GeneHub-GEPIS is a web application that performs digital expression analysis in human and mouse tissues based on an integrated gene database. Using aggregated expressed sequence tag (EST) library information and EST counts, the application calculates the normalized gene expression levels across a large panel of normal and tumor tissues, thus providing rapid expression profiling for a given gene.
The study of gene families is pivotal for the understanding of gene evolution across different organisms and such phylogenetic background is often used to infer biochemical functions of genes. Modern high-throughput experiments offer the possibility to analyze the entire transcriptome of an organism; however, it is often difficult to deduct functional information from that data.
Large-scale RNAseq has substantially changed the transcriptomics field, as it enables an unprecedented amount of high resolution data to be acquired. However, the analysis of these data still poses a challenge to the research community. Many tools have been developed to overcome this problem, and to facilitate the study of miRNA expression profiles and those of their target genes. While a few of these enable both kinds of analysis to be performed, they also present certain limitations in terms of their requirements and/or the restrictions on data uploading.
The use of induced pluripotent stem cells (iPSC) derived from independent patients and sources holds considerable promise to improve the understanding of development and disease. However, optimized use of iPSC depends on our ability to develop methods to efficiently qualify cell lines and protocols, monitor genetic stability, and evaluate self-renewal and differentiation potential. To accomplish these goals, 57 stem cell lines from 10 laboratories were differentiated to 7 different states, resulting in 248 analyzed samples.
Understanding how regulatory networks globally coordinate the response of a cell to changing conditions, such as perturbations by shifting environments, is an elementary challenge in systems biology which has yet to be met. Genome-wide gene expression measurements are high dimensional as these are reflecting the condition-specific interplay of thousands of cellular components.
Lung cancer is the most common cause of cancer-related mortality with more than 1.4 million deaths per year worldwide. To search for significant somatic alterations in lung cancer, we analyzed, integrated and manually curated various data sets and literatures to present an integrated genomic database of non-small cell lung cancer (IGDB.NSCLC, http://igdb.nsclc.ibms.sinica.edu.tw).
miRNAs are the most abundant class of small non-coding RNAs, and they are involved in post-transcriptional regulations, playing a crucial role in the refinement of genetic programming during plant development. Here we present a comprehensive picture of miRNA regulation in Vitis vinifera L. plant during its complete life cycle. Furthering our knowledge about the post-transcriptional regulation of plant development is fundamental to understand the biology of such an important crop.
MicroRNAs (miRNAs) are small regulatory RNAs that play important roles in animals, plants, and viruses. Deep-sequencing technology has been widely adopted in miRNA investigations. However, it is still a big mysterious why nearly all sequencing data contain miRNA sequences from exogenous species, called exo-miRNAs. In this study, we developed a novel platform, exo-miRExplorer, for mining and identifying exo-miRNAs from high-throughput small RNA sequencing experiments which originated from tissues and cell lines of multiple organisms.
MicroRNAs (miRNAs) present diverse regulatory functions in a wide range of biological activities. Studies on miRNA functions generally depend on determining miRNA expression profiles between libraries by using a next-generation sequencing (NGS) platform. Currently, several online web services are developed to provide small RNA NGS data analysis. However, the submission of large amounts of NGS data, conversion of data format, and limited availability of species bring problems. In this study, we developed miRSeq to provide alternatives.
Several techniques have been tailored to the quantification of microRNA expression, including hybridization arrays, quantitative PCR (qPCR), and high-throughput sequencing. Each of these has certain strengths and limitations depending both on the technology itself and the algorithm used to convert raw data into expression estimates. Reliable quantification of microRNA expression is challenging in part due to the relatively low abundance and short length of the miRNAs.
Our database aim to make it easy for researchers to find the right tools or data source for their own specific study in miRNA field. Now we have collect >1000 tools. This database will update when every new 100 tools add in. Last update time 22nd April, 2018 (v1.2).
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