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Related Experiment Video

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Making Record-efficiency SnS Solar Cells by Thermal Evaporation and Atomic Layer Deposition

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Entropy predicts sensitivity of pseudorandom seeds.

Benjamin Dominik Maier¹, Kristoffer Sahlin²

¹Department of Mathematics, Stockholm University, 106 91 Stockholm, Sweden.

Genome Research

|May 22, 2023

Summary

This summary is machine-generated.

New strobemer seeds improve sequence similarity searches, even with high error rates and indels. A new model links seed entropy to sensitivity, aiding the design of more effective seeds for read mapping and ANI estimation.

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Sensitive Detection of Proteopathic Seeding Activity with FRET Flow Cytometry

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Published on: December 8, 2015

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Seed design is crucial for sequence similarity searches like read mapping and average nucleotide identity (ANI) estimation.
Traditional seeds (k-mers, spaced k-mers) show reduced sensitivity at high error rates, especially with indels.
Strobemers, a pseudorandom seeding construct, demonstrated high sensitivity at high indel rates, but the underlying reasons were not fully understood.

Purpose of the Study:

To develop a model for estimating seed entropy and understand its relationship with match sensitivity.
To design novel strobemer seed constructs with improved performance.
To evaluate the effectiveness of new strobemer constructs in read mapping and ANI estimation.

Main Methods:

Proposed a model to estimate the entropy of a seed.
Introduced three new strobemer seed constructs: mixedstrobes, altstrobes, and multistrobes.
Utilized simulated and biological data for performance evaluation.

Main Results:

Established a positive correlation between seed entropy and match sensitivity.
Demonstrated that new strobemer constructs (mixedstrobes, altstrobes, multistrobes) outperform existing strobemers in sequence matching sensitivity.
Showcased improved performance in read mapping (30% faster, 0.2% higher accuracy with minimap2) and ANI estimation (higher rank correlation with true ANI for high entropy seeds).

Conclusions:

Seed entropy is a key factor in determining match sensitivity, providing a framework for designing superior seeds.
The novel strobemer constructs offer enhanced sensitivity for sequence similarity searches.
Strobemers represent a significant advancement for applications like read mapping and ANI estimation, particularly in the presence of high error rates and indels.