Research and Interests

Applied Bioinformatics

Over the past decade and a half I’ve accumulated a significant amount of experience across a wide spectrum of research topics, both computational and biological, with the unifying theme of "big data". Today I consider myself a genomicist; meaning, I conduct research in the three primary facets of genome research: data generation, data analysis and new method development. My research path has progressed from computational and problem solving algorithms in my undergraduate and masters years to applied bioinformatic analysis and new method development during my PhD and research associate appointments and finally to where I am today, as an genomics experimentalist and Director of a state of the art genomics core facility. My current research is on the generation, computational manipulation and interpretation of very large data sets across a wide range of disciplines, often applying techniques not originally designed for a particular data type or experiment in order to ask, and answer, new and interesting biological questions. I have had the pleasure of collaborating on projects in a wide range of biological subjects and problems including: functional genomics, evolutionary genomics, behavior genomics, comparative genomics, human and environmental microbiome, epigenomics, whole genome association studies and many others.

Microarrays - Single Feature Polymorphisms

Gene expression microarrays are a tool used by ecologists, to study the genome-wide transcriptional variation that underlies complex interactions among and between organisms and their environment. Microarrays allow for the simultaneous measurement of thousands of gene products across many samples. These measurements rely on sequence similarity for efficient hybridization of sample mRNA targets to the microarray probes. Genetic polymorphisms located within a microarray's probe affects this hybridization efficiency. These differences in probe level hybridization efficiencies are known as single feature polymorphisms (SFPs). SFPs are both a source of error, they negatively impact the estimate for transcript abundance, and a source of information, they are genetic markers. The Affymetrix 3' IVT microarray platform uses 11 to 16 short-oligonucleotide probes, covering different regions of a gene, to measure transcript abundance. The redundancy of probes within the same gene, allows for the capability to detect candidate SFPs from the data itself. My previous work presented three new results pertaining to SFPs in Affymetrix gene expression microarray data. The first result is a novel, improved algorithm for the accurate detection of SFPs. The second result evaluates the impact of SFPs to false positives in both differential expression analysis and the detection of SFPs themselves and offers a solution to reduce false positives induced by SFPs. Finally, the third result demonstrates how SFPs can be used to provide a new means for evaluating the accuracy of critical microarray preprocessing steps. These results will provide ecologists with new tools and techniques when using Affymetrix 3' IVT microarrays in ecological microarray studies.

Breeding Swarms: A Genetic Algorithm/Particle Swarm Optimization Hybrid Algorihtm

Genetic Algorithms and Particles Swarm Optimization are both population based stochastic search algorithms that have proven to be successful in solving very difficult problems. However, both models have strengths and weaknesses. Previous researchers have suggested that a hybrid of the GA and PSO models could lead to further advances. Breeding Swarms, combines the strengths of GAs with those of PSO. The Breeding Swarms algorithm improves search results for real valued problems by controlling the amount of diversity in the population. We performed experiments on optimizing standard benchmark functions and recurrent neural networks. Results show that the Breeding Swarm algorithm produces significantly better results than either GA or PSO, scales well to large problems and is more consistent over different problem domains.


dbcAmplicons: Analsyis of double-barcoded massively multiplexed Illumina amplicons

ARC: ARC is a pipeline which facilitates iterative, reference guided de novo assemblies.

SeqyClean: a Software Tool for Comprehensive Preprocessing of Sequence Data.

OTUbase: Provides a platform for Operational Taxonomic Unit based analysis within R.

rSFFreader: rSFFreader is a R package to read in sequence data (sequence, quality and clip points) from both Roche 454 sequencers and Life Sciences Ion Torrent Seuqencers.


Total Publications: 47.

h-index: 12, total citations: 456 on 40 publications via Web of Knowledge, .

h-index: 14, total citations: 625 on 34 articles via Google Scholar, Google Scholar.

Updated: November 6, 2013.

In Review


47. Yahvah KM, Brooker SL, Williams JE, Settles ML, McGuire MA, McGuire MK (2014) Elevated dairy fat intake in lactating women alters milk lipid and fatty acids without detectible changes in expression of genes related to lipid uptake or synthesis. Nutrition Research, Accepted Dec, 2014. In Press

46. Liang S, Gliniewicz K, Mendes-Soares H, Settles ML, Forney L, Coats E, McDonald A (2014) Comparative Analysis of Microbial Community of Novel Lactic Acid Fermentation Inoculated with Different Undefined Mixed Cultures. Bioresource Technology, Accepted Dec, 2014. In Press.

