The document summarizes research characterizing newly isolated bacteriophages (viruses that infect bacteria) that prey on Mycobacterium smegmatis bacteria. Twelve bacteriophages were found and nine were analyzed by electron microscopy, showing a tailed "siphoviridae" structure. The genome of one phage, Bipolar, was fully sequenced and found to be similar to the F1 subcluster. The document then analyzes whether two genes, Tape Measure Protein (TMP) and Lysin A, can accurately predict phage cluster relationships on their own. Results showed that TMP was highly accurate, while Lysin A was less so, supporting the hypothesis that Lysin A is more diverse
John Quackenbush, PhD, a professor of biostatistics and computational biology talks about genomics, the human genome and what the study of it means for our understanding of diseases and, specifically, cancer.
Inferring microbial gene function from evolution of synonymous codon usage bi...Fran Supek
Introduction: Thousands of microbial genomes are available, yet even for the model organisms, a sizable portion of the genes have unknown function. Phyletic profiling is a technique that can predict their function by comparing the presence/absence profiles of their homologs across genomes. In addition, prokaryotic genomes contain an evolutionary signature of gene expression levels in the codon usage biases, where highly expressed genes prefer the codons better adapted to the cellular tRNA pools.
Objectives: We aimed to augment the existing phyletic profiling approaches by incorporating more detailed knowledge of gene evolutionary history, and create a very large database of predicted gene functions direcly usable for microbiologists.
Materials & methods: We used the OMA groups of orthologs and the paralogy relationships inferred through OMA's „witness of non-orthology“ rule. Genes were assigned to Gene Ontology categories and the phyletic profiles compared using the CLUS classifier that performs a hierarchical multilabel classification using decision trees. We quantified significant codon biases using a Random Forest randomization test that compares against the composition of intergenic DNA. Codon biases in COG gene families were contrasted between microbes inhabiting different enviroments, while controlling for phylogenetic inertia.
Results: The genomic co-occurence patterns of both the orthologs and the paralogs (the homologs separated by a speciation and by a duplication event, respectively) were informative and synergistic in a phylogenetic profiling setup, even though paralogy relationships are thought to conserve function less well. The resulting ~400,000 gene function predictions for 998 prokaryotes (at FDR<10%)> method to systematically link codon adaptation within COG gene families to microbial phenotypes and environments (thus functionally characterizing the COGs) and experimentally validated the predictions for novel E. coli genes relevant for surviving oxidative, thermal or osmotic stress.
Conclusion: Our work towards ehnancing phylogenetic profiling, as well as developing complementary genomic context approaches, will contribute to prioritizing experimental investigation of microbial gene function, cutting time and cost needed for discovery.
The 'omics' revolution: How will it improve our understanding of infections a...WAidid
This slideset explains the ‘Omics’ technology and its role in the study of infections and vaccination. It is a revolution as it offers powerful tools to interrogate the animal / human immune response to vaccines and infections.
John Quackenbush, PhD, a professor of biostatistics and computational biology talks about genomics, the human genome and what the study of it means for our understanding of diseases and, specifically, cancer.
Inferring microbial gene function from evolution of synonymous codon usage bi...Fran Supek
Introduction: Thousands of microbial genomes are available, yet even for the model organisms, a sizable portion of the genes have unknown function. Phyletic profiling is a technique that can predict their function by comparing the presence/absence profiles of their homologs across genomes. In addition, prokaryotic genomes contain an evolutionary signature of gene expression levels in the codon usage biases, where highly expressed genes prefer the codons better adapted to the cellular tRNA pools.
Objectives: We aimed to augment the existing phyletic profiling approaches by incorporating more detailed knowledge of gene evolutionary history, and create a very large database of predicted gene functions direcly usable for microbiologists.
Materials & methods: We used the OMA groups of orthologs and the paralogy relationships inferred through OMA's „witness of non-orthology“ rule. Genes were assigned to Gene Ontology categories and the phyletic profiles compared using the CLUS classifier that performs a hierarchical multilabel classification using decision trees. We quantified significant codon biases using a Random Forest randomization test that compares against the composition of intergenic DNA. Codon biases in COG gene families were contrasted between microbes inhabiting different enviroments, while controlling for phylogenetic inertia.
