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1.  Models of gene gain and gene loss for probabilistic reconstruction of gene content in the last universal common ancestor of life 
Biology Direct  2013;8:32.
Background
The problem of probabilistic inference of gene content in the last common ancestor of several extant species with completely sequenced genomes is: for each gene that is conserved in all or some of the genomes, assign the probability that its ancestral gene was present in the genome of their last common ancestor.
Results
We have developed a family of models of gene gain and gene loss in evolution, and applied the maximum-likelihood approach that uses phylogenetic tree of prokaryotes and the record of orthologous relationships between their genes to infer the gene content of LUCA, the Last Universal Common Ancestor of all currently living cellular organisms. The crucial parameter, the ratio of gene losses and gene gains, was estimated from the data and was higher in models that take account of the number of in-paralogs in genomes than in models that treat gene presences and absences as a binary trait.
Conclusion
While the numbers of genes that are placed confidently into LUCA are similar in the ML methods and in previously published methods that use various parsimony-based approaches, the identities of genes themselves are different. Most of the models of either kind treat the genes found in many existing genomes in a similar way, assigning to them high probabilities of being ancestral (“high ancestrality”). The ML models are more likely than others to assign high ancestrality to the genes that are relatively rare in the present-day genomes.
Reviewers
This article was reviewed by Martijn A Huynen, Toni Gabaldón and Fyodor Kondrashov.
doi:10.1186/1745-6150-8-32
PMCID: PMC3892064  PMID: 24354654
2.  Measuring gene expression divergence: the distance to keep 
Biology Direct  2010;5:51.
Background
Gene expression divergence is a phenotypic trait reflecting evolution of gene regulation and characterizing dissimilarity between species and between cells and tissues within the same species. Several distance measures, such as Euclidean and correlation-based distances have been proposed for measuring expression divergence.
Results
We show that different distance measures identify different trends in gene expression patterns. When comparing orthologous genes in eight rat and human tissues, the Euclidean distance identified genes uniformly expressed in all tissues near the expression background as genes with the most conserved expression pattern. In contrast, correlation-based distance and generalized-average distance identified genes with concerted changes among homologous tissues as those most conserved. On the other hand, correlation-based distance, Euclidean distance and generalized-average distance highlight quite well the relatively high similarity of gene expression patterns in homologous tissues between species, compared to non-homologous tissues within species.
Conclusions
Different trends exist in the high-dimensional numeric data, and to highlight a particular trend an appropriate distance measure needs to be chosen. The choice of the distance measure for measuring expression divergence can be dictated by the expression patterns that are of interest in a particular study.
Reviewers
This article was reviewed by Mikhail Gelfand, Eugene Koonin and Subhajyoti De (nominated by Sarah Teichmann).
doi:10.1186/1745-6150-5-51
PMCID: PMC2928186  PMID: 20691088
3.  A family of GFP-like proteins with different spectral properties in lancelet Branchiostoma floridae 
Biology Direct  2008;3:28.
Background
Members of the green fluorescent protein (GFP) family share sequence similarity and the 11-stranded β-barrel fold. Fluorescence or bright coloration, observed in many members of this family, is enabled by the intrinsic properties of the polypeptide chain itself, without the requirement for cofactors. Amino acid sequence of fluorescent proteins can be altered by genetic engineering to produce variants with different spectral properties, suitable for direct visualization of molecular and cellular processes. Naturally occurring GFP-like proteins include fluorescent proteins from cnidarians of the Hydrozoa and Anthozoa classes, and from copepods of the Pontellidae family, as well as non-fluorescent proteins from Anthozoa. Recently, an mRNA encoding a fluorescent GFP-like protein AmphiGFP, related to GFP from Pontellidae, has been isolated from the lancelet Branchiostoma floridae, a cephalochordate (Deheyn et al., Biol Bull, 2007 213:95).
Results
We report that the nearly-completely sequenced genome of Branchiostoma floridae encodes at least 12 GFP-like proteins. The evidence for expression of six of these genes can be found in the EST databases. Phylogenetic analysis suggests that a gene encoding a GFP-like protein was present in the common ancestor of Cnidaria and Bilateria. We synthesized and expressed two of the lancelet GFP-like proteins in mammalian cells and in bacteria. One protein, which we called LanFP1, exhibits bright green fluorescence in both systems. The other protein, LanFP2, is identical to AmphiGFP in amino acid sequence and is moderately fluorescent. Live imaging of the adult animals revealed bright green fluorescence at the anterior end and in the basal region of the oral cirri, as well as weaker green signals throughout the body of the animal. In addition, red fluorescence was observed in oral cirri, extending to the tips.
Conclusion
GFP-like proteins may have been present in the primitive Metazoa. Their evolutionary history includes losses in several metazoan lineages and expansion in cephalochordates that resulted in the largest repertoire of GFP-like proteins known thus far in a single organism. Lancelet expresses several of its GFP-like proteins, which appear to have distinct spectral properties and perhaps diverse functions.
Reviewers
This article was reviewed by Shamil Sunyaev, Mikhail Matz (nominated by I. King Jordan) and L. Aravind.
doi:10.1186/1745-6150-3-28
PMCID: PMC2467403  PMID: 18598356
4.  Evolutionary history of bacteriophages with double-stranded DNA genomes 
Biology Direct  2007;2:36.
Background
Reconstruction of evolutionary history of bacteriophages is a difficult problem because of fast sequence drift and lack of omnipresent genes in phage genomes. Moreover, losses and recombinational exchanges of genes are so pervasive in phages that the plausibility of phylogenetic inference in phage kingdom has been questioned.
Results
We compiled the profiles of presence and absence of 803 orthologous genes in 158 completely sequenced phages with double-stranded DNA genomes and used these gene content vectors to infer the evolutionary history of phages. There were 18 well-supported clades, mostly corresponding to accepted genera, but in some cases appearing to define new taxonomic groups. Conflicts between this phylogeny and trees constructed from sequence alignments of phage proteins were exploited to infer 294 specific acts of intergenome gene transfer.
Conclusion
A notoriously reticulate evolutionary history of fast-evolving phages can be reconstructed in considerable detail by quantitative comparative genomics.
Open peer review
This article was reviewed by Eugene Koonin, Nicholas Galtier and Martijn Huynen.
doi:10.1186/1745-6150-2-36
PMCID: PMC2222618  PMID: 18062816
5.  Similarity searches in genome-wide numerical data sets 
Biology Direct  2006;1:13.
We present psi-square, a program for searching the space of gene vectors. The program starts with a gene vector, i.e., the set of measurements associated with a gene, and finds similar vectors, derives a probabilistic model of these vectors, then repeats search using this model as a query, and continues to update the model and search again, until convergence. When applied to three different pathway-discovery problems, psi-square was generally more sensitive and sometimes more specific than the ad hoc methods developed for solving each of these problems before.
Reviewers
This article was reviewed by King Jordan, Mikhail Gelfand, Nicolas Galtier and Sarah Teichmann.
doi:10.1186/1745-6150-1-13
PMCID: PMC1489924  PMID: 16734895

Results 1-5 (5)