家蚕茧壳的多组学分析。

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Panagiota Fragkou, Ioannis Martakos, Georgia Rouni, Demetrios Vasilakos, Evangelos Koutsoukos, Alesssio Saviane, Silvia Cappellozza, Nikolaos S Thomaidis, Marios G Kostakis, Martina Samiotaki, Sotiris Kotsiantis, Mariana Barcenas, Skarlatos G Dedos
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引用次数: 0

摘要

在这项研究中,我们结合了表型组学、蛋白质组学、代谢组学和脂质组学对历史和当代家蚕茧壳的分析,以研究人类驱动的家蚕在欧洲的引入和多样化。根据最近关于该物种祖先和茧壳颜色的基因组变异性的发现,我们对148个茧壳进行了光学和荧光成像分析,以确定显性和隐性表型性状,并对80个茧壳样品采用LC-MS/MS分析方案,确定该物种茧壳平均含有98±13个(Mean±SD)蛋白质,同时我们鉴定了141种代谢物和981种脂质。我们通过多种验证协议、一系列降维方法和聚类算法以及历史档案和手稿的叙述来验证这些生成的数据集。我们的多组学数据集为从多个科学角度进一步开发蚕茧壳提供了有价值的基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multiomics analysis of the Silkworm cocoon shell.

In this study we combined phenomics, proteomics, metabolomics and lipidomics analyses of historical and contemporary Bombyx mori cocoon shells to case-study the human-driven introduction and diversification of this species in Europe. Prompted by recent findings on the genomic variability that underlies the ancestry and cocoon shell colour of this species, we carried out optical and fluorescence imaging analysis of 148 cocoons shells to identify overt and covert phenotypic traits and employed LC-MS/MS analyses protocols for 80 cocoon shell samples to identify that the cocoon shell of this species contains on average 98 ± 13 (Mean ± SD) proteins, while we identified 141 metabolites and 981 lipids. We validated these generated datasets through multiple validation protocols, through a series of dimensionality reduction methods and clustering algorithms and through narratives from historical archives and manuscripts. Our multiomics datasets provide a valuable foundation for advancing further exploitations of silkworm cocoon shells in multiple scientific perspectives.

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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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