Results: The genomic co-occurence patterns of both the orthologs and the paralogs (the homologs separated by a speciation and by a duplication event, respectively) were informative and synergistic in a phylogenetic profiling setup, even though paralogy relationships are thought to conserve function less well. The resulting ~400,000 gene function predictions for 998 prokaryotes (at FDR<10%)> method to systematically link codon adaptation within COG gene families to microbial phenotypes and environments (thus functionally characterizing the COGs) and experimentally validated the predictions for novel E. coli genes relevant for surviving oxidative, thermal or osmotic stress.
Conclusion: Our work towards ehnancing phylogenetic profiling, as well as developing complementary genomic context approaches, will contribute to prioritizing experimental investigation of microbial gene function, cutting time and cost needed for discovery.
The 'omics' revolution: How will it improve our understanding of infections a...WAidid
This slideset explains the ‘Omics’ technology and its role in the study of infections and vaccination. It is a revolution as it offers powerful tools to interrogate the animal / human immune response to vaccines and infections.
Personalized Medicine and the Omics Revolution by Professor Mike SnyderThe Hive
Personalized medicine is expected to benefit from the combination of genomic information with the global monitoring of molecular components and physiological states. To ascertain whether this can be achieved, we determined the whole genome sequence of an individual at high accuracy and performed an integrated Personal Omics Profiling (iPOP) analysis, combining genomic, transcriptomic, proteomic, metabolomic, and autoantibodyomic information, over a 38-month period that included healthy and two virally infected states. Our iPOP analysis of blood components revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways across healthy and disease conditions. Importantly, genomic information was also used to estimate medical risks, including Type 2 Diabetes, whose onset was observed during the course of our study. Our study demonstrates that longitudinal personal omics profiling can relate genomic information to global functional omics activity for physiological and medical interpretation of healthy and disease states.
Meet the speaker, Professor Michael Snyder (Stanford):
Michael Snyder is the Stanford Ascherman Professor, Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. He received his Ph.D. from the California Institute of Technology and postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks. Seminal findings from the Snyder laboratory include; the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species. He has also combined different state-of–the-art omics technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine. He is a co-founder of several biotechnology companies including; Protometrix (now part of Life Technologies), Affomix (now part of Illumina), Excelix, and Personalis, and he presently serves on the board of a number of companies.
Synonymous mutations as drivers in human cancer genomes.Fran Supek
Synonymous mutations change the sequence of a gene without directly altering the sequence of the encoded protein. Here, we present evidence that these "silent" mutations frequently contribute to human cancer. Selection on synonymous mutations in oncogenes is cancer-type specific, and although the functional consequences of cancer-associated synonymous mutations may be diverse, they recurrently alter exonic motifs that regulate splicing and are associated with changes in oncogene splicing in tumors. The p53 tumor suppressor (TP53) also has recurrent synonymous mutations, but, in contrast to those in oncogenes, these are adjacent to splice sites and inactivate them. We estimate that between one in two and one in five silent mutations in oncogenes have been selected, equating to ~6%- 8% of all selected single-nucleotide changes in these genes. In addition, our analyses suggest that dosage-sensitive oncogenes have selected mutations in their 3' UTRs.
Slides from a Comparative Genomics and Visualisation course (part 1) presented at the University of Dundee, 7th March 2014. Other materials are available at GitHub (https://github.com/widdowquinn/Teaching)
Guest lecture on comparative genomics for University of Dundee BS32010, delivered 21/3/2016
Workshop/other materials available at DOI:10.5281/zenodo.49447
Managing Health and Disease Using Omics and Big DataLaura Berry
Presented at the NGS Tech and Applications Congress: USA. To find out more, visit:
www.global-engage.com
Michael Snyder is a Professor, Chair of Genetics and Director of the Stanford Center for Genomics and Personalized Medicine at Stanford University. In this presentation Michael discusses using omics and big data to predict disease risk and catch early disease onset.
