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[This corrects the article DOI: 10.1371/journal.pone.0184681.].

Phytolacca americana L. (pokeweed) has metal phytoremediation potential, but little is known about its metal accumulation-related genes. In this study, the de novo sequencing of total RNA produced 53.15 million reads covering 10.63 gigabases of transcriptome raw data in cadmium (Cd)-treated and untreated pokeweed. Of the 97,502 assembled unigenes, 42,197 had significant matches in a public database and were annotated accordingly. An expression level comparison between the samples revealed 1515 differentially expressed genes (DEGs), 923 down- and 592 up-regulated under Cd treatment. A KEGG pathway enrichment analysis of DEGs revealed that they were involved in 72 metabolism pathways, with photosynthesis, phenylalanine metabolism, ribosome, phenylpropanoid biosynthesis, flavonoid biosynthesis and carbon fixation in photosynthetic organisms containing 24, 18, 72, 14, 7 and 15 genes, respectively. Genes related to heavy metal tolerance, absorption, transport and accumulation were also identified, including 11 expansins, 8 nicotianamine synthases, 6 aquaporins, 4 ZRT/IRT-like proteins, 3 ABC transporters and 3 metallothioneins. The gene expression results of 12 randomly selected DEGs were validated using quantitative real-time PCR, and showed different response patterns to Cd in their roots, stems and leaves. These results may be helpful in increasing our understanding of heavy metal hyperaccumulators and in future phytoremediation applications.

Genomic copy number alteration and allelic imbalance are distinct features of cancer cells, and recent advances in the genotyping technology have greatly boosted the research in the cancer genome. However, the complicated nature of tumor usually hampers the dissection of the SNP arrays. In this study, we describe a bioinformatic tool, named GIANT, for genome-wide identification of somatic aberrations from paired normal-tumor samples measured with SNP arrays. By efficiently incorporating genotype information of matched normal sample, it accurately detects different types of aberrations in cancer genome, even for aneuploid tumor samples with severe normal cell contamination. Furthermore, it allows for discovery of recurrent aberrations with critical biological properties in tumorigenesis by using statistical significance test. We demonstrate the superior performance of the proposed method on various datasets including tumor replicate pairs, simulated SNP arrays and dilution series of normal-cancer cell lines. Results show that GIANT has the potential to detect the genomic aberration even when the cancer cell proportion is as low as 5∼10%. Application on a large number of paired tumor samples delivers a genome-wide profile of the statistical significance of the various aberrations, including amplification, deletion and LOH. We believe that GIANT represents a powerful bioinformatic tool for interpreting the complex genomic aberration, and thus assisting both academic study and the clinical treatment of cancer.