Objectives: We've been taught since we're young that bacteria are everywhere but are they really everywhere? To address this question, we created Bacillus pangenomes. Analysis of the pangenomes allowed us to answer questions such as whether biogeography affected the pangenome and its structure. Material & Methods: In this study, we relied heavily on high performance computing to generate the necessary data. Genomes were retrieved from NCIB and pangenomes were created with the micropan package for R, a software for statistical computing on Oklahoma State University's "Pete" compute cluster. Micropan and FigTree were used to create the blast distance and 16s rRNA phylogenetic trees, respectively. The calculated genomic differenced allowed us to compare how the 16s rRNA tree differed from the full genome tree. Principal Component Analysis (PCA) plots were also constructed to show the relationship between species in different environments and regions. Results: Our data indicated the pangenome size to differ based on environment and region. Heaps analysis showed the pangenomes to be open with an alpha value much lower than one independent from the number of genomes included in the pangenome. Conclusion: There is still much work that needed to be done but our preliminary results suggest that species within a genus tend to cluster together regardless of external factors and that the Bacillus has an open pangenome

Yang, Wenjian Ryan

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1Oklahoma State University, Department of Microbiology & Molecular Genetics *Thesis director, advisor **Second reader, advisor The Bacillus Pangenome And the Answers Hidden Within Ryan Yang1, Noha Youssef1*, Wouter Hoff1** Abstract. Objectives: We’ve been taught since we’re young that bacteria are everywhere but are they really everywhere? To address this question, we created Bacillus pangenomes. Analysis of the pangenomes allowed us to answer questions such as whether biogeography affected the pangenome and its structure. Material & Methods: In this study, we relied heavily on high performance computing to generate the necessary data. Genomes were retrieved from NCIB and pangenomes were created with the micropan package for R, a software for statistical computing on Oklahoma State University’s “Pete” compute cluster. Micropan and FigTree were used to create the blast distance and 16s rRNA phylogenetic trees, respectively. The calculated genomic differenced allowed us to compare how the 16s rRNA tree differed from the full genome tree. Principal Component Analysis (PCA) plots were also constructed to show the relationship between species in different environments and regions. Results: Our data indicated the pangenome size to differ based on environment and region. Heaps analysis showed the pangenomes to be open with an alpha value much lower than one independent from the number of genomes included in the pangenome. Conclusion: There is still much work that needed to be done but our preliminary results suggest that species within a genus tend to cluster together regardless of external factors and that the Bacillus has an open pangenome. Introduction  The genus Bacillus is capable of producing spores that can be picked up by wind and can be found everywhere on Earth. Our goal here is to determine whether Bacillus genomes from different geographical locations and different environments differed as an adaptation or whether they remained relatively indifferent. As the cost of sequencing have dramatically decreased over the years, more and more genomes have been uploaded, enabling us to retrieve a large amount of Bacillus genomes from NCBI to carry out the pangenome analysis, yielding insights into evolutionary history of Bacillus. Pangenomic analysis would yield core and accessory genomes, or in other word would inform us about the set of genes that are common to all genomes (core genes) and these that are present in one or two genomes but not the rest (accessory genes). Pangenome is the sum of the core and accessory genes. As the size of the core genome increases, the size of the accessory genome should decrease leading to a smaller (closed) pangenome. On the other hand, open pangenomes have a small core size and a very large accessory genome size. We asked the question whether the Bacillus pangenome is open or closed, and whether Bacillus genomes coming from the same geographic location or from the same environment would have a more similar genome than these from a different location or environment. Or is the pangenome dependent on phylogeny, or in other words would Bacillus genomes from the same species be more similar regardless of their geographic location or environment.   