Single Nucleotide Polymorphism Analysis
Predictive Analytics and Data Science Conference May 27-28
Asst. Prof. Vitara Pungpapong, Ph.D.
Department of Statistics
Faculty of Commerce and Accountancy
Chulalongkorn University
Metagenomics is the study of a collection of genetic material (genomes) from a mixed community of organisms. Metagenomics usually refers to the study of microbial communities.
BourseIndia is a Stock Advisory company, which provide you accurate Equity Market Tips. We are here to provide tips for Stock Cash, Equity Market Tips, Stock Futures and traded in both NSE and BSE. These tips will be profitable and will help to get better profit in Stock market financial services
Personalized Medicine and the Omics Revolution by Professor Mike SnyderThe Hive
Personalized medicine is expected to benefit from the combination of genomic information with the global monitoring of molecular components and physiological states. To ascertain whether this can be achieved, we determined the whole genome sequence of an individual at high accuracy and performed an integrated Personal Omics Profiling (iPOP) analysis, combining genomic, transcriptomic, proteomic, metabolomic, and autoantibodyomic information, over a 38-month period that included healthy and two virally infected states. Our iPOP analysis of blood components revealed extensive, dynamic and broad changes in diverse molecular components and biological pathways across healthy and disease conditions. Importantly, genomic information was also used to estimate medical risks, including Type 2 Diabetes, whose onset was observed during the course of our study. Our study demonstrates that longitudinal personal omics profiling can relate genomic information to global functional omics activity for physiological and medical interpretation of healthy and disease states.
Meet the speaker, Professor Michael Snyder (Stanford):
Michael Snyder is the Stanford Ascherman Professor, Chair of Genetics and the Director of the Center of Genomics and Personalized Medicine. He received his Ph.D. from the California Institute of Technology and postdoctoral training at Stanford University. He is a leader in the field of functional genomics and proteomics, and one of the major participants of the ENCODE project. His laboratory study was the first to perform a large-scale functional genomics project in any organism, and has launched many technologies in genomics and proteomics. These including the development of proteome chips, high resolution tiling arrays for the entire human genome, methods for global mapping of transcription factor binding sites (ChIP-chip now replaced by ChIP-seq), paired end sequencing for mapping of structural variation in eukaryotes, de novo genome sequencing of genomes using high throughput technologies and RNA-Seq. These technologies have been used for characterizing genomes, proteomes and regulatory networks. Seminal findings from the Snyder laboratory include; the discovery that much more of the human genome is transcribed and contains regulatory information than was previously appreciated, and a high diversity of transcription factor binding occurs both between and within species. He has also combined different state-of–the-art omics technologies to perform the first longitudinal detailed integrative personal omics profile (iPOP) of person and used this to assess disease risk and monitor disease states for personalized medicine. He is a co-founder of several biotechnology companies including; Protometrix (now part of Life Technologies), Affomix (now part of Illumina), Excelix, and Personalis, and he presently serves on the board of a number of companies.
Synonymous mutations as drivers in human cancer genomes.Fran Supek
Synonymous mutations change the sequence of a gene without directly altering the sequence of the encoded protein. Here, we present evidence that these "silent" mutations frequently contribute to human cancer. Selection on synonymous mutations in oncogenes is cancer-type specific, and although the functional consequences of cancer-associated synonymous mutations may be diverse, they recurrently alter exonic motifs that regulate splicing and are associated with changes in oncogene splicing in tumors. The p53 tumor suppressor (TP53) also has recurrent synonymous mutations, but, in contrast to those in oncogenes, these are adjacent to splice sites and inactivate them. We estimate that between one in two and one in five silent mutations in oncogenes have been selected, equating to ~6%- 8% of all selected single-nucleotide changes in these genes. In addition, our analyses suggest that dosage-sensitive oncogenes have selected mutations in their 3' UTRs.
