SparseAssembler [二代基因测序组装软件: Sparse k-mer Graph for Memory Efficient de novo Genome Assembly). The core algorithm (Sparse k-mer) was used in BGI’s SoapDenovo-II, the updated version of BGI’s flagship software SoapDenovo]. Available at:
Faculty 1000 of Biology and Medicine, UK (2008-2016)
1. Ma ZS (2020) Critical network structures and medical ecology mechanisms underlying human microbiome-associated diseases. iScience.doi: 10.1016/j.isci.2020.101195.
2.Ma ZS (2020) Testing the Anna Karenina Principle in human microbiome-associated diseases. iScience, 23(4):101007.
3.Ma ZS (2020) Heterogeneity-disease relationship in the human microbiome associated diseases. FEMS Microbiol Ecol, fiaa093. doi:10.1093/femsec/fiaa093.
4.Ma ZS, Li LW, Zhang YP (2020) Defining individual-level genetic diversity and similarity profiles. Scientific Reports, 10(1): 5805.
5. Li LW, Li WD, Zou Q, Ma ZS (2020) Network analysis of the hot spring microbiome sketches possible niche differentiations among ecological guilds. Ecological Modelling, vol. 431. doi: 10.1016/j.ecolmodel.2020.109147.
6.Li LW, Ma ZS (2020) Species sorting and neutral theory analyses reveal archaeal and bacterial communities are assembled differently in hot springs. Frontiers in Bioengineering and Biotechnology, 8. doi: 10.3389/fbioe.2020.00464.
7.Li WD, Ma ZS (2020) FBA Ecological Guild: Trio of Firmicutes-Bacteroidetes Alliance against actinobacteria in human oral microbiome. Scientific Reports, 10(1): 287.
8. Ma ZS & Taylor RAJ (2020) Human reproductive system microbiomes exhibited significantly different heterogeneity scaling with gut microbiome, but the intra-system scaling is invariant. Oikos, 129 (6): 903-911.
9.Ma ZS (2020) Estimating the optimum coverage and quality of amplicon sequencing with taylor's power law extensions. Frontiers in Bioengineering and Biotechnology, 8:372.
10.Ma ZS (2020) Assessing and interpreting the metagenome heterogeneity with power law. Frontiers in Microbiology, 11: 648.
11. Luo J, Chai J, et al. (2020) From asymmetrical to balanced genomic diversification during rediploidization: Subgenomic evolution in allotetraploid fish. Science Advances. DOI: 10.1126/sciadv.aaz7677
12.Ma ZS (2020) Spatiotemporal fluctuation scaling law and metapopulation modeling of the novel coronavirus (COVID-19) and SARS outbreaks. (preprint) https://arxiv.org/abs/2003.03714
13.Ma ZS (2020) A simple mathematical model for estimating the inflection points of COVID-19 outbreaks (preprint) https://www.medrxiv.org/content/10.1101a/2020.03.25.20043893v1
14.Ma ZS & AM Ellsion (2020) Risks and etiology of bacterial vaginosis revealed by species dominance network analysis (preprint) https://www.medrxiv.org/content/10.1101/2020.05.23.20104208v1
15.Ma ZS & AM Ellsion (2020) Towards a unifying diversity-area relationship (DAR) of species- and gene-diversity. (preprint) https://www.biorxiv.org/content/10.1101/2020.05.16.099861v1
16.Li WD & Ma ZS (2020) A theoretic approach to the mechanism of gut microbiome translocation in SIV-infected Asian macaques. FEMS Microbiology Ecology (in Revision)
17.Li WD & Ma ZS (2020) Population-level diversity-disease relationship (DDR) in the human microbiome associated diseases. Frontiers in Microbiology. (in Revision)
18.Li WD & Ma ZS (2020) Species dominance network analysis approach to the stability mechanism under the Lactobacillus-deficient vaginal microbiome. (Under Review)
19.Li WD, Sun Y, et al (2020) Ecological and network analyses reveal four microbial species with potential significance for the diagnosis/treatment of ulcerative colitis (UC). (Under Review)
20.Li LW, Ma ZS (2020) Modeling the microbiome assembly of breast tissues with tumors via neutral, near neutral and niche-neutral hybrid models. (Under Review)
21.Sun Y, Li LW et al (2020) Does ulcerative colitis influence the spatial heterogeneity of the intestinal mucosal microbiome? Evolutionary Bioinformatics.(in Revision)
22.Ma ZS, Li WD, Shi P (2020) Microbiome-diversity—host-phylogeny relationship in animal gastrointestinal tract microbiome. (Under Review)
23.Ma ZS (2020) Towards unifying microbiome ecology of metagenomic genes and microbial taxa: critical ecological processes and network structures (Under Review)
24.Rahman MDM & ZS Ma (2020) The Deep Learning AI Technology in Bioinformatics: A Brief Review (Under Review)
25.Ma ZS (2020) Niche-neutral theoretic approach to mechanisms underlying biodiversity and biogeography of human microbiomes. Molecular Ecology (in Revision)
26.Ma ZS, Li WD (2019) How man and woman are different in their microbiome: Ecological and network analyses of the microgenderome. Advanced Science, 6(23): 1902054.
