DIALIGN-T: An improved algorithm for segment-based multiple sequence alignment

Subramanian, Alap R.; Weyer-Menkhoff, Jan; Kaufmann, Michael; Morgenstern, Burkhard

doi:10.1186/1471-2105-6-66

Cited by 134 publications

(47 citation statements)

References 31 publications

(38 reference statements)

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“…For each model we also checked its absolute quality by analyzing the QMEAN score, QMEAN Z-score, and the GA341 score [39][40][41][42][43].…”

Section: Model Assessmentmentioning

confidence: 99%

“…The |QMEAN Z-scores| between 1 and 3 are acceptable quality and |QMEAN Z-scores| > 3 are considered "bad quality structures". The GA341 is a score for the reliability of a model [40,41]. A model is predicted to be reliable when the model score is higher than a prespecified cutoff of 0.7.…”

Section: Model Assessmentmentioning

confidence: 99%

“…The superposition of the modeled HMG core regions (81 -146) from ROBETTA with the template 2E6O had a higher RMSD values than the superposition of the HMG core determined using the SWISS-MODEL workspace and Swiss-PDB viewer (value of 1.71 Ǻ for Model 1_SWM verses an average of 2.46 Ǻ for Model 1 ROB through Model 5 ROB). The quality of the models was analyzed by the QMEAN, QMEAN Z-score [41][42][43] and the GA341 score from ModEval [39,40]. The QMEAN, QMEAN Z-score and GA341 are briefly discussed in the methods section.…”

Section: The C-terminal Region Makes An Essentialmentioning

confidence: 99%

See 2 more Smart Citations

Modeling the Structure of Yeast MAT<i>α</i>1: An HMG-Box Motif with a C-Terminal Helical Extension

Jackson¹,

Lawson²,

Villafañe³

et al. 2013

OJBIPHY

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The yeast MATα1 is required for the activation of α-specific genes in Saccharomyces cerevisiae and thus confers the α-cell identity of the yeast. MATα1 contains a domain called the α-domain which has significant sequence identity to the HMG-box family of proteins. A multiple sequence alignment of several α-domains and various structurally determined HMG-box domains have revealed that both domains possess very similar structural and functional residues. We found that the basic amino acids of the N-terminal loop, the intercalating hydrophobic residues of the first helix, and the hydrophobic residues required for interactions within the core of the protein are remarkably conserved in α-domains and HMG-box proteins. Our generated molecular models suggest that the first and third helix will be shorter and that the HMG-box core is not an isolated domain. The region beyond the conserved HMG-box motif contains an extended helical region for about 20 -30 amino acids. Structural models generated by comparative modeling and ab initio modeling reveal that this region will add two or more additional α-helices and will make significant contacts to helix III, II and I of the HMG-box core. We were able to illustrate how the extended α-domain would bind to DNA by merging of the α-domain and the LEF-1/DNA complex. The models we are reporting will be helpful in understanding how MATα1 binds to DNA with its partner MCM1 and activates transcription of α-specific genes. These models will also aid in future biophysical studies of MATα1 including the crystallization and structure determination.

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“…For each model we also checked its absolute quality by analyzing the QMEAN score, QMEAN Z-score, and the GA341 score [39][40][41][42][43].…”

Section: Model Assessmentmentioning

confidence: 99%

Section: Model Assessmentmentioning

confidence: 99%

Section: The C-terminal Region Makes An Essentialmentioning

confidence: 99%

See 1 more Smart Citation

Modeling the Structure of Yeast MAT<i>α</i>1: An HMG-Box Motif with a C-Terminal Helical Extension

Jackson¹,

Lawson²,

Villafañe³

et al. 2013

OJBIPHY

View full text Add to dashboard Cite

show abstract

“…Kjer 1995Kjer , 2004Hickson et al 1996;Jennings et al 2001;Simossis and Heringa 2004). As an example of the perceived importance of this relationship, evaluation of the quality of procedures for multiple sequence alignment is now frequently assessed using structure-based reference alignments as the best estimate of the 'correct' alignment, including BAliBASE (Thompson et al 1999a(Thompson et al , 2005Bahr et al 2001), OXBench (Raghava et al 2003), PREFAB (Edgar 2004b), SABmark (Van Walle et al 2005), IRMBase (Subramanian et al 2005) and BRAliBASE (Gardner et al 2005); and a similar approach has been taken to evaluation of pairwise alignment for database searches and structure prediction (Brenner et al 1998;Domingues et al 2000;Sauder et al 2000;Marsden and Abagyan 2004;Marti-Renom et al 2004). Note that this approach explicitly uses a biological criterion to judge the quality of the alignments, rather than whatever mathematical criterion was used to produce the alignments.…”

Section: Structure Function and Homologymentioning

confidence: 99%

Multiple sequence alignment for phylogenetic purposes

Morrison¹

2006

Aust. Systematic Bot.