Slides from a Comparative Genomics and Visualisation course (part 1) presented at the University of Dundee, 7th March 2014. Other materials are available at GitHub (https://github.com/widdowquinn/Teaching)
Guest lecture on comparative genomics for University of Dundee BS32010, delivered 21/3/2016
Workshop/other materials available at DOI:10.5281/zenodo.49447
Managing Health and Disease Using Omics and Big DataLaura Berry
Presented at the NGS Tech and Applications Congress: USA. To find out more, visit:
www.global-engage.com
Michael Snyder is a Professor, Chair of Genetics and Director of the Stanford Center for Genomics and Personalized Medicine at Stanford University. In this presentation Michael discusses using omics and big data to predict disease risk and catch early disease onset.
Single Nucleotide Polymorphism Analysis
Predictive Analytics and Data Science Conference May 27-28
Asst. Prof. Vitara Pungpapong, Ph.D.
Department of Statistics
Faculty of Commerce and Accountancy
Chulalongkorn University
Metagenomics is the study of a collection of genetic material (genomes) from a mixed community of organisms. Metagenomics usually refers to the study of microbial communities.
BourseIndia is a Stock Advisory company, which provide you accurate Equity Market Tips. We are here to provide tips for Stock Cash, Equity Market Tips, Stock Futures and traded in both NSE and BSE. These tips will be profitable and will help to get better profit in Stock market financial services
Genomic gene expression changes resulting from Trypanosomiasis: a horizontal study Examining expression changes elucidated by micro arrays in seminal tissues associated with the pathophysiology of Trypanosomiasis during disease progression
Molecular Identification of Specific Virulence Genes in EnteropathogenicEsche...iosrjce
IOSR Journal of Pharmacy and Biological Sciences(IOSR-JPBS) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of Pharmacy and Biological Science. The journal welcomes publications of high quality papers on theoretical developments and practical applications in Pharmacy and Biological Science. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
What is bioinformatics?
About human genome
Human genome project
Aim of human genome project
History
Sequencing Strategy
Benefits of Human Genome Project research
Disadvantages of human genome project
Conclusion
References
MIMG 199 P. acnes Poster Final - Lauren and Rachelle
HHMI Research poster -6-9-2014 Bipolar
1. Methods
• Twelve mycobacteriophages were isolated from the College of Idaho campus and 9 of
these were characterized by electron microscopy. All are consistent with siphoviridae
morphotypes (bacteriophages with head and tail).
• The genome of mycobacteriophage Bipolar was sequenced. Comparison by whole
genome nucleotide alignment places Bipolar within the F1 mycobacteriophage subcluster.
• Annotation of the Bipolar genome indicates that the Bipolar genome is 58985bp, including
107 predicted genes. The genome contains a predicted programmed translational
frameshift at position 9096bp (in the putative tail assembly chaperone gene).
• The gene encoding Tape Measure Protein (TMP) was found to accurately predict the
subcluster assignment for Bipolar. More broadly, TMP was found to accurately predict the
subcluster assignment for 166 mycobacteriophages (97.5% accuracy) based on dot plot
analysis.
• The gene encoding Lysin A was found to accurately predict the subcluster assignment for
Bipolar. Lysin A was somewhat less accurate (93.4%) in predicting the subcluster
assignments for other mycobacteriophages.
• Our hypothesis was supported. These results are important for understanding
mycobacteriophage diversity and the genes involved in lysis of Mycobacterium
smegmatis.
Conclusions
Genomic Analysis of the Novel F1 Subcluster Mycobacteriophage Bipolar
Shandee J. Tachick, McKayla M. Stevens, Aliza M. Auces, Ljuvica R. Kolich, Hana L. Hoang, Pamela A. Dockstader, Ann P. Koga, and Richard L. Daniels
Biology Department, The College of Idaho, Caldwell, Idaho 83605.
Acknowledgements
We would like to thank the students in the Fall BIO201 (Molecules to Cells) course for mycobacteriophage isolation,
especially our TAs Macey Horch, Laura Holden, Megan Brock, Jessie Lambright and Juan Cervantes. We are very
grateful to the HHMI SEA Staff, the laboratory of Graham Hatfull (University of Pittsburgh) and DNA sequencing facility
at Virginia Commonwealth University. Karthik Chinnathambi and Rick Ubic provided technical assistance with electron
microscopy at the Boise State University Center for Materials Characterization. The project described was supported in
part by the INBRE Program, NIH Grant Nos. P20 RR016454 (National Center for Research Resources) and P20
GM103408 (National Institute of General Medical Sciences).
