Identifying siRNA Targets Using Bioinformatics

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Questions and Answers

Match the key steps for identifying siRNA targets with their descriptions:

Target sequence selection = Scan mRNA for AA dinucleotides and record 19 nucleotides downstream; consider GG-starting sequences Sequence characteristics = Evaluate for high GC content and absence of internal repeats Secondary structure prediction = Use algorithms to predict protein folding stability Thermodynamic stability = Assess relative stability of siRNA duplex ends; favor sequences promoting antisense strand incorporation into RISC

Match the considerations for siRNA design with the correct description:

Off-target prediction = Perform BLAST searches and use tools like GESS to identify potential off-target effects Conservation analysis = Focus on variable regions of the target gene Immune stimulation avoidance = Avoid motifs known to stimulate immune responses Machine learning approaches = Utilize advanced computational methods for sequence analysis

Match the steps in the siRNA target identification process with their main focuses:

Experimental validation = Validate most promising siRNA candidates experimentally Thermodynamic stability = Assess the incorporation of the antisense strand into the RISC complex Conservation analysis = Identify highly conserved regions within the target gene Off-target prediction = Improve accuracy through computational off-target analysis

Match the sequence evaluation criteria to their characteristics:

<p>Sequence characteristics = Evaluate for low GC content and A/U-rich 5' end Immune stimulation avoidance = Incorporate motifs that enhance immune responses Secondary structure prediction = Focus on accessible mRNA regions using algorithms Machine learning approaches = Apply classical statistical methods to predict target efficacy</p> Signup and view all the answers

Match the key siRNA design steps with their actions:

<p>Target sequence selection = Identify sequences following AA dinucleotides Thermodynamic stability = Favor sequences that promote RISC incorporation Off-target prediction = Utilize bioinformatics tools to manage unintended targets Experimental validation = Conduct laboratory experiments on selected siRNA candidates</p> Signup and view all the answers

Flashcards

Target sequence selection for siRNA

Scanning the mRNA sequence for AA dinucleotides and recording the 19 nucleotides downstream, with a preference for GG-starting sequences.

Sequence characteristics for siRNA

Evaluating the sequence for optimal characteristics like low GC content (36-52%), specific base preferences, absence of internal repeats, and an A/U-rich 5' end.

Secondary structure prediction for siRNA

Predicting the mRNA's secondary structure using algorithms to identify accessible regions where the siRNA can bind.

Thermodynamic stability for siRNA

Assessing the stability of the siRNA duplex ends, favoring sequences that promote the antisense strand's incorporation into the RISC complex.

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Off-target prediction for siRNA

Using BLAST searches and tools like GESS to identify potential off-target effects, ensuring the siRNA specifically targets the intended gene without affecting others.

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Study Notes

Identifying New siRNA Targets Using Bioinformatics

Select target sequences by scanning mRNA for AA dinucleotides, recording 19 nucleotides downstream, and prioritizing GG-starting sequences.
Assess sequence characteristics, favoring low GC content (36-52%), specific base preferences, absence of internal repeats, and A/U-rich 5' ends.
Predict mRNA secondary structure using algorithms, focusing on accessible sites.
Evaluate thermodynamic stability of siRNA duplex ends, favoring sequences promoting antisense strand incorporation into RISC (RNA-induced silencing complex).
Predict off-target effects with BLAST searches and tools like GESS.
Prioritize highly conserved target gene regions.
Avoid siRNA sequences containing motifs known to stimulate immune responses, such as UGUGU, GUCCUUCA, and CUGAAUU.
Utilize machine learning methods like Graph Neural Networks or transformer-based models for advanced computational analysis.
Validate promising siRNA candidates through experimental procedures.