We previously developed a web server CPGAVAS for annotation, visualization and GenBank submission of plastome sequences. Here, we upgrade the server into CPGAVAS2 to address the following challenges: (i) inaccurate annotation in the reference sequence likely causing the propagation of errors; (ii) difficulty in the annotation of small exons of genes petB, petD and rps16 and trans-splicing gene rps12; (iii) lack of annotation for other genome features and their visualization, such as repeat elements; and (iv) lack of modules for diversity analysis of plastomes. In particular, CPGAVAS2 provides two reference datasets for plastome annotation. The first dataset contains 43 plastomes whose annotation have been validated or corrected by RNA-seq data. The second one contains 2544 plastomes curated with sequence alignment. Two new algorithms are also implemented to correctly annotate small exons and trans-splicing genes. Tandem and dispersed repeats are identified, whose results are displayed on a circular map together with the annotated genes. DNA-seq and RNA-seq data can be uploaded for identification of single-nucleotide polymorphism sites and RNA-editing sites. The results of two case studies show that CPGAVAS2 annotates better than several other servers. CPGAVAS2 will likely become an indispensible tool for plastome research and can be accessed from http://www.herbalgenomics.org/cpgavas2.

No abstract

The success of deep neural networks often relies on a large amount of labeled examples, which can be difficult to obtain in many real scenarios. To address this challenge, unsupervised methods are strongly preferred for training neural networks without using any labeled data. In this paper, we present a novel paradigm of unsupervised representation learning by Auto-Encoding Transformation (AET) in contrast to the conventional Auto-Encoding Data (AED) approach. Given a randomly sampled transformation, AET seeks to predict it merely from the encoded features as accurately as possible at the output end. The idea is the following: as long as the unsupervised features successfully encode the essential information about the visual structures of original and transformed images, the transformation can be well predicted. We will show that this AET paradigm allows us to instantiate a large variety of transformations, from parameterized, to non-parameterized and GAN-induced ones. Our experiments show that AET greatly improves over existing unsupervised approaches, setting new state-of-the-art performances being greatly closer to the upper bounds by their fully supervised counterparts on CIFAR-10, ImageNet and Places datasets.

No abstract

No abstract