Classification of Myoviridae bacteriophages using protein sequence similarity

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1471-2180

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Classification of Myoviridae bacteriophages using protein sequence similarity ; volume:9 ; number:1 ; day:26 ; month:10 ; year:2009 ; pages:1-16 ; date:12.2009
BMC microbiology ; 9, Heft 1 (26.10.2009), 1-16, 12.2009

Creator: Lavigne, Rob

Contributor: Darius, Paul
Summer, Elizabeth J.
Seto, Donald
Mahadevan, Padmanabhan
Nilsson, Anders S.
Ackermann, Hans W.
Kropinski, Andrew M.
SpringerLink (Online service)

DOI: 10.1186/1471-2180-9-224

URN: urn:nbn:de:1111-2016030223254

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:22 AM CEST

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Lavigne, Rob
Darius, Paul
Summer, Elizabeth J.
Seto, Donald
Mahadevan, Padmanabhan
Nilsson, Anders S.
Ackermann, Hans W.
Kropinski, Andrew M.
SpringerLink (Online service)

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Classification of Myoviridae bacteriophages using protein sequence similarity

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Other Objects (12)

Sequence variability of Campylobactertemperate bacteriophages

Detailed protein sequence alignment based on Spectral Similarity Score (SSS)

Prediction of enzyme function by combining sequence similarity and protein interactions

Quantitative assessment of relationship between sequence similarity and function similarity

Automated classification of tailed bacteriophages according to their neck organization

Mining for class-specific motifs in protein sequence classification

Classification and Learning of Similarity Measures

Improving protein structure similarity searches using domain boundaries based on conserved sequence information

Protein sequence-similarity search acceleration using a heuristic algorithm with a sensitive matrix

Towards an automatic classification of protein structural domains based on structural similarity

Clustering protein sequences with a novel metric transformed from sequence similarity scores and sequence alignments with neural networks

Clustering biological sequences with dynamic sequence similarity threshold

Sequence variability of Campylobactertemperate bacteriophages

Detailed protein sequence alignment based on Spectral Similarity Score (SSS)

Prediction of enzyme function by combining sequence similarity and protein interactions

Quantitative assessment of relationship between sequence similarity and function similarity

Automated classification of tailed bacteriophages according to their neck organization

Mining for class-specific motifs in protein sequence classification

Classification and Learning of Similarity Measures

Improving protein structure similarity searches using domain boundaries based on conserved sequence information

Protein sequence-similarity search acceleration using a heuristic algorithm with a sensitive matrix

Towards an automatic classification of protein structural domains based on structural similarity

Clustering protein sequences with a novel metric transformed from sequence similarity scores and sequence alignments with neural networks

Clustering biological sequences with dynamic sequence similarity threshold

Sequence variability of Campylobactertemperate bacteriophages

Detailed protein sequence alignment based on Spectral Similarity Score (SSS)

Prediction of enzyme function by combining sequence similarity and protein interactions

Quantitative assessment of relationship between sequence similarity and function similarity

Automated classification of tailed bacteriophages according to their neck organization

Mining for class-specific motifs in protein sequence classification

Classification and Learning of Similarity Measures

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Protein sequence-similarity search acceleration using a heuristic algorithm with a sensitive matrix

Towards an automatic classification of protein structural domains based on structural similarity

Clustering protein sequences with a novel metric transformed from sequence similarity scores and sequence alignments with neural networks

Clustering biological sequences with dynamic sequence similarity threshold

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