MOTIVATION: A reconstruction of full-length transcripts observed by next-generation sequencer or tiling arrays is an essential technique to know all phenomena of transcriptomes. Several techniques of the reconstruction have been developed. However, problems of high-level noises and biases still remain and interrupt the reconstruction. A method is required that is robust against noise and bias and correctly reconstructs transcripts regardless of equipment used. RESULTS: We propose a completely new statistical method that reconstructs full-length transcripts and can be applied on both next-generation sequencers and tiling arrays. The method called ARTADE2 analyzes 'positional correlation', meaning correlations of expression values for every combination on genomic positions of multiple transcriptional data. ARTADE2 then reconstructs full-length transcripts using a logistic model based on the positional correlation and the Markov model. ARTADE2 elucidated 17 591 full-length transcripts from 55 transcriptome datasets and showed notable performance compared with other recent prediction methods. Moreover, 1489 novel transcripts were discovered. We experimentally tested 16 novel transcripts, among which 14 were confirmed by reverse transcription-polymerase chain reaction and sequence mapping. The method also showed notable performance for reconstructing of mRNA observed by a next-generation sequencer. Moreover, the positional correlation and factor analysis embedded in ARTADE2 successfully detected regions at which alternative isoforms may exist, and thus are expected to be applied for discovering transcript biomarkers for a wide range of disciplines including preemptive medicine. AVAILABILITY: http://matome.base.riken.jp CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In supervised and unsupervised image classification, it is known that contextual classification methods based on Markov random fields (MRF) improve non-contextual classifiers successfully. In this paper, we consider unsupervised unmixing problem by introduction of a new MRF. First, spectral vectors observed at mixels are assumed to follow Gaussian mixtures. Second, vectors representing fractions of categories are supposed to follow MRF over the observed area. Then, we derive an unsupervised unmixing method, which is also useful for unsupervised classification. The proposed method was evaluated through a synthetic data set and a benchmark data set for classification, and it showed an excellent performance.

We consider contextual classification of hyperspectral data based on the boosting method. Bootstrap AdaBoost proposed by Kawaguchi and Nishii (2006) is applied to Spatial Boosting for contextual classification. The paper proposes a recursive version of Spatial Boosting. Posterior probabilities of each pixel are updated by the contextual classification function derived from Spatial Boosting and this is repeated. The proposed method with random stumps shows excellent performance for classification of AVIRIS data. Furthermore, it is superior to other well-known contextual classification methods including MRF based classifiers.

Over the past few decades, a considerable number of studies have been made on statistical classification methods for hyperspectral imagery. For classification of hyperspectral data, we must take care of a curse of dimension and computation cost. For the problem, we propose AdaBoost by decision stumps based on composed feature variables. We show that the method can be processed in acceptable time for AVIRIS data. The proposed method obtains a more accurate result compared to kernel based NN and SVM. We also assess features of hyperspectral data from the obtained classifiers. The proposed method can imply the relative importance of the feature for classification.