Jean — Molecular Biology & Genomics

The built-in starter workflows — id, name, what each does and which tools it chains. Pass an `id` to get that template’s graph back, ready to hand to `run_workflow`. This server is stateless, so these are the shipped templates only, not anything a person has saved in their own editor.

Would this workflow graph run? Returns the blocking errors — an unknown tool, a wire into a port that does not exist, a cycle — each naming the node and the port, without running anything.

Run a workflow graph: nodes naming tools, wires carrying one node’s output into another’s input, `$whenExpression` to make a step conditional, and `scatter` to run a step once per element of a list. Returns the run record — per-node status, outputs and errors, in topological order.

Read one window of an answer that came back cut to the size budget: the tool, the same arguments, the field its notice named, and which items or characters to return. The answer is derived again, so the window is the one that was cut.

The full description of each named tool: what it computes, its models and options, and what it refuses. The listing on this endpoint carries only each description’s opening.

Return the plasmid/sequence currently open in the editor. Emits it as a `record`, so it is the SOURCE node of a pipeline — wire its record into any downstream node's $subject.

Read a CHOSEN project document as a record — the pipeline's data-reader source, for running on a specific file instead of whatever happens to be open in the editor.

List the project's sequence documents (id, name, length, topology, kind).

Cut positions of the given enzymes on the open plasmid (top/bottom cut, overhang, kind).

Where the given enzymes could ALSO cut the open plasmid under non-standard conditions, from REBASE's own star-activity list and its one-base rule.

Whether the host's own methylation blocks the given enzymes ON THIS PLASMID, from REBASE's methylation-sensitivity tables localised onto your sites.

The reaction a digest needs, from NEB's own data: the NEBuffer that serves every enzyme together (scored on the WORST enzyme's percent activity, a star-activity buffer disqualified at any percent), the incubation temperature(s), any cofactor that must be added (SAM or ATP), and each product's heat-inactivation step, or the products NEB cannot heat-inactivate.

Per-enzyme cut counts on the open plasmid (the EcoRI² superscript-badge numbers).

Render the open record's map to a publication SVG string, circular or linear — the same figure the Export panel draws, with the same knobs and the same defaults.

Resolve a gel lane (ladder / plasmid / digest / pcr) into [bp, form] bands for the open plasmid.

Run an agarose gel and photograph it: every lane's fragments migrated by the validated pydna model, drawn as a gel-doc image with the marker ladder's bp marks and each lane's name.

Where a primer anneals on the open plasmid: 3'-anchored sites with identity % and the ✓/⚠/~/✗ verdict, graded at a bar you can move and that the answer states.

Lay a set of oligos out as a supplier's PLATE order sheet: one worksheet per plate, in the well order the supplier's own order form fills, with the column shape its loading direction requires.

Turn a record into the SUBMISSION SET NCBI's `table2asn` consumes: a `.fsa` whose defline carries the source modifiers, and a 5-column feature table (`.tbl`) beside it.

Turn a record into what ENA's Webin-CLI takes for an annotated sequence (`-context sequence`): an ENA flat file (`.embl`) and its manifest.

Fold a set of oligos and check them against each other: every oligo's hairpin, its self-dimer anywhere and its self-dimer at the 3' end, plus the cross-dimer with every other oligo in the set, at any position and at a 3' end.

Measure a sequence against what a NAMED synthesis vendor publishes it can make: length, average and windowed GC, homopolymer runs, tandem repeats, long repeats and hairpin-forming inverted repeats.

Write the sheet IDT's gene-fragment PLATE upload takes for one or more designed fragments: one worksheet per 96-well plate, wells in the template's own order, and the template's four columns (well, name, sequence, 5' phosphorylation).

Which Golden Braid / MoClo / Yeast ToolKit grammar, position and level the open plasmid represents (or null).

Pairwise-align ONE sequencing read against a reference and hand on the two gapped rows, so the read can be graded.

Trim sequencing adapters and low-quality ends off FASTQ reads before mapping, and report what was removed.

FastQC's report on FASTQ reads, every module a default FastQC run reports: per-base quality boxes, per-tile quality (when the headers carry a tile), per-sequence quality, per-base sequence content, per-sequence GC against FastQC's theoretical normal, per-base N content, sequence length distribution, duplication levels, overrepresented sequences with their possible source, and adapter content, each with FastQC's own pass/warn/fail grade and the limit it was graded on.

Map sequencing reads (FASTQ or FASTA) or assembled contigs onto a reference and return the alignment as SAM, the file a pileup viewer, a variant caller or `samtools` reads next.

Grade a sequencing read against a reference (✓ verified / ⚠ near / ~ partial / ✗ divergent) from an alignment.

Read the QC files a sequencing provider returns alongside the reads: a per-base COVERAGE table, or a Plasmidsaurus summary (k-mer composition and the contamination estimate with its named source).

Silently recode the internal Type-IIS sites inside a part's coding sequence so the part can be used in a Golden Gate assembly — same protein, no recognition site.

Design the PCR primers that ADD Type-IIS fusion sites to a part: enzyme site, spacer and the four-base overhang you chose, on a Tm-matched annealing footprint.

Extract @plasmid / !action / &gel cross-references from a lab-notebook markdown body.

Render a construct's build history into a numbered bench-recipe protocol.

Propose the minimal SILENT synonymous edits that remove forbidden restriction sites from a CDS (a design/write).

Minimum-free-energy secondary structure (dot-bracket + ΔG) of an RNA/DNA sequence (Turner-2004).

Amplify the subject template with a forward + reverse primer (pydna Anneal). Emits the first product as a NEW sequence record so downstream tools (digest, gel, map) operate on the amplicon, not the template — the tool→tool hand-off.

