Björn D. Lindahl

Molecular Biology · 2007–2023

A researcher in the Science corpus: 9 papers; trusts 1 researcher and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

Whom Björn D. Lindahl trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Michael P. Cummings
  2. Christopher Quince
  3. Jens Reeder
  4. Micah Hamady
  5. Rob Knight
  6. Leho Tedersoo
  7. Jaikoo Lee
  8. Qiong Wang
  9. Stephen F. Altschul
  10. D. Read

Who trusts Björn D. Lindahl overall

  1. Surono
  2. Christopher W. Fernandez
  3. Petr Baldrian
  4. S. Allison
  5. P. Högberg
  6. K. Narisawa
  7. Hayssam Soueidan
  8. G. D. De Deyn
  9. P. Kennedy
  10. M. Buée

Papers from Björn D. Lindahl's perspective

Ordered by how strongly the researchers Björn D. Lindahl trusts endorse each paper. Same corpus, a different order for every researcher.

  1. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB (Nucleic Acids Research)
  2. Genome sequencing in microfabricated high-density picolitre reactors (Nature)
  3. Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates. (Environmental Microbiology)
  4. Accurate determination of microbial diversity from 454 pyrosequencing data (Nature Methods)
  5. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs.
  6. Ironing out the wrinkles in the rare biosphere through improved OTU clustering (Environmental Microbiology)
  7. QIIME allows analysis of high-throughput community sequencing data (Nature Methods)
  8. Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities (Applied and Environmental Microbiology)
  9. Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy (Applied and Environmental Microbiology)
  10. Rapid denoising of pyrosequencing amplicon data: exploiting the rank-abundance distribution (Nature Methods)

Their papers