Tape Measure Protein
Figure 2. Bipolar genome and
single gene analysis.
A) Dot plot of 166
mycobacteriophage genomes. Colors
on axes represent the phage clusters
as depicted on the legend. Graph
generated by Genome Pair - Rapid
Dotter (GEPARD). B) Dot plot of
Tape Measure Protein (TMP) gene
from 166 mycobacteriophages. C)
Dot plot of Lysin A gene from 166
mycobacteriophages.
A
Hypothesis
Kalliden
CuteCollette
Alcor
MrMud
Gary1
Achille13
BipolarBactina
Oink
Figure 1. Electron
micrographs of 9
mycobacteriophages
isolated from soil samples
on the College of Idaho
campus. All isolates were
identified as siphoviridae
(tailed bacteriophages). Using
ImageJ (a freely available
image analysis program
provided by the National
Institutes of Health) we found
an average capsid diameter
of 73.3 nm and the average
tail length of 164.8 nm.
Lysin A
Bacteriophages are viruses that infect bacteria. As a group, bacteriophages are the single most
abundant biological entity in the biosphere, with a population estimated at 1031 particles1. As part
of the HHMI SEA-PHAGES program, the College of Idaho’s Fall 2013 BIO201 class isolated and
characterized 12 mycobacteriophages from Southwestern Idaho that infect Mycobacterium
smegmatis. Electron microscopy revealed that each of these mycobacteriophages displayed
morphological characteristics consistent with a siphoviridae morphotype (tailed bacteriophages).
One of these newly-isolated mycobacteriophages (Bipolar) was selected for sequencing and
further genomic analysis. We found that the Bipolar genome is 59.0kb in length and contains 107
predicted protein-coding genes, with a nucleotide sequence similar to the F1 mycobacteriophage
subcluster. This annotation included gene function predictions and the results are being reviewed
for submission to Genbank, a repository for biological sequence information hosted by the National
Institutes of Health’s National Center for Biotechnology Information (NCBI).
Following the genomic annotation of Bipolar, we further investigated whether the clustering
relationships that result from whole genome similarity are also evident when single genes are
analyzed. A recent study demonstrated that mycobacteriophages could be accurately assigned
into their respective clusters and subclusters based on the nucleotide sequence of two genes:
Tape Measure Protein (TMP) and Major Capsid Protein2. Here we examine whether ubiquitous
non-structural genes, such as Lysin A, can also be used to predict phage cluster assignments.
While structural genes are generally found in a similar 5’ region of mycobacteriophage genomes
and are highly conserved, Lysin A exhibits a greater degree of positional freedom and does not
display the same high degree of similarity between mycobacteriophages3. We hypothesize that this
diversity makes Lysin A a less suitable gene for cluster assignment. Using dot plot analysis and
phylogenetic trees, we test this hypothesis by visualizing the relationships predicted by both whole
genome analysis and single gene analysis of Tape Measure Protein (TMP) and Lysin A from 166
mycobacteriophages.
REFERENCES
1. Hatfull et al., Comparative genomic analysis of sixty mycobacteriophage genomes: Genome clustering, gene acquisition and
gene size. J Mol Biol. 2010.
2. Smith et al., Phage cluster relationships identified through single gene analysis. BMC Genomics. 2013.
3. Payne and Hatfull, Mycobacteriophage Endolysins: Diverse and Modular Enzymes with Multiple Catalytic Activiites. PLOS
ONE. 2012.
