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Topic · Genetics

Genome Rearrangement Algorithms

Genome Rearrangement Algorithms is a research topic within Genetics. Science Explorer counts 3k research works in it since 1954. 22.4% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the study of genomic rearrangements, evolutionary genomics, and ancestral genome reconstruction. It explores topics such as comparative genomics, chromosome evolution, phylogenetic analysis based on gene order, genome distance estimation, breakpoint reuse in evolution, and the role of duplications and inversions in shaping genomes.

  • Genomic Rearrangements
  • Evolutionary Genomics
  • Ancestral Genome Reconstruction
  • Comparative Genomics
  • Chromosome Evolution
  • Phylogenetic Analysis
  • Gene Order Phylogeny
  • Genome Distance Estimation
  • Breakpoint Reuse
  • Duplication and Inversion
Research works
3k
fractional, since 1954
In the world top 10%
667
per year above
Top-10% rate
22.4%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
-17%
the tick is no change

Which countries lead Genome Rearrangement Algorithms research?

By volume, the United States and China publish the most (81 and 48 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 81 works
  2. 2 China 48 works
  3. 3 Germany 24 works
  4. 4 France 20 works
  5. 5 United Kingdom 13 works
  6. 6 India 12 works
  7. 7 Canada 12 works
  8. 8 Russia 11 works
  9. 9 Brazil 11 works
  10. 10 Italy 10 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 33.3%China: 20.0%Germany: 10.0%France: 8.4%6 others listed: 28.2%33%largest
United States81 · 33.3%China48 · 20.0%Germany24 · 10.0%France20 · 8.4%6 others listed68 · 28.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Genome Rearrangement Algorithms research?

By volume in 2022–2025, Universidade Estadual de Campinas (UNICAMP) publishes the most Genome Rearrangement Algorithms research, followed by Centre National de la Recherche Scientifique and Pennsylvania State University.

By volume, 2022–2025

  1. 1 Universidade Estadual de Campinas (UNICAMP)Brazil 4 works
  2. 2 Centre National de la Recherche ScientifiqueFrance 4 works
  3. 3 Pennsylvania State UniversityUnited States 3 works
  4. 4 University of HelsinkiFinland 3 works
  5. 5 Shandong University of Science and TechnologyChina 3 works
  6. 6 Amrita Vishwa VidyapeethamIndia 3 works
  7. 7 Bielefeld UniversityGermany 2 works
  8. 8 University of California San DiegoUnited States 2 works
  9. 9 Université de SherbrookeCanada 2 works
  10. 10 University of WarsawPoland 2 works

Who are the leading researchers in Genome Rearrangement Algorithms?

The most-cited researchers publishing on Genome Rearrangement Algorithms include David Haussler, David Bryant and Peter F. Stadler.

  1. 1 David Haussler United States 3.4k citations
  2. 2 David Bryant New Zealand 1.9k citations
  3. 3 Peter F. Stadler Austria 1.8k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Genome Rearrangement Algorithms research done?

The largest centres of Genome Rearrangement Algorithms research in 2022–2025 are Beijing (China), Paris (France), Moscow (Russia) and Qingdao (China).

Largest cities, 2022–2025

  1. 1 Beijing China 11 works
  2. 2 Paris France 6 works
  3. 3 Moscow Russia 6 works
  4. 4 Qingdao China 5 works
  5. 5 Campinas Brazil 4 works
  6. 6 New York United States 4 works
  7. 7 Helsinki Finland 3 works
  8. 8 Saint Petersburg Russia 3 works
  9. 9 Tokyo Japan 3 works
  10. 10 State College United States 3 works
See Genome Rearrangement Algorithms on the map

Is Genome Rearrangement Algorithms research growing?

Output in 2018–2022 was 17% lower than in 2013–2017, peaking in 2026.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.