com.seqbench/workbench

Look up a UniProt accession in the AlphaFold Protein Structure Database (CC-BY 4.0). Returns confidence, model version and structure file URLs, or {found:false} when no prediction exists for that accession.

Design antisense-oligonucleotide (ASO) gapmers against an mRNA target: scans candidate sites, builds the antisense oligo in the standard 5-10-5 architecture (chemically-modified wings, central DNA gap for RNase H1, phosphorothioate backbone), and screens each for known liabilities (G-quadruplex motifs, CpG immunostimulation, self-complementarity, GC extremes). No transcriptome-wide off-target search.

Enumerate the specific wrong plasmids a multi-part Golden Gate or Gibson assembly can produce — a part dropped, inverted, duplicated, two parts swapped, the backbone self-circularized — as full sequences, ranked by how few independent mis-ligations each needs. Golden Gate outcomes are annotated with the MEASURED overhang cross-talk they would have to exploit (Potapov/Pryor ligation data). Feed the result to diagnostic_digest to pick a screening enzyme. Reports no probability per outcome: the ligation data does not measure transformation or vector background.

Explain a band you measured on a gel. Given the template, both primers and the observed size, it enumerates every pair of priming sites — including a single primer priming both strands — that would give a product that size, and ranks them by how much of each primer's 3' end matches without interruption, which is what decides whether a mispriming event can extend at all. Reports no yield and assigns no share of the band: the band is the input, not the output. Says plainly when nothing on this template explains the size, and what that points to instead.

Take a set of barcodes and report the minimum edit distance in it, which pairs sit at that distance, any duplicates, the GC range and the longest homopolymer. The pairs are the point: 'minimum distance 2' is a number, and 'these two barcodes are one substitution apart' is something to fix. Use before committing a pool you inherited or assembled by hand — a single close pair makes two constructs indistinguishable in the data and nothing downstream can detect it.

Build a set of DNA barcodes that are pairwise at least a chosen edit distance apart, so a read error cannot turn one barcode in the pool into another. Honours a GC window, a homopolymer cap and excluded motifs (the restriction sites you clone with) during the search rather than filtering afterwards, and can extend a set you already have rather than replacing it. Reports the distance ACHIEVED — re-derived over the finished pool, not assumed from the search — plus how many read errors that distance lets you correct and detect. Deterministic given a seed. Returns fewer barcodes than asked, with a reason, when the constraints leave no room.

Quantify CBE/ABE base editing from a pair of Sanger traces — an unedited control and the edited pool — without NGS. At each editable position in the activity window the edited trace is treated as a mixture of the unedited and converted peaks, and the control's OWN alt-channel signal at that same position is subtracted as background, because dye crosstalk is position- and context-dependent and a global constant would be wrong per position. Significance comes from a null built from the same sample (every control position outside the window carrying the same base), so the threshold adapts to the run's chemistry instead of being hardcoded. Returns per-position percentages with z-scores, the target and its bystanders, the background distribution (including a robust estimate of its spread and a count of its outliers), and the noise floor — the percentage the background alone reaches, or null when the run's own null has no spread to derive one from. A window position whose control already carries the converted base is reported but not quantified, because the (1 − b) rescale amplifies error by 1/(1 − b) and turns a 0.1-point wobble into half the pool. Locate the window with an editor id plus the protospacer, or give it explicitly. Blind to indels, which shift the trace rather than mixing a base. PREDICTED, NOT MEASURED. None is published for this implementation. Every run instead reports what it rests on: the background mean, sd, robust (MAD-based) sd, outlier count and n, and a noiseFloorPercent that says how much apparent editing the background alone reaches at the significance threshold — null, rather than 0, when the run has no null with any spread to derive a limit from. This implementation's recovery of known synthetic mixtures (to under a percentage point) is deliberately NOT offered as validation — it tests the arithmetic and the coordinate handling, not whether the linear mixture model fits a real capillary trace. Two properties ARE characterized. The bias is dire…

Design cytosine (CBE, C→T) or adenine (ABE, A→G) base-editing gRNAs for an SpCas9 target: for each NGG gRNA it reports every editable base inside the editor's activity window, flags bystander edits (more than one editable base in the window), and — with a CDS reading frame — classifies each edit's amino-acid consequence (silent / missense / nonsense / stop-loss). Bystander-free guides are ranked first. Handles both strands (a C→T on the protospacer of a reverse-strand guide is reported as the forward-strand G→A).

