Automatic clustering of orthologs and in-paralogs from pairwise species comparisons.

By Maido Remm, Christian E. V. Storm, Erik L. L. Sonnhammer. Journal of Molecular Biology, 2001

1,285 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4d00a66475f5bbc04ae6eac5fe4eb6e8d590f11c

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Citations in the corpus

OMA, A Comprehensive, Automated Project for the Identification of Orthologs from Complete Genome Data: Introduction and First Achievements (2005, background): “A noticeable shortcoming of Inparanoid is the fact that it only handles pairs of genomes at a time.”
Robust and accurate prediction of residue–residue interactions across protein interfaces using evolutionary information (2014, background): “Constructing paired-sequence alignments for more complex genomic architectures is more involved and requires the ability to distinguish orthologs from paralogs, the subject of active research ( Remm et al., 2001 ; Datta et al., 2009 ).”
PlnTFDB: an integrative plant transcription factor database (2007, methodology): “The INPARANOID software implements a variation of the best-reciprocal-BLAST-hits method to search for orthologs between pairs of species [25].”
Combining Bioinformatics and Phylogenetics to Identify Large Sets of Single-Copy Orthologous Genes (COSII) for Comparative, Evolutionary and Systematic Studies: A Test Case in the Euasterid Plant Clade (2006, background): “It is worth noting that previous studies have shown that gene duplication/retention plays a key role in the evolution of transcription factors in Arabidopsis where transcription factor gene families have much higher copy numbers than in other organisms (R iechmann and R atcliffe 2000).”
PlnTFDB: updated content and new features of the plant transcription factor database (2009, methodology): “As orthologous genes often have the same function in different species (23), we have used InParanoid (24) to detect clusters of orthologous genes between pairs of species in PlnTFDB.”
Inparanoid: a comprehensive database of eukaryotic orthologs (2004, methodology): “The Inparanoid program was developed speciﬁcally to identify clusters of true orthologs while avoiding inclusion of closely related but non-orthologous proteins (1).”
Proteinortho: Detection of (Co-)orthologs in large-scale analysis (2011, methodology): “The most widely used approach to identify (putative) orthologs between two species is the reciprocal best alignment heuristic [11-15].”
A human protein-protein interaction network: a resource for annotating the proteome. (2005, positive): “to utilize the large interaction data sets of model organisms for the identification of higher confidence interactions by loop motif analysis, we determined which of the interacting proteins from our network have orthologous proteins in D. melanogaster (Dm), C.elegans (Ce), and S. cerevisiae (Sc) ( ”
OrthoMCL-DB: querying a comprehensive multi-species collection of ortholog groups (2005, background): “65 paralogous relationships, and incorporating edges connecting the resulting co-orthologs, overcomes the inability of simple reciprocal best hit approaches to detect many-to-many relationships (3,4).”