Dongxiang wild rice (Oryza rufipogon Griff.) is the progenitor of cultivated rice (Oryza sativa L.), and is well known for its superior level of tolerance against cold, drought and diseases. To date, however, little is known about the salt-tolerant character of Dongxiang wild rice. To elucidate the molecular genetic mechanisms of salt-stress tolerance in Dongxiang wild rice, the Illumina HiSeq 2000 platform was used to analyze the transcriptome profiles of the leaves and roots at the seedling stage under salt stress compared with those under normal conditions. The analysis results for the sequencing data showed that 6,867 transcripts were differentially expressed in the leaves (2,216 up-regulated and 4,651 down-regulated) and 4,988 transcripts in the roots (3,105 up-regulated and 1,883 down-regulated). Among these differentially expressed genes, the detection of many transcription factor genes demonstrated that multiple regulatory pathways were involved in salt stress tolerance. In addition, the differentially expressed genes were compared with the previous RNA-Seq analysis of salt-stress responses in cultivated rice Nipponbare, indicating the possible specific molecular mechanisms of salt-stress responses for Dongxiang wild rice. A large number of the salt-inducible genes identified in this study were co-localized onto fine-mapped salt-tolerance-related quantitative trait loci, providing candidates for gene cloning and elucidation of molecular mechanisms responsible for salt-stress tolerance in rice.
Summary: Genetic modifications that cause pivotal protein inactivation or abnormal activation may lead to cell signaling pathway change or even dysfunction, resulting in cancer and other diseases. In turn, dysfunction will further produce 'novel proteins' that do not exist in the canonical human proteome. Identification of novel proteins is meaningful for identifying promising drug targets and developing new therapies. In recent years, several tools have been developed for identifying DNA or RNA variants with the extensive application of nucleotide sequencing technology. However, these tools mainly focus on point mutation and have limited performance in identifying large-scale variants as well as the integration of mutations. Here we developed a hybrid Sequencing Analysis bioinformatic pipeline by integrating all relevant detection Kits(SAKit): this pipeline fully integrates all variants at the genomic and transcriptomic level that may lead to the production of novel proteins defined as proteins with novel sequences compare to all reference sequences by comprehensively analyzing the long and short reads. The analysis results of SAKit demonstrate that large-scale mutations have more contribution to the production of novel proteins than point mutations, and long-read sequencing has more advantages in large-scale mutation detection. Availability and implementation: SAKit is freely available on docker image (https://hub.docker.com/repository/docker/therarna/sakit), which is mainly implemented within a Snakemake framework in Python language.
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