We developed a genome-wide transcriptomic atlas of grapevine (Vitis vinifera) based on 54 samples representing green and woody tissues and organs at different developmental stages as well as specialized tissues such as pollen and senescent leaves. Together, these samples expressed ;91% of the predicted grapevine genes. Pollen and senescent leaves had unique transcriptomes reflecting their specialized functions and physiological status. However, microarray and RNA-seq analysis grouped all the other samples into two major classes based on maturity rather than organ identity, namely, the vegetative/ green and mature/woody categories. This division represents a fundamental transcriptomic reprogramming during the maturation process and was highlighted by three statistical approaches identifying the transcriptional relationships among samples (correlation analysis), putative biomarkers (O2PLS-DA approach), and sets of strongly and consistently expressed genes that define groups (topics) of similar samples (biclustering analysis). Gene coexpression analysis indicated that the mature/woody developmental program results from the reiterative coactivation of pathways that are largely inactive in vegetative/green tissues, often involving the coregulation of clusters of neighboring genes and global regulation based on codon preference. This global transcriptomic reprogramming during maturation has not been observed in herbaceous annual species and may be a defining characteristic of perennial woody plants.

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Abstract. Clustering of sequential or temporal data is more challenging than traditional clustering as dynamic observations should be processed rather than static measures. This paper proposes a Hidden Markov Model (HMM)-based technique suitable for clustering of data sequences. The main aspect of the work is the use of a probabilistic model-based approach using HMM to derive new proximity distances, in the likelihood sense, between sequences. Moreover, a novel partitional clustering algorithm is designed which alleviates computational burden characterizing traditional hierarchical agglomerative approaches. Experimental results show that this approach provides an accurate clustering partition and the devised distance measures achieve good performance rates. The method is demonstrated on real world data sequences, i.e. the EEG signals due to their temporal complexity and the growing interest in the emerging field of Brain Computer Interfaces.