Haris Vikalo – Recent software

Algorithms for Haplotype Assembly

SDhaP, package for haplotype assembly based on semi-definite programming. Applicable to both diploids and polyploids (the latter may be bi-allelic or poly-allelic). The method is described in the following publication:
- S. Das and H. Vikalo, "SDhaP: haplotype assembly for diploids and polyploids via semi-definite programming," BMC Genomics, 16:260, April 2015.
AltHap, haplotype assembler based on matrix/tensor factorization. Significant speed improvement over competing methods. Details of the method can be found in the following publication:
- A. Hashemi, B. Zhu and H. Vikalo, "Sparse tensor decomposition for haplotype assembly of diploids and polyploids," BMC Genomics, 19(Suppl 4):191, March 2018.
BFBP interprets haplotype assembly as the problem of decoding in communication systems and solves it via iterative belief propagation algorithm. Limited to bi-allelic variants. Details are in the following paper:
- Z. Puljiz and H. Vikalo, "Decoding genetic variations: Communications-inspired haplotype assembly," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 13, no. 3, June 2016, pp. 518-530.

Software for Viral Quasispecies Reconstruction

TenSQR is particularly suitable for reconstruction of viral quasispecies that consist of highly imbalanced populations (but applies more broadly). It relies on tensor factorization with successive read removal. Background is in the following article:
- S. Ahn, Z. Ke and H. Vikalo, "Viral quasispecies reconstruction via tensor factorization with successive read removal," Bioinformatics, vol. 34, no. 13, July 2018, pp. i23–i31. [Proceedings of the 2019 ISMB Conference.]
QSdpR extends the ideas of SDhaP to the problem of reconstructing viral communities. For details about the method, please see the paper below:
- S. Barik, S. Das, and H. Vikalo, "Viral quasispecies reconstruction via correlation clustering," Genomics, vol. 110, no. 6, November 2018, pp. 375-381.
To reconstruct quasispecies characterized by very low diversity, aBayesQR combines ideas from unsupervised learning and Bayesian inference. Details are here:
- S. Ahn and H. Vikalo, "aBayesQR: A Bayesian method for reconstruction of viral populations characterized by low diversity," Journal of Computational Biology, vol. 25, no. 7, July 2018, pp: 637-648.