STEP 1
Input target sequences (paste FASTA text or upload files).
crRNA
crRNA Design - Cas12a(Cpf1)
Complete target sequence input, parameter configuration, and candidate site filtering in the browser. No local client installation or environment setup is required, so you can focus on experimental design.
Desktop is recommended for a complete parameter configuration experience.
Product Introduction
crRNA Design is an online design tool for CRISPR/Cas12a (Cpf1) workflows. It automatically screens, scores, and ranks candidate crRNA (Guide RNA) sites based on the target sequence.
Built on CHOPCHOP algorithm capabilities, it lets you complete parameter setup, task submission, result analysis, and history management online without installing local software or configuring a command-line environment.
It supports PAM recognition, guide length configuration, GC content analysis, self-complementarity analysis, and off-target risk assessment, helping researchers quickly obtain candidate crRNAs for experimental validation.
Clear Purpose
Designed for Cas12a (Cpf1) scenarios to automatically search, filter, and rank crRNA candidate sites.
Core Scenarios Covered
Suitable for early-stage gene-editing design, candidate comparison, pre-experiment parameter exploration, and workflow planning.
Practical Bottlenecks Solved
Web-enables command-line workflows, reduces environment setup friction, and supports history-based review with fast iteration.
Online Collaboration Workflow
Supports task-state tracking and history management for easier team sharing and iterative optimization.
How to use?
STEP 2
Configure General / Cpf1 Setting / Primers Options parameters based on experiment needs.
STEP 3
Click "Start Design" to submit the task and begin automatic computation.
STEP 4
After completion, view results in a new tab and download full files.
Parameter Details
Cpf1 Setting
crRNA length without PAM
Sets crRNA length (excluding PAM), affecting candidate scanning window and site quantity.
5'-PAM
Specifies PAM mode. When Non-standard is selected, custom PAM input is supported.
Efficiency score
Sets the efficiency scoring model used for activity-related candidate ranking.
Check for self-complementarity
Enables self-complementarity checks. When enabled, candidates with higher structure risk are filtered out.
Check for complementarity versus backbone
Checks complementarity risk against the selected backbone.
Standard backbone
Uses the standard backbone template for complementarity assessment.
Extended backbone
Uses the extended backbone template for stricter assessment.
Custom backbone
Uses a custom backbone sequence for assessment; custom input is required after selection.
Input
Input Sequence
Inputs the target sequence to design against (FASTA text supported). If both text and file are provided, text input takes priority.
Upload FASTA File
Recommended for long sequences or external sequence files. Upload standard .fa/.fasta/.txt files; valid sequences will be extracted for analysis.
General
Target specific region of gene
Defines the search scope (e.g., CODING / WHOLE / PROMOTER / ONLY TARGET EXON(S)), which determines candidate crRNA scan range.
Upstream / Downstream (Promoter mode)
When target region is Promoter, defines upstream and downstream search ranges around TSS.
Exon (Only target exon(s))
Used when selecting specific exons only, restricting candidates to designated exon regions.
Restrict targeting
Restricts candidate boundaries/ranges to control search size and result volume.
Isoform consensus determined by
Sets isoform consensus strategy (e.g., intersection/union), affecting candidate retention across transcripts.
Pre-filtering
Initial candidate filtering parameters used to eliminate low-quality sites early.
GC content range (Minimum / Maximum)
Defines candidate GC content range. Too low may reduce binding stability; too high may increase structural risk.
Self-complementarity maximum
Sets the upper bound of self-complementarity to reduce potential hairpin/self-pairing risks.
Color scoring ignore one off-target without mismatches
Controls display scoring strategy and affects visual score presentation.
Displayed flanking sequence length
Sets upstream/downstream context length shown in results for easier manual review.
Primers Options
Design primers
Determines whether primers are co-designed based on crRNA candidates. Enables more complete output with increased runtime.
Product size (From / To)
Limits amplicon size range to control experimental feasibility and detection stability.
Primer size (From / To / Optimal)
Sets primer length range and optimal value to balance specificity and amplification efficiency.
Primer Tm (From / To / Optimal)
Sets primer melting temperature range and optimal value to improve amplification condition matching.
Minimum distance from primer to target site
Sets the minimum distance between primer and target site to avoid proximity-related amplification/interpretation issues.
Cpf1 Setting
crRNA length without PAM
Sets crRNA length (excluding PAM), affecting candidate scanning window and site quantity.
