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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
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The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
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Step-growth or condensation polymerization is a stepwise reaction of bi or multifunctional monomers to form long-chain polymers. As all the monomers are reactive, most of the monomers are consumed at the early stages of the reaction to form small chains of reactive oligomers, which then combine to form long polymer chains in the late stages. Hence, the reaction has to proceed for a long time to achieve high molecular weight polymers.
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Related Experiment Video

Updated: Jul 15, 2025

A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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Pangenome graph layout by Path-Guided Stochastic Gradient Descent.

Simon Heumos1,2, Andrea Guarracino3,4, Jan-Niklas M Schmelzle5,6

  • 1Quantitative Biology Center (QBiC), University of Tübingen, Tübingen 72076, Germany.

Biorxiv : the Preprint Server for Biology
|October 4, 2023
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Summary
This summary is machine-generated.

Visualizing large pangenome graphs is challenging. A new Path-Guided Stochastic Gradient Descent (PG-SGD) algorithm efficiently creates low-dimensional layouts, revealing genomic diversity and features.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Pangenome graphs are essential for studying genomic variability across populations.
  • Visualizing these large graphs in low dimensions is crucial for understanding genomic similarity and diversity.
  • Existing graph layout methods face scalability challenges with gigabase-scale pangenome data.

Conclusions:

  • PG-SGD offers an efficient solution for visualizing large pangenome graphs.
  • The algorithm aids in uncovering biological insights from population-scale genomic data.
  • The integration into the ODGI software facilitates broader accessibility and application.