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Related Concept Videos

Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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Genome Size and the Evolution of New Genes03:21

Genome Size and the Evolution of New Genes

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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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IR Frequency Region: Fingerprint Region01:03

IR Frequency Region: Fingerprint Region

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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Vesicular Tubular Clusters01:45

Vesicular Tubular Clusters

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After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
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Genomic Imprinting and Inheritance02:30

Genomic Imprinting and Inheritance

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Diploid organisms inherit genetic material through chromosomes from both parents. Copies of the same gene are known as alleles. In most cases, both alleles are simultaneously expressed and allow various cellular processes to function optimally. If one of the alleles is missing or mutated, the expression of the other allele can compensate; however, this is not true for all genes.
The expression of some genes depends on which parent passed the gene to the offspring, through a phenomenon known as...
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Spatial Separation of Molecular Conformers and Clusters
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Genomic region detection via Spatial Convex Clustering.

John Nagorski1, Genevera I Allen1,2,3

  • 1Department of Statistics, Rice University, Houston, TX, United States of America.

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Summary

Spatial Convex Clustering (SpaCC) identifies biologically relevant genomic regions from large datasets. This unsupervised method improves disease biomarker discovery and data analysis for methylation and copy number variation.

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Cancer Epigenetics

Background:

  • Modern genomic technologies generate vast amounts of data from spatially registered probes across chromosomes.
  • Individual probes are less informative; the goal is to identify contiguous genomic regions for biological interpretation and biomarker discovery.
  • Existing methods often struggle with the scale and complexity of genomic data, necessitating advanced analytical techniques.

Purpose of the Study:

  • To introduce an unsupervised feature learning technique, Spatial Convex Clustering (SpaCC), for mapping technological units (probes) to biological units (genomic regions).
  • To develop a method specifically for detecting multi-subject regions of methylation and segments of copy number variation.
  • To demonstrate SpaCC's utility as a pre-processing technique for dimension reduction in large-scale genomics data.

Main Methods:

  • Developed Spatial Convex Clustering (SpaCC), an unsupervised technique leveraging fusion penalties and convex clustering.
  • Formulated the method as a convex optimization problem with a massively parallelizable algorithm for efficient computation.
  • Incorporated automated approaches for handling missing values and determining optimal tuning parameters.

Main Results:

  • SpaCC effectively identifies biologically interpretable genomic regions common across multiple subjects.
  • Simulation studies using real methylation and copy number variation data showed significant performance gains over existing methods.
  • Demonstrated SpaCC's effectiveness in cancer epigenetics case studies for subtype discovery, network estimation, and epigenetic-wide association studies.

Conclusions:

  • SpaCC is a powerful tool for unsupervised feature learning in genomics, transforming probe-level data into meaningful genomic regions.
  • The method offers significant advantages for biomarker discovery, disease subtyping, and network analysis in cancer epigenetics.
  • SpaCC provides a robust and scalable approach for pre-processing large-scale genomic datasets, enhancing downstream analyses.