Journal articles: 'Evolutionary bioinformatics'

1

Pagel, Mark. "Evolution, Bioinformatics and Evolutionary Bioinformatics Online." Evolutionary Bioinformatics 2 (January 2006): 117693430600200. http://dx.doi.org/10.1177/117693430600200006.

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Pagel, M. "Phylogenetic-evolutionary approaches to bioinformatics." Briefings in Bioinformatics 1, no. 2 (2000): 117–30. http://dx.doi.org/10.1093/bib/1.2.117.

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Sherbakov, Dmitry, Yuri Panchin, and Ancha Baranova. "Extracting Evolutionary Insights Using Bioinformatics." International Journal of Genomics 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/376235.

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Pal, S. K., S. Bandyopadhyay, and S. S. Ray. "Evolutionary computation in bioinformatics: a review." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 36, no. 5 (2006): 601–15. http://dx.doi.org/10.1109/tsmcc.2005.855515.

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Raymer, Michael L. "Book Review: Evolutionary Computation in Bioinformatics." Genetic Programming and Evolvable Machines 6, no. 2 (2005): 229–30. http://dx.doi.org/10.1007/s10710-005-7581-6.

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Pinho, Jorge, João Luis Sobral, and Miguel Rocha. "Parallel evolutionary computation in bioinformatics applications." Computer Methods and Programs in Biomedicine 110, no. 2 (2013): 183–91. http://dx.doi.org/10.1016/j.cmpb.2012.10.001.

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Ye, Kai, Gert Vriend, and Adriaan P. IJzerman. "Tracing evolutionary pressure." Bioinformatics 24, no. 7 (2008): 908–15. http://dx.doi.org/10.1093/bioinformatics/btn057.

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Li, Shan, Liying Kang, and Xing-Ming Zhao. "A Survey on Evolutionary Algorithm Based Hybrid Intelligence in Bioinformatics." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/362738.

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With the rapid advance in genomics, proteomics, metabolomics, and other types of omics technologies during the past decades, a tremendous amount of data related to molecular biology has been produced. It is becoming a big challenge for the bioinformatists to analyze and interpret these data with conventional intelligent techniques, for example, support vector machines. Recently, the hybrid intelligent methods, which integrate several standard intelligent approaches, are becoming more and more popular due to their robustness and efficiency. Specifically, the hybrid intelligent approaches based on evolutionary algorithms (EAs) are widely used in various fields due to the efficiency and robustness of EAs. In this review, we give an introduction about the applications of hybrid intelligent methods, in particular those based on evolutionary algorithm, in bioinformatics. In particular, we focus on their applications to three common problems that arise in bioinformatics, that is, feature selection, parameter estimation, and reconstruction of biological networks.

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Leman, S. C., M. K. Uyenoyama, M. Lavine, and Y. Chen. "The evolutionary forest algorithm." Bioinformatics 23, no. 15 (2007): 1962–68. http://dx.doi.org/10.1093/bioinformatics/btm264.

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Harmon, L. J., J. T. Weir, C. D. Brock, R. E. Glor, and W. Challenger. "GEIGER: investigating evolutionary radiations." Bioinformatics 24, no. 1 (2007): 129–31. http://dx.doi.org/10.1093/bioinformatics/btm538.

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Drost, Hajk-Georg, Alexander Gabel, Jialin Liu, Marcel Quint, and Ivo Grosse. "myTAI: evolutionary transcriptomics with R." Bioinformatics 34, no. 9 (2017): 1589–90. http://dx.doi.org/10.1093/bioinformatics/btx835.

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Münch, Philipp C., Bärbel Stecher, and Alice C. McHardy. "EDEN: evolutionary dynamics within environments." Bioinformatics 33, no. 20 (2017): 3292–95. http://dx.doi.org/10.1093/bioinformatics/btx394.

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Jungck, J. R., and A. E. Weisstein. "Mathematics and evolutionary biology make bioinformatics education comprehensible." Briefings in Bioinformatics 14, no. 5 (2013): 599–609. http://dx.doi.org/10.1093/bib/bbt046.

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Jungck, John R., Noppadon Khiripet, Rawin Viruchpinta, and Jutarat Maneewattanapluk. "Evolutionary Bioinformatics: Making Meaning of Microbes, Molecules, Maps." Microbe Magazine 1, no. 8 (2006): 365–71. http://dx.doi.org/10.1128/microbe.1.365.1.