Background & Introduction
Whole Genome
Singleton
A
B
C
D
E
F
G
H
I
J
K
L
M
O
P
Q
R
N
B
C
Melvin(A4)
Dhanush(A4)
BellusTerra(A4)
ICleared(A4)
Flux(A4)Arturo(A4)
Pukovnik(A2)
SkiPole(A1)
Kugel(A1)
KSSJEB(A1)
BPBiebs31(A1)
Lesedi(A1)JC27(A1)
Rosebush(B2)
Qyrzula(B2)
Arbiter(B2)
DaVinci(A6)
EricB
(A6)
Jeffabunny(A6)
G
ladiator(A6)
Ham
m
er(A6)
Blue7
(A6)
Zaka
(A6)
HINdeR
(A7)
Tim
shel (A7)
Thibault (J)
Courthouse (J)
Cucu (A5)
George (A5)
LittleCherry (A5)
Tiger (A5)
RidgeCB (A1)
Saintus (A8)
Astro (A8)
Winky (L2)
Faith1 (L2)
Crossroads (L2)
Muddy (Singleton)
Predator (H1)
BigNuz (P)
Konstantine (H1)
Nova (D1)
Troll4 (D1)
Butterscotch (D1)
Adjustor (D1)
Gumball (D1)
SirHarley (D1)
Avani (F2)
Jovo (A5)
Toto (E)Babsiella (I1)Hedgerow (B2)Ares (B2)
Send513 (R)
Papyrus (R)
Akoma (B3)
Phyler (B3)
Daisy (B3)
Heathcliff (B3)
Liefie (G)
Hope
(G)Angel (G
)
G
iles
(Q
)
Suffolk
(B1)
Firecracker(O
)
Corndog
(O)
Dylan
(O)
BAKA
(J)
Optimus(J)
MacnCheese(K3)
Pixie(K3)
Phaedrus(B3)
PackMan(A9)
Alma(A9)
LinStu(C1)
Pleione(C1)
Nappy(C1)
Dandelion(C1)
Spud(C1)ArcherS7(C1)
Ava3(C1)
Brujita(I1)
Island3(I1)
Dori(Singleton)
Butters(N)
Fishburne(P)Donovan(P)
Jebeks(P)
LittleE(J)
Phrux(E)
Rakim(E)
Phatbacter(E)
Murphy(E)
Phaux(E)
Bask21(E)
DS6A(Singleton)Trixie(A2)
Echild(A2)
L5(A2)
EagleEye(A2)
D29(A2)
Adzzy(A2)
Gadjet(B3)
Bongo
(M
)
PegLeg
(M
)
IsaacEli (B1)
ThreeO
h3D2
(B1)
Kinbote
Draft (Q)
UncleHowie
(B1)
Fang (B1)
TallGrassMM
(B1)
Thora (B1)
RockyHorror (F1)
Fruitloop (F1)
Shauna1 (F1)
DeadP (F1)
Gumbie (F1)Bipolar (F1)ChrisnMich (B4)JAMal (B4)Zemanar (B4)Nigel (B4)
Stinger (B4)
Cooper (B4)
Anaya (K1)
Angelica (K1)
Adephagia (K1)
JAWS (K1)
BarrelRoll (K1)
CrimD (K1)
Validus (K1)
Yoshi (F2)
Redi (N)
Charlie (N)
ZoeJ (K2)
Reprobate (B5)
Acadian (B5)
Twister (A10)
Rebeuca (A10)
Goose (A10)
Rockstar (A3)
HelDan (A3)
Jobu08 (A3)
JHC117 (A3)
M
icrowolf (A3)
Vix
(A3)
M
ethuselah
(A3)
ElTiger69
(A5)
Benedict(A5)
SG
4
(F1)
Bernardo
(B3)
Ramsey(F1)
Jabbawokkie(F2)
TM4(K2)
Rey(M)
Patience(Singleton)
Rumpelstiltskin(L2)
UPIE(L1)
JoeDirt(L1)LeBron(L1)
Single-gene analysis of Tape Measure Protein (TMP)
more accurately predicts mycobacteriophage cluster
relationships than Lysin A.
A
Figure 3. Phylogenetic
trees generated from
166 A) Lysin A nucleotide
sequences using
Maximum Likelihood (ML)
analysis. B) Tape
Measure Protein (TMP)
nucleotide sequences. C)
whole mycobacteriophage
genomes. Phylogenetic
trees created using
Molecular Evolutionary
Genetics Analysis
software (MEGA6).
