Sequence clustering

From Wikipedia, the free encyclopedia
Jump to navigation Jump to search

In bioinformatics, sequence clustering algorithms attempt to group biological sequences that are somehow related. The sequences can be either of genomic, "transcriptomic" (ESTs) or protein origin. For proteins, homologous sequences are typically grouped into families. For EST data, clustering is important to group sequences originating from the same gene before the ESTs are assembled to reconstruct the original mRNA.

Some clustering algorithms use single-linkage clustering, constructing a transitive closure of sequences with a similarity over a particular threshold. UCLUST[1] and CD-HIT[2] use a greedy algorithm that identifies a representative sequence for each cluster and assigns a new sequence to that cluster if it is sufficiently similar to the representative; if a sequence is not matched then it becomes the representative sequence for a new cluster. The similarity score is often based on sequence alignment. Sequence clustering is often used to make a non-redundant set of representative sequences.

Sequence clusters are often synonymous with (but not identical to) protein families. Determining a representative tertiary structure for each sequence cluster is the aim of many structural genomics initiatives.

Sequence clustering algorithms and packages[edit]

  • CD-HIT[2]
  • UCLUST in USEARCH[1]
  • Starcode:[3] a fast sequence clustering algorithm based on exact all-pairs search.[4]
  • OrthoFinder:[5] a fast, scalable and accurate method for clustering proteins into gene families (orthogroups)[6]
  • Linclust:[7] first algorithm whose runtime scales linearly with input set size, very fast, part of MMseqs2 [8] software suite for fast, sensitive sequence searching and clustering of large sequence sets
  • TribeMCL: a method for clustering proteins into related groups[9]
  • BAG: a graph theoretic sequence clustering algorithm[10]
  • JESAM:[11] Open source parallel scalable DNA alignment engine with optional clustering software component
  • UICluster:[12] Parallel Clustering of EST (Gene) Sequences
  • BLASTClust single-linkage clustering with BLAST[13]
  • Clusterer:[14] extendable java application for sequence grouping and cluster analyses
  • PATDB: a program for rapidly identifying perfect substrings
  • nrdb:[15] a program for merging trivially redundant (identical) sequences
  • CluSTr:[16] A single-linkage protein sequence clustering database from Smith-Waterman sequence similarities; covers over 7 mln sequences including UniProt and IPI
  • ICAtools[17] - original (ancient) DNA clustering package with many algorithms useful for artifact discovery or EST clustering
  • Skipredudant EMBOSS tool[18] to remove redundant sequences from a set
  • CLUSS Algorithm[19] to identify groups of structurally, functionally, or evolutionarily related hard-to-align protein sequences. CLUSS webserver [20]
  • CLUSS2 Algorithm[21] for clustering families of hard-to-align protein sequences with multiple biological functions. CLUSS2 webserver [20]

Non-redundant sequence databases[edit]

  • PISCES: A Protein Sequence Culling Server[22]
  • RDB90[23]
  • UniRef: A non-redundant UniProt sequence database[24]
  • Uniclust: A clustered UniProtKB sequences at the level of 90%, 50% and 30% pairwise sequence identity.[25]
  • Virus Orthologous Clusters:[26] A viral protein sequence clustering database; contains all predicted genes from eleven virus families organized into ortholog groups by BLASTP similarity

See also[edit]

References[edit]

  1. ^ a b "USEARCH". drive5.com.
  2. ^ a b "CD-HIT: a ultra-fast method for clustering protein and nucleotide sequences, with many new applications in next generation sequencing (NGS) data". cd-hit.org.
  3. ^ "Starcode repository". 2018-10-11.
  4. ^ Zorita E; Cuscó P; Filion GJ. (Jun 2015). "Starcode: sequence clustering based on all-pairs search". Bioinformatics. 31 (12): 1913–1919. doi:10.1093/bioinformatics/btv053. PMC 4765884. PMID 25638815.
  5. ^ "OrthoFinder". Steve Kelly Lab.
  6. ^ Emms DM; Kelly S. (Aug 2015). "OrthoFinder: solving fundamental biases in whole genome comparisons dramatically improves orthogroup inference accuracy". Genome Biology. 16 (157): 157. doi:10.1186/s13059-015-0721-2. PMC 4531804. PMID 26243257.
  7. ^ Steinegger M.; Söding J. (June 2018). "Clustering huge protein sequence sets in linear time". Nature Communications. 9 (1): 2542. Bibcode:2018NatCo...9.2542S. doi:10.1038/s41467-018-04964-5. PMC 6026198. PMID 29959318.
  8. ^ Steinegger M.; Söding J. (Oct 16, 2017). "MMseqs2 enables sensitive protein sequence searching for the analysis of massive data sets". Nature Biotechnology. 35 (11): 1026–1028. doi:10.1038/nbt.3988. PMID 29035372.
  9. ^ Enright AJ; Van Dongen S; Ouzounis CA. (Apr 2002). "An efficient algorithm for large-scale detection of protein families". Nucleic Acids Res. 30 (7): 1575–84. doi:10.1093/nar/30.7.1575. PMC 101833. PMID 11917018.
  10. ^ "Archived copy". Archived from the original on 2003-12-06. Retrieved 2004-02-19.CS1 maint: Archived copy as title (link)
  11. ^ "Bioinformatics Paper: JESAM: CORBA software components for EST alignments and clusters". littlest.co.uk.
  12. ^ http://ratest.eng.uiowa.edu/pubsoft/clustering/
  13. ^ "NCBI News: Spring 2004-BLASTLab". nih.gov.
  14. ^ "Clusterer: extendable java application for sequence grouping and cluster analyses". bugaco.com.
  15. ^ "Index of /pub/nrdb".
  16. ^ "Archived copy". Archived from the original on 2006-09-24. Retrieved 2006-11-23.CS1 maint: Archived copy as title (link)
  17. ^ "Introduction to the ICAtools". littlest.co.uk.
  18. ^ "EMBOSS: skipredundant". pasteur.fr.
  19. ^ Kelil, Abdellali; Wang, Shengrui; Brzezinski, Ryszard; Fleury, Alain (2007). "CLUSS Algorithm : Clustering non-alignable protein sequences". Prospectus.usherbrooke.ca. 8: 286. doi:10.1186/1471-2105-8-286. PMC 1976428. PMID 17683581.
  20. ^ a b "CLUSS Home Page".
  21. ^ Kelil, Abdellali; Wang, Shengrui; Brzezinski, Ryszard (January 2008). "CLUSS2 : Alignment-independent algorithm for clustering protein families with multiple biological functions". International Journal of Computational Biology and Drug Design. 1 (2): 122–140. doi:10.1504/IJCBDD.2008.02019 (inactive 2019-05-19).
  22. ^ "Dunbrack Lab". fccc.edu.
  23. ^ Holm L1, Sander C. (Jun 1998). "Removing near-neighbour redundancy from large protein sequence collections". Bioinformatics. 14 (5): 423–9. doi:10.1093/bioinformatics/14.5.423. PMID 9682055.
  24. ^ "About UniProt". uniprot.org.
  25. ^ Mirdita M; von den Drisch L.; Galiez C.; Soeding J.; Steinegger M. (Nov 2016). "Uniclust databases of clustered and deeply annotated protein sequences and alignments". Nucleic Acids Res. 45 (D1): D170–D176. doi:10.1093/nar/gkw1081. PMC 5614098. PMID 27899574.
  26. ^ "VOCS - Viral Bioinformatics Resource Center". uvic.ca.