An effective haplotype assembly algorithm based on hypergraph partitioning

A chaotic viewpoint-based approach to solve haplotype assembly using hypergraph model

Decreasing the cost of high-throughput DNA sequencing technologies, provides a huge amount of data that enables researchers to determine haplotypes for diploid and polyploid organisms. Although various methods have been developed to reconstruct haplotypes in diploid form, their accuracy is still a challenging task. Also, most of the current methods cannot be applied to polyploid form. In this paper, an iterative method is proposed, which employs hypergraph to reconstruct haplotype. The proposed method by utilizing chaotic viewpoint can enhance the obtained haplotypes. For this purpose, a haplotype set was randomly generated as an initial estimate, and its consistency with the input fragments was described by constructing a weighted hypergraph. Partitioning the hypergraph specifies those positions in the haplotype set that need to be corrected. This procedure is repeated until no further improvement could be achieved. Each element of the finalized haplotype set is mapped to a line by chaos game representation, and a coordinate series is defined based on the position of mapped points. Then, some positions with low qualities can be assessed by applying a local projection. Experimental results on both simulated and real datasets demonstrate that this method outperforms most other approaches, and is promising to perform the haplotype assembly.

NCMHap: a novel method for haplotype reconstruction based on Neutrosophic c-means clustering

BACKGROUND

Single individual haplotype problem refers to reconstructing haplotypes of an individual based on several input fragments sequenced from a specified chromosome. Solving this problem is an important task in computational biology and has many applications in the pharmaceutical industry, clinical decision-making, and genetic diseases. It is known that solving the problem is NP-hard. Although several methods have been proposed to solve the problem, it is found that most of them have low performances in dealing with noisy input fragments. Therefore, proposing a method which is accurate and scalable, is a challenging task.

RESULTS

In this paper, we introduced a method, named NCMHap, which utilizes the Neutrosophic c-means (NCM) clustering algorithm. The NCM algorithm can effectively detect the noise and outliers in the input data. In addition, it can reduce their effects in the clustering process. The proposed method has been evaluated by several benchmark datasets. Comparing with existing methods indicates when NCM is tuned by suitable parameters, the results are encouraging. In particular, when the amount of noise increases, it outperforms the comparing methods.

CONCLUSION

The proposed method is validated using simulated and real datasets. The achieved results recommend the application of NCMHap on the datasets which involve the fragments with a huge amount of gaps and noise.

AROHap: An effective algorithm for single individual haplotype reconstruction based on asexual reproduction optimization

In this paper, a method for single individual haplotype (SIH) reconstruction using Asexual reproduction optimization (ARO) is proposed. Haplotypes, as a set of genetic variations in each chromosome, contain vital information such as the relationship between human genome and diseases. Finding haplotypes in diploid organisms is a challenging task. Experimental methods are expensive and require special equipment. In SIH problem, we encounter with several fragments and each fragment covers some parts of desired haplotype. The main goal is bi-partitioning of the fragments with minimum error correction (MEC). This problem is addressed as NP-hard and several attempts have been made in order to solve it using heuristic methods. The current method, AROHap, has two main phases. In the first phase, most of the fragments are clustered based on a practical metric distance. In the second phase, ARO algorithm as a fast convergence bio-inspired method is used to improve the initial bi-partitioning of the fragments in the previous step. AROHap is implemented with several benchmark datasets. The experimental results demonstrate that satisfactory results were obtained, proving that AROHap can be used for SIH reconstruction problem.