High-throughput data

Reconstructed monomers were used to design a satellite-specific fluorescent in situ hybridization (FISH) probe. The data were also analysed within a phylogenetic framework built using the internal transcribed spacer (ITS) sequences of 45S nuclear ribosomal DNA.

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The consensus on how to choose a reference gene for serum or plasma miRNA expression qPCR studies has not been reached and none of the potential candidates have yet been convincingly validated. We proposed a new in silico approach of finding a suitable reference for human, circulating miRNAs and identified a new set of endogenous reference miRNA based on miRNA profiling experiments from Gene Expression Omnibus.

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After over a decade of developments in field collection, laboratory methods and advances in highthroughput sequencing, contamination remains a key issue in ancient DNA research. Currently, human and microbial contaminant DNA still impose challenges on cost-effective sequencing and accurate interpretation of ancient DNA data.

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Many tools exist in the analysis of bacterial RNA sequencing (RNA-seq) transcriptional profiling experiments to identify differentially expressed genes between experimental conditions. Generally, the workflow includes quality control of reads, mapping to a reference, counting transcript abundance, and statistical tests for differentially expressed genes.

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RNA molecules fold into complex three-dimensional shapes, guided by the pattern of hydrogen bonding between nucleotides. This pattern of base pairing, known as RNA secondary structure, is critical to their cellular function.

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Recently, rapid improvements in technology and decrease in sequencing costs have made RNA-Seq a widely used technique to quantify gene expression levels. Various normalization approaches have been proposed, owing to the importance of normalization in the analysis of RNA-Seq data.

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A rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to highthroughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition.

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Recent cancer genome studies on many human cancer types have relied on multiple molecular highthroughput technologies. Given the vast amount of data that has been generated, there are surprisingly few databases which facilitate access to these data and make them available for flexible analysis queries in the broad research community.

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Unsupervised clustering represents one of the most widely applied methods in analysis of highthroughput ‘omics data. A variety of unsupervised model-based or parametric clustering methods and nonparametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. N ≥ 500.

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The European Community has adopted very restrictive policies regarding the dissemination and use of genetically modified organisms (GMOs). In fact, a maximum threshold of 0.9% of contaminating GMOs is tolerated for a “GMOfree” label.

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Various efforts to understand the relationship between biological information and disease have been done using many different types of highthroughput data such as genomics and metabolomics. However, information obtained from previous studies was not satisfactory, implying that new direction of studies is in need.

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Next-generation sequencing (NGS) is a high throughput sequencing technology, which has revolutionized both basic and clinical research of the human genetic disorders. This technology is also called massively parallel sequencing (MPS) due to its ability to generate a huge amount of output data in a cost- and time-effective manner. NGS is widely utilized for different sequencing applications such as targeted sequencing (a group of candidate genes), exome sequencing (all coding regions), and whole genome sequencing (the entire human genome).

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Institute of Medical Physics and Biophysics, Charité, Berlin, Germany. †Max Planck Institute for Molecular Genetics, Berlin, Germany. ‡Max Planck Institute for Infection Biology, Berlin, Germany. Correspondence: Hans Lehrach. E-mail: lehrach@molgen.mpg.de comment reviews Published: 16 April 2004 Genome Biology 2004, 5:322 The electronic version of this article is the complete one and can be found online at http://genomebiology.

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