Methods & Materials We began by retrieving as many Bacillus genomes as possible from NCBI and recorded all relevant information into a spreadsheet. These genomes originated from Bacillus around the globe and from a variety of different environments so genomes without location and environmental data were omitted and the remaining were assigned a genome ID (GID). Based on the country of origin, the genomes were grouped into the respective regions (see Appendix A). Regions below the threshold of 15 genomes were omitted from the study. The same genomes were also grouped together based on which environments they originated from, resulting in a total of 11 subgroups (Table 1).   Figure 1: A world view of where the Bacillus genomes originated.                                                Region                         Environment        Asia 181  Engineered 32        Africa 24  Food 46                                Latin America & Caribbean 26  Freshwater 32        North America 89  Host-Associated 131        Western Europe 109  Marine 18           Terrestrial 166        TABLE 1:  Regional and environmental subgroups along with how many genomes were included in each. Genomes are not mutually exclusive.  Pangenome creation was done via the micropan package for R following the authors’ recommended pipeline. Genes were predicted with Prodigal and protein files were prepared. Next, the protein files were compared against each other. With 632 genomes, this generated 399424 (632*632) blast comparisons. Blast distances were then computed in order identify clusters and produce a pan-matrix. Subsequently, a phylogenetic tree based on blast distances was constructed. Clustering was done using the bclust function within the micropan package. A threshold value of 0.75 was used. Due to the size of our pangenomes and the exponential RAM requirements, the single linkage parameter was used. Core genome size and total pangenome size estimations were carried out with the binomixEstimate function, fitting binomial mixture models to the computed pan-matrix data. Principal component analysis (PCA) was performing using R. The pan-matrix data was loaded and read as a table and subsequently plotted. Unique numbers were assigned to different members of the same species.   To investigate whether or not difference in pangenome sizes were caused by having a different number of genomes making each subgroup, random trials were performed. Subsampling was done by randomly selecting 18 genomes from each subgroup (18 was chosen as it represented the size of the smallest subgroup). The entire analysis procedure was performed. This was repeated five times.  Results  TABLE 2: SUBGROUP DATA. (A CLUSTER IS A GENE FAMILY)  Pangenome sizes across locations and environments. Our data indicated pangenome sizes differed by location and environment (Table 2 and Appendix B). By performing random Pangenome  number of genomes pangenome size Estimated core size (clusters) closed/ open alpha Jaccard Africa 24 26491 621 open 0.680735 0.488927 Asia 181 71758 10 open 0.393525 0.583958 Latin America & Caribbean 26 23404 769 open 0.731071 0.437667 North America 89 41417 475 open 0.481170 0.490172 Western Europe 109 43772 457 open 0.456434 0.573861        Engineered 32 30510 851 open 0.661623 0.542938 Food 46 27722 825 open 0.641062 0.516747 Freshwater 32 42465 641 open 0.415799 0.607756 Host 131 43067 493 open 0.528638 0.506518 Marine 18 39092 272 Open 0.46046 0.550491 Terrestrial 166 60758 1 open 0.458459 0.586594 subsampling, we were able to rule out the differing number of genomes as the cause. Furthermore, subsampling was able to confirm that the pangenomes are indeed open. The calculated alpha values were far below 1 for all subsamples except for one North American subsample. In addition, when comparing the alpha and Jaccard values of subgroups (Table 2) to the respective subsample averages (Appendix B), the differences between values were small. This is also clear when the number of clusters are plotted against the number of genomes both for the subsamples (Figure 2) and the total (Figure 3), where the collector’s curves are not showing a plateau. These results indicate that much more sampling (or in other words much more genomes) is required in order to see a closed pangenome. Which factors affected genome clustering; geographic location, environment, or phylogeny?  