135

113

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I have addressed the biological rather than bioinformatics aspects of molecular sequence alignment by covering a series of topics that have been under-valued, particularly within the context of phylogenetic analysis. First, phylogenetic analysis is only one of the many objectives of sequence alignment, and the most appropriate multiple alignment may not be the same for all of these purposes. Phylogenetic alignment thus occupies a specific place within a broader context. Second, homology assessment plays an intricate role in phylogenetic analysis, with sequence alignment consisting of primary homology assessment and tree building being secondary homology assessment. The objective of phylogenetic alignment thus distinguishes it from other sorts of alignment. Third, I summarise what is known about the serious limitations of using phenetic similarity as a criterion for automated multiple alignment, and provide an overview of what is currently being done to improve these computerised procedures. This synthesises information that is apparently not widely known among phylogeneticists. Fourth, I then consider the recent development of automated procedures for combining alignment and tree building, thus integrating primary and secondary homology assessment. Finally, I outline various strategies for increasing the biological content of sequence alignment procedures, which consists of taking into account known evolutionary processes when making alignment decisions. These procedures can be objective and repeatable, and can involve computerised algorithms to automate much of the work. Perhaps the most important suggestion is that alignment should be seen as a process where new sequences are added to a pre-existing alignment that has been manually curated by the biologist.

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“…Benchmark datasets like OXBench [37], HOMSTRAD [38], PREFAB [39], Bali-BASE [40], and SABmark [41] are built on real sequences by aligning structural elements and in some cases with hand-curation. Others like IRMBASE [42] are generated by simulating sequence evolution based on specific molecular evolutionary models.…”

Section: Multiple Sequence Alignmentmentioning

confidence: 99%

Genome sequencing and next-generation sequence data analysis: A comprehensive compilation of bioinformatics tools and databases

Jiménez-López¹,

Gachomo²,

Sharma³

et al. 2013

AJMB

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Genomics has become a ground-breaking field in all areas of the life sciences. The advanced genomics and the development of high-throughput techniques have lately provided insight into whole-genome characterization of a wide range of organisms. In the post-genomic era, new technologies have revealed an outbreak of prerequisite genomic sequences and supporting data to understand genome wide functional regulation of gene expression and metabolic pathways reconstruction. However, the availability of this plethora of genomic data presents a significant challenge for storage, analyses and data management. Analysis of this mega-data requires the development and application of novel bioinformatics tools that must include unified functional annotation, structural search, and comprehensive analysis and identification of new genes in a wide range of species with fully sequenced genomes. In addition, generation of systematically and syntactically unambiguous nomenclature systems for genomic data across species is a crucial task. Such systems are necessary for adequate handling genetic information in the context of comparative functional genomics. In this paper, we provide an overview of major advances in bioinformatics and computational biology in genome sequencing and next-generation sequence data analysis. We focus on their potential applications for efficient collection, storage, and analysis of genetic data/information from a wide range of gene banks. We also discuss the importance of establishing a unified nomenclature system through a functional and structural genomics approach.

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DIALIGN-T: An improved algorithm for segment-based multiple sequence alignment

Cited by 134 publications

References 31 publications

Modeling the Structure of Yeast MAT<i>α</i>1: An HMG-Box Motif with a C-Terminal Helical Extension

Modeling the Structure of Yeast MAT<i>α</i>1: An HMG-Box Motif with a C-Terminal Helical Extension

Multiple sequence alignment for phylogenetic purposes

Genome sequencing and next-generation sequence data analysis: A comprehensive compilation of bioinformatics tools and databases

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DIALIGN-T: An improved algorithm for segment-based multiple sequence alignment

Cited by 134 publications

References 31 publications

Modeling the Structure of Yeast MAT&lt;i&gt;α&lt;/i&gt;1: An HMG-Box Motif with a C-Terminal Helical Extension

Modeling the Structure of Yeast MAT&lt;i&gt;α&lt;/i&gt;1: An HMG-Box Motif with a C-Terminal Helical Extension

Multiple sequence alignment for phylogenetic purposes

Genome sequencing and next-generation sequence data analysis: A comprehensive compilation of bioinformatics tools and databases

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Product

Resources

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Modeling the Structure of Yeast MAT<i>α</i>1: An HMG-Box Motif with a C-Terminal Helical Extension

Modeling the Structure of Yeast MAT<i>α</i>1: An HMG-Box Motif with a C-Terminal Helical Extension