27.Ma ZS, Li LW, Gotelli NJ (2019) Diversity-disease relationships and shared species analyses for human microbiome-associated diseases. The ISME Journal, 13: 1911–1919.
28.Ma ZS, Ellison AM (2019) Dominance network analysis provides a new framework for studying the diversity-stability relationship. Ecological Monographs. 89(2), DOI: 10.1002/ecm.1358.
29.Li LW & Ma ZS (2019) Comparative power law analysis for the spatial heterogeneity scaling of the hot-spring and human microbiomes. Molecular Ecology, 28(11): 2932-2943.
30.Ma ZS (2019) A new DTAR (diversity–time–area relationship) model demonstrated with the indoor microbiome. Journal of Biogeography, 46(1). DOI: 10.1111/jbi.13636
31.Ma ZS, Li LW, Ye CX, Peng MS, Zhang YP (2019) Hybrid assembly of ultra-long Nanopore reads augmented with 10×-genomics contigs: Demonstrated with a human genome. Genomics, 111(6): 1896-1901.
32.Li LW, Ma ZS (2019) Global microbiome diversity scaling in hot springs with DAR (diversity-area relationship) profiles. Frontiers in Microbiology, vol. 10, article 118.
33.Li WD, Ma ZS (2019) Diversity scaling of human vaginal microbial communities. Zoological Research. 40(6): 587-594.
34.Ma ZS & AM Ellison (2018) A unified concept of dominance applicable at both community and species scale. Ecosphere, https://doi.org/10.1002/ecs2.2477.
35.Ma ZS (2018) Extending species-area relationships (SAR) to diversity-area relationships (DAR), Ecology and Evolution, 8(20): 10023-10038.
36.Ma ZS (2018) Diversity time-period and diversity-time-area relationships exemplified by the human microbiome. Scientific Reports, 8(1): 7214.
37.Ma ZS (2018) Sketching the human microbiome biogeography with DAR (diversity-area relationship) profiles. Microbial Ecology, vol. 76, https://doi.org/10.1007/s00248-018-1245-6
38.Ma ZS, Li LW, Li W (2018) Assessing and interpreting the within-Body biogeography of human microbiome diversity. Frontiers in Microbiology, 9:1619.
39.Ma ZS, Li LW (2018) Measuring metagenome diversity and similarity with Hill numbers. Molecular Ecology Resources, 18(6): 1339-1355.
40.Li W, Yuan Y, et al. (2018) A cross-scale neutral theory approach to the influence of obesity on community assembly of human gut microbiome. Frontiers in Microbiology, 9: 2320.
41.Sun Y, Li LW, et al. (2018) The gut microbiota heterogeneity and assembly changes associated with the IBD. Scientific Reports, 9(1): 440.
42.Ma ZS, Li LW (2018) Semen microbiome biogeography: an analysis based on a Chinese population study. Frontiers in Microbiology, vol. 9: article 3333.
43.Ma ZS, Ye DD (2017) Trios—promising in silico biomarkers for differentiating the effect of disease on the human microbiome network. Scientific Reports, 7(1): 13259.
44.Ma ZS (2017) The P/N (Positive-to-Negative Links) ratio in complex networks—a promising in silico biomarker for detecting changes occurring in the human microbiome. Microbial Ecology, 75(4): 1063-1073.
45.Ma ZS, Li L (2017) Quantifying the human vaginal community state types (CSTs) with the species specificity index. Peer J, 2017, 5: e3366.
46.Chen HJ, Peng S, et al. (2017) Oral microbial community assembly under the influence of periodontitis. PLoS One, 12(8): e0182259.
47.Dai L, Kou H, et al. (2017) Does colorectal cancer significantly influence the assembly of gut microbial communities? Peer J, 5(8): e3383. DOI:10.7717/peerj.3383.
48.Wei L, Xing PW, et al. (2017) CPPred-RF: A Sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. Journal of Proteome Research, 16(5): 2044‐2053.