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Gadaleta, Emanuela, Stefano Pirrò, Abu Zafer Dayem Ullah, Jacek Marzec, and Claude Chelala. "BCNTB bioinformatics: the next evolutionary step in the bioinformatics of breast cancer tissue banking." Nucleic Acids Research 46, no. D1 (2017): D1055—D1061. http://dx.doi.org/10.1093/nar/gkx913.

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Rodriguez, A. A., T. Bompada, M. Syed, P. K. Shah, and N. Maltsev. "Evolutionary analysis of enzymes using Chisel." Bioinformatics 23, no. 22 (2007): 2961–68. http://dx.doi.org/10.1093/bioinformatics/btm421.

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17

Gil, Nelson, and Andras Fiser. "Identifying functionally informative evolutionary sequence profiles." Bioinformatics 34, no. 8 (2017): 1278–86. http://dx.doi.org/10.1093/bioinformatics/btx779.

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18

DiNardo, Zach, Kiran Tomlinson, Anna Ritz, and Layla Oesper. "Distance measures for tumor evolutionary trees." Bioinformatics 36, no. 7 (2019): 2090–97. http://dx.doi.org/10.1093/bioinformatics/btz869.

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Abstract Motivation There has been recent increased interest in using algorithmic methods to infer the evolutionary tree underlying the developmental history of a tumor. Quantitative measures that compare such trees are vital to a number of different applications including benchmarking tree inference methods and evaluating common inheritance patterns across patients. However, few appropriate distance measures exist, and those that do have low resolution for differentiating trees or do not fully account for the complex relationship between tree topology and the inheritance of the mutations labeling that topology. Results Here, we present two novel distance measures, Common Ancestor Set distance (CASet) and Distinctly Inherited Set Comparison distance (DISC), that are specifically designed to account for the subclonal mutation inheritance patterns characteristic of tumor evolutionary trees. We apply CASet and DISC to multiple simulated datasets and two breast cancer datasets and show that our distance measures allow for more nuanced and accurate delineation between tumor evolutionary trees than existing distance measures. Availability and implementation Implementations of CASet and DISC are freely available at: https://bitbucket.org/oesperlab/stereodist. Supplementary information Supplementary data are available at Bioinformatics online.

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19

Sadowski, M. I., and W. R. Taylor. "Evolutionary inaccuracy of pairwise structural alignments." Bioinformatics 28, no. 9 (2012): 1209–15. http://dx.doi.org/10.1093/bioinformatics/bts103.

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20

Mailund, T., and C. N. S. Pedersen. "QDist--quartet distance between evolutionary trees." Bioinformatics 20, no. 10 (2004): 1636–37. http://dx.doi.org/10.1093/bioinformatics/bth097.

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21

Huson, D. H. "SplitsTree: analyzing and visualizing evolutionary data." Bioinformatics 14, no. 1 (1998): 68–73. http://dx.doi.org/10.1093/bioinformatics/14.1.68.

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22

Kumar, S., K. Tamura, I. B. Jakobsen, and M. Nei. "MEGA2: molecular evolutionary genetics analysis software." Bioinformatics 17, no. 12 (2001): 1244–45. http://dx.doi.org/10.1093/bioinformatics/17.12.1244.

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23

Taylor, Philip. "A program for drawing evolutionary trees." Bioinformatics 4, no. 4 (1988): 441–43. http://dx.doi.org/10.1093/bioinformatics/4.4.441.

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24

O'Connor, Timothy D., and Nicholas I. Mundy. "Genotype–phenotype associations: substitution models to detect evolutionary associations between phenotypic variables and genotypic evolutionary rate." Bioinformatics 25, no. 12 (2009): i94—i100. http://dx.doi.org/10.1093/bioinformatics/btp231.

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25

Jungck, J. R., S. S. Donovan, A. E. Weisstein, N. Khiripet, and S. J. Everse. "Bioinformatics education dissemination with an evolutionary problem solving perspective." Briefings in Bioinformatics 11, no. 6 (2010): 570–81. http://dx.doi.org/10.1093/bib/bbq028.

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26

Vlachakis, Dimitrios, Athanasia Pavlopoulou, Dorothea Kazazi, and Sophia Kossida. "Unraveling microalgal molecular interactions using evolutionary and structural bioinformatics." Gene 528, no. 2 (2013): 109–19. http://dx.doi.org/10.1016/j.gene.2013.07.039.

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27

Bielejec, F., A. Rambaut, M. A. Suchard, and P. Lemey. "SPREAD: spatial phylogenetic reconstruction of evolutionary dynamics." Bioinformatics 27, no. 20 (2011): 2910–12. http://dx.doi.org/10.1093/bioinformatics/btr481.