Run one batchable SeqBench tool over many records. Returns a table of per-record results and typed failures.

One-paste 'tell me everything': auto-detects DNA/RNA/protein, then reports composition, ORFs, single-cutter enzymes, end primers or protein properties, plus a BLAST link.

Work out why a cloning experiment failed: no colonies, every clone empty vector, or no PCR band. Takes your design (method, parts, enzymes, primers, host methylation state) plus what you actually observed (colony counts on the plate and on each control, screening tally, band sizes, whether the ladder ran) and returns causes ranked by evidence — each with the deterministic fact from the design or the observation that implicates it, the cheapest observation that would separate it from the next candidate, and the next experiment. Causes the observations eliminate are reported as eliminated, naming the observation that did it; causes the design makes impossible are not listed. No probability is computed anywhere — the ordering is of evidence, not of likelihood, and `ranking.evidenceBased` says so when the inputs separate nothing.

Rank every observation you have not yet made by how many open causes it settles WHICHEVER WAY IT COMES OUT, then return the smallest set of observations that settles all of them. Takes the same arguments as cloning_diagnose. The ranking is computed by re-running the diagnosis at each possible outcome of each observation and intersecting, so every count is a worst case rather than an average — and causes that no observation can settle are named, because those need a different experiment rather than more observing. No probabilities anywhere.

Assemble fragments by Gibson/overlap, Golden Gate (Type IIS), restriction–ligation (sticky or blunt), TOPO/TA, LIC or SLIC (T4-polymerase chew-back) or In-Fusion/CPEC, returning the product, the junctions and — for the primer-design methods — the junction primers. Each method is modeled as its own chemistry rather than as one product model with different labels: LIC's chew-back stops at the first occurrence of the single dNTP supplied, so a tail carrying that base stops it early and a tail without one lets it run past the junction, and both are refused with the offending base and position named.

Codon Adaptation Index (CAI) and per-codon relative adaptiveness of a CDS against an expression host, with rare-codon and GC3 analysis.

Codon-optimize a protein (or coding DNA) for an expression host by picking the most-frequent codon per residue.

Iteratively substitutes synonymous codons to resolve unwanted restriction sites (domestication for Golden Gate), homopolymers, tandem repeats, predicted secondary structure, cryptic RBS/polyA motifs and hidden alternate-frame ORFs that construct_qc flags — without changing the encoded protein (verified). Does NOT touch premature stops or GC extremes; re-run construct_qc afterward to confirm. A native TypeScript alternative to a constraint-solver sidecar.

Lint a coding DNA sequence for premature stops, internal RBS/polyA motifs, unwanted restriction sites, GC extremes and repeats.

Find and score candidate guide RNAs (protospacer + PAM) in a target DNA for common nucleases (SpCas9, SpCas9-NG, SaCas9, Cas12a). PREDICTED, NOT MEASURED. No held-out skill statistic is claimed. Both are pre-2016 models, superseded by Rule Set 3 (DeWeirdt et al., Nat Commun 2022) and by DeepHF. Rule Set 3 IS now shipped here, as crispr_ontarget, and unlike these two it carries a held-out calibration: on an independent tiling library, 87.7% / 74.9% / 82.0% of its lowest-scoring guides landed in the bottom two activity quintiles. Prefer it for SpCas9 with an NGG PAM; DeepHF is still not shipped. Treat these two as a ranking aid, not an efficiency prediction. Valid for: SpCas9 with an NGG PAM and a 20 nt spacer, and only when enough genomic flanking context is present to build the model's 30-mer / 35-mer window — both scores are null rather than padded otherwise. Nothing is predicted for SaCas9, Cas12a or SpCas9-NG.

Build an HDR donor (homology arms flanking an edit) from a target sequence and either an explicit edit window (editStart/editEnd) or a guide's cut site (guideStart/guideEnd/guideStrand/nuclease — SpCas9-family only; Cas12a's staggered cut needs an explicit editStart/editEnd). Also designs genotyping primers spanning the edit site on the original sequence (a real size-shift or sequencing target to confirm the edit), reusing the same primer-design engine as primer_design.

