The configuration space of homologous proteins: A theoretical and practical framework to reduce the diversity of the protein sequence space after massive all-by-all sequence comparisons

Bastien, Olivier; Ortet, Philippe; Roy, Sylvaine; Maréchal, Éric

doi:10.1016/j.future.2006.07.016

Cited by 7 publications

(8 citation statements)

References 59 publications

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“…This property is an advantage for the clustering of large databases of biological sequences, since the addition of new sequences does not necessarily requires the recalculation of previous alignments. This is why different clustering methods based on pairwise comparisons of proteins have been proposed, using either E-value (COG [6], TribeMCL [7], ProtoNet [8], ProtoMap [9], SIMAP [10], SYSTERS [11]) or Z-value statistics (Decrypthon [12], TeraProt [12], PhytoProt [13], CluSTr [14]). Recent use of PAB classification for an automatic inference of phylogeny includes OrthoMCL [15], based on pairwise BLAST comparisons and the computation of evolutionary distance based on E-value statistics (for review, [12]).…”

Section: Introductionmentioning

confidence: 99%

“…In previous case studies, TULIP trees where shown to be consistent with phylogenetic trees [23]. The higher accuracy of Z-value over E-value statistics has been discussed and tested [12,[23][24][25]. In particular, Z-value statistics are valid when comparing sequences of very different amino acid compositions, an interesting feature to help the analysis of compositionally biased sequences.…”

Section: Introductionmentioning

confidence: 99%

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TULIP Software and Web Server: Automatic Classification of Protein Sequences Based on Pairwise Comparisons and Z-Value Statistics

Grando¹,

Ortet²,

Joubert³

et al. 2009

TOBIOIJ

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A configuration space of homologous protein sequences (or CSHP) has been recently constructed based on pairwise comparisons, with probabilities deduced from Z-value statistics (Monte Carlo methods applied to pairwise comparisons) and following evolutionary assumptions. A Z-value cut-off is applied so as proteins are placed in the CSHP only when the similarity of pairs of sequences is significant following the Theorem of the Upper Limit of a score Probability (TULIP theorem). Based on the positions of similar protein sequences in the CSHP, a classification can be deduced, which can be visualized as trees, called TULIP trees. In previous case studies, TULIP trees where shown to be consistent with phylogenetic trees. To date, no tool has been made available to allow the computation of TULIP trees following this model. The availability of methods to cluster proteins based on pairwise comparisons and following evolutionary assumptions should be useful for evaluation and for the future improvements they might inspire. We developed a web server allowing the local or online computation of TULIP trees based on the CSHP probabilities. The input is a set of homologous protein sequences in multi-FASTA format. Pairwise comparisons are conducted using the Smith-Waterman method, with 100-1,000 sequence shuffling to estimate pairwise Z-values. Obtained Z-value matrix is used to infer a tree which is then written to a file. Output consists therefore of a Z-value matrix, a distance matrix, a TULIP treefile in NEWICK format, and a TULIP tree visualisation. The TULIP server provides an easy-to-use interface to the TULIP software, and allows a classification of protein sequences based on pairwise alignments and following evolutionary assumptions. TULIP trees are consistent with phylogenies in numerous cases, but they can be inconsistent for multi-domain proteins in which some domains have been conserved in all branches. Thus TULIP trees cannot be considered as conventional phylogenetic trees, following the MIAPA (Minimum Information About a Phylogenetic Analysis) recommendations. A major strength of the TULIP classification is its statistical validity when analysing samples including compositionally unbiased and biased sequences (i.e. with biased amino acid distributions), like sequences from Plasmodium falciparum. The TULIP web server is a service of the Malaria Portal of the University of Pretoria, South Africa, and is available at

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TULIP Software and Web Server: Automatic Classification of Protein Sequences Based on Pairwise Comparisons and Z-Value Statistics