To answer this question, we used Principal Component Analyses (PCA) based on the clustering information. We plotted PCA for each subgroup including the five location and 6 environment subgroups shown in the first column of Table 2 above. We then examined each of these PCA to see whether the genomes clustered by their geography, their environment, or merely by their phylogeny. The analysis was also repeated using the clustering information from the 5 random subsamples (with 18 genomes each) for each of these subgroups. After examining these PCA plots, it was evident that clustering was mainly based on phylogeny as genomes from the same species or from supergroups always clustered closely together regardless of the location or the environment from which they were obtained (Appendix C).  Conclusion Our analysis suggests that the Bacillus pangenome is an open pangenome. The alpha values were well below the threshold of 1. As we added more genomes, the slope of the clusters vs number of genomes line does decrease. It is possible that our study did not include enough genomes to tell the whole story. It was also obvious that the there was an uneven geographical and environmental distribution of Bacillus genomes uploaded to NCBI. Further studies with more genomes with need to be done. However, with an increasing number of genomes, exponentially more computational resources are required unless more efficient methods are discovered. Time also scales exponentially unless advances are made to enable parallelization.   Figure 2: As the number of genomes increased, the numbers also increased. Unlike a closed pangenome, the slopes of the lines presented here does not appear to be approaching a limit. Vertical bars represent standard deviations at each x value. 30005000700090001100013000150001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18ClustersNumber of GenomesRegional SubsamplesWestern EuropeNorth AmericaLatin America &CaribbeanAsia3000500070009000110001300015000170001 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18ClustersNumber of GenomesEnvironmental SubsamplesMarineTerrestrialHostFreshwaterFoodEngineered Figure 3: When looking at the clusters vs number of genome graphs for the subgroups, we see the number of clusters rapidly increase then slow down. However, the slopes still do not appear to be approaching a limit, in line with the calculated alpha values. Vertical bars represent standard deviations at each x value. 2000700012000170002200027000320000 20 40 60 80 100 120 140 160 180ClustersNumber of GenomesEnvironmentalEngineered Food Freshwater Host Terrestrial Marine2000700012000170002200027000320000 20 40 60 80 100 120 140 160 180 200ClustersNumber of GenomesRegionalAfrica Asia LAMC WEU North America  Appendix A: Region classification by Country of Origin Asia Baltics Latin America & Caribbean Africa Western Europe Afghanistan Bangladesh Bhutan Brunei Burma Cambodia China East Timor Hong Kong India Indonesia Iran Japan North Korea South Korea Laos Macau Malaysia Maldives Mongolia Nepal Pakistan Philippines Singapore Sri Lanka Taiwan Thailand Vietnam   Estonia Latvia Lithuania  C.W. OF IND. STATES Armenia Azerbaijan Belarus Georgia Kazakhstan Kyrgyzstan Moldova Russia Tajikistan Turkmenistan Ukraine Uzbekistan Former Soviet Union  Near East Bahrain Cyprus Gaza Strip Iraq Israel Jordan Kuwait Lebanon Oman Qatar Saudi Arabia Syria Turkey United Arab Emirates West Bank Yemen  North America Bermuda Canada Greenland St Pierre & Miquelon United States Anguilla Antigua & Barbuda Argentina Aruba Bahamas, The  Barbados Belize Bolivia Brazil British Virgin Is. Cayman Islands Chile Colombia Costa Rica Cuba Dominica Dominican Republic Ecuador El Salvador French Guiana Grenada Guadeloupe Guatemala Guyana Haiti Honduras Jamaica Martinique Mexico Montserrat Netherlands Antilles Nicaragua Panama Paraguay Peru Puerto Rico Saint Kitts & Nevis Saint Lucia Saint Vincent and the Grenadines Suriname Trinidad & Tobago Turks & Caicos Is Uruguay Venezuela Virgin Islands Algeria Angola Benin Botswana Burkina Faso Burundi Cameroon Cape Verde Central African Rep. Chad Comoros Congo, Dem. Rep.  Congo, Dem. Rep.  