49.Zou Q, Wan S, Zeng X, Ma ZS (2017) Reconstructing evolutionary trees in parallel for massive sequences. BMC Systems Biology, 11(S6): 100. DOI: 10.1186/s12918-017-0476-3.
50.Ma ZS (2017) Measuring microbiome diversity and similarity with Hill Numbers. Chapter 8, in “Metagenomics” https://doi.org/10.1016/B978-0-08-102268-9.00008-2, Elsevier
51.Ye CX & ZS Ma (2016) Sparc: a sparsity-based consensus algorithm for long erroneous sequencing reads. PeerJ. 4:e2016. DOI 10.7717/peerj.2016 https://peerj.com/articles/2016/
52.Ye CX, C Hill, J Ruan, ZS Ma (2016) DBG2OLC: Efficient Assembly of Large Genomes Using Long Erroneous Reads of the Third Generation Sequencing Technologies. Sci Rep, 6: 31900.
53.Li LW & Ma ZS (2016) Testing the Neutral Theory of Biodiversity with Human Microbiome Datasets. Scientific Reports, 6:31448.
54.Ma ZS, Li LW, et al. (2016) Integrated network-diversity analyses suggest suppressive effect of Hodgkin’s lymphoma and slightly relieving effect of chemotherapy on human milk microbiome. Scientific Reports, 6:28048.
55.Wang Y, Wang R, et al. (2016) Sex Ratio Elasticity Influences the Selection of Sex Ratio Strategy. Scientific Reports, 6(1):39807.
56.Ma ZS, et al. (2016) A Brief Review on the Ecological Network Analysis with Applications in the Emerging Medical Ecology. pp 7-41, In “Hydrocarbon and Lipid Microbiology Protocols, Springer Protocols Handbooks”. Editors: McGenity T. et al. Springer.
57.Ma ZS, et al. (2015) Network analysis suggests a potentially ‘evil’ alliance of opportunistic pathogens inhibited by a cooperative network in human milk bacterial communities. Scientific Reports, 5: 8275.
58.Ma ZS (2015) Power law analysis of the human microbiome. Molecular Ecology, 24(21):5428‐5445.
59.Ma ZS (2015) Towards computational models of animal cognition, an introduction for computer scientists. Cognitive Systems Research, 33:42-69.
60.Ma ZS (2015) Towards computational models of animal communication, an introduction for computer scientists. Cognitive Systems Research, 33:70-99.
61.Ye CX, Hill C, Ruan J, Ma ZS (2014) DBG2OLC: efficient assembly of large genomes using the compressed overlap graph. Paper: http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:1410.2801 Software: http://sites.google.com/site/dbg2olc/
62.Li H, Ye DD, et al. (2014) Soil bacterial communities of different natural forest types in China. Plant and Soil, 383.
63.Guan Q & Ma ZS (2014) Ecological analysis of the human milk microbiome. Chinese Science Bulletin, 59(22): 2205-2212. http://www.eurekalert.org/pub_releases/2014-09/scp-ahb090214.php
64.Ma ZS , Yang L, Neilson RP, Hess A, Millar R (2014) A survivability-centered research agenda for cloud computing supported emergency response and management systems. 17pp. The 35th IEEE-AIAA Aerospace Conference (Aerospace 2014), Big Sky, Montana, USA, March 7-15th, doi: 10.1109/AERO.2014.6836515
65.Zhang ZG, Geng JW, et al. (2014) Spatial heterogeneity and co-occurrence patterns of human mucosal associated intestinal microbiota. The ISME Journal, 8(4): 881‐893.
66.Ma ZS (2013) Stochastic Populations, Power Law, and Fitness Aggregation in Genetic Algorithms. Fundamenta Informaticae, vol. 122, pp173-206.
67.Ma ZS (2013) First passage time and first passage percolation models for analyzing network resilience and effective strategies in strategic information warfare research. I. J. Information and Computer Security, 5(4): 334-358.
69.Ma ZS (2012) A unified definition for reliability, survivability and resilience inspired by the handicap principle and ecological stability. I. J. of Critical Infrastructures, 8(2): 242-272.
70.Ma ZS (2012) A note on extending Taylor’s power law for characterizing human microbial communities: inspiration from comparative studies on the distribution patterns of insects and galaxies, and as a case study for medical ecology and personalized medicine. http://adsabs.harvard.edu/abs/2012arXiv1205.3504M
71.Ye CX, Ma ZS, et al. (2012) Exploiting sparseness in de novo genome assembly. BMC Bioinformatics, 13(Suppl 6):S1.
