Project description:Four cancer cell line, ie SW480-Vector, SW480-TET2, SW620-Vector and SW620-TET2 were treated with tgfb1, repsox and control. Then the total RNA of these samples were extracted and sequenced with Illumina Nextseq 500.
Project description:Three cancer cell line, ie SW480, SW620 and Caco-2, were treated with TET2, TET2CD and control vector lentivirus. The expression of TET2 was validated using qPCR. Then the total RNA of these samples were extracted and sequenced with Illumina Nextseq 500.
Project description:This study evaluates the effects of 96h stimulus with Interleukin 4 (100 ng/mL) on the transcriptome of human umbilical cord blood derived mast cells. Through this approach, we identify upregulation of key intraepithelial mast cell-associated transcripts and downregulation of subepithelial mast cell-associated transcripts. Replicates are technical duplicates. Samples were sequenced on an Illumina NextSeq 500.
Project description:Three libraries from 100 HEK293 cells each were prepared using a Smartseq based custom library preparation approach with unique molecular identifiers. Libraries were sequenced on a Illumina NextSeq 500
Project description:Total RNA was isolated from serum samples by the Qiagen miRNeasy Serum/Plasma extraction kit and QIAcube automation. All samples were quantified using the Nanodrop spectrophotometer prior to plating. Small RNA-seq libraries were prepared using the Norgen Biotek Small RNA Library Prep Kit and then sequenced on the Illumina NextSeq 500 platform at 51bp single end reads. ExceRpt was employed to assess the read quality and annotate miRNAs. The read count was log transformed and normalized by quantile normalization.
Project description:Precision medicine is being enabled in high-income countries by the growing availability of health data, increasing knowledge of the genetic determinants of disease and variation in response to treatment (pharmacogenomics), and the decreasing costs of data generation, which promote routine application of genomic technologies in the health sector. However, there is uncertainty about the feasibility of applying precision medicine approaches in low- and middle-income countries, due to the lack of population-specific knowledge, skills, and resources. The Human Heredity and Health in Africa (H3Africa) initiative was established to drive new research into the genetic and environmental basis for human diseases of relevance to Africans as well as to build capacity for genomic research on the continent. Precision medicine requires this capacity, in addition to reference data on local populations, and skills to analyze and interpret genomic data from the bedside. The H3Africa consortium is collectively processing samples and data for over 70,000 participants across the continent, accompanied in most cases by rich clinical information on a variety of non-communicable and infectious diseases. These projects are increasingly providing novel insights into the genetic basis of diseases in indigenous populations, insights that have the potential to drive the development of new diagnostics and treatments. The consortium has also invested significant resources into establishing high-quality biorepositories in Africa, a bioinformatic network, and a strong training program that has developed skills in genomic data analysis and interpretation among bioinformaticians, wet-lab researchers, and health-care professionals. Here, we describe the current perspectives of the H3Africa consortium and how it can contribute to making precision medicine in Africa a reality.
Project description:Purpose: The study was designed to identify transcriptional differences of P0 liver HSCs with different genotypes or different cell size. Method: For RNA-seq, libraries were prepared according to the Smart-seq2 protocol from 100-200 sorted HSCs. Samples were sequenced by NextSeq500 (Illumina) with single-end 75-bp read length using the NextSeq 500/550 High Output v2 Kit (75 cycles, Illumina). The RNA-seq pipeline from Basepair (www.basepairtech.com) was used for the analysis. Expression count was analyzed by STAR and differential expression by DESeq2 (P < 0.05 and fold-change > 2).