45. Dai J, Gliniewicz K, Settles ML, Coats ER, McDonald, AG (2014) Influence of organic loading rate and solid retention time on polyhydroxybutyrate production from hybrid poplar hydrolysates using mixed microbial cultures. Bioresource Technology, 175C:23-33. PMID: 25459800

44. Li H, Ye D, Wang X, Settles ML, Wang J, Hao Z, Zhou L, Dong P, Jiang Y, Ma Z (2014) Soil bacterial communities of different natural forest types in Northeast China. Plant and Soil, 383(1-2):203-216.

43. Dhimolea E, Wadia P, Murray T, Settles ML, Calafat AM, Treitman JD, Sonnenschein C, Shioda T, Soto A (2014) Prenatal exposure to BPA alters the epigenome of the rat mammary gland and increases the propensity to neoplastic development. PLoS One. 9(7):e99800. PMID: 24988533

42. Jacobson JC, Turok DK, Dermish AI, Nygaard IE, Settles ML. (2014) Vaginal microbiome changes with levonorgestrel intrauterine system placement. Contraception, 90(2):130-135. PMID: 24835828

41. Dziewanowska K, Settles M, Hunter S, Linquist I, Schilkey F, Hartzel PL. (2014) Phase variation in Myxococcus xanthus yields cells specialized for iron sequestration. PLoS One. 9(4):e95189. PMID: 24733297

40. Hunter SS, Yano H, Loftie-Eaton W, Hughes J, De Gelder L, Stragier P, De Vos P, Set- tles ML, Top EM. (2014) Draft genome sequence of Pseudomonas moraviensis R28-S. Genome Announc. 2(1):e00035-14. PMID: 24558233

39. Chapalamadugu KC, VandeVoort CA, Settles ML, Robison BD, Murdoch GK (2014) Maternal Bisphenol A Exposure Impacts the Fetal Heart Transcriptome. PLoS ONE 9(2): e89096. PMID: 24586524


38. Hou, D, Zhou, X, Zhong, X, Settles, ML, Herring, J, Wang, L, Abdo, Z, Xu, C, Forney, LJ. Microbiota of the seminal fluid from healthy and infertile men Accepted to Journal of Clinical Microbiology. July 2013. PMID: 23993888

37. Zhbannikov I.Y.; Hunter S.S; Settles M.L.; Foster J.A. (2013) SlopMap: A Software Applica- tion Tool for Quick and Flexible Identification of Similar Sequences Using Exact K-Mer Matching. J Data Mining Genomics Proteomics 4:133.

36. Benner, M, Settles, ML, Murdoch, G, Hardy, R, Robison, BD. (2013)Sex specific transcriptional responses of the zebrafish (Danio rerio) brain selenoproteome to acute sodium selenite supplementation. Physiological Genomics 45(15):653-666. PMID: 23737534

35. Fremgen, S, Williams, A, Furusawa, G, Dziewanowska, K, Settles, M, Hartzell, P. MasABK Proteins Interact with Proteins of the Type IV Pilin System to Affect Social Motility of Myxococcus xanthus. PLoS ONE 8(1): e54557. (2013) PMID: 23342171


34. Rosenblum EB, Poorten TJ, Joneson S, Settles M. Substrate-Specific Gene Expression in Batrachochytrium dendrobatidis, the Chytrid Pathogen of Amphibians. PLoS One, 7(11), e49924. (2012) PMID: 23185485

33. Garrison, E, Treeck, M, Ehret, E, Butz, H, Garbuz, T, Oswald, BP, Settles, M, Boothroyd, J and Arrizabalaga, G. A forward genetic screen reveals calcium-dependent protein kinase 3 regulates egress in Toxoplasma. PLoS Pathogens. 8(11):e1003049. (2012). PMID: 23209419

32. Guerrero-Bosagna, C., Covert, T., Haque, M., Settles, M., Nilsson, E., Anway, M., Skinner, M. Epigenetic Transgenerational Inheritance of Vinclozolin Induced Mouse Adult Onset Disease and Associated Sperm Epigenome Biomarkers. Reproductive Toxicology, 34(4):694-707. (2012). PMID: 23041264

31. Settles, ML, Coram, TE, Soule, T and Robison, BD An improved algorithm for the detection of genomic variation using short oligonucleotide expression microarrays. Molecular Ecology Resources, 12(6):1079–1089, (2012). PMID: 22966828

