Trying to determine which variations in genomes are stochastic(random) or selectively chosen by natural selection is difficult and takes a in depth understanding of the ecological and clinical conditions the strain has undergone. The natural selection that strains undergo is mostly due to host adaption.There are two hypotheses that surround how within the same region such different strains can arise.
- Multiple Niche Polymorphism(MNP): The different strains are able to survive and thrive in the same region because they occupy different niches; different hosts, tissue types, and which organism carries the disease. Host adaption.
- Negative Frequency-dependent Selection(NFD): The ability to evade a hosts immune system requires many varying strains because they have to adapt to their specific host. Immune-escape mechanisms.
In order to test which hypothesis is correct you can see whether the house keeping genes are maintained or not. A high amount of non-synonymous DNA in the house keeping genes would imply that NFD is the correct hypothesis and a high amount of synonymous data would suggest MNP is correct. There is also a chance that both hypothesi are correct it could be the working together of immune escape mechanisms and host adaption.
Mapping the genome and tracking adaptions is also helpful for predicting what future threats lyme disease could cause. Once you predict the variation in the strains then you could hopefully predict what changes have occured ecologically, climate wise, and migration wise.
Based on the current genomes that we have mapped we can recognize that some swapping of DNA has occurred. We can look at when recombination began occurring between strains based on how much recombination has occurred. If it is low, then it is recent, high then it started further back in history.
Sequencing genomes would also allow researchers to track the migration of a strain based on the crossing over that occurred between species and using the fact listed right above this. By comparing the date of migration and ecological events that occurred in the area they migrated from you can predict what will cause a strain to migrate.
With the strains of lyme disease become more and more diverse as well as more and more spread recombination will occur at a far faster rate creating an even more diverse selection of strains. The more diverse the more threatening lyme disease is to the public.
What bioinformatics can do for this field
Bigger picture:
Understanding a highly adaptive bacteria genus like Borrelia will help researchers understanding how bacterial genomes evolve.
In order to tackle the problems listed above, I need to master population genomics, phylogenomics and phylogeography.
Key facts to understand about Borrelia:
Mapping the genome and tracking adaptions is also helpful for predicting what future threats lyme disease could cause. Once you predict the variation in the strains then you could hopefully predict what changes have occured ecologically, climate wise, and migration wise.
Based on the current genomes that we have mapped we can recognize that some swapping of DNA has occurred. We can look at when recombination began occurring between strains based on how much recombination has occurred. If it is low, then it is recent, high then it started further back in history.
Sequencing genomes would also allow researchers to track the migration of a strain based on the crossing over that occurred between species and using the fact listed right above this. By comparing the date of migration and ecological events that occurred in the area they migrated from you can predict what will cause a strain to migrate.
With the strains of lyme disease become more and more diverse as well as more and more spread recombination will occur at a far faster rate creating an even more diverse selection of strains. The more diverse the more threatening lyme disease is to the public.
What bioinformatics can do for this field
- Creating a linkage map between all the SNP's of the Borrelia genome using population genomics
- Figuring out which genetic changes are associated with which strain and what that change does and what causes it using phylogenomics
- Testing the ecological hypotheses mentioned in this paper (MNP, NFD) by estimating population size, migration rate, strain frequency and the crossing of genomes by using genome-level phylogeography.
Progress to keep in mind:
Bigger picture:
Understanding a highly adaptive bacteria genus like Borrelia will help researchers understanding how bacterial genomes evolve.
In order to tackle the problems listed above, I need to master population genomics, phylogenomics and phylogeography.
Key facts to understand about Borrelia:
- Plasmids of bacteria differ but they have a set genome outside of the plasmid.
- PF54 gene is responsible for evading immune system
- OspC, dbpA,vls, b08, and a07 are all antigen genes responsible for diversifying selection
Statistics: MUST RESEARCH LATER ON
- Bayesian
- Markovian
- Hidden Markov Models (HMMs) are statistical models for sequential data.
- They are used for programing artificial intelligence, modeling biological sequences, pattern recognition (can be used in basic programing?)
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