Despite the high economic and ecological importance of forests, our knowledge of the genomic evolution of trees under salt stress remains very limited. Here we report the genome sequence of the desert poplar, Populus euphratica, which exhibits high tolerance to salt stress. Its genome is very similar and collinear to that of the closely related mesophytic congener, P. trichocarpa. However, we find that several gene families likely to be involved in tolerance to salt stress contain significantly more gene copies within the P. euphratica lineage. Furthermore, genes showing evidence of positive selection are significantly enriched in functional categories related to salt stress. Some of these genes, and others within the same categories, are significantly upregulated under salt stress relative to their expression in another salt-sensitive poplar. Our results provide an important background for understanding tree adaptation to salt stress and facilitating the genetic improvement of cultivated poplars for saline soils.

Summary: With the rapid advances and prevalence of high-throughput genomic technologies, integrating information of multiple relevant genomic studies has brought new challenges. Microarray meta-analysis has become a frequently used tool in biomedical research. Little effort, however, has been made to develop a systematic pipeline and user-friendly software. In this article, we present MetaOmics, a suite of three R packages MetaQC, MetaDE and MetaPath, for quality control, differentially expressed gene identification and enriched pathway detection for microarray meta-analysis. MetaQC provides a quantitative and objective tool to assist study inclusion/exclusion criteria for meta-analysis. MetaDE and MetaPath were developed for candidate marker and pathway detection, which provide choices of marker detection, meta-analysis and pathway analysis methods. The system allows flexible input of experimental data, clinical outcome (case–control, multi-class, continuous or survival) and pathway databases. It allows missing values in experimental data and utilizes multi-core parallel computing for fast implementation. It generates informative summary output and visualization plots, operates on different operation systems and can be expanded to include new algorithms or combine different types of genomic data. This software suite provides a comprehensive tool to conveniently implement and compare various genomic meta-analysis pipelines. Availability:  http://www.biostat.pitt.edu/bioinfo/software.htm Contact:  ctseng@pitt.edu Supplementary Information:  Supplementary data are available at Bioinformatics online.