30. Hughes, J, Lohman, B, Deckert, G, Nichols, E, Settles, M, Abdo, Z and Top, E The Role of Clonal Interference in the Evolutionary Dynamics of Plasmid-Host Adaptation mBio, 3(4):e00077-12. (2012). PMID: 22761390

29. Drew, RE, Settles, ML, Churchill, EJ, Williams, SM, Balli, S and Robison, BD Brain transcriptome variation among behaviorally distinct strains of zebrafish (Danio rerio). BMC genomics, 13(1):323. (2012). PMID: 22817472

28. Copeland, W, Krishnan, V, Beck, D, Settles, M, Foster, J, Cho, KC, Day, M, Hickey, R, Schütte, U, Zhou, X, Williams, C, Forney, L and Abdo, Z mcaGUI: Microbial Community Analysis R-GUI. Bioinformatics, 28(16):2198-2199. (2012). PMID: 22692220

27. Minozzi, G, Williams, JL, Stella, A, Strozzi, F, Luini, MV, Settles, M, Taylor, JF, Whitlock, RH, Zanella, R and Neibergs, HL Meta-analysis of Two Genome-Wide Association Studies of Bovine Paratuberculosis. PLoS One, 7(3):e32578. (2012). PMID: 22396781

26. Rosenblum, EB, Poorten, T, Settles, M and Murdoch, GK Only skin deep: shared genetic response to the deadly chytrid fungus in susceptible frog species. Molecular Ecology, 21(13):3110-3120. (2012). PMID: 22332717

25. Turner, G, Cuthbertson, D, Voo, S, Settles, M, Grimes, H and Lange, M. Experimental sink removal induces stress responses, including shifts in amino acid and phenylpropanoid metabolism, in soybean leaves. Planta, 235(5):939-954. (2012). PMID: 22109846


24. Murdoch, BM, Murdoch, GK, Settles, M, McKay, S, Williams, JL and Moore, S Genome-Wide Scan Identifies Loci Associated with Classical BSE Occurrence. PLoS ONE, 6(11):e26819. (2011). PMID: 22073200

23. Furusawa, G, Dziewanowska, K, Stone, H, Settles, M and Hartzell, P. Global analysis of phase variation in Myxococcus xanthus. Molecular Microbiology, 81(3):784-804. (2011). PMID: 21722202

22. Beck, D, Settles, M and Foster, J OTUbase: an R infrastructure package for operational taxonomic unit data. Bioinformatics, 27(12):1700-1701. (2011). PMID: 21498398(*corresponding author)

21. Zanella, R, Settles, M, McKay, S, Schnabel, R, Taylor, J, Whitlock, R, Schukken, Y, Van Kessel, J, Smith, JM and Neibergs, H Identification of Loci Associated with Tolerance to Johne's Disease in Holstein Cattle. Animal Genetics, 42(1):28-38. (2011). PMID: 20477805


20. Guerrero-Bosagna, C, Settles, M, Lucker, B and Skinner, MK. Epigenetic Transgenerational Actions of Vinclozolin on Promoter Regions of the Sperm Epigenome. PLoS ONE, 5(9):e13100. (2010). PMID: 20927350

19. Neibergs, HL, Settles, ML, Whitlock, RH and Taylor, JF. GSEA-SNP identifies genes associated with Johne's disease in cattle. Mammalian genome, 21(7):419-425. (2010). PMID: 20706723

18. Johnson, KA, Neibergs, H, Michal, JJ, Carstens, GE, Settles, M, Hafla, A, Forbes, TD, Holloway, JW, Brosh, A. Differential expression of mitochondrial genes in liver from beef calves with divergent phenotypes for feed efficiency. EAAP Scientific Series, 127(1):75-76. (2010).

17. Clement TM, Savenkova MI, Settles, M, Anway MD and Skinner MK Alterations in the developing testis following embryonic vinclozolin exposure. Reproductive Toxicology, 30(3):353-363. (2010). PMID: 20566332

16. Murdoch, BM., Clawson, ML, Yue, S, Basu, U, McKay, S, Settles, M, Capoferri, R, Laegreid, WW, Williams, JL, Moore, SS PRNP Haplotype Associated with Classical BSE Incidence in European Holstein Cattle. PLoS ONE, 5(9):e12786. (2010). PMID: 20862290

