• Publications
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T-Coffee: A novel method for fast and accurate multiple sequence alignment.
TLDR
A new method for multiple sequence alignment that provides a dramatic improvement in accuracy with a modest sacrifice in speed as compared to the most commonly used alternatives but avoids the most serious pitfalls caused by the greedy nature of this algorithm. Expand
The FAIR Guiding Principles for scientific data management and stewardship
TLDR
This Comment is the first formal publication of the FAIR Principles, and includes the rationale behind them, and some exemplar implementations in the community. Expand
PRALINE: a multiple sequence alignment toolbox that integrates homology-extended and secondary structure information
TLDR
PRALINE can integrate information from database homology searches to generate a homology-extended multiple alignment and provides a choice of seven different secondary structure prediction programs that can be used individually or in combination as a consensus for integrating structural information into the alignment process. Expand
An analysis of protein domain linkers: their classification and role in protein folding.
TLDR
A linker database intended for the rational design of linkers for domain fusion is constructed and two main types of linker were identified; helical and non-helical. Expand
General secretion signal for the mycobacterial type VII secretion pathway
TLDR
Data show that the YxxxD/E motif is a general secretion signal that is present in all known mycobacterial T7S substrates or substrate complexes, indicating that an additional signal(s) provides system specificity. Expand
Tracking repeats using significance and transitivity
TLDR
The results show that TRUST is a useful and reliable tool for mining tandem and non-tandem repeats in protein sequence databases, capable of predicting multiple repeat types with varying intervening segments within a single sequence. Expand
Homology-extended sequence alignment
TLDR
It is shown that owing to the incorporation of the pre-alignment information into a standard progressive multiple alignment routine, the alignment quality between distant sequences increases significantly and outperforms state-of-the-art methods, such as T-COFFEE and MUSCLE. Expand
A simple and fast secondary structure prediction method using hidden neural networks
TLDR
A secondary structure prediction method YASPIN that unlike the current state-of-the-art methods utilizes a single neural network for predicting the secondary structure elements in a 7-state local structure scheme and then optimizes the output using a hidden Markov model, which results in providing more information for the prediction. Expand
Lagrangian Relaxation Applied to Sparse Global Network Alignment
TLDR
This paper presents a method for global network alignment that is fast and robust, and can flexibly deal with various scoring schemes taking both node-to-node correspondences as well as network topologies into account, and finds that it outperforms alternative state-of-the-art methods with respect to quality and running time. Expand
SnapDRAGON: a method to delineate protein structural domains from sequence data.
TLDR
The method to identify protein domain boundaries from sequence information alone based on the assumption that hydrophobic residues cluster together in space is described, and domain boundaries are delineated with an accuracy of 51.8 %. Expand
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