Project description:The nascent polypeptide-associated (NAC) complex was described in yeast as a heterodimer composed of two subunits, α and β, and was shown to bind to the nascent polypeptides newly emerging from the ribosome. Although NAC function was widely described in yeast, less is known about its role in plants. The knock down of individual NAC subunit(s) led usually to a higher sensitivity to stress. In Arabidopsis thaliana genome, there are five genes coding for NACα subunit, and two genes coding for NACβ. Double homozygous mutant in both genes coding for NACβ was acquired, which showed a delayed development compared to the wild type, had abnormal number of flower organs, shorter siliques and greatly reduced seed set. Herein, both NACβ genes were characterized by complementation analysis, overexpression, subcellular localization, and promoter analysis. Since flowers were the most affected organs by nacβ mutation, the flower buds transcriptome was identified by RNA sequencing, and their proteome by gel-free approach. The differential expression analyses of transcriptomic and proteomic datasets suggest the involvement of NACβ subunits in stress responses and male gametophyte development.
Project description:In order to more accurately discover the cause of drug resistance in tumor treatment, and to provide a new basis for precise treatment.
Therefore, based on the umbrella theory of precision medicine, we carried out this single-center, prospective, and observational study to include patients with liver metastases from colorectal cancer. By combining genome, transcriptome, and proteomic sequencing data, we established a basis for colorectal cancer liver Transfer the multi-omics data of the sample, describe the reason for the resistance of the first-line treatment, and search for new therapeutic targets.
Project description:Proteomics data from a combind transcriptome/proteome study of three sexually deceptive orchids of the genus Ophrys. Data are from labella of mature, unpollinated flowers of (1) Ophrys exaltata subsp. archipelagi, (2) O. sphegodes, and (3) O. garganica. Proteomics data were searched against SwissProt and TAIR databases and further against organism-specific databases obtained from transcriptome sequencing (454, Sanger ESTs and Solexa data). Thirteen trypsinised gel slices per sample were subjected to electrospray ionisation-based LC-MS/MS analysis with a 2D linear ion trap Finnigan LTQ (Thermo Electron Corporation) equipped with an Ultimate Nano HPLC System (Dionex Corporation). Mass spectra were searched against SwissProt and Arabidopsis TAIR9 protein databases to identify peptides. Additionally, spectra were searched against protein databases created from the Ophrys reference transcriptome obtained in this study. Stringent criteria were used for the assignment of spectra to peptides (95% peptide identification probability) in Scaffold 3.3 (Proteome Software Inc., USA). In order to maximise the utility of proteomics data for uncovering proteins predicted by the orchid transcriptome, a minimum of one unique peptide was used for protein identification, while using two different stringency levels for the probabilistic assignment of peptides to proteins (99% for highest quality, HQ; 90% to maximise protein discovery, PD, in the absence of a fully sequenced genome). Concerning the sequencing and transcriptomics results: Three normalised cDNA libraries were constructed from three different Ophrys species, O. exaltata, O. garganica, and O. sphegodes. These libraries were 454 pyrosequenced and all the high quality reads generated in this study are available in the Sequence Read Archive (SRA) of the National Centre for Biotechnology Information (NCBI) with the accession number SRA060767. Additional sequencing of O. sphegodes flower labella yielded 1.7 Mbp of Sanger (dbEST library LIBEST_028084; dbEST IDs 77978749-77979571; GenBank accessions JZ163765-JZ164587) and 2.5 Gbp of Illumina Solexa (SRA060767) data.
Project description:This dataset contains the transcriptome sequence of Zostera marina as produced by Illumina sequencing. Four tissues were sequenced, female flower in late and early stages of development, the male flower, the root and leaf tissue. Full transcriptome sequencing of four tissues, including female flower at two time points in development
Project description:In this study, we aim to present a global view of transcriptome dynamics during flower development in chickpea. We generated around 234 million high-quality reads for eight flower development stages (ranging from 16 to 40 million reads for each stage) and 91 million high-quality reads from three vegetative tissues using Illumina high-throughput sequencing GAII platform. Because of non-availability of reference genome sequence, we mapped the reads to chickpea transcriptome comprised of 34,760 transcripts for estimation of their transcriptional activity in different tissue samples. The transcriptome dynamics was studied by comparison of gene expression during flower development stages with vegetative tissues.
Project description:In this study, we aim to present a global view of transcriptome dynamics during flower development in chickpea. We generated around 234 million high-quality reads for eight flower development stages (ranging from 16 to 40 million reads for each stage) and 91 million high-quality reads from three vegetative tissues using Illumina high-throughput sequencing GAII platform. Because of non-availability of reference genome sequence, we mapped the reads to chickpea transcriptome comprised of 34,760 transcripts for estimation of their transcriptional activity in different tissue samples. The transcriptome dynamics was studied by comparison of gene expression during flower development stages with vegetative tissues. We collected different tissue samples used in this study and total RNA isolated was subjected to Illumina sequencing. The sequenced data was further filtered using NGS QC Toolkit to obtain high-quality reads. The filtered reads were mapped to 34760 chickpea transcripts and reads per kilobase per million (RPKM) was calculated for each gene in all the sample to measure their gene expression. Differential expression analysis was performed using DESeq software. The genes preferentially expression during various stages of flower development as compared to vegetative stages and those with speciifc expression were identified.