INDEX: Incremental depth extension approach for protein–protein interaction networks alignment

Mir, Abolfazl; Naghibzadeh, Mahmoud; Saadati, Nayyereh

doi:10.1016/j.biosystems.2017.08.005

Cited by 22 publications

(10 citation statements)

References 42 publications

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“…To our knowledge, in order to gain biological relevance, every other network alignment algorithm applied to PPI networks has had to guide the alignment with objective functions that include protein-pair sequence similarities, in order to encourage sequence-and thus functionally-similar protein pairs to align to each other. This fact has led to a widespread belief in a so-called "sequence-topology trade-off" 23,28 , so that virtually every alignment algorithm either imposes sequence-based restrictions [25][26][27][30][31][32][33][34][35][36][37] , or has a literal trade-off in an objective function of the form αT (a) + (1 − α)S(a), where a is an alignment, T (a) measures the topological similarity exposed by the alignment according to some topological measure, S(a) is an objective based on sequence similarity, and α is a balancing parameter that makes the trade-off explicit 18,23,[38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53] . Virtually all methods that use the explicit trade-off have found that functional similarity is positively correlated with the weight given to sequence, and negatively correlated with the weight given to topology 15,23,35,38,40 .…”

Section: The Sequence-topology "Trade-off"mentioning

confidence: 99%

On the current failure -- but bright future -- of topology-driven biological network alignment

Wang,

Chen,

Frederisy

et al. 2022

Preprint

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Since the function of a protein is defined by its interaction partners, and since we expect similar interaction patterns across species, the alignment of protein-protein interaction (PPI) networks between species, based on network topology alone, should uncover functionally related proteins across species. Surprisingly, despite the publication of more than fifty algorithms aimed at performing PPI network alignment, few have demonstrated a statistically significant link between network topology and functional similarity, and none have demonstrated that orthologs can be recovered using network topology alone. We find that the major contributing factors are to this surprising failure are: (i) edge densities in most currently available experimental PPI networks are demonstrably too low to expect topological network alignment to succeed; (ii) in the few cases where the edge densities are high enough, some measures of topological similarity easily uncover functionally similar proteins while others do not; and (iii) most network alignment algorithms to date perform poorly at optimizing even their own topological objective functions, hampering their ability to use topology effectively. We demonstrate that SANA-the Simulated Annealing Network Aligner-significantly outperforms existing aligners at optimizing their own objective functions, even achieving near-optimal solutions when optimal solution is known. We offer the first demonstration of global network alignments based on topology alone that align functionally similar proteins with p-values in some cases below 10 −300 . We predict that topological network alignment has a bright future as edge densities increase towards the value where good alignments become possible. We demonstrate that when enough common topology is present at high enough edge densities-for example in the recent, partly synthetic networks of the Integrated Interaction Database-topological network alignment easily recovers most orthologs, paving the way towards high-throughput functional prediction based on topology-driven network alignment.

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Section: The Sequence-topology "Trade-off"mentioning

confidence: 99%

On the current failure -- but bright future -- of topology-driven biological network alignment

Wang,

Chen,

Frederisy

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…If it passes the position test then go to actual sequences, i.e., reference genome and patient's Chromosome 9. Use a seed and extend approach [13] to find the actual inverted string which might be longer than 6 nucleotides. The limitation of this method is that inversions of shorter lengths than 6 nucleotides cannot be found.…”

Section: Algorithm 1 Production Of Hash-based Genome Index Void Hashi...mentioning

confidence: 99%

Linked List Elimination from Hashing Methods

Naghibzadeh

2021

Research Reports on Computer Science

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Hashing has been used for decades in many fields such as encryption, password verification, and pattern search. Hash systems consist mainly of three components: the hash function, the hash table, and the linked lists that are attached to the hash table. One of the major benefits of using a hash function is reduction in the runtime of the hash-based software systems. However, their linked lists are a major source of time consumption. In this paper, an innovative method is proposed to remove all the linked lists attached to the hash table and collect the necessary information in a one-dimensional array. The method can be used to create an index for the human genome. The human genome is the size of a million-page book with no index, and it is difficult to find the needed information. The proposed method transforms list search operations with linear time complexity into array searches with logarithmic time complexity. In a sample problem, finding inversions in genomic sequences, the proposed indexing system is compared with traditional hashing systems with linked lists. It is demonstrated that, in addition to time complexity reduction, the proposed method reduces the space required for the hash system to one half of what is used by linked list based methods.

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“…Moreover, the topological structure information of the network, which is an external attribute of proteins, can also be incorporated. The majority of the PPI network alignment algorithms use a combination of sequence similarity and topological similarity [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Pairwise Biological Network Alignment Based on Discrete Bat Algorithm

Chen

Zhang

Xia

2021

Computational and Mathematical Methods in Medicine

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The development of high-throughput technology has provided a reliable technical guarantee for an increased amount of available data on biological networks. Network alignment is used to analyze these data to identify conserved functional network modules and understand evolutionary relationships across species. Thus, an efficient computational network aligner is needed for network alignment. In this paper, the classic bat algorithm is discretized and applied to the network alignment. The bat algorithm initializes the population randomly and then searches for the optimal solution iteratively. Based on the bat algorithm, the global pairwise alignment algorithm BatAlign is proposed. In BatAlign, the individual velocity and the position are represented by a discrete code. BatAlign uses a search algorithm based on objective function that uses the number of conserved edges as the objective function. The similarity between the networks is used to initialize the population. The experimental results showed that the algorithm was able to match proteins with high functional consistency and reach a relatively high topological quality.

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INDEX: Incremental depth extension approach for protein–protein interaction networks alignment

Cited by 22 publications

References 42 publications

On the current failure -- but bright future -- of topology-driven biological network alignment

On the current failure -- but bright future -- of topology-driven biological network alignment

Linked List Elimination from Hashing Methods

Pairwise Biological Network Alignment Based on Discrete Bat Algorithm

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