Screen a guide's protospacer for off-target sites (protospacer match + valid PAM, both strands) against a small curated set of common lab reference genomes (see genomesChecked) — NOT a whole human/mouse genome search. For SpCas9 with a 20 nt spacer each site also gets a Doench 2016 CFD score, so sites are ranked by predicted cut likelihood rather than by mismatch count alone, and the guide gets an aggregate specificity. Use this the same way primer_specificity is used: a useful sanity check within the covered organisms, not a clearance guarantee for a mammalian expression host. PREDICTED, NOT MEASURED. Best of the common off-target scores on the authors' GUIDE-Seq comparison, at Pearson r = 0.40 over 9 guides and 402 sites (vs CCTop 0.31, Hsu-Zhang 0.26) — a useful ranking, not a reliable magnitude. Weights were measured for SINGLE mismatches; multiple mismatches are multiplied, and Listgarten et al. 2018 note the training data never contained a mismatch and an alternative PAM together, so that combination is extrapolation. Valid for: SpCas9 with a 20 nt spacer, which is the only case scored — every other nuclease returns null rather than a number from the wrong enzyme's table. One locus, one cell line. Substitutions only: DNA/RNA bulges are neither searched nor scorable, and a low score is not a claim that a site is safe.

Score SpCas9 guides for on-target activity with Rule Set 3 (DeWeirdt et al., Nat Commun 2022) — the current successor to the Doench 2014 and CRISPRscan scores crispr_grna_design reports. Give a target sequence to find and score every NGG guide in it, or give 30-mer contexts directly. The tracrRNA MATTERS and is not cosmetic: Rule Set 3 models it as a feature, and the paper measures its accuracy dropping when the wrong one is specified. Returns a z-scored activity, which ranks guides against each other; it is not a percentage and not a probability of editing. PREDICTED, NOT MEASURED. Held out six datasets (23,629 context sequences) from training; Rule Set 3 (Sequence) had the highest Spearman correlation on three of the six. On a separate tiling library generated for the paper, with every spacer any model had seen removed, it significantly outperformed all other models (p < 0.002) when the correct tracrRNA was given. Calibration on that independent set: of the lowest-scoring guides, 87.7% / 74.9% / 82.0% landed in the bottom two activity quintiles (Hsu / Chen / DeWeirdt tracrRNA), and of the highest-scoring, 77.0% / 69.0% / 75.5% landed in the top two. No single held-out Spearman is quoted here because the paper reports it per gene as a distribution rather than as one figure, and inventing a headline number from a figure would be the fit-residual mistake in a different costume. Valid for: SpCas9 with an NGG PAM and a 20 nt spacer, scored over a 30-mer (4 nt upstream + spacer + PAM + 3 nt downstream) — nothing is padded, so a guide without that flank is skipped rather than estimated. Knockout activity in mammalian pooled screens with a U6/Pol III promoter; the authors expect it to generalize less well to Pol II-transcribed sgRNAs, and in vitro transcribed sgRNAs (as used in zebrafish) are known to be poorly predicted by models trained this way. The tracrRNA must be the one you will actually use. This is Rule Set 3 (Sequence) only: the Sequence+Target model, which add…

Screen two oligos for the most stable heterodimer (cross-dimer) between them.

Pick the restriction digest that tells your intended construct apart from the wrong ones on a screening gel. Digests every candidate, works out which bands would actually resolve at the chosen agarose percentage (size ratio, the gel's resolving window, and whether a band is too faint to score), and ranks single enzymes — then buffer-checked pairs if no single one works. The criterion is separating the INTENDED construct from every alternative; telling the alternatives apart from each other is reported as a bonus. Get the alternatives from assembly_outcomes.

Nucleic-acid quantity conversions: molar mass, amount (pmol/nmol), molar and mass concentration, and copy number, from mass ± volume and either a length or a sequence.

Recommend a single NEB buffer (and flag caveats) for digesting with two enzymes in one tube.

