# sgRNA Design: Three-Tiered Approach Find or design sgRNAs by **prioritizing validated sequences before computational predictions**. Always start at Option 1 and only descend to the next tier when the current one yields nothing usable. Ported from the Biomni `sgRNA_design_guide.md` (snap-stanford/Biomni), with the data parsing corrected and the literature step wired to this environment's search tools. ## When to use - "Give me an sgRNA to knock out TP53 in human cells" - "Design CRISPR guides to activate OCT4" / "CRISPRi guides for MYC" - "What guide RNA should I use for with SpCas9 / SaCas9 / Cas12a?" - Selecting guides for an arrayed or pooled CRISPR screen ## Inputs - **Gene symbol** (required), e.g. TP53, BRCA1, AAVS1. - **Organism** (default human), e.g. human/mouse/rat or NCBI TAXID. - **Application** (default knockout): knockout / activation (CRISPRa) / inhibition (CRISPRi). - **Cas enzyme** (default SpCas9): SpCas9, SaCas9, AsCas12a, enAsCas12a. ## Outputs - `_selected_sgRNAs.csv` — unified table of 3–4 recommended guides (sequence, source, rank/score, exon/position, PAM, citation/dataset, notes). - `_sgRNA_summary.md` — which tier was used and why, the picks, and caveats. Save both to the user's results directory. ## Bundled resources (work offline; refresh via `references/refresh_resources.md`) - `references/resource/addgene_grna_sequences.csv` — 321 validated sgRNAs, 197 genes (Addgene). - `references/resource/CRISPick_download_links.txt` — 238 CRISPick dataset URLs, 13 organisms. ## Scripts | Script | Purpose | | --- | --- | | `scripts/search_addgene.py` | Tier 1 / Method 1 — search the Addgene database (handles the HTML-wrapped IDs and messy species values). | | `scripts/find_crispick_dataset.py` | Tier 2 / Step 1 — resolve the correct CRISPick download URL for organism+enzyme+application. | | `scripts/select_crispick_sgrnas.py` | Tier 2 / Step 3 — filter a downloaded CRISPick file to your gene, rank, pick 3–4. | | `scripts/check_design_rules.py` | Tier 3 — sanity-check a candidate guide (length, GC, TTTT, PAM). | | `scripts/export_results.py` | Write the unified CSV + markdown summary. | --- ## Option 1 — Validated sequences (ALWAYS try first) > You MUST complete **both** Method 1 and Method 2 before considering Option 2. Do not skip > Method 2 even if Method 1 finds nothing — many validated guides live only in the literature. ### Method 1 — Bundled Addgene database ``` import sys; sys.path.insert(0, "scripts") from search_addgene import search_addgene hits = search_addgene("TP53", species="human", application="knockout") print(len(hits)) # 0 for TP53 -> still do Method 2 before Option 2 ``` Each hit carries a clean `Target Sequence`, `pubmed_id`/`pubmed_url`, `plasmid_id`/`plasmid_url`, and `Depositor`. **Cite the PubMed ID** of the original publication in your methods. `application` accepts intent words and maps them to Addgene's vocabulary: knockout→`cut`, activation→`activate`, inhibition/CRISPRi→`interfere`/`RNA targeting`. ### Method 2 — Literature & web search (REQUIRED) This environment does not expose `advanced_web_search_claude`; use the available tools instead. Run **both** for coverage: - `LiteratureSearch` — peer-reviewed papers (validated guides, supplements). - `WebSearch` — vendor/database hits (GenScript, Horizon, lab protocols). Query templates (substitute the gene): ``` "sgRNA" OR "guide RNA" "" validated experimental "CRISPR knockout" "" sgRNA sequence validated "" sgRNA "cutting efficiency" OR "on-target" ``` Scan ≥10–15 results and check supplementary materials. For any validated guide, record the sequence, citation (PMID/DOI), and validation details (cell line, cutting efficiency). **Decision:** if