alexa On Evaluation of Rankings in Analysis of NGS Data
ISSN 2469-9853

Journal of Next Generation Sequencing & Applications
Open Access

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On Evaluation of Rankings in Analysis of NGS Data

Margaret R Donald1 and Susan R Wilson1,2*
1School of Mathematics and Statistics, University of New South Wales, Kensington, NSW 2052, Australia
2Mathematical Sciences Institute, Australian National University, Canberra, ACT 2601, Australia
*Corresponding Author : Susan R Wilson
School of Mathematics and Statistics
University of New South Wales, Kensington
NSW 2052, Australia
Tel: +61 2 6125 4460
E-mail: [email protected]
Rec date: Dec 15, 2015; Acc date: Jan 25, 2016; Pub date: Jan 28, 2016
Citation: Donald MR, Wilson SR (2016) On Evaluation of Rankings in Analysis of NGS Data. Next Generat Sequenc & Applic S1:003. doi: 10.4172/2469-9853.S1-003
Copyright: © 2016 Donald MR, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


A ranked list of genes (or proteins or regions) is a common output from analysis of NGS data. Many choices will have been made in the analysis (either explicitly or implicitly) and there is no ‘correct’ method to use for the analysis. So if two different and appropriate methods are used, an important question is the following: How similar are the two rankings? Allowing a looser definition of agreement than ‘exact’ agreement, and using a Bayesian logit model with O’Sullivan penalized splines, a useful visualisation has been developed giving the probability of agreement at each point and the credible interval at which the sequence degenerates into noise. The approach is illustrated on some typical RNA-seq data. The estimate of the point at which the agreement between the rankings degenerates into noise, as well as the credible interval, will be over-estimates of their true values. From a practical perspective, it is usually better to estimate a slightly larger set of top-ranked data than one that is smaller. Even so, the estimates found for NGS data are relatively small compared with the total length of the sequence.


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