Difference between revisions of "Part:BBa K4712063"
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<partinfo>BBa_K4712063 parameters</partinfo> | <partinfo>BBa_K4712063 parameters</partinfo> | ||
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| − | <table | + | Protocol: |
| + | 1. For a 20μL reaction system: | ||
| + | |||
| + | <table><tr><th>Reagent</th><th>Stock Concentration</th><th>Volume Added（μL）</th></tr><tr><td>Forward Primer</td><td>10μM</td><td>1</td></tr><tr><td>Reverse Primer</td><td>10μM</td><td>1</td></tr><tr><td>Rehydration Buffer (2X)</td><td></td><td>10</td></tr><tr><td>DNA Template</td><td>10nM/L</td><td>2</td></tr><tr><td>ddH2O</td><td></td><td>To 18</td></tr><tr><td>Starter (10X)</td><td></td><td>2</td></tr></table> | ||
| + | |||
| + | 2. Gently tap to mix several times, briefly centrifuge, repeat 3 times (mix gently to avoid vigorous vortexing). | ||
| + | |||
| + | 3. Incubate at 37°C for 20 minutes. | ||
| + | |||
| + | 4. After heating at 65°C for 10 minutes, proceed to gel electrophoresis. | ||
| + | |||
| + | https://static.igem.wiki/teams/4712/wiki/result/fig1c.png | ||
| + | |||
| + | Electrophoresis of RPA Primer Screening for Neisseria meningitidis (NME) | ||
| + | |||
| + | https://static.igem.wiki/teams/4712/wiki/result/fig3b.png | ||
| + | |||
| + | The figure above correspond to the fluorescence intensity of crRNA targeting Neisseria meningitidis after CRISPR reaction under Bright and UV illumination. The figure utilize pseudocolor to facilitate analysis using software (Image Lab 6). No crRNA is added into negative control. The concentration of DNA template is 10nM/L. | ||
| + | |||
| + | https://static.igem.wiki/teams/4712/wiki/result/fig3a.png | ||
| + | |||
| + | The linear graphs sequentially correspond to the efficiency verification of crRNAs targeting the DNA sequences of Neisseria meningitidis pathogen. No crRNA is added into negative control. The concentration of DNA template is 10nM/L. | ||
| + | |||
| + | https://static.igem.wiki/teams/4712/wiki/result/fig10c-left.png | ||
| + | https://static.igem.wiki/teams/4712/wiki/result/fig10c-right.png | ||
| + | |||
| + | Linear graphs and figure correspond to the fluorescence intensity of crRNAs targeting NME after one-tube reaction of RPA and CRISPR under Bright and UV illumination. The figures utilize pseudocolor to facilitate analysis using software (Image Lab 6). No crRNA is added into negative control. The concentration of DNA template is 10nM/L. | ||
| + | |||
| + | Machine Learning | ||
| + | https://static.igem.wiki/teams/4712/wiki/engineering/engineering/fig1.png | ||
| + | |||
| + | In the initial phase, we trained the model with a limited dataset of 74 data points, followed by an evaluation that proved that the support vector machine model fits into the evaluation. | ||
| + | |||
| + | Subsequently, during the second round, our focus shifted to fine-tuning the SVM model parameters. Notably, this round of training also relied on the same 74 data points. It is pertinent to mention that both the first and second rounds of training and evaluation employed randomly partitioned datasets. | ||
| + | |||
| + | In the third round, our model made predictions for the performance of 40 entirely new primers, which were unlabeled. The practical utility of these predictions was validated through experimental verification, confirming the high accuracy and reliability of our model. The alignment between the predicted class and the experimental class was particularly strong. | ||
| + | |||
| + | This approach effectively underscores the potential of machine learning to reduce the number of experiments required. | ||
Latest revision as of 13:33, 12 October 2023
NME-R4
The primers were designed using NCBI BLAST and SanpGene to achieve efficient and specific amplification. This primer in RPA serves as the initial binding point for the amplification process, ensuring the specificity of the reaction by targeting the desired Neisseria meningitidis DNA or RNA sequences. The primers provided data for mathematical modeling for further primer design.
Sequence and Features
- 10COMPATIBLE WITH RFC[10]
- 12COMPATIBLE WITH RFC[12]
- 21COMPATIBLE WITH RFC[21]
- 23COMPATIBLE WITH RFC[23]
- 25COMPATIBLE WITH RFC[25]
- 1000COMPATIBLE WITH RFC[1000]
Protocol:
1. For a 20μL reaction system:
| Reagent | Stock Concentration | Volume Added（μL） |
|---|---|---|
| Forward Primer | 10μM | 1 |
| Reverse Primer | 10μM | 1 |
| Rehydration Buffer (2X) | 10 | |
| DNA Template | 10nM/L | 2 |
| ddH2O | To 18 | |
| Starter (10X) | 2 |
2. Gently tap to mix several times, briefly centrifuge, repeat 3 times (mix gently to avoid vigorous vortexing).
3. Incubate at 37°C for 20 minutes.
4. After heating at 65°C for 10 minutes, proceed to gel electrophoresis.
Electrophoresis of RPA Primer Screening for Neisseria meningitidis (NME)
The figure above correspond to the fluorescence intensity of crRNA targeting Neisseria meningitidis after CRISPR reaction under Bright and UV illumination. The figure utilize pseudocolor to facilitate analysis using software (Image Lab 6). No crRNA is added into negative control. The concentration of DNA template is 10nM/L.
The linear graphs sequentially correspond to the efficiency verification of crRNAs targeting the DNA sequences of Neisseria meningitidis pathogen. No crRNA is added into negative control. The concentration of DNA template is 10nM/L.
Linear graphs and figure correspond to the fluorescence intensity of crRNAs targeting NME after one-tube reaction of RPA and CRISPR under Bright and UV illumination. The figures utilize pseudocolor to facilitate analysis using software (Image Lab 6). No crRNA is added into negative control. The concentration of DNA template is 10nM/L.
Machine Learning
In the initial phase, we trained the model with a limited dataset of 74 data points, followed by an evaluation that proved that the support vector machine model fits into the evaluation.
Subsequently, during the second round, our focus shifted to fine-tuning the SVM model parameters. Notably, this round of training also relied on the same 74 data points. It is pertinent to mention that both the first and second rounds of training and evaluation employed randomly partitioned datasets.
In the third round, our model made predictions for the performance of 40 entirely new primers, which were unlabeled. The practical utility of these predictions was validated through experimental verification, confirming the high accuracy and reliability of our model. The alignment between the predicted class and the experimental class was particularly strong.
This approach effectively underscores the potential of machine learning to reduce the number of experiments required.