15. Murdoch, BM, Clawson, M, Laegreid, W, Stothard, P, Settles, M, McKay, S, Prasad, A, Wang, Z, Moore, SS and Williams, J A 2cM genome-wide scan of European Holstein cattle affected by classical BSE. BMC Genetics, 11(1). (2010). PMID: 20350325

14. Coram, TE, Xueling, H, Zhan, G, Settles, ML and Chen, X Meta-analysis of transcripts associated with race-specific resistance to stripe rust in wheat demonstrates common induction of blue copper-binding protein, heat-stress transcription factor, pathogen-induced WIR1A protein, and ent-kaurene synthase transcripts. Functional and Integrative Genomics, 10(3):383-392. (2010). PMID: 19937262


13. Rosenblum EB, Poorten TJ, Settles, M, Murdoch GK, Robert J, Maddox, N. and Eisen, MB. Genome-Wide Transcriptional Response of Silurana (Xenopus) tropicalis to Infection with the Deadly Chytrid Fungus. PLoS ONE 4(8):e6494. (2009). PMID: 19701481

12. Coram, TE, Settles, ML and Chen, X Large-scale analysis of antisense transcription in wheat using the Affymetrix GeneChip Wheat Genome Array. BMC Genomics, 10(1):253. (2009). PMID: 19480707

11. Settles, ML, Zanella, R, McKay, SD, Schnabel, RD, Taylor, JF, Whitlock, R, Schukken, Y, Van Kessel, JS, Smith, JM and Neibergs, H A whole genome association analysis identifies loci associated with Mycobacterium avium subsp. paratuberculosis infection status in US holstein cattle. Animal Genetics, 40(5):655-662. (2009). PMID: 19422364


10. Drew, RE, Rodnick, KJ, Settles, M, Wacyk, J, Churchill, E, Powell, M, Hardy, R, Murdoch, G, Hill, R and Robison, BD Effect of Starvation on Gene Expression in Multiple Tissues of Zebrafish. Physiological Genomics 35(3):283-295. (2008). PMID: 18728227

9. Coram, TE, Settles, ML, Wang, M and Chen, X Surveying expression level polymorphism and single-feature polymorphism in near-isogenic wheat lines differing for the Yr5 stripe rust resistance locus. TAG Theoretical and Applied Genetics, 117(3):401-411. (2008). PMID: 18470504

8. Robison BD, Drew, RE, Murdoch, G, Powell M, Rodnick KJ, Settles, M, Stone, D, Churchill, E, Hill, R, Papasani, M, Lewis, S and Hardy, R Sexual dimorphism in hepatic gene expression and the response to dietary carbohydrate manipulation in the zebrafish (Danio rerio). Comparative Biochemistry and Physiology Part D: Genomics and Proteomics, 3(2):141-154. (2008). PMID: 20483215

7. Coram, TE, Settles, ML and Chen, X Transcriptome analysis of high-temperature adult-plant resistance conditioned by Yr39 during the wheat-Puccinia striiformis f. sp. tritici interaction. Molecular Plant Pathology, 9(4):479-493. (2008). PMID: 18705862


6. Settles, M and Soule, T Choosing an Algorithm: An Experimental Comparison of the Genetic Algorithm and Binary Particle Swarm. In: Proceedings of the $17^{th}$ IMACS World Congress on Scientific Computation, Applied Mathematics and Simulation. Paris, France. (2005).

5. Settles, M and Soule, T Breeding Swarms: A GA/PSO Hybrid. In: Proceeding of the Genetic and Evolutionary Computation Conference. pp. 161-168, Washington D.C. (2005).

4. Settles, M, Nathan, P and Soule, T Breeding Swarms: A New Approach to Recurrent Neural Network Training. In: Proceeding of the Genetic and Evolutionary Computation Conference. pp. 185-192, Washington D.C. (2003).


3. Settles, M, Rodebaugh, B and Soule, T Comparison of genetic algorithm and particle swarm optimizer when evolving a recurrent neural network In: Proceeding of the Genetic and Evolutionary Computation Conference. volume 2723, pp. 148-149, Chicago, IL. (2003).

2. Settles, M and Soule, T A Hybrid GA/PSO to Evolve Artificial Recurrent Neural Networks. In: Intelligent Engineering Systems Through Artificial Neural Networks, volume 13, pp. 51-56, St. Louis, MO. (2003).


1. Settles, M and Rylander, B Neural network learning using particle swarm optimizers. In: Advances in Information Science and Soft Computing, pp. 224-226. (2002). WSEAS Press.