Browsing by Subject "Ranking"

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  • Topa, Hande; Honkela, Antti (2018)
    Background: Genome-wide high-throughput sequencing (HIS) time series experiments are a powerful tool for monitoring various genomic elements over time. They can be used to monitor, for example, gene or transcript expression with RNA sequencing (RNA-seq), DNA methylation levels with bisulfite sequencing (BS-seq), or abundances of genetic variants in populations with pooled sequencing (Pool-seq). However, because of high experimental costs, the time series data sets often consist of a very limited number of time points with very few or no biological replicates, posing challenges in the data analysis. Results: Here we present the GPrank R package for modelling genome-wide time series by incorporating variance information obtained during pre-processing of the HIS data using probabilistic quantification methods or from a beta-binomial model using sequencing depth. GPrank is well-suited for analysing both short and irregularly sampled time series. It is based on modelling each time series by two Gaussian process (GP) models, namely, time-dependent and time-independent GP models, and comparing the evidence provided by data under two models by computing their Bayes factor (BF). Genomic elements are then ranked by their BFs, and temporally most dynamic elements can be identified. Conclusions: Incorporating the variance information helps GPrank avoid false positives without compromising computational efficiency. Fitted models can be easily further explored in a browser. Detection and visualisation of temporally most active dynamic elements in the genome can provide a good starting point for further downstream analyses for increasing our understanding of the studied processes.
  • Topa, Hande; Honkela, Antti (BioMed Central, 2018)
    Abstract Background Genome-wide high-throughput sequencing (HTS) time series experiments are a powerful tool for monitoring various genomic elements over time. They can be used to monitor, for example, gene or transcript expression with RNA sequencing (RNA-seq), DNA methylation levels with bisulfite sequencing (BS-seq), or abundances of genetic variants in populations with pooled sequencing (Pool-seq). However, because of high experimental costs, the time series data sets often consist of a very limited number of time points with very few or no biological replicates, posing challenges in the data analysis. Results Here we present the GPrank R package for modelling genome-wide time series by incorporating variance information obtained during pre-processing of the HTS data using probabilistic quantification methods or from a beta-binomial model using sequencing depth. GPrank is well-suited for analysing both short and irregularly sampled time series. It is based on modelling each time series by two Gaussian process (GP) models, namely, time-dependent and time-independent GP models, and comparing the evidence provided by data under two models by computing their Bayes factor (BF). Genomic elements are then ranked by their BFs, and temporally most dynamic elements can be identified. Conclusions Incorporating the variance information helps GPrank avoid false positives without compromising computational efficiency. Fitted models can be easily further explored in a browser. Detection and visualisation of temporally most active dynamic elements in the genome can provide a good starting point for further downstream analyses for increasing our understanding of the studied processes.
  • Davies, Caelum John (Helsingin yliopisto, 2020)
    Where is best? Much like the pay-for-access services, profiteering, and mystery that in-part defines the nation brand ranks that form the subject of this work; cross my palm with enough money and it might just be you when the results of this work’s index are revealed! Provocation aside; the concepts of nation branding and nation brands have quickly entered the spotlight of the world’s stage since Anholt first coined the term in 1996. Quickly, it has become big business. From Cool Britainia to ESTonia, nations have been quick in ‘corporatising’ their image to gain attraction and favour around the world. This work is not interested in the brands created by countries per say, rather it is interested in a country’s brand strength, that is how effective countries are in achieving the goals they set out to accomplish through their branding efforts. This work is not the first to be interested in such a thing, for within a decade of Anholt coining the term, he had developed a rank to measure and compare the strength of nation’s brands himself. Jump forward to 2020 and the world has multiple such organisations - often consultancy firms - seeking to do the same through the development of their own ranks. This work seeks to cast a critical eye over these ranks, developing an index of European country brand strength itself. Specifically, this work does three things. Firstly, it provides an understanding of ‘nation brand’ from a country level perspective, generating its findings based on literature (and lack of literature) from thirty-five countries. Secondly, it critically assesses the success and failures of nine prominent nation brand ranks, and in doing so draws from outside literature on University ranking and ranking in general. Thirdly, the crux of the work. Based on the findings gleaned from the previous aim’s outcomes, it develops an original index of country brand strength that is less analytically flawed than its comparators. Through the building its own index of country brand strength, a more holistic understanding of the challenges of indexing and ranking is developed, whist also evidencing that at least some of the shortcomings of its comparators can be overcome. This undertaking is done following OECD guidance, and inspired by the 2010 work of Marc Fetscherin. To compliment its aims, the work provides a detailed discussion on key interlinked and underlying concepts including soft power, geoeconomics, and globalisation. The index is not without fault, failing one test of soundness, but it does yield that Denmark, the Netherlands, Austria, Sweden, Ireland and Estonia share the strongest country brands within the EU. The ranks it casts a critical eye over are not without fault either, with the biggest problems reviled to be those of black boxing, subjectivity in surveying, and enablement of misinterpretation through presenting only rank positions of countries, and not index scores.