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Data for The genetics monopolistic industry, as expected, is missing information two inches beyond their nose: in this case, transcripts

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Zenodo2022-07-27 更新2026-05-25 收录
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<strong>De novo transcriptome assembly is one of the many fundamental pieces of new research in genomics. It is, for example, the preferred method for studying non-model organisms, since it is easier and cheaper than building a genome, and reference methods are not possible without an existing genome. The transcriptomes of these organisms can thus reveal novel proteins and their isoforms that are implicated in such unique biological phenomena. This technique is also useful in cancer research as it makes possible to detect potentially significant chimeric transcripts in cancer and normal somatic tissues.</strong> <strong>Given that the genetics industry is organized in the form of a monopoly controlled by hidden lobbies who also control Academia, all the available software for de novo transcriptome assembly is being developed by academic researchers under the open-source paradigm. We report here, that as anyone could have very easily deduced from past experiences in other industries, this unethical form of organization in the industry is resulting in incompetence whose effects include missing a significant portion of the available information that could be obtained from some genomics studies. In this case, missing transcripts in transcriptome studies. We won't deep in on the consequences, but these could include overpricing, over costs, and failing to achieve the goals of some studies.</strong>

从头转录组组装(de novo transcriptome assembly)是基因组学新兴研究的核心方向之一。例如,该方法是研究非模式生物(non-model organisms)的首选方案:相较于构建全基因组,其操作更简便、成本更低廉,且若无已有的参考基因组,便无法使用基于参考的组装方法。因此,这类生物的转录组能够揭示与独特生物学现象相关的新型蛋白质及其剪接异构体(isoforms)。该技术在癌症研究中同样具有重要应用价值,可用于检测癌组织与正常体细胞组织中具有潜在研究意义的嵌合转录本(chimeric transcripts)。 鉴于遗传学行业由掌控学术界的隐性游说团体以垄断形式运作,当前所有可用的从头转录组组装软件均由学术研究者基于开源范式(open-source paradigm)开发。正如我们可从其他行业的过往经验中轻易推断的那样,这种违背伦理的行业组织形式正引发能力缺失问题,其负面影响包括丢失部分可从部分基因组学研究中获取的重要信息——就本案例而言,即转录组研究中遗漏转录本的问题。本文暂不深入探讨其后续影响,但此类问题可能包括定价过高、研发成本攀升,以及部分研究无法达成既定目标。

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Zenodo
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2022-07-27
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