5'-PAM
Specifies PAM mode. When Non-standard is selected, custom PAM input is supported.
Efficiency score
Sets the efficiency scoring model used for activity-related candidate ranking.
Check for self-complementarity
Enables self-complementarity checks. When enabled, candidates with higher structure risk are filtered out.
Check for complementarity versus backbone
Checks complementarity risk against the selected backbone.
Standard backbone
Uses the standard backbone template for complementarity assessment.
Extended backbone
Uses the extended backbone template for stricter assessment.
Custom backbone
Uses a custom backbone sequence for assessment; custom input is required after selection.
Primers Options
Design primers
Determines whether primers are co-designed based on crRNA candidates. Enables more complete output with increased runtime.
Product size (From / To)
Limits amplicon size range to control experimental feasibility and detection stability.
Primer size (From / To / Optimal)
Sets primer length range and optimal value to balance specificity and amplification efficiency.
Primer Tm (From / To / Optimal)
Sets primer melting temperature range and optimal value to improve amplification condition matching.
Minimum distance from primer to target site
Sets the minimum distance between primer and target site to avoid proximity-related amplification/interpretation issues.
Input
Input Sequence
Inputs the target sequence to design against (FASTA text supported). If both text and file are provided, text input takes priority.
Upload FASTA File
Recommended for long sequences or external sequence files. Upload standard .fa/.fasta/.txt files; valid sequences will be extracted for analysis.
General
Target specific region of gene
Defines the search scope (e.g., CODING / WHOLE / PROMOTER / ONLY TARGET EXON(S)), which determines candidate crRNA scan range.
Upstream / Downstream (Promoter mode)
When target region is Promoter, defines upstream and downstream search ranges around TSS.
Exon (Only target exon(s))
Used when selecting specific exons only, restricting candidates to designated exon regions.
Restrict targeting
Restricts candidate boundaries/ranges to control search size and result volume.
Isoform consensus determined by
Sets isoform consensus strategy (e.g., intersection/union), affecting candidate retention across transcripts.
Pre-filtering
Initial candidate filtering parameters used to eliminate low-quality sites early.
GC content range (Minimum / Maximum)
Defines candidate GC content range. Too low may reduce binding stability; too high may increase structural risk.
Self-complementarity maximum
Sets the upper bound of self-complementarity to reduce potential hairpin/self-pairing risks.
Color scoring ignore one off-target without mismatches
Controls display scoring strategy and affects visual score presentation.
Displayed flanking sequence length
Sets upstream/downstream context length shown in results for easier manual review.
Calculation Principle
STEP 1
Input and Preprocessing
01The service receives target sequences (text input or FASTA upload).
02Sequence format is normalized (remove blank lines, merge line wraps, standardize casing), then valid target sequence is extracted for computation.
03Basic validation is performed for character validity and length limits. On failure, an error is returned and the task is terminated.
STEP 2
Parameter Assembly and Task Modeling
01User parameters are loaded: target region (e.g., CODING/WHOLE/PROMOTER/TARGETEXON), guide length, PAM, GC range, self-complementarity threshold, etc.
02Candidate search rules and filter rules are constructed from parameters.
03If primer design is enabled, primer constraints are assembled simultaneously (amplicon length, primer length, Tm range).
STEP 3
Candidate Site Scanning (Initial Screening)
01Sliding-window scanning is performed across target regions based on PAM + guide length to generate candidate sites.
02Sites that clearly violate basic requirements are removed in initial screening.
03An initial candidate set is generated; if empty, this typically appears as 'No target sites'.
STEP 4
Candidate Filtering and Quality Evaluation
01GC filtering: candidate GC must fall within user-defined range.
02Self-complementarity filtering: candidates exceeding threshold are removed.
03If backbone information is provided, assessment is further aligned to real-use scenarios.
STEP 5
Off-target Retrieval and Comprehensive Ranking
01Off-target-related retrieval is performed on retained candidates to count potential non-target hits.
02Candidate risks are evaluated by mismatch tiers and related indicators.
03Ranking outputs are generated by scoring models, forming a candidate list ready for direct screening.
STEP 6
Optional RPA Primer & Probe Design
01When primer design is enabled, primer design is performed for candidate sites.
02Primer schemes are generated under amplicon size, primer size, and Tm constraints.
03Outputs include both candidate results and primer-related results (with typically increased runtime).
