Difference between revisions of "Part:BBa K5490000:Design"

Revision as of 17:24, 27 September 2024

23-nt sequence binds CasRx to cleave WNV genome modifiable target 1.

Assembly Compatibility:

10
COMPATIBLE WITH RFC[10]
12
COMPATIBLE WITH RFC[12]
21
COMPATIBLE WITH RFC[21]
23
COMPATIBLE WITH RFC[23]
25
COMPATIBLE WITH RFC[25]
1000
COMPATIBLE WITH RFC[1000]

Design Notes

ATROPOS required the use of good quality CRISPR guide RNAs (gRNAs). To this end the Dry Lab was tasked with providing a software solution that would generate those gRNAs for our system. After investigating the literature, we discovered an existing solution to the problem of gRNA generation by Wessels et al. [1] and an improved version by Keng et al. [2]. Wessel’s solution called Cas13designGuidePredictor is an R script [3] that takes as input RNA sequences and produces 23-mer gRNAs. It first generates a pool of all possible 23-mers from a reference RNA sequence and then generates a GuideScore value for each based on a Random-Forest AI algorithm. The algorithm is fitted to data provided with the script and then can predict the guide score for any 23-mer given as input. The data consists of 23-mers, calculated physical characteristics (base counts, minimum free energy and others) along with a known GuideScore. The Random-Forest model is able to fit the data and produce GuideScores for 23-mers it has not seen before, given their physical characteristics. Keng improves on this model with Cas13gRNAtor [4]. Cas13gRNAtor is a collection of Python scripts working along with Cas13designGuidePredictor. Except from calculating Wessel’s GuideScore, Cas13gRNAtor filters out offtargets to the human genome and also calculates conservation scored based on a consensus sequence. The consensus RNA sequence is derived by choosing the most common base along all the input sequences. Due to this Cas13gRNAtor requires not only a reference RNA sequence but also as many RNA sequence samples as we can provide. These sequences are first aligned to the reference before being given to Cas13gRNAtor.

The Cas13gRNAtor was chosen to be used by the Dry Lab as it fitted the needs of ATROPOS more directly. Cas13gRNAtor is provided as open source software, but it is not provided in an executable format, only the source code is provided. This means that, in order to utilize it, the user needs to have installed the necessary dependencies. Dependencies are other pieces of software usually open source themselves that the program can not run without, so they have to be downloaded along with their own dependencies. In most cases installing dependencies is easily done, but when the number of dependencies is large this task becomes more difficult. To run Cas13gRNAtor we decided on using a Personal Computer (PC) with the Ubuntu Linux operating system. The workload is low so consumer PC hardware will suffice for running the program, there is no need for High Performance Computing (HPC). Linux as an operating system is supported by both Python and R and makes it easy to find and install any other dependencies we might need.

References

Source

Source:Silvana Konermann Laboratory of Molecular and Cell Biology, Salk Institute for Biological Studies, 10010 N Torrey Pines Rd, La Jolla, CA 92037, USA Direct repeat sequence,firstly identified at Ruminococcus Spacer sequence desingned by iGEM IOANNINA 2024 dry lab team

@@ Line 7: / Line 7: @@
 ===Design Notes===
-The gRNAs are complex in their design, particularly because they are part of a composite system. Each gRNA is expressed as a polycistronic RNA, which, upon expression, interacts with the CasRx protein. This interaction leads to the maturation of the gRNA and its subsequent cleavage into three distinct gRNAs, each targeting different regions of the virus.
-Given this complexity, it is crucial to validate the functionality of each individual gRNA before assembling them into the composite array. The gRNA array consists of two main components:
-Direct Repeats (DRs): These sequences produce secondary structures that are essential for the CasRx protein to bind, initiating the maturation process of the gRNAs.
-Spacer Sequences: These sequences are complementary to specific regions of the virus and guide the endonuclease activity of CasRx to its target.
-By validating each gRNA separately, we ensure that each component of the array functions correctly before combining them into the final composite part. This step is essential for achieving precise targeting and effective cleavage of the viral genome.
-First, for the individual gRNAs, we ordered three separate inserts, each consisting of a direct repeat (DR) sequence upstream and a specific spacer sequence downstream. These inserts were designed with recognition sites for the Type IIS restriction enzyme BbsI, positioned at both the 5' and 3' ends. This design ensures that after cleavage, the recognition sites are removed, leaving only the desired gRNA sequence. The BbsI recognition sites were placed facing each other to facilitate this separation.
+ATROPOS required the use of good quality CRISPR guide RNAs (gRNAs). To this end the Dry Lab was tasked with providing a software solution that would generate those gRNAs for our system. After investigating the literature, we discovered an existing solution to the problem of gRNA generation by Wessels et al. [1] and an improved version by Keng et al. [2]. Wessel’s solution called Cas13designGuidePredictor is an R script [3] that takes as input RNA sequences and produces 23-mer gRNAs. It first generates a pool of all possible 23-mers from a reference RNA sequence and then generates a GuideScore value for each based on a Random-Forest AI algorithm. The algorithm is fitted to data provided with the script and then can predict the guide score for any 23-mer given as input. The data consists of 23-mers, calculated physical characteristics (base counts, minimum free energy and others) along with a known GuideScore. The Random-Forest model is able to fit the data and produce GuideScores for 23-mers it has not seen before, given their physical characteristics. Keng improves on this model with Cas13gRNAtor [4]. Cas13gRNAtor is a collection of Python scripts working along with Cas13designGuidePredictor. Except from calculating Wessel’s GuideScore, Cas13gRNAtor filters out offtargets to the human genome and also calculates conservation scored based on a consensus sequence. The consensus RNA sequence is derived by choosing the most common base along all the input sequences. Due to this Cas13gRNAtor requires not only a reference RNA sequence but also as many RNA sequence samples as we can provide. These sequences are first aligned to the reference before being given to Cas13gRNAtor.
+The Cas13gRNAtor was chosen to be used by the Dry Lab as it fitted the needs of ATROPOS more directly. Cas13gRNAtor is provided as open source software, but it is not provided in an executable format, only the source code is provided. This means that, in order to utilize it, the user needs to have installed the necessary dependencies. Dependencies are other pieces of software usually open source themselves that the program can not run without, so they have to be downloaded along with their own dependencies. In most cases installing dependencies is easily done, but when the number of dependencies is large this task becomes more difficult. To run Cas13gRNAtor we decided on using a Personal Computer (PC) with the Ubuntu Linux operating system. The workload is low so consumer PC hardware will suffice for running the program, there is no need for High Performance Computing (HPC). Linux as an operating system is supported by both Python and R and makes it easy to find and install any other dependencies we might need.
+References