Efficient targeted transcript discovery via array-based normalization of RACE libraries.

Rapid amplification of cDNA ends (RACE) is a widely used approach for transcript identification. Random clone selection from the RACE mixture, however, is an ineffective sampling strategy if the dynamic range of transcript abundances is large. To improve sampling efficiency of human transcripts, we hybridized the products of the RACE reaction onto tiling arrays and used the detected exons to delineate a series of reverse-transcriptase (RT)-PCRs, through which the original RACE transcript population was segregated into simpler transcript populations. We independently cloned the products and sequenced randomly selected clones. This approach, RACEarray, is superior to direct cloning and sequencing of RACE products because it specifically targets new transcripts and often results in overall normalization of transcript abundance. We show theoretically and experimentally that this strategy leads indeed to efficient sampling of new transcripts, and we investigated multiplexing the strategy by pooling RACE reactions from multiple interrogated loci before hybridization.

Subtelomeric 6p deletion: clinical and array-CGH characterization in two patients

We report on two patients with de novo subtelomeric terminal deletion of chromosome 6p. Patient 1 is an 8-month-old female born with normal growth parameters, typical facial features of 6pter deletion, bilateral corectopia, and protruding tongue. She has severe developmental delay, profound bilateral neurosensory deafness, poor visual contact, and hypsarrhythmia since the age of 6 months. Patient 2 is a 5-year-old male born with normal growth parameters and unilateral hip dysplasia; he has a characteristic facial phenotype, bilateral embryotoxon, and moderate mental retardation. Further characterization of the deletion, using high-resolution array comparative genomic hybridization (array-CGH; Agilent Human Genome kit 244 K), revealed that Patient 1 has a 8.1 Mb 6pter-6p24.3 deletion associated with a contiguous 5.8 Mb 6p24.3-6p24.1 duplication and Patient 2 a 5.7 Mb 6pter-6p25.1 deletion partially overlapping with that of Patient 1. Complementary FISH and array analysis showed that the inv del dup(6) in Patient 1 originated de novo. Our results demonstrate that simple rearrangements are often more complex than defined by standard techniques. We also discuss genotype-phenotype correlations including previously reported cases of deletion 6p.

Cluster-based active learning for compact image classification

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.