet-Oases suffer from the worst results, while the EvidentialGene strategy can provide the most comprehensive and accurate transcriptome of Lilium ledebourii (Baker) Boiss. The BLAST results also confirmed the superiority of EvidentialGene, so it could capture even up to 59% more (than Velvet-Oases) unique gene hits. To promote assembly optimization, with the help of normalized benchmarking, PCA and AHC, it is emphasized that each metric can only provide part of the transcriptome status, and one should never settle for just a few evaluation criteria. This study supplies a framework for benchmarking and optimizing the efficiency of assembly approaches to analyze RNA-Seq data and reveals that selecting an inefficient assembly strategy might result in less identification of unique gene hits.
SN 2223-7747
YR 2022
FD 2022-09-10
LK http://hdl.handle.net/10668/21561
UL http://hdl.handle.net/10668/21561
LA en
DS RISalud
RD Apr 10, 2025