Project description:Purpose: The goal of this study is to provided a comprehensive genomic information for functional genomic studies in Q. mongolica. Methods:The Quercus mongolica leaves were generated by deep sequencing, using Illumina Hiseq 4000. The high-quality reads were obtained by removing the reads that contained adaptor contamination, low quality bases and undetermined bases.The transcriptome were de novo assembly. Results:A total of 52934562 raw reads were obtained from Illumina sequencing platform. After filtering out the low quality reads, we obtained 52076914 clean reads, which assembled into 39130 transcripts with a mean length of 742 bp and GC content of 42.12%, and 24196 unigenes with a mean length of 732 bp and GC content of 42.34%, based on Trinity assembly platform. Conclusions:RNA-Seq was applied to polyadenylate-enriched mRNAs from leaves of Q. mongolica to obtain the transcriptome. De novo assembly was then applied followed by gene annotation and functional classification. The SSRs and SNPs were also obtained using assembled transcripts as reference sequences. The results of this study lay the foundation for further research on genetic diversity of Quercus.
2019-01-29 | GSE125799 | GEO
Project description:Raw sequence reads of Zygophyllum xanthoxylum and Tetraena mongolica
Project description:This study intends to explore the clinicopathological characteristics and survival prognosis of locally recurrent colorectal cancer patients with different treatment modes by retrospectively analyzing the medical records of locally recurrent colorectal cancer patients who received hospitalization in our center. Transcriptome sequencing and public databases were used to screen for molecular markers related to locally recurrent colorectal cancer and to explore molecular markers’ regulatory role in the progression of locally recurrent colorectal cancer.
