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EPIC-seq, (short for Epigenetic Expression Inference by Cell-free DNA Sequencing), is a high-throughput method that specifically targets gene promoters using cell-free DNA (cfDNA) sequencing. By employing non-invasive techniques such as blood sampling, it infers the expression levels of targeted genes. It consists of both wet and dry lab stages.
Any platform that can allow for the ligated fragments to be sequenced across the NheI junction (Roche 454) or by paired-end or mate-paired reads (Illumina GA and HiSeq platforms) would be suitable for Hi-C. [4] Before high-throughput sequencing, the quality of the library should be verified using Sanger sequencing, wherein the long sequencing ...
High-throughput screening (HTS) is a method for scientific discovery especially used in drug discovery and relevant to the fields of biology, materials science [1] and chemistry. [ 2 ] [ 3 ] Using robotics , data processing/control software, liquid handling devices, and sensitive detectors, high-throughput screening allows a researcher to ...
eDNA metabarcoding has applications to diversity monitoring across all habitats and taxonomic groups, ancient ecosystem reconstruction, plant-pollinator interactions, diet analysis, invasive species detection, pollution responses, and air quality monitoring. eDNA metabarcoding is a unique method still in development and will likely remain in flux for some time as technology advances and ...
The primary goal of bioinformatics is to increase the understanding of biological processes. What sets it apart from other approaches is its focus on developing and applying computationally intensive techniques to achieve this goal. Examples include: pattern recognition, data mining, machine learning algorithms, and visualization.
Massive parallel sequencing or massively parallel sequencing is any of several high-throughput approaches to DNA sequencing using the concept of massively parallel processing; it is also called next-generation sequencing (NGS) or second-generation sequencing.
Hi-C uses high-throughput sequencing to find the nucleotide sequence of fragments [2] [22] and uses paired end sequencing, which retrieves a short sequence from each end of each ligated fragment. As such, for a given ligated fragment, the two sequences obtained should represent two different restriction fragments that were ligated together in ...
In general, a machine learning system can usually be trained to recognize elements of a certain class given sufficient samples. [30] For example, machine learning methods can be trained to identify specific visual features such as splice sites. [31] Support vector machines have been extensively used in cancer genomic studies. [32]