ArticleCalifornian journal of health promotion2018
Comparative De Novo Transcriptome Assembly of
Article in Californian journal of health promotion, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
background and purposeThe reduction of cost and ease of using core laboratories or commercial sequencing companies have allowed biomedical and health researchers alike to employ reference-based genomic or transcriptomic sequencing (RNA-seq) projects to expand their work. Non-reference based data analysis, in cases of inexperienced researchers, become more challenging despite the availability of many open source and commercial software programs.
methodsWe performed de novo assembly of RNA-seq data obtained from a non-model organism (Eastern Newt skin) to compare data output of two commercially available software workflows.
resultsOur results show that the software packages performed satisfactorily albeit with differences in how the annotated and novel transcripts were identified and listed.
conclusionOverall, we conclude that the use of commercial software platforms has a clear advantage to that of open source programs because of convenience with data analysis workflows. One caveat is that users need to know the software's basic algorithm and technical approach, in order to determine the precision and validity of the data output. Thus, it is imperative that researchers fully evaluate the software according to their needs to determine their suitability.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.