Adephagia(K1)
JAWS(K1)
BarrelRoll(K1)
Anaya(K1)
Angelica(K1)
Pixie(K3)
EagleEye(A2)
Jabbawokkie(F2)
Alma(A9)
Courthouse(J)
Liefie(G)
Redi(N)
Bongo(M)Fang(B1)
Twister(A10)D29(A2)
SkiPole(A1)
Rosebush(B2)
Ares(B2)
Hedgerow
(B2)
Arbiter(B2)
Q
yrzula
(B2)
Validus
(K1)
Akom
a
(B3)
Heathcliff (B3)
Bernardo
(B3)
Gadjet (B3)
Phlyer (B3)
Daisy (B3)
Cuco (A5)
George (A5)
Tiger (A5)
Phaedrus (B3)
Brujita (I1)
Island3 (I1)
HINdeR (A7)
Timshel (A7)
Suffolk(B1)
ThreeOh3D2 (B1)
IsaacEli (B1)
Thora (B1)
TallGrassMM (B1)
UncleHowie (B1)
Astro (A8)
Saintus (A8)
ElTiger69 (A5)
Phelemich (B5)
Rockstar (A3)
HelDan (A3)PackMan (A9)Pukovnik (A2)BellusTerra (A4)
TiroTheta9 (A4)Flux (A4)Melvin (A4)
ICleared (A4)
Arturo (A4)
Methuselah (A3)
Trixie (A2)
Goose (A10)
Rebeuca (A10)
Reprobate (B5)
Kayacho
(B4)
Rey (M
)
Butters
(N)
C
harlie
(N)
C
orndog
(O
)
BPs
(G
)
Hope
(G
)
Avrafan
(G)
Angel(G)
Phrux(E)
Phatbacter(E)
Phaux(E)
Rakim
(E)
Bask21(E)
Murphy(E)
Toto(E)
Bipolar(F1)Shauna1(F1)
DeadP(F1)SG4(F1)
Fruitloop(F1)
GUmbie(F1)
Ramsey(F1)
RockyHorror(F1)
DS6A(Singleton)
Avani(F2)
Yoshi(F2)
Giles(Q)
Kinbote(Q)HH92(Q)
JHC117(A3)
Vix(A3)
Jobu08(A3)
Microwolf(A3)
PegLeg(M)
TM4(K2)
Echild(A2)
CrimD(K1)
MacnCheese(K3)
JAMal(B4)
Zemanar(B4)
ArcherS7(C1)
Dandelion(C1)
Spud(C1)
Nappy(C1)
Ava3
(C1)
Pleione
(C1)
N
igel(B4)
Stinger (B4)
Acadian
(B5)
Dylan
(O)
Firecracker (O)
ZoeJ (K2)
Blue7 (A6)
Hammer (A6)
Gladiator (A6)
DaVinci (A6)
EricB (A6)L5 (A2)LinStu (C1)Kugel (A1)Lesedi (A1)Dhanush (A4)JC27 (A1)RidgeCB (A1)Donovan (P)
Jebeks (P)
Fishburne (P)
BigNuz (P)
LittleE (J)
BAKA (J)
Optimus (J)
Thibault (J)
ChrisnMich (B4)
Dori (Singleton)
LittleCherry (A5)
Jovo (A5)
Jeffabunny (A6)
Zaka (A6)
BPBiebs31 (A1)
KSSJEB (A1)
Benedict (A5)
Adzzy (A2)
Babsiella (I1)
Cooper (B4)
Predator (H1)
Gumball (D1)
Nova (D1)
SirHarley (D1)
Troll4
(D1)
Adjutor (D1)
Butterscotch
(D
1)
Konstantine
(H1)
Papyrus
(R)
Send513
(R)
Patience(Singleton)
Faith1(L2)
W
inky(L2)Crossroads(L2)
Rumpelstiltskin(L2)
Muddy(Singleton)
UPIE(L1)
JoeDirt(L1)LeBron(L1)
C
B