Quantify a whole plate of edited samples against ONE untreated control trace and return a single sortable table — the plate-scale form of sanger_indel_spectrum, base_edit_quant and sanger_knockin_quant, chosen with `mode`. Each sample gives one row keyed by its id, carrying the headline number for that mode (edited fraction / editing at the target base / intended knock-in percentage), the fit-quality numbers behind it (R², or the background n and noise floor for base mode), and fitAdequate — the single-sample tool's own gate verdict on that row, so the plate cannot drift from the per-well answer. Failure is isolated per well: a sample whose read is short, mismatched or unfittable becomes a failed ROW with its error message and the other 95 still come back, while an error about the control trace, the mode or the work ceilings throws, because it is wrong for every row. Duplicate sample ids are suffixed (against the whole plate, so the suffix never lands on another well's name) rather than merged. Arguments are strict: an argument belonging to another mode, an unknown argument, an out-of-range limit, and an `offset` override (which is a property of one pair of reads, not of a plate) are all rejected rather than ignored or clamped, because at plate scale a substituted setting rewrites every row identically and nothing in the table looks odd. Returns the rows in input order, a tally, and a CSV. Comparing two wells' percentages is only meaningful when both rows are fitAdequate, which is why the plate summary is computed over those rows alone. PREDICTED, NOT MEASURED. None is published for this implementation, and being a batch does not soften that: each row is exactly the claim the corresponding single-sample tool makes. What every row instead reports is what it rests on — R² for the indel and knock-in modes, the background n, sd and noise floor for base mode — plus fitAdequate, which is the single-sample tool's OWN gate verdict on that row rather than a threshold re-inv…

Report how many promoters, sgRNA handles and neutral spacers are available at each maximum-shared-length threshold, and therefore the longest extra-long sgRNA array that can be built without reusing a part. Ask this before designing: the answer is a property of the measured parts collection, not of your guides, and it is the constraint that decides the design. Deterministic — it is a selection over a fixed table.

Build a multiplexed CRISPR array that expresses many sgRNAs from one cassette and shares no long stretch with itself. Twenty sgRNAs built the obvious way carry twenty copies of the same Cas9 scaffold, promoter and terminator — a construct that recombines in the cell and that synthesis vendors refuse — so this draws each transcription unit's parts from the measured non-repetitive collection of Reis et al. (2019), selecting ACROSS promoters, handles and spacers at once rather than within each type, then re-measures the assembled molecule for repeats the junctions created. Returns the sequence, an annotated GenBank file and the pool usage. Takes guides; it does not choose or score them — use crispr_grna_design and crispr_offtarget_check for that.

Generate a downloadable Beckman/Labcyte Echo acoustic-liquid-handler picklist CSV (columns: Source Plate Name, Source Plate Type, Source Well, Destination Plate Name, Destination Well, Transfer Volume, Name — the header row reproduced from PyEcho, a real open-source Echo-picklist generator) for the given PCR reactions, at the same well positions export_plate_layout assigns. Assumes a 5 uL Echo-scale PCR reaction (master mix 2500 nL, each primer 250 nL, template 250 nL, water 1750 nL) — a commonly used acoustic-dispensing miniaturization scale, not a universal standard; rescale the volumes for your own protocol. Source/Destination Plate Type uses a placeholder Echo plate-type code (384PP_AQ_BP) — replace with the exact type from your own Echo Plate Type Library. Each distinct template label gets its own well on the TemplateSource plate, row-major (A1, A2, … A24, then B1, …) across that 384-well source plate.

Generate a downloadable Opentrons Python Protocol API (v2, OT-2) script that sets up the given PCR reactions on a 96-well PCR plate, at the same well positions export_plate_layout assigns. Uses real Opentrons labware/pipette API names confirmed against docs.opentrons.com and the Opentrons shared-data labware-definitions repository (opentrons_96_wellplate_200ul_pcr_full_skirt, opentrons_96_tiprack_20ul, opentrons_24_tuberack_nest_1.5ml_snapcap, nest_12_reservoir_15ml, p20_single_gen2) and the confirmed load_labware/load_instrument/transfer method signatures. Master-mix/primer/template/water volumes are clearly-labeled placeholder constants at the top of the script — this is a starting point to review and adapt for your own enzyme and instrument, not a certified ready-to-run protocol.

Assign a set of PCR reactions (name + forward/reverse primer + optional template label) to wells on a 96-well plate, row-major (A1, A2, … A12, then B1, B2, … up to H12). Returns the well-assignment data for rendering a plate diagram; export_opentrons_protocol and export_echo_picklist build their downloadable files from this exact same layout, so all three always agree.

Hierarchically cluster a genes x samples expression matrix (UPGMA/average, complete, or single linkage; Euclidean or correlation distance) and return the row/column leaf order, dendrogram merge trees, and row-z-scored values for the Clustered Expression Heatmap visualization.