72.Gajer P, Brotman RM, et al. (2012) Temporal Dynamics of the Human Vaginal Microbiota. Sci Transl Med., 4(132):132ra52.
73.Ma ZS, et al. (2012) A Bird's Eye View of Microbial Community Dynamics. In “Microbial Ecological Theory: Current Perspectives.” Editors: LA Ogilvie and PR Hirsch. Caister Academic Press.
74.Ma ZS & Krings AW. (2011) Dynamic hybrid fault modeling and extended evolutionary Game theory for reliability, survivability and fault tolerance analyses. IEEE Transactions on Reliability, 60(1):180-196.
75.Ma ZS (2011) Ecological ‘theater’ for evolutionary computing ‘play’: some insights from population ecology and evolutionary ecology. I. Journal of Bio-Inspired Computing, 4(1):31-55.
76.Ma ZS (2011) Frailty modeling for risk analysis in network security and survivability. I. J. Computer and Information Security, 4:276-294.
77.Ma ZS, et al. (2011) Insect navigation and communication in flight and migration: a potential model for joining and collision avoidance in MAVs (Micro-Aerial Vehicle) and mobile robots fleet control. Proc. of the 32nd IEEE-AIAA Aerospace Conference. 14pp, Big Sky, Montana, USA.
78.Ye CX, Cannon C, Ma ZS*, Yu DW, Pop M* (2011) SparseAssembler2: Sparse k-mer Graph for Memory Efficient Genome Assembly. http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:1108.3556
79.Ma ZS & AW Krings et al. (2011) Has the cyber warfare threat been overstated? A cheap talk game-theoretic perspective. The 7th Cyberspace Sciences and Information Intelligence Research Workshop, 7th CSIIRW11. October 14-16, 2011. Oak Ridge National Lab, Oak Ridge, USA.
80.Ma ZS, et al. (2011) Caring about trees in the forest: incorporating frailty in risk analysis for personalized medicine. Personalized Medicine, 8(6): 681-688
81.Ma ZS (2010) Is Strategic Information Warfare Really Asymmetric?—a New Perspective from the Handicap Principle. Journal of Information Warfare, 9(3): 51-61.
82.Ma ZS, et al. (2010) Logics in Animal Cognition: Are They Important to Brain Computer Interfaces (BCI) and Future Space Missions? Proc. 31st IEEE-AIAA Aerospace Conference 2010, 8pp. Big Sky, Montana, USA.
83.Ma ZS (2010) An integrated approach to network intrusion detection with block clustering analysis, generalised logistic regression and linear discriminant analysis. I. J. Information and Computer Security. Vol. 4(1):76-97
84.Ma ZS, FT Sheldon, AW Krings (2010) The handicap principle, strategic information warfare and the paradox of asymmetry. The 6th Cyberspace Sciences and Information Intelligence Research Workshop, 6th CSIIRW10, Oak Ridge National Lab, Oak Ridge, USA.
85.Ma ZS (2010) A New Extended Evolutionary Game Theory Approach to Strategic Information Warfare Research. J. of Information Warfare, vol. 8(2).
86.Ma ZS & AW Krings (2009) Is Chaos Theory Relevant to Reliability and Survivability? IEEE-AC paper #1697, 10pp, The 30th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14th, 2009.
87.Ma ZS (2009) Dragonfly Preying on Flying Insects, Rendezvous Search Games, and Rendezvous and Docking in Space Explorations. IEEE-AC paper #1698, 8pp, The 30th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14th, 2009.
88.Ma ZS (2009) Cognitive Ecology and Social Learning Inspired Machine Learning: with Particular Reference to the Evolving of Resilient Airborne Networks (AN). IEEE-AC paper #1706, 14pp, The 30th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14th, 2009.
89.Ma ZS, AW Krings, RE Hiromoto (2009) Dragonfly as a model for UAV/MAV Flight and Communication Controls. IEEE-AC paper #1718, 8pp, The 30th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 7-14th, 2009.
90.Ma ZS & AW Krings (2009) Insect sensory systems inspired computing and communications. Ad Hoc Networks 7(4): 742-755.
91.Ma ZS, AW Krings et al. (2009) The Handicap Principle for Trust in Computer Security, the Semantic Web and Social Networking. Springer Lecture Notes in Computer Science, vol. 5854, pp458-468
92.Ma ZS (2009) Towards a Population Dynamics Theory for Evolutionary Computing: Learning from Biological Population Dynamics in Nature. Springer Lecture Notes in Artificial Intelligence, vol. 5855, pp 195-205.
