A single-molecule long-read survey of the human transcriptome

Sharon, Donald; Tilgner, Hagen; Grubert, Fabian; Snyder, M

doi:10.1038/nbt.2705

Cited by 563 publications

(535 citation statements)

References 25 publications

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“…In addition, the decision to apply a weighting of exactly 1.00 or −1.00 to the regression coefficients associated with each of these genes, thus assigning an identical magnitude of effect to every gene, seems to be completely arbitrary. A recent article (10) found the human transcriptome to be extremely complex, with >100,000 distinct transcripts coding for around 20,000 protein-coding genes. In that context, the gene expression model assumed by the authors appears to be rather simplistic.…”

Section: Theoretical and Methodological Issuesmentioning

confidence: 99%

A critical reanalysis of the relationship between genomics and well-being

Brown

MacDonald

Samanta³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

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Fredrickson et al. Proc Natl Acad Sci USA 110(33):13684-13689] claimed to have observed significant differences in gene expression related to hedonic and eudaimonic dimensions of well-being. Having closely examined both their claims and their data, we draw substantially different conclusions. After identifying some important conceptual and methodological flaws in their argument, we report the results of a series of reanalyses of their dataset. We first applied a variety of exploratory and confirmatory factor analysis techniques to their self-reported well-being data. A number of plausible factor solutions emerged, but none of these corresponded to Fredrickson et al.'s claimed hedonic and eudaimonic dimensions. We next examined the regression analyses that purportedly yielded distinct differential profiles of gene expression associated with the two well-being dimensions. Using the best-fitting two-factor solution that we identified, we obtained effects almost twice as large as those found by Fredrickson et al. using their questionable hedonic and eudaimonic factors. Next, we conducted regression analyses for all possible two-factor solutions of the psychometric data; we found that 69.2% of these gave statistically significant results for both factors, whereas only 0.25% would be expected to do so if the regression process was really able to identify independent differential gene expression effects. Finally, we replaced Fredrickson et al.'s psychometric data with random numbers and continued to find very large numbers of apparently statistically significant effects. We conclude that Fredrickson et al.'s widely publicized claims about the effects of different dimensions of well-being on health-related gene expression are merely artifacts of dubious analyses and erroneous methodology.genomic perspectives | leukocytes | transcriptional response | epigenetics

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Section: Theoretical and Methodological Issuesmentioning

confidence: 99%

A critical reanalysis of the relationship between genomics and well-being

Brown

MacDonald

Samanta³

et al. 2014

Proc. Natl. Acad. Sci. U.S.A.

View full text Add to dashboard Cite

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“…Although sizeable error rates (∼13%) have been reported [8], these errors are random, in contrast to context-specific errors (e.g. palindromic sequences or GC rich contents) that are generally observed in other techniques, such that multiple lower-quality base calls can be aligned to derive high-quality (de-novo) sequence data [9,10].…”

Section: Introductionmentioning

confidence: 99%

Graphene nanodevices for DNA sequencing

2016

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Fast, cheap, and reliable DNA sequencing could be one of the most disruptive innovations of this decade as it will pave the way for personalised medicine. In pursuit of such technology, a variety of nanotechnology-based approaches have been explored and established including sequencing with nanopores. Due to its unique structure and properties, graphene provides interesting opportunities for the development of a new sequencing technology. In recent years, a wide range of creative ideas for graphene sequencers have been theoretically proposed and the first experimental demonstrations have begun to appear. Here, we review the different approaches to using graphene nanodevices for DNA sequencing, which involve DNA passing through graphene nanopores, nanogaps, and nanoribbons, and the physisorption of DNA on graphene nanostructures. We discuss the advantages and problems of each of these key techniques, and provide a perspective on the use of graphene in future DNA sequencing technology.

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“…Here we explored NLR enrichment in combination with long read singlemolecule real time (SMRT) sequencing using the PacBio RSII platform [11][12][13] (SMRT RenSeq). We used our Solanum NLR bait library 4,10 to capture the NLR complement from two DNA libraries of different size (1.5 kb and 2.5 kb fragments), derived from the same resistant SP1102 accession, and sequenced these on one and two SMRT cells, respectively.…”

mentioning

confidence: 99%