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Related Concept Videos

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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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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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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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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.
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The present-day mitochondrial and chloroplast genomes have retained some of the characteristics of their ancestral prokaryotes and also have acquired new attributes during their evolution within eukaryotic cells. Like prokaryotic genomes, mitochondrial and chloroplast genomes neither bind with histone-like proteins nor show complex packaging into chromosome-like structures, as observed in eukaryotes. Unlike mitotic cell divisions observed in eukaryotic cells, mitochondria and chloroplasts...
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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iSeg: an efficient algorithm for segmentation of genomic and epigenomic data.

Senthil B Girimurugan1, Yuhang Liu2, Pei-Yau Lung2

  • 1Department of Mathematics, Florida Gulf Coast University, Fort Myers, FL, USA.

BMC Bioinformatics
|April 13, 2018
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Summary

A new algorithm, iSeg, efficiently segments genomic and epigenomic data with high accuracy. This tool improves upon existing methods, offering speed and flexibility for complex biological datasets.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Genomic segmentation is crucial for identifying functional elements.
  • Existing algorithms struggle with accuracy and speed for complex genomic and epigenomic data.
  • There's a need for improved computational tools in genomics research.

Purpose of the Study:

  • To develop an efficient and accurate algorithm for genomic and epigenomic profile segmentation.
  • To create a general computational framework applicable to various genomic datasets.
  • To enhance the speed and accuracy of segment-calling in large-scale genomic analyses.

Main Methods:

  • Developed iSeg, an algorithm utilizing dynamic programming and a novel binary tree data structure.
  • Employed an objective function based on p-values for segment significance testing and refinement.
  • Utilized t-tests for p-value computation across diverse datasets.

Main Results:

  • iSeg demonstrates comparable or superior accuracy to popular segment-calling algorithms.
  • The algorithm efficiently segments various genomic and epigenomic data types (e.g., DNA copy number variation, nucleosome occupancy).
  • iSeg is computationally efficient, suitable for large datasets and long sequences.

Conclusions:

  • iSeg is an efficient, general-purpose segmentation tool for genomic data analysis.
  • The algorithm offers tunable parameters for flexibility with different data characteristics.
  • iSeg provides accurate and fast segmentation, outperforming many existing methods.