93.Ma ZS (2009) Towards an Extended Evolutionary Game Theory with Survival Analysis and Agreement Algorithms for Modeling Uncertainty, Vulnerability, and Deception. Springer Lecture Notes in Artificial Intelligence, vol. 5855, pp 608-618.
94.Ma ZS, AW. Krings, FT. Sheldon (2009) An outline of the three-layer survivability analysis architecture for strategic information warfare research. The 5th Cyberspace Sciences and Information Intelligence Research Workshop, 5th CSIIRW09, Oak Ridge National Lab, Oak Ridge, USA. CSIIRW 2009: 28
95.Ma ZS, AW Krings, RC Millar (2009) Introduction of first passage time (FPT) analysis for software reliability and network security. The 5th Cyberspace Sciences and Information Intelligence Research Workshop, 5th CSIIRW09, Oak Ridge National Lab, Oak Ridge, USA. CSIIRW 2009: 63
96.Krings AW & Ma ZS (2009) Surviving Attacks and Intrusions: What can we Learn from Fault Models. HICSS (Hawaii International Conference on System Sciences) 2009: 1-8. https://hicss.hawaii.edu/
97.Ma ZS(2011) Did we miss some evidence of chaos in laboratory insect populations? Population Ecology, 53:405–412.
98.Ma ZS(2010) Survival Analysis Approach to Life Table Analysis and Hypothesis Testing for Russian Wheat Aphid Populations. Bulletin of Entomological Research. vol. 100(3): 315-324
99.Ma ZS (2009) A new modelling approach to insect reproduction with same-shape reproduction distribution and rate summation: with particular reference to Russian wheat aphid. Bulletin of Entomological Research, vol. 99(5): 445-455
100.Ma ZS, Bechinski EJ (2008) Life tables and demographic statistics of Russian wheat aphid under different temperatures and host plant stages. European Journal of Entomology, vol. 106:205–210
101.Ma, Z.S. & Bechinski, E.J. (2008) Accelerated failure time modeling of the development and survival of Russian wheat aphid, Diuraphis noxia (Mordvilko). Population Ecology51(4), 543–548
102.Ma ZS & EJ Bechinski (2008) A survival-analysis-based simulation model for Russian wheat aphid population dynamics. Ecological Modelling, vol. 216(3):323-332
103.Ma ZS & EJ Bechinski (2008) Developmental and Phenological Modeling of Russian Wheat Aphid (Hemiptera: Aphididae). Annals of the Entomological Society of America, Vol. 101(2):351-361
104.Ma ZS, AW Krings, RE Hiromoto (2008) Insect Sensory Systems Inspired Communications and Computing (II): An Engineering Perspective. Funchal, Maderia, Prtugal, Jan 28-31, 2008, IEEE BIOSIGNALS, 292-297
105.Ma ZS & AW Krings (2008) Dynamic hybrid fault models and the applications to wireless sensor networks (WSNs). ACM-IEEE MSWiM 2008, Vancouver, Canada, 100-108.
106.Ma ZS & AW Krings (2008) Spatial Distribution Patterns, Power Law, and the Agent-based Directed Diffusion Sensor Networks. 2008 IEEE PerCom (IEEE Pervasive Computing), Hong Kong, March 17-21, 2008: 596-601.
107.Ma ZS & AW Krings (2008) Dynamic populations in genetic algorithms. The 23rd Annual ACM Symposium on Applied Computing, March 16-20, 2008, Fortaleza, Ceara, Brazil. ACM-SAC 2008: 1807-1811
108.Ma ZS & AW Krings (2008) Bio-Robustness and Fault Tolerance: A New Perspective on Reliable, Survivable and Evolvable Network Systems. Paper #1650, 20pp. The 29th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8th, 2008.
109.Ma ZS & AW Krings (2008) Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management (PHM). Paper #1618, 21pp. The 29th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8th, 2008.
110.Ma ZS & AW Krings (2008) Competing Risks Analysis of Reliability, Survivability, and Prognostics and Health Management (PHM). Paper #1626, 20pp. The 29th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8th, 2008.
111.Ma ZS & AW Krings (2008) Multivariate Survival Analysis (I): Shared Frailty Approaches to Reliability and Dependence Modeling. Paper #1634, 21pp. The 29th IEEE-AIAA Aerospace Conference, Big Sky, Montana, USA, March 1-8th, 2008.
112.Krings, AW & Ma ZS (2006) Fault-Models in Wireless Communication: Towards Survivable Ad Hoc Networks. 2006 MILCOM, DOI: 10.1109/MILCOM.2006.302349, 2006 IEEE Military Communications Conference. 3-25 October 2006, Washington, DC.