Cote d'Ivoire Djibouti Egypt Equatorial Guinea Eritrea Ethiopia Gabon Gambia, The  Ghana Guinea Guinea-Bissau Kenya Lesotho Liberia Libya Madagascar Malawi Mali Mauritania Mauritius Mayotte Morocco Mozambique Namibia Niger Nigeria Reunion Rwanda Saint Helena Sao Tome & Principe Senegal Seychelles Sierra Leone Somalia South Africa Sudan Swaziland Tanzania Togo Tunisia Uganda Western Sahara Zambia Zimbabwe Andorra Austria Belgium Denmark Faroe Islands Finland France Germany Gibraltar Greece Guernsey Iceland Ireland Isle of Man Italy Jersey Liechtenstein Luxembourg Malta Monaco Netherlands Norway Portugal San Marino Spain Sweden Switzerland  Scotland United Kingdom  Eastern Europe Albania Bosnia & Herzegovina Bulgaria Croatia Czechoslovakia Czech Republic Hungary Macedonia Poland Romania Serbia Slovakia Slovenia     Appendix B: Subsample Data       Pangenome pangenome size Estimated core size (clusters) closed/open alpha Jaccard Average Pan size Average core Average alpha Average Jaccard StDev PanSize StDev core Stdev Alpha StDev Jaccard Africa 1 22005 593 open 0.4553537 0.433882         Africa 2 27792 345 open 0.4563117 0.529493         Africa 3 26928 1103 open 0.4521538 0.512742         Africa 4 26443 273 open 0.7050855 0.529934         Africa 5 19900 744 open 0.7785115 0.47349 24613.6 611.6 0.56948324 0.4959081 3457.896658 333.5697828 0.15943676 0.041589815 Asia 1 34580 695 open 0.6721211 0.567932         Asia 2 19485 1019 open 0.6301683 0.495048         Asia 3 27507 853 open 0.5016329 0.556166         Asia 4 21472 898 open 0.3543851 0.487318         Asia 5 25378 799 open 0.479846 0.554265 25684.4 852.8 0.52763068 0.53214588 5889.064552 119.8549123 0.12685103 0.037857103 Engineered 1 23590 877 open 0.708943 0.572166         Engineered 2 22064 950 open 0.7259468 0.556165         Engineered 3 15704 1104 open 0.6709187 0.530243         Engineered 4 20686 350 open 0.6923687 0.533681         Engineered 5 26098 929 open 0.6502747 0.526212 21628.4 842 0.68969038 0.5436933 3873.034314 287.7351212 0.02999306 0.019703817 Food 1 11252 1316 open 0.8309799 0.468074         Food 2 24048 761 open 0.7139766 0.563266         Food 3 15704 1104 open 0.6709187 0.530243         Food 4 18110 1075 open 0.642987 0.553348         Food 5 17942 1179 open 0.5452709 0.493223 17411.2 1087 0.68082662 0.52163068 4627.835909 204.703444 0.10434352 0.040276893 Freshwater 1 37187 722 open 0.3632943 0.618665         Freshwater 2 28207 467 open 0.3768284 0.62452         Freshwater 3 24184 803 open 0.6409266 0.593168         Freshwater 4 34266 846 open 0.4247286 0.609472         Freshwater 5 24670 829 open 0.6489047 0.582388 29702.8 733.4 0.49093652 0.60564266 5806.455261 156.3339375 0.14243214 0.017585572 Host 1 27560 1067 open 0.6964876 0.542498         Host 2 32549 84 open 0.6314323 0.547085         Host 3 23338 1100 open 0.7242915 0.544012         Host 4 17639 895 open 0.777438 0.464703         Host 5 30771 958 open 0.4669348 0.499903 26371.4 820.8 0.65931684 0.5196401 6008.084495 420.0508303 0.11973343 0.036322513 Latin America & Caribbean 1 23499 1096 open 0.6838306 0.556342         Latin America & Caribbean 2 28157 1096 open 0.7711738 0.550643         Latin America & 19191 712 open 0.8188463 0.544046         Each subset contained 18 genomes. Marine 1-5 are identical because it was the smallest pangenome, which only contained 18 genomes.  Caribbean 3 Latin America & Caribbean 4 27717 16 open 0.709396 0.568584         Latin America & Caribbean 5 35354 0 open 0.6625861 0.56735 26783.6 584 0.72916656 0.55739298 6013.430618 548.7148622 0.06458762 0.010597225 Marine 1 39092 272 open 0.4854578 0.550491         Marine 2 39092 272 open 0.4854578 0.550491         Marine 3 39092 272 open 0.4854578 0.550491         Marine 4 39092 272 open 0.4854578 0.550491         Marine 5 39092 272 open 0.4854578 0.550491 39092        North America 1 28228 0 open 0.7383063 0.513465         North America 2 25896 1 open 0.6940126 0.548129         North America 3 25808 578 open 0.7419565 0.537815         North America 4 11975 1160 closed 1.124703 0.440259         North America 5 24451 711 open 0.7981726 0.525639 23271.6 490 0.8194302 0.5130613 6459.312216 496.161768 0.17461114 0.042723121 Terrestrial 1 33683 857 open 0.5857039 0.56566         Terrestrial 2 31464 819 open 0.6379748 0.572156         Terrestrial 3 25344 0 open 0.6593896 0.568993         Terrestrial 4 30102 953 open 0.678333 0.557079         Terrestrial 5 29035 23 open 0.4257995 0.566002 29925.6 530.4 0.59744016 0.5659781 3094.352646 476.2686217 0.10200820 0.00562509 Western Europe 1 27628 991 open 0.5932333 0.609213         Western Europe 2 19534 1027 open 0.7556711 0.589953         Western Europe 3 31016 896 open 0.4100924 0.565506         Western Europe 4 25065 993 open 0.6962406 0.572009         Western Europe 5 27838 1072 open 0.7294422 0.570945 26216.2 995.8 0.63693592 0.58152506 4290.645429 64.75106177 0.14101454 0.018005398 Appendix C: PCA plots  

Bacillus pangenome and the answers hidden within

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Bacillus pangenome and the answers hidden within

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