either method yields usable validated guides, select 3–4 and export. Only if **both** come up empty, go to Option 2. --- ## Option 2 — CRISPick precomputed designs Use when no validated guides exist, you need genome-wide coverage, or you want ranked options. ### Step 1 — Resolve the dataset URL ``` from find_crispick_dataset import find_crispick_dataset info = find_crispick_dataset("human", cas="SpCas9", application="knockout") print(info["matches"]) # GRCh38 + GRCh37 dataset URLs print(info["warning"]) # Cas12a variant warning, if applicable ``` > **AsCas12a vs enAsCas12a are different enzymes.** Guides for one may not work with the other. > The finder matches the exact enzyme token so datasets never cross-contaminate. ### Step 2 — Download & extract (files are 50–700 MB; not bundled) ``` wget '' gunzip sgRNA_design_*.txt.gz ``` ### Step 3 — Filter, rank, select ``` from select_crispick_sgrnas import select_crispick_sgrnas picks = select_crispick_sgrnas("sgRNA_design_..._CRISPRko_....txt", "TP53", n=4) ``` Ranks by **Combined Rank** (lower = better) by default; `rank_by="on_target"` or `"off_target"` to prioritize efficiency or specificity. Spreads picks across distinct exons for redundancy. Optional filters: `exon=`, `cut_position_range=`, `max_target_cut_pct=` (knockout). Column names are resolved defensively (handles both real CRISPick and abbreviated layouts); see `references/crispick_file_format.md`. If the gene is absent → Option 3. --- ## Option 3 — De-novo design (last resort) For genes/organisms not covered above. Follow `references/design_rules.md`: - Length: 20 bp (SpCas9/SaCas9), 23–25 bp (Cas12a). PAM: SpCas9 NGG, SaCas9 NNGRRT, Cas12a TTTV (5'). - GC 40–60%; avoid TTTT and homopolymer runs >4 nt. - KO → early exons (first ~50%); CRISPRa → −200 to +1 of TSS; CRISPRi → −50 to +300 of TSS. ``` from check_design_rules import check_design_rules, format_report print(format_report(check_design_rules("GAGGTTGTGAGGCGCTGCCC", "SpCas9", pam="AGG"))) ``` This checks rules only — for real off-target assessment use Cas-OFFinder/CRISPOR or CRISPick ranks. --- ## Export (all tiers) ``` from export_results import from_addgene, from_crispick, export unified = from_addgene(hits, application="knockout", enzyme="SpCas9") # or from_crispick(...) export(unified, gene="TP53", tier="Option 1 (validated Addgene)", outdir="/path/to/results", rationale="Validated guides found via Method 1.") ``` ## Universal best practice **Test 3–4 sgRNAs per gene experimentally regardless of predicted scores**, and validate edits (Sanger sequencing; TIDE/T7E1 for indels). Prediction scores guide selection but do not replace empirical validation. ## Citations & acknowledgments (preserve in user methods) - **Validated guides (Option 1):** Addgene (https://www.addgene.org). Cite the PubMed ID of each guide's original publication. Acknowledge: "Validated sgRNA sequences obtained from Addgene." - **CRISPick (Option 2):** "Guide designs provided by the CRISPick web tool of the GPP at the Broad Institute." - Cas9 (SpCas9, SaCas9): Sanson KR, et al. *Nat Commun.* 2018;9(1):5416. PMID: 30575746. - Cas12a (AsCas12a, enAsCas12a): DeWeirdt PC, et al. *Nat Biotechnol.* 2021;39(1):94–104. PMID: 32661438. (Specify which Cas12a variant you used.) ## Scientific caveats - Bundled Addgene/CRISPick files are a fixed snapshot (197 genes / 238 datasets); literature search (Method 2) is mandatory precisely because the snapshot is incomplete. - Genome build matters: human defaults GRCh38 (GRCh37 also available), mouse GRCm38 — match coordinates to your reference. - The skill does not perform genome-wide off-target alignment beyond CRISPick's precomputed ranks.