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28

Ames, Ryan M., Daniel Money, Vikramsinh P. Ghatge, Simon Whelan, and Simon C. Lovell. "Determining the evolutionary history of gene families." Bioinformatics 28, no. 1 (2011): 48–55. http://dx.doi.org/10.1093/bioinformatics/btr592.

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29

Gibson, Todd A., and Debra S. Goldberg. "Improving evolutionary models of protein interaction networks." Bioinformatics 27, no. 3 (2010): 376–82. http://dx.doi.org/10.1093/bioinformatics/btq623.

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30

Hodzic, Ermin, Raunak Shrestha, Salem Malikic, et al. "Identification of conserved evolutionary trajectories in tumors." Bioinformatics 36, Supplement_1 (2020): i427—i435. http://dx.doi.org/10.1093/bioinformatics/btaa453.

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Abstract Motivation As multi-region, time-series and single-cell sequencing data become more widely available; it is becoming clear that certain tumors share evolutionary characteristics with others. In the last few years, several computational methods have been developed with the goal of inferring the subclonal composition and evolutionary history of tumors from tumor biopsy sequencing data. However, the phylogenetic trees that they report differ significantly between tumors (even those with similar characteristics). Results In this article, we present a novel combinatorial optimization method, CONETT, for detection of recurrent tumor evolution trajectories. Our method constructs a consensus tree of conserved evolutionary trajectories based on the information about temporal order of alteration events in a set of tumors. We apply our method to previously published datasets of 100 clear-cell renal cell carcinoma and 99 non-small-cell lung cancer patients and identify both conserved trajectories that were reported in the original studies, as well as new trajectories. Availability and implementation CONETT is implemented in C++ and available at https://github.com/ehodzic/CONETT. Supplementary information Supplementary data are available at Bioinformatics online.

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31

Esmaili-Taheri, A., M. Ganjtabesh, and M. Mohammad-Noori. "Evolutionary solution for the RNA design problem." Bioinformatics 30, no. 9 (2014): 1250–58. http://dx.doi.org/10.1093/bioinformatics/btu001.

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32

Dib, Linda, Daniele Silvestro, and Nicolas Salamin. "Evolutionary footprint of coevolving positions in genes." Bioinformatics 30, no. 9 (2014): 1241–49. http://dx.doi.org/10.1093/bioinformatics/btu012.

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33

Christensen, Sarah, Juho Kim, Nicholas Chia, Oluwasanmi Koyejo, and Mohammed El-Kebir. "Detecting evolutionary patterns of cancers using consensus trees." Bioinformatics 36, Supplement_2 (2020): i684—i691. http://dx.doi.org/10.1093/bioinformatics/btaa801.

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Abstract Motivation While each cancer is the result of an isolated evolutionary process, there are repeated patterns in tumorigenesis defined by recurrent driver mutations and their temporal ordering. Such repeated evolutionary trajectories hold the potential to improve stratification of cancer patients into subtypes with distinct survival and therapy response profiles. However, current cancer phylogeny methods infer large solution spaces of plausible evolutionary histories from the same sequencing data, obfuscating repeated evolutionary patterns. Results To simultaneously resolve ambiguities in sequencing data and identify cancer subtypes, we propose to leverage common patterns of evolution found in patient cohorts. We first formulate the Multiple Choice Consensus Tree problem, which seeks to select a tumor tree for each patient and assign patients into clusters in such a way that maximizes consistency within each cluster of patient trees. We prove that this problem is NP-hard and develop a heuristic algorithm, Revealing Evolutionary Consensus Across Patients (RECAP), to solve this problem in practice. Finally, on simulated data, we show RECAP outperforms existing methods that do not account for patient subtypes. We then use RECAP to resolve ambiguities in patient trees and find repeated evolutionary trajectories in lung and breast cancer cohorts. Availability and implementation https://github.com/elkebir-group/RECAP. Supplementary information Supplementary data are available at Bioinformatics online.

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34

Skums, Pavel, Viachaslau Tsyvina, and Alex Zelikovsky. "Inference of clonal selection in cancer populations using single-cell sequencing data." Bioinformatics 35, no. 14 (2019): i398—i407. http://dx.doi.org/10.1093/bioinformatics/btz392.

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Abstract Summary Intra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single-cell sequencing (scSeq), which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here, we present Single Cell Inference of FItness Landscape (SCIFIL), a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from scSeq data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy our approach, and show how it could be applied to experimental tumor data to study clonal selection and infer evolutionary history. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer. Availability and implementation Its source code is available at https://github.com/compbel/SCIFIL.