STEP 7
Result Output and History Retention
01The system outputs main results and detailed data for review and secondary filtering.
02Task states progress as 'queued -> running -> completed/failed/timeout'.
03Task history is retained for reviewing parameter-result differences and supporting follow-up tuning iterations.
Project Reference
Project: CHOPCHOPv2 - Apache License 2.0
Project URL: https://bitbucket.org/valenlab/chopchop/src/master/
Frequently Asked Questions
Q1: What sequence input methods are supported?
Two methods are supported: paste sequence text directly, or upload a FASTA file.
Q2: How are multiple sequences handled?
The current workflow uses the first valid sequence for calculation.
Q3: Why does it say the sequence is invalid?
Common causes include illegal characters (e.g., N or special symbols) or invalid content mixed into the format.
Q4: Why does it say the sequence is too long?
The input sequence exceeds the system limit. It is recommended to trim to the target analysis region before submission.
Q5: Does uppercase/lowercase matter?
No. The system normalizes sequence casing automatically.
Q6: Why do I still get errors after uploading a file?
Usually because the file format is invalid or no recognizable valid sequence is present.
Q7: When should I enable primer design?
It is recommended when you already have satisfactory candidates and are moving into experimental validation.
Q8: I am new to tuning parameters. Any suggestions?
Start with relaxed parameters to get candidates, then tighten settings step by step to improve specificity.
Q9: If submission returns success, is computation finished?
No. It means submission was accepted; the task is queued or running.
Q10: Why can wait time be long sometimes?
Tasks are processed in a queue. If many users submit simultaneously, your task may wait.
Q11: What kinds of tasks are usually slower?
Typical cases include large target regions, complex filtering combinations, and primer design enabled.
Q12: Why does timeout happen?
The task did not finish within the allowed time window, often due to heavy parameters or oversized search ranges.
Q13: Can I still get results after timeout?
Usually no. Timeout means the task ended without complete output. Adjust parameters and rerun.
Q14: Does refreshing the page affect backend computation?
Usually not. The task continues on the server side.
Q15: Why do I get 'No target sites'?
Common causes include overly strict combinations of PAM + guide length + GC + self-complementarity filters, too small target regions, or inherently limited candidate sites in the sequence.
Q16: What if there are too few results?
Try widening GC range, moderately reducing guide length, relaxing self-complementarity filter, or expanding the target region.
Q17: What if there are too many results?
Try narrowing GC range, increasing filter strictness, constraining target regions more specifically, and keeping only top-ranked candidates.
Q18: Is it useful to keep failed tasks?
Yes. Failed history helps review parameter combinations and quickly optimize the next run.
Q19: Will repeated submissions of the same sequence always match?
Usually yes if parameters, algorithm version, and database version are unchanged; parameter changes can significantly alter outputs.
Q20: What should I prioritize in results?
Prioritize candidate ranking and off-target-related metrics, then refine according to experimental objectives.
Q21: How should GC range be set more robustly?
Start with a moderate range. If too few results, widen it gradually; if specificity is insufficient, tighten it gradually.
Q22: Is longer Guide Size always better?
Not always. Longer guides may be stricter but can reduce candidate count. Balance usable candidate volume with specificity.
Q23: How should I choose PAM?
Choose based on your system/tool requirements. Stricter PAM settings usually yield fewer candidates.
Q24: Is backbone mandatory?
No. If you have clear vector/backbone information, filling it in usually gives results closer to real use.
Q25: Why can changing one parameter alter results a lot?
Parameters are strongly coupled. PAM, guide length, GC, and filtering thresholds jointly affect usable candidate counts.
Q26: How can I compare two runs effectively?
Keep the same sequence and change only one key parameter per run to identify impact sources clearly.
Q27: How can I improve reproducibility?
Fix the input sequence, use stable parameter templates, and retain run history records.
Q28: What workflow do you recommend?
Start broad to obtain candidates -> tighten for higher specificity -> enable primer design for validation prep.
Q29: When should I rerun instead of continuing with current results?
Rerun first when you see No target sites, timeout, or outputs that clearly do not meet experiment requirements.
Q30: I am not sure why a run failed. Fastest troubleshooting path?
Check in order: 1) sequence validity; 2) sequence length limit; 3) whether parameters are too strict; 4) whether heavy computation options are enabled (e.g., primer design); 5) retry after moderate relaxation.
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