A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling

<p>Abstract</p> <p>Background</p> <p>RNA-Seq exploits the rapid generation of gigabases of sequence data by Massively Parallel Nucleotide Sequencing, allowing for the mapping and digital quantification of whole transcriptomes. Whilst previous comparisons between RNA-Seq and microarrays have been performed at the level of gene expression, in this study we adopt a more fine-grained approach. Using RNA samples from a normal human breast epithelial cell line (MCF-10a) and a breast cancer cell line (MCF-7), we present a comprehensive comparison between RNA-Seq data generated on the Applied Biosystems SOLiD platform and data from Affymetrix Exon 1.0ST arrays. The use of Exon arrays makes it possible to assess the performance of RNA-Seq in two key areas: detection of expression at the granularity of individual exons, and discovery of transcription outside annotated loci.</p> <p>Results</p> <p>We found a high degree of correspondence between the two platforms in terms of exon-level fold changes and detection. For example, over 80% of exons detected as expressed in RNA-Seq were also detected on the Exon array, and 91% of exons flagged as changing from Absent to Present on at least one platform had fold-changes in the same direction. The greatest detection correspondence was seen when the read count threshold at which to flag exons Absent in the SOLiD data was set to <it>t</it><1 suggesting that the background error rate is extremely low in RNA-Seq. We also found RNA-Seq more sensitive to detecting differentially expressed exons than the Exon array, reflecting the wider dynamic range achievable on the SOLiD platform. In addition, we find significant evidence of novel protein coding regions outside known exons, 93% of which map to Exon array probesets, and are able to infer the presence of thousands of novel transcripts through the detection of previously unreported exon-exon junctions.</p> <p>Conclusions</p> <p>By focusing on exon-level expression, we present the most fine-grained comparison between RNA-Seq and microarrays to date. Overall, our study demonstrates that data from a SOLiD RNA-Seq experiment are sufficient to generate results comparable to those produced from Affymetrix Exon arrays, even using only a single replicate from each platform, and when presented with a large genome.</p

The utility of MAS5 expression summary and detection call algorithms

<p>Abstract</p> <p>Background</p> <p>Used alone, the MAS5.0 algorithm for generating expression summaries has been criticized for high False Positive rates resulting from exaggerated variance at low intensities.</p> <p>Results</p> <p>Here we show, with replicated cell line data, that, when used alongside detection calls, MAS5 can be both selective and sensitive. A set of differentially expressed transcripts were identified that were found to be changing by MAS5, but unchanging by RMA and GCRMA. Subsequent analysis by real time PCR confirmed these changes. In addition, with the Latin square datasets often used to assess expression summary algorithms, filtered MAS5.0 was found to have performance approaching that of its peers.</p> <p>Conclusion</p> <p>When used alongside detection calls, MAS5 is a sensitive and selective algorithm for identifying differentially expressed genes.</p

The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

BACKGROUND: The number of gene expression studies in the public domain is rapidly increasing, representing a highly valuable resource. However, dataset-specific bias precludes meta-analysis at the raw transcript level, even when the RNA is from comparable sources and has been processed on the same microarray platform using similar protocols. Here, we demonstrate, using Affymetrix data, that much of this bias can be removed, allowing multiple datasets to be legitimately combined for meaningful meta-analyses. RESULTS: A series of validation datasets comparing breast cancer and normal breast cell lines (MCF7 and MCF10A) were generated to examine the variability between datasets generated using different amounts of starting RNA, alternative protocols, different generations of Affymetrix GeneChip or scanning hardware. We demonstrate that systematic, multiplicative biases are introduced at the RNA, hybridization and image-capture stages of a microarray experiment. Simple batch mean-centering was found to significantly reduce the level of inter-experimental variation, allowing raw transcript levels to be compared across datasets with confidence. By accounting for dataset-specific bias, we were able to assemble the largest gene expression dataset of primary breast tumours to-date (1107), from six previously published studies. Using this meta-dataset, we demonstrate that combining greater numbers of datasets or tumours leads to a greater overlap in differentially expressed genes and more accurate prognostic predictions. However, this is highly dependent upon the composition of the datasets and patient characteristics. CONCLUSION: Multiplicative, systematic biases are introduced at many stages of microarray experiments. When these are reconciled, raw data can be directly integrated from different gene expression datasets leading to new biological findings with increased statistical power

Holier than thou? Identity buffers and adoption of controversial practices in the Islamic banking category