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35

Dupanloup, Isabelle, and Henrik Kaessmann. "Evolutionary simulations to detect functional lineage-specific genes." Bioinformatics 22, no. 15 (2006): 1815–22. http://dx.doi.org/10.1093/bioinformatics/btl280.

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36

Guillaume, F., and J. Rougemont. "Nemo: an evolutionary and population genetics programming framework." Bioinformatics 22, no. 20 (2006): 2556–57. http://dx.doi.org/10.1093/bioinformatics/btl415.

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37

Lin, Y., and B. M. E. Moret. "Estimating true evolutionary distances under the DCJ model." Bioinformatics 24, no. 13 (2008): i114—i122. http://dx.doi.org/10.1093/bioinformatics/btn148.

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38

Mather, William H., Jeff Hasty, and Lev S. Tsimring. "Fast stochastic algorithm for simulating evolutionary population dynamics." Bioinformatics 28, no. 9 (2012): 1230–38. http://dx.doi.org/10.1093/bioinformatics/bts130.

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39

Cohen, Ofir, Haim Ashkenazy, David Burstein, and Tal Pupko. "Uncovering the co-evolutionary network among prokaryotic genes." Bioinformatics 28, no. 18 (2012): i389—i394. http://dx.doi.org/10.1093/bioinformatics/bts396.

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Sankararaman, Sriram, Fei Sha, Jack F. Kirsch, Michael I. Jordan, and Kimmen Sjölander. "Active site prediction using evolutionary and structural information." Bioinformatics 26, no. 5 (2010): 617–24. http://dx.doi.org/10.1093/bioinformatics/btq008.

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Ting, Chuan-Kang, Wei-Ting Lin, and Yao-Ting Huang. "Multi-objective tag SNPs selection using evolutionary algorithms." Bioinformatics 26, no. 11 (2010): 1446–52. http://dx.doi.org/10.1093/bioinformatics/btq158.

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Pham, Son K., and Pavel A. Pevzner. "DRIMM-Synteny: decomposing genomes into evolutionary conserved segments." Bioinformatics 26, no. 20 (2010): 2509–16. http://dx.doi.org/10.1093/bioinformatics/btq465.

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43

Chor, B., and T. Tuller. "Maximum likelihood of evolutionary trees: hardness and approximation." Bioinformatics 21, Suppl 1 (2005): i97—i106. http://dx.doi.org/10.1093/bioinformatics/bti1027.

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44

Holmes, Ian. "Using evolutionary Expectation Maximization to estimate indel rates." Bioinformatics 21, no. 10 (2005): 2294–300. http://dx.doi.org/10.1093/bioinformatics/bti177.

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Abstract Motivation The Expectation Maximization (EM) algorithm, in the form of the Baum–Welch algorithm (for hidden Markov models) or the Inside-Outside algorithm (for stochastic context-free grammars), is a powerful way to estimate the parameters of stochastic grammars for biological sequence analysis. To use this algorithm for multiple-sequence evolutionary modelling, it would be useful to apply the EM algorithm to estimate not only the probability parameters of the stochastic grammar, but also the instantaneous mutation rates of the underlying evolutionary model (to facilitate the development of stochastic grammars based on phylogenetic trees, also known as Statistical Alignment). Recently, we showed how to do this for the point substitution component of the evolutionary process; here, we extend these results to the indel process. Results We present an algorithm for maximum-likelihood estimation of insertion and deletion rates from multiple sequence alignments, using EM, under the single-residue indel model owing to Thorne, Kishino and Felsenstein (the ‘TKF91’ model). The algorithm converges extremely rapidly, gives accurate results on simulated data that are an improvement over parsimonious estimates (which are shown to underestimate the true indel rate), and gives plausible results on experimental data (coronavirus envelope domains). Owing to the algorithm's close similarity to the Baum–Welch algorithm for training hidden Markov models, it can be used in an ‘unsupervised’ fashion to estimate rates for unaligned sequences, or estimate several sets of rates for sequences with heterogenous rates. Availability Software implementing the algorithm and the benchmark is available under GPL from http://www.biowiki.org/ Contact ihh@berkeley.edu

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45

Kumar, Sudhir, Koichiro Tamura, and Masatoshi Nei. "MEGA: Molecular Evolutionary Genetics Analysis software for microcomputers." Bioinformatics 10, no. 2 (1994): 189–91. http://dx.doi.org/10.1093/bioinformatics/10.2.189.