Grando¹,

Ortet²,

Joubert³

et al. 2009

TOBIOIJ

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“…Automatic analysis of biological sequences is crucial for the treatment of massive genomic outputs. Our understanding of more than 90 % of protein sequences stored in public databases, deduced from automatic translation of gene sequences, will not result from direct experimentation, but from our ability to predict informative features using in silico workflows [ 1 , 2 ]. An underlying postulate is that the molecular sequences determined in biological individuals or species, which have evolved from a common ancestor sequence and are therefore homologous, have conserved enough of the original features to be similar.…”

Section: Introductionmentioning

confidence: 99%

Evolution of biological sequences implies an extreme value distribution of type I for both global and local pairwise alignment scores

Bastien

Maréchal

2008

BMC Bioinformatics

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Background: Confidence in pairwise alignments of biological sequences, obtained by various methods such as Blast or Smith-Waterman, is critical for automatic analyses of genomic data. Two statistical models have been proposed. In the asymptotic limit of long sequences, the Karlin-Altschul model is based on the computation of a P-value, assuming that the number of high scoring matching regions above a threshold is Poisson distributed. Alternatively, the Lipman-Pearson model is based on the computation of a Z-value from a random score distribution obtained by a Monte-Carlo simulation. Z-values allow the deduction of an upper bound of the P-value (1/Z-value 2 ) following the TULIP theorem. Simulations of Z-value distribution is known to fit with a Gumbel law. This remarkable property was not demonstrated and had no obvious biological support.

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“…However, such manual pretreatment cannot be undertaken for all known genes. Alternatively, high throughput molecular phylogenies can be derived from massive all-against-all comparisons, based on pairwise alignments [68]. The questions of the statistical accuracy and maintenance of high throughput phylogenetic reconstruction are critical when including compositionally atypical and high insert containing sequences.…”

Section: Introductionmentioning

confidence: 99%

“…As mentioned above, and discussed recently [68] for massive comparisons based on BlastP/ E-values , i.e . COG [71], Tribe [72], ProtoMap [73], ProtNet [74], SIMAP [75] and SYSTERS release 4 [76], there is no theoretical support to justify that an E-value table can be converted into a rigorous and stable metric.…”

Section: Introductionmentioning

confidence: 99%

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

Birkholtz

Bastien

Wells

et al. 2006

Malar J

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The organization and mining of malaria genomic and post-genomic data is important to significantly increase the knowledge of the biology of its causative agents, and is motivated, on a longer term, by the necessity to predict and characterize new biological targets and new drugs. Biological targets are sought in a biological space designed from the genomic data from Plasmodium falciparum, but using also the millions of genomic data from other species. Drug candidates are sought in a chemical space containing the millions of small molecules stored in public and private chemolibraries. Data management should, therefore, be as reliable and versatile as possible. In this context, five aspects of the organization and mining of malaria genomic and post-genomic data were examined: 1) the comparison of protein sequences including compositionally atypical malaria sequences, 2) the high throughput reconstruction of molecular phylogenies, 3) the representation of biological processes, particularly metabolic pathways, 4) the versatile methods to integrate genomic data, biological representations and functional profiling obtained from Xomic experiments after drug treatments and 5) the determination and prediction of protein structures and their molecular docking with drug candidate structures. Recent progress towards a grid-enabled chemogenomic knowledge space is discussed.

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The configuration space of homologous proteins: A theoretical and practical framework to reduce the diversity of the protein sequence space after massive all-by-all sequence comparisons

Cited by 7 publications

References 59 publications

TULIP Software and Web Server: Automatic Classification of Protein Sequences Based on Pairwise Comparisons and Z-Value Statistics

TULIP Software and Web Server: Automatic Classification of Protein Sequences Based on Pairwise Comparisons and Z-Value Statistics

Evolution of biological sequences implies an extreme value distribution of type I for both global and local pairwise alignment scores

Integration and mining of malaria molecular, functional and pharmacological data: how far are we from a chemogenomic knowledge space?

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