Phatbacter(E)
Phaux(E)Murphy(E)
Bask21(E)
Phrux(E)
Rakim(E)
Toto(E)
BigNuz(P)Donovan(P)
Fishburne(P)
Jebeks(P)
Rebeuca(A10)Twister(A10)Goose(A10)
Microwolf(A3)
Jobu08(A3)
JHC117(A3)Vix(A3)
Methuselah
(A3)
ElTiger69
(A5)
Benedict(A5)
LeBron
(L1)
U
PIE
(L1)
JoeDirt (L1)
Rum
pelstiltskin
(L2)
Faith1
(L2)
W
inky (L2)
Crossroads (L2)
Arturo (A4)
Flux (A4)
ICleared (A4)
Adzzy (A2)
Pukovnik (A2)
EagleEye (A2)
Tiger (A5)
Jovo (A5)
Cuco (A5)
George (A5)
LittleCherry (A5)
ArcherS7 (C1)
Ava3 (C1)
Pleione (C1)
Nappy (C1)
Dandelion(C1)
TiroTheta9 (A4)
Dhanush (A4)
BellusTerra (A4)
Melvin (A4)
HINdeR (A7)Timshel (A7)Trixie (A2)Echild (A2)L5 (A2)D29 (A2)DaVinci (A6)EricB (A6)Zaka (A6)
Jeffabunny (A6)
Gladiator (A6)
Blue7 (A6)
Hammer (A6)
Yoshi (F2)
Avani (F2)
Jabbawokkie
(F2)
Predator (H1)
Konstantine
(H
1)
Lesedi(A1)
BPBiebs31
(A1)
SkiPole
(A1)
JC27
(A1)
KSSJEB
(A1)
Kugel(A1)
RidgeCB(A1)
Brujita(I1)
Island3(I1)
Babsiella(I1)
TM4(K2)
ZoeJ(K2)
DS6A(Singleton)
TallGRassMM(B1)
Firecracker(O)
Corndog(O)
Dylan(O)
KinboteDraft(Q)
Giles(Q)
HH92(Q)
Nova(D1)
Troll4(D1)
Adjutor(D1)
Butterscotch(D1)SirHarley(D1)Gumball(D1)
Patience(Singleton)
PackMan(A9)Alma(A9)
Rockstar(A3)
HelDan(A3)
Saintus(A8)
Astro(A8)
LinStu(C1)
Spud(C1)
LittleE(J)
Courthouse(J)
Thibault(J)
BAKA
(J)
Optimus(J)
Send513
(R)
PAPYRUS
(R)
M
uddy
(Singleton)
R
ey
(M
)
Bonga
(M
)
PegLeg
(M
)
Phlyer (B3)
Phaedrus (B3)
Heathcliff (B3)
Akoma (B3)
Bernardo (B3)
Gadjet (B3)
Daisy (B3)
Charlie (N)Redi (N)Butters (N)Validus (K1)CrimD (K1)BPs (G)
Avrafan (G)Hope (G)
Angel (G)
Liefie (G)
MacnCheese (K3)
Pixie (K3)
Anaya (K1)
Adephagia (K1)
Angelica (K1)
JAWS (K1)
BarrelRoll (K1)
Fang (B1)
ThreeOh3D2 (B1)
Thora (B1)
IsaacEli (B1)
UncleHowie (B1)
Suffolk (B1)
Cooper (B4)
Stinger (B4)
Nigel (B4)
JAMal (B4)
ChrisnMich (B4)
Zemanar (B4)
Reprobate (B5)
Phelem
ich
(B5)
Acadian
(B5)
Kayacho
(B4)
Ares
(B2)
R
osebush
(B2)
Hedgerow
(B2)
Q
yrzula
(B2)
Arbiter(B2)
Dori(Singleton)
Ramsey(F1)
RockyHorror(F1)
DeadP(F1)
SG4(F1)
Shauna1(F1)Fruitloop(F1)
Bipolar(F1)GUmbie(F1)