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46

Holmes, I., and W. J. Bruno. "Evolutionary HMMs: a Bayesian approach to multiple alignment." Bioinformatics 17, no. 9 (2001): 803–20. http://dx.doi.org/10.1093/bioinformatics/17.9.803.

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47

Wang, Xue Chen, Xiao Guang Yue, Qing Guo Ren, and Zi Qiang Zhao. "Research on Coal Mine Rescue Robot Model." Applied Mechanics and Materials 340 (July 2013): 801–4. http://dx.doi.org/10.4028/www.scientific.net/amm.340.801.

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According to the situation of frequently domestic mining safety accidents, the basic theory and related concepts of bioinformatics' gene expression programming and multi-agent system are discussed. Related concepts of Bioinformatics and biological evolution and evolutionary computation are described in this paper. A coal mine rescue robot working model is discussed based on bioinformatics gene expression programming algorithm and multi-agent system theory.

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48

Hung, Che-Lun, and Chun-Yuan Lin. "Open Reading Frame Phylogenetic Analysis on the Cloud." International Journal of Genomics 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/614923.

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Phylogenetic analysis has become essential in researching the evolutionary relationships between viruses. These relationships are depicted on phylogenetic trees, in which viruses are grouped based on sequence similarity. Viral evolutionary relationships are identified from open reading frames rather than from complete sequences. Recently, cloud computing has become popular for developing internet-based bioinformatics tools. Biocloud is an efficient, scalable, and robust bioinformatics computing service. In this paper, we propose a cloud-based open reading frame phylogenetic analysis service. The proposed service integrates the Hadoop framework, virtualization technology, and phylogenetic analysis methods to provide a high-availability, large-scale bioservice. In a case study, we analyze the phylogenetic relationships amongNorovirus. Evolutionary relationships are elucidated by aligning different open reading frame sequences. The proposed platform correctly identifies the evolutionary relationships between members ofNorovirus.

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Quadeer, Ahmed A., David Morales-Jimenez, and Matthew R. McKay. "RocaSec: a standalone GUI-based package for robust co-evolutionary analysis of proteins." Bioinformatics 36, no. 7 (2019): 2262–63. http://dx.doi.org/10.1093/bioinformatics/btz890.

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Abstract Summary Patterns of mutational correlations, learnt from protein sequences, have been shown to be informative of co-evolutionary sectors that are tightly linked to functional and/or structural properties of proteins. Previously, we developed a statistical inference method, robust co-evolutionary analysis (RoCA), to reliably predict co-evolutionary sectors of proteins, while controlling for statistical errors caused by limited data. RoCA was demonstrated on multiple viral proteins, with the inferred sectors showing close correspondences with experimentally-known biochemical domains. To facilitate seamless use of RoCA and promote more widespread application to protein data, here we present a standalone cross-platform package ‘RocaSec’ which features an easy-to-use GUI. The package only requires the multiple sequence alignment of a protein for inferring the co-evolutionary sectors. In addition, when information on the protein biochemical domains is provided, RocaSec returns the corresponding statistical association between the inferred sectors and biochemical domains. Availability and implementation The RocaSec software is publicly available under the MIT License at https://github.com/ahmedaq/RocaSec. Supplementary information Supplementary data are available at Bioinformatics online.

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Zhang, Yan, and Junge Zheng. "Bioinformatics of Metalloproteins and Metalloproteomes." Molecules 25, no. 15 (2020): 3366. http://dx.doi.org/10.3390/molecules25153366.

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Trace metals are inorganic elements that are required for all organisms in very low quantities. They serve as cofactors and activators of metalloproteins involved in a variety of key cellular processes. While substantial effort has been made in experimental characterization of metalloproteins and their functions, the application of bioinformatics in the research of metalloproteins and metalloproteomes is still limited. In the last few years, computational prediction and comparative genomics of metalloprotein genes have arisen, which provide significant insights into their distribution, function, and evolution in nature. This review aims to offer an overview of recent advances in bioinformatic analysis of metalloproteins, mainly focusing on metalloprotein prediction and the use of different metals across the tree of life. We describe current computational approaches for the identification of metalloprotein genes and metal-binding sites/patterns in proteins, and then introduce a set of related databases. Furthermore, we discuss the latest research progress in comparative genomics of several important metals in both prokaryotes and eukaryotes, which demonstrates divergent and dynamic evolutionary patterns of different metalloprotein families and metalloproteomes. Overall, bioinformatic studies of metalloproteins provide a foundation for systematic understanding of trace metal utilization in all three domains of life.

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Journal articles on the topic 'Evolutionary bioinformatics'

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