FastQC-style deep quality-control report for a FASTQ file: per-base quality and content, GC and length distributions, sequence duplication levels, overrepresented sequences, and adapter content — each with a warn/fail verdict against FastQC's own published thresholds.

Trim FASTQ reads: an ungapped sliding-suffix adapter match (against the same named Illumina adapters as the QC report) followed by a BWA-style 3' quality trim (the same algorithm Cutadapt's own -q option reuses), then drops reads below a minimum length. Returns the trimmed FASTQ plus before/after read-count, mean-length and mean-quality stats.

Find open reading frames (ATG…stop) across all six frames.

Clean, case-fold, DNA↔RNA convert, reverse and line-wrap a sequence.

Over-representation analysis: test which GO terms (biological process / molecular function / cellular component) and Reactome pathways are statistically enriched in a query gene list versus a background, using the hypergeometric test with Benjamini-Hochberg FDR correction across all tested terms. Uses bundled GO Consortium + Reactome reference data (human only). KEGG is not included (its license does not permit bundling gene sets).

GC content, AT content and per-base composition of a sequence.

A gene/drug-target dossier fanned out to five independent sources in one call: Open Targets (function, tractability, top associated diseases), an NCBI/UniProt plain-English function summary, ChEMBL (known drugs and their mechanism/clinical phase, cross-referenced with indications), ClinicalTrials.gov (trials by gene/condition term), and Europe PMC (top cited papers). Each source fails independently — a down source returns null/empty for its own section rather than failing the whole call, and every failure is listed in "sourceErrors" rather than silently omitted.

A gene's tissue-expression fingerprint: per-tissue median TPM from GTEx (v8) and subcellular localization / RNA tissue-specificity / protein class from the Human Protein Atlas, in one call.

The real exon/UTR/CDS structure of a human gene's canonical transcript, fetched live from Ensembl (the same exon/CDS map the HGVS Converter tool uses) — for rendering an exon diagram.

CHOOSE a set of 4-base Golden Gate/MoClo junction overhangs, rather than scoring one you already have. Maximizes the fidelity of the set's WEAKEST junction against the same published T4-ligase ligation data golden_gate_fidelity scores with, subject to every member actually ligating well — an overhang can score a perfect ratio simply because nothing was ever measured cross-reacting with it, and 94% of the ligation matrix is zeros. Pin the overhangs your vector already commits you to with `fixed`, forbid others with `forbidden`, and give `junctions` when the junctions sit at real positions in real sequence and may only slide a few bases. Reports whether the search was exhaustive (provably the best available) or budget-limited (the best found).

Score a candidate set of 4-base Golden Gate/MoClo junction overhangs against real published T4-ligase ligation-count data: per-overhang specificity, the weakest link in the set, and any risky cross-reacting pairs. Optionally compare against a named published overhang set. This is SeqBench's own transparent scoring methodology — it does not reproduce NEB's/Potapov's own published aggregate fidelity percentages for named sets (their exact formula isn't disclosed anywhere accessible).

Golden Gate as the reaction runs: digest pre-domesticated part plasmids with a Type IIS enzyme and assemble them in the order their OVERHANGS dictate. The fragment released from each part is the one carrying no recognition site (the site goes out with the backbone, which is why a mis-ordered assembly is not re-cut), and the assembly order is an OUTPUT — a set whose overhangs do not close into a single cycle has no product, and the reason is the answer. Distinct from cloning_simulate's `goldengate` method, which does the other job: designing the primers that ADD the sites to BARE parts, assembled in the order you list them.

Parse an HGVS "c." variant description (by gene symbol, RefSeq NM_, or Ensembl ENST accession), convert it to genomic (g.) coordinates via a real, live-fetched Ensembl exon/CDS map (transcripts resolved through the bundled MANE RefSeq<->Ensembl crosswalk), apply 3'-rule normalization to any del/dup/ins, and predict the protein (p.) effect where that is safely computable. Refuses cleanly — rather than guessing — for circular/mitochondrial genomes, RNA-level or protein-level input, uncertain/mosaic syntax, splice-junction-adjacent or inversion protein effects, and non-MANE/non-Ensembl transcripts.

Check a UniProt id-mapping job submitted via id_map_submit. Returns {status, ready:false} while still running; once FINISHED, also returns the mapped ids (normalized regardless of which target database was requested) and any ids that failed to map.