Similar Datasets
Project description:Kernel development is accompanied by complex gene networks. Expression quantitative trait loci (eQTL) analysis is an efficient way to detect the regulatory elements of genes, especially the trans-eQTLs help to construct the regulatory networks of genes and contribute to a better understanding of the intrinsic mechanisms of biological processes. Till now, the 15 DAP (day after pollination) eQTL has been elucidated in maize kernel, but little is known about the early stage. Here, we conduct eQTL analysis for 5 DAP maize kernel using 318 maize inbred lines. The results will provide insights into the genetic basis of early kernel development.
2019-10-01 | GSE110315 | GEO
Project description:We performed a molecular characterization of the maize small kernel mutant with endosperm developmental deficiency phenotypes.To elucidate how SMK8 affects kernel development, we performed RNA sequencing (RNA-seq), and the results revealed numerous differentially expressed genes related to storage proteins and starch biosynthesis and biosynthesis of amino acids.
2020-07-09 | GSE154016 | GEO
Project description:Tumor formation is in part driven by copy number alterations (CNAs), which can be measured using array Comparative Genomic Hybridization (aCGH). Identifying regions of DNA that are gained or lost in a significant fraction of tumor samples can facilitate identification of genes possibly related to the development of cancer. Until now, no method has been described that provides a statistical framework in which these regions can be identified without prior discretization of the aCGH data. Kernel Convolution - a Statistical Method for Aberrant Region deTection (KC-SMART) is a new approach which inputs continuous aCGH data to identify regions that are significantly aberrant across an entire tumor set. KC-SMART uses kernel convolution to generate a Kernel Smoothed Estimate (KSE) of CNAs across the genome, aggregated over all tumors. By varying the width of the kernel function, a scale space is created which enables the detection of aberrations of varying size. In an analysis of 89 human sporadic breast tumors KC-SMART performs better than a previously published method, STAC. Our method not only identified aberrations that are strongly associated with clinical parameters, but also showed stronger enrichment for known cancer genes in the detected regions. Furthermore, KC-SMART identifies 18 aberrant regions in mammary tumors from p53 conditional knock-out mice. These regions, combined with gene expression micro-array data, point to known cancer genes and novel candidate cancer genes. Keywords: Comparative Genomic Hybridization, aCGH
2008-02-01 | GSE7794 | GEO
Project description:Maize kernel is an important source of food, feed and industrial raw materials. The illustration of the molecular mechanisms of maize kernel development will be helpful for the manipulation of maize improvements. Although a great many researches based on molecular biology and gecetics have greatly increased our understanding on the kernel development, many of the mechanisms controlling this important process remain elusive. In current study, a microarray with approximately 58,000 probes was used to study the dynamic gene expression during kernel development from the fertilization to physiological maturity. Samples from two consecutive time-points were paired and labeled using different fluorescent dyes (Cy3 and Cy5) and hybridized in the same array. Hybridization of slides was performed according to the manufacturer’s instructions (http://www.maizearray.org/). The hybridized slides were scanned by a Genepix 4000B (Axon, USA). A loop design was applied for running the microarray. Two replicates of each pair of samples were carried out to test both the reproducibility and quality of the chip hybridizations. By comparing six consecutive time-points, namely 1, 5, 10, 15, 25 and 35 days after pollination (DAP), 3,445 differentially expressed genes were identified. These genes were then grouped into 10 clusters showing specific expression patterns using a K-means clustering algorithm. An investigation of function and expression patterns of genes expanded our understanding of the regulation mechanism underlying the important developmental processes, cell division and kernel filling. The differential expression of genes involved in plant hormone signaling pathways suggested that phytohormone might play a critical role in the kernel developmental process. Moreover, regulation of some transcription factors and protein kinases might be involved in the whole developmental process. Keywords: Time course, development
2008-04-09 | GSE9386 | GEO