Existing scholarship on categories frequently highlights how some category members may violate codes that others diligently abide by. In this paper, we take into account the differences in identity across category members, and ask how these relative differences determine their response to a code-violating change. Taking a case where category members are clearly identified as ‘insiders’ and ‘outsiders’, we argue that insiders’ reaction to a code violation depends upon the extent to which they believe their identity to be distinct from the code violator’s, who might be an insider or an outsider. Specifically, we suggest that it is the presence or absence of an ‘identity buffer’ – i.e., a relative identity advantage – which determines insiders’ reaction. We hypothesize that when a code violation is introduced by a fellow category insider, the focal insider will be more likely to refrain from the practice. When it is an outsider who introduces the code violation, insiders will be more likely to adopt the code violation as long as they can retain an identity buffer. We further posit that when outsiders adopt code-preserving behavior, thus narrowing the identity buffer between insiders and outsiders, it will mitigate insiders’ likelihood of code violation adoption. We test and find support for our hypotheses using data on Islamic banking industry in 12 countries (2003-2014)

Predicting the vulnerability of great apes to disease : the role of superspreaders and their potential vaccination

Charlotte Carne was funded by a scholarship from the University of Roehampton (http://www.roehampton.ac.uk/home/). The Royal Zoological Society of Scotland (http://www.rzss.org.uk/) provided core funding for the Budongo Conservation Field Station. The orang-utan field research was funded by the Wildlife Conservation Society (WCS: http://www.wcs.org/), the US Fish and Wildlife Service Great Ape Conservation Fund (http://www.fws.gov/international/wildlif​e-without-borders/great-ape-conservation​-fund.html), Orang-utan Tropical Peatland Project (OuTrop: http://www.outrop.com/), Primate Conservation Inc. (http://www.primate.org/), and the L.S.B. Leakey Foundation (http://leakeyfoundation.org/).Disease is a major concern for the conservation of great apes, and one that is likely to become increasingly relevant as deforestation and the rise of ecotourism bring humans and apes into ever closer proximity. Consequently, it is imperative that preventative measures are explored to ensure that future epidemics do not wipe out the remaining populations of these animals. In this paper, social network analysis was used to investigate vulnerability to disease in a population of wild orang-utans and a community of wild chimpanzees. Potential 'superspreaders' of disease - individuals with disproportionately central positions in the community or population - were identified, and the efficacy of vaccinating these individuals assessed using simulations. Three resident female orang-utans were identified as potential superspreaders, and females and unflanged males were predicted to be more influential in disease spread than flanged males. By contrast, no superspreaders were identified in the chimpanzee network, although males were significantly more central than females. In both species, simulating the vaccination of the most central individuals in the network caused a greater reduction in potential disease pathways than removing random individuals, but this effect was considerably more pronounced for orang-utans. This suggests that targeted vaccinations would have a greater impact on reducing disease spread among orang-utans than chimpanzees. Overall, these results have important implications for orang-utan and chimpanzee conservation and highlight the role that certain individuals may play in the spread of disease and its prevention by vaccination.Publisher PDFPeer reviewe

High correspondence between Affymetrix exon and standard expression arrays

Exon arrays aim to provide comprehensive gene expression data at the level of individual exons, similar to that provided on a per-gene basis by existing expression arrays. This report describes the performance of Affymetrix GeneChip® Human Exon 1.0 ST array by using replicated RNA samples from two human cell lines, MCF7 and MCF10A, hybridized both to Exon 1.0 ST and to HG-U133 Plus2 arrays. Cross-comparison between array types requires an appropriate mapping to be found between individual probe sets. Three possible mappings were considered, reflecting different strategies for dealing with probe sets that target different parts of the same transcript. Irrespective of the mapping used, Exon 1.0 ST and HG-U133 Plus2 arrays show a high degree of correspondence. More than 80% of HG-U133 Plus2 probe sets may be mapped to the Exon chip, and fold changes are found well preserved for over 96% of those probe sets detected present. Since HG-U133 Plus2 arrays have already been extensively validated, these results lend a significant degree of confidence to exon arrays

Amplification protocols introduce systematic but reproducible errors into gene expression studies

The desire to perform microarray experiments with small starting amounts of RNA has led to the development of a variety of protocols for preparing and amplifying mRNA. This has consequences not only for the standardization of experimental design, but also for reproducibility and comparability between experiments. Here we investigate the differences between the Affymetrix standard and small sample protocols and address the data analysis issues that arise when comparing samples and experiments that have been processed in different ways. We show that data generated on the same platform using different protocols are not directly comparable. Further, protocols introduce systematic biases that can be largely accounted for by using the correct data analysis techniques. </jats:p