CAST: an iterative algorithm for the complexity analysis of
  sequence tracts

Promponas, Vasilis J.; Enright, Anton J.; Tsoka, Sophia; Kreil, David P.; Leroy, C.; Hamodrakas, Stavros J.; Sander, Chris; Ouzounis, Christos A.

doi:10.1093/bioinformatics/16.10.915

Cited by 166 publications

(125 citation statements)

References 17 publications

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“…To derive gene families, we used protein translations and excluded short genes (less than 60aa), pre-masked both the query and the database using the CAST algorithm 16 and applied blastp 17 with no default filtering, an e-value threshold of e-10 and effective database length set to 5 million. As a measure of sequence similarity we used 'conservation score' which is derived as a pairwise comparison score divided by the smallest self-score of the two proteins 18 .…”

Section: Gene-centric Comparative Analysis Of Ebpr Sludges To Other Mmentioning

confidence: 99%

“…Shotgun sequencing applied directly to environmental samples has recently demonstrated that near complete genomes can be obtained for dominant populations in a community without the need for cultivation [15][16] . Therefore, it was anticipated that a near complete genome could be obtained for A. phosphatis from lab-scale EBPR enrichment cultures, allowing a comprehensive metabolic reconstruction.…”

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confidence: 99%

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Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities

et al. 2006

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Enhanced Biological Phosphorus Removal (EBPR) is not well understood at the metabolic level despite being one of the best-studied microbially-mediated industrial processes due to its ecological and economic relevance. Here we present a metagenomic analysis of two lab-scale EBPR sludges dominated by the uncultured bacterium, "Candidatus Accumulibacter phosphatis". This analysis sheds light on several controversies in EBPR metabolic models and provides hypotheses explaining the dominance of A. phosphatis in this habitat, its lifestyle outside EBPR and probable cultivation requirements.Comparison of the same species from different EBPR sludges highlights recent evolutionary dynamics in the A. phosphatis genome that could be linked to mechanisms for environmental adaptation. In spite of an apparent lack of phylogenetic overlap in the flanking communities of the two sludges studied, common functional themes were found, at least one of them complementary to the inferred metabolism of the dominant organism.The present study provides a much-needed blueprint for a systems-level understanding of EBPR and illustrates that metagenomics enables detailed, often novel, insights into even well-studied biological systems. 3Excessive inorganic phosphate (Pi) supply to freshwater negatively affects water quality and ecosystem balance through a process known as eutrophication 1 . Limitations on allowable Pi discharges from municipal and industrial sources via wastewater treatment have proven effective in reducing Pi levels in many waterways 2 . Increasingly stringent Pi limits for effluent wastewater are expected in the future and hence efficient and reliable Pi removal methods are required. Due to the massive quantity of wastewater treated daily (more than 120 billion liters in the US alone 3 ), any improvement in existing methods should have tangible economic and ecological consequences.Enhanced Biological Phosphorus Removal (EBPR) is a treatment process in which microorganisms remove Pi from wastewater by accumulating it inside their cells as polyphosphate. These polyphosphate-accumulating organisms (PAOs) are then allowed to settle in a separate tank (clarifier), leaving the effluent water largely Pi-depleted. EBPR is more economical in the long term 2 and has a lower environmental impact 4 than traditional (chemical) Pi removal 5 , but is prone to unpredictable failures due to loss or reduced activity of microbial populations responsible for Pi removal 6 . This is primarily because the design process is highly empirical due to an incomplete understanding of sludge microbial ecology. Environmental engineers and microbiologists have been studying EBPR since its introduction in municipal wastewater treatment plants over thirty years ago 5 with the goal of making it a more reliable industrial process. Typically, EBPR is studied in lab-scale sequencing batch reactors (SBRs) where the microbial community can be better monitored and perturbed, and PAOs can be enriched to much higher levels than in full scale systems 7 .For th...

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Section: Gene-centric Comparative Analysis Of Ebpr Sludges To Other Mmentioning

confidence: 99%

mentioning

confidence: 99%

Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities

et al. 2006

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“…falciparum sequences were filtered for compositional bias using CAST (Promponas et al 2000), with a threshold of 20, before being queried. Sequences were then searched with the HMMs using HMMER version 2.2 (Eddy 1998), and any that matched an HMM with a score greater than the lowest score for sequences included in the Pfam full alignment of the family were considered as hits.…”

Section: Hmm Searchesmentioning

confidence: 99%

Comparative Genomics of Transcriptional Control in the Human Malaria Parasite Plasmodium falciparum

Coulson¹,

Hall²,

Ouzounis³

2004

Genome Res.

238

250

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The life cycle of the parasite Plasmodium falciparum, responsible for the most deadly form of human malaria, requires specialized protein expression for survival in the mammalian host and insect vector. To identify components of processes controlling gene expression during its life cycle, the malarial genome-along with seven crown eukaryote group genomes-was queried with a reference set of transcription-associated proteins (TAPs). Following clustering on the basis of sequence similarity of the TAPs with their homologs, and together with hidden Markov model profile searches, 156 P. falciparum TAPs were identified. This represents about a third of the number of TAPs usually found in the genome of a free-living eukaryote. Furthermore, the P. falciparum genome appears to contain a low number of sequences, which are highly conserved and abundant within the kingdoms of free-living eukaryotes, that contribute to gene-specific transcriptional regulation. However, in comparison with these other eukaryotic genomes, the CCCH-type zinc finger (common in proteins modulating mRNA decay and translation rates) was found to be the most abundant in the P. falciparum genome. This observation, together with the paucity of malarial transcriptional regulators identified, suggests Plasmodium protein levels are primarily determined by posttranscriptional mechanisms.

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“…biol.uoa.gr/PRED-GPCR. Available options for this application include selection of an E-value threshold and a filter that implements the CAST algorithm [23], allowing, low-complexity region detection and selective masking. This filter can significantly improve the selectivity of the method, since the sequence score takes into account all scoring domains and could, therefore, return false positives in case of low scoring domains repeated along the sequence.…”

Section: Resultsmentioning

confidence: 99%

A Novel method for GPCR recognition and family classification from sequence alone using signatures derived from profile hidden Markov models

Papasaikas

Bagos²,

Litou³

et al. 2003

SAR and QSAR in Environmental Research

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G-protein coupled receptors (GPCRs) constitute a broad class of cell-surface receptors, including several functionally distinct families, that play a key role in cellular signalling and regulation of basic physiological processes. GPCRs are the focus of a significant amount of current pharmaceutical research since they interact with more than 50% of prescription drugs, whereas they still comprise the best potential targets for drug design. Taking into account the excess of data derived by genome sequencing projects, the use of computational tools for automated characterization of novel GPCRs is imperative. Typical computational strategies for identifying and classifying GPCRs involve sequence similarity searches (e.g. BLAST) coupled with pattern database analysis (e.g. PROSITE, BLOCKS). The diagnostic method presented here is based on a probabilistic approach that exploits highly discriminative profile Hidden Markov Models, excised from low entropy regions of multiple sequence alignments, to derive potent family signatures. For a given query, a P-value is obtained, combining individual hits derived from the same family. Hence a best-guess family membership is depicted, allowing GPCRs' classification at a family level, solely using primary structure information. A web-based version of the application is freely available at

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CAST: an iterative algorithm for the complexity analysis of sequence tracts

Abstract: CAST (version 1.0) executable binaries are available to academic users free of charge under license. Web site entry point, server and additional material: http://www.ebi.ac.uk/research/cgg/services/cast/

Cited by 166 publications

References 17 publications

Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities

Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities

Comparative Genomics of Transcriptional Control in the Human Malaria Parasite Plasmodium falciparum

A Novel method for GPCR recognition and family classification from sequence alone using signatures derived from profile hidden Markov models

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