Design site-directed mutagenesis (SOE-PCR/QuikChange) primers for a point change on a CDS: the ranked inner mutagenic pairs, the constant outer pair, and the single spanning primer that replaces an outer when the site sits too near a CDS end.

Design site-saturation mutagenesis (SSM) primers: one overlapping forward/reverse pair per site, each carrying a degenerate codon, sharing ONE reaction-level overlap and 3' temperature so every site amplifies in the same tube (both of Mutation Maker's solvers: fast approximation by default, exhaustive search on request).

Design QCLM (quick-change lightning multi-site) oligos: one primer per mutated site, all annealing in one reaction, with the codons drawn from an organism's usage table.

Reverse-complement the subject sequence. Emits a NEW record, so downstream nodes (map, digest, gel, ORF) run on the revcomp rather than the original.

Translate the subject DNA to protein (Biopython `Bio.Seq.translate` semantics). The SUBJECT is the selection when there is one, the named feature when `feature` is given, and the whole record otherwise — the `region` output says which, so never paste bases in to scope it.

Find open reading frames on BOTH strands of the subject (origin-spanning when circular).

Digest the subject with the given enzymes into real fragments (sequence + both overhangs).

GC fraction of the subject — whole-sequence, or a sliding window profile (DnaChisel).

What the subject record is MADE OF: molecular weight under its own topology and strand count, GC and AT over the resolved bases, and the per-symbol census with the ambiguity codes broken out.

Molecular weight (average + monoisotopic), molecular formula, isoelectric point, instability index and amphipathicity (Boman index, Eisenberg hydrophobic moment) of a protein sequence (Biopython ProtParam + `peptides` + `pyteomics`).

Windowed physicochemical profile along a protein (ExPASy ProtScale). Pass `scaleId` for any of the 57 scales ProtScale publishes plus the 14 Biopython tables it does not, with optional 0-1 normalisation; or pass `kind` for the five ProtParam profiles (hydropathy over the 28 GRAVY scales, Vihinen flexibility, Hopp & Woods hydrophilicity, Emini surface accessibility, Janin interior-to-surface transfer).

Signal-peptidase cleavage sites in a protein, by von Heijne's (1986) weight matrix as implemented by EMBOSS sigcleave.

Eisenberg hydrophobic-moment plot along a protein: per-window mean hydrophobicity and moment, each classified Globular / Transmembrane / Surface-seeking, plus those windows collapsed into residue spans.

Transmembrane topology of a protein by the HMM-TM 114-state hidden Markov model (JUCHMME, Bagos et al.), decoded residue by residue into inside / transmembrane-helix / outside runs.

Intrinsic disorder along a protein by FoldIndex (Prilusky 2005): I_F = 2.785·⟨H⟩ − |⟨R⟩| − 1.151 over a sliding window, where ⟨H⟩ is mean normalized Kyte-Doolittle hydropathy and ⟨R⟩ the mean net charge per residue.

Per-residue intrinsic disorder by metapredict (Emenecker, Griffith & Holehouse, Biophys J 2021; V2/V3 the lab's successors), a bidirectional LSTM trained to reproduce a consensus of disorder predictors, run locally.

In-silico proteolytic digest of a protein (ExPASy PeptideCutter / PeptideMass): peptides with coordinates, average and monoisotopic masses, m/z per charge state, and the MS mass-window filter. 49 proteases and chemicals — or an enzyme of your own written in RPG rule grammar — one or several at a time; a multi-enzyme digest runs concurrently (all at once, products re-digested until nothing changes) or sequentially (one result per enzyme).

Monoisotopic and average mass of a peptide, and its m/z at each charge state (validated against pyteomics).

Theoretical MS/MS fragment ion ladder of a peptide: a, b, c from the N-terminus and x, y, z from the C-terminus, with neutral-loss and radical variants, at each charge up to a maximum (validated against pyteomics, cross-checked against OpenMS).

The most probable isotopic composition of a peptide (e.g. how many 13C atoms it most likely carries) and that composition's relative abundance, from natural isotope abundances (validated against pyteomics).

Coarse isotope pattern of a peptide or of an elemental formula (C29H31N7O, as a compound's formula reads): the relative heights and masses of its M, M+1, M+2 … peaks as an instrument that does not resolve isotopic fine structure sees them, computed by per-element convolution from natural abundances (validated against OpenMS).

Score how well an MS/MS spectrum matches a peptide with the X!Tandem HyperScore (validated against OpenMS): b and y ions are generated for the peptide, matched to the observed peaks within a tolerance, and scored as ln(1 + dot product) + ln(matched b!) + ln(matched y!).

Read the MS/MS spectra of a Mascot Generic Format (MGF) file: each spectrum's title, precursor m/z, precursor intensity, declared charge states and peak list, interpreted as pyteomics reads MGF (validated on every spectrum of PXD000001).

Read the spectra of a PSI mzML run file: each spectrum's native id, MS level, centroid or profile, scan start time and unit, base peak, total ion current, precursor (selected ion m/z, charge, intensity, isolation window) and peak arrays, interpreted as pyteomics reads mzML (validated on every spectrum of three real runs).

Protein inference from peptide-spectrum matches (validated against OpenMS BasicProteinInferenceAlgorithm on PXD000001): maps each peptide to the protein database, keeps the best match per spectrum, scores each protein by aggregating its peptides' best scores, reports the proteins its evidence cannot tell apart as indistinguishable groups, and the percent of each sequence the matches cover.

Bayesian protein inference, Epifany (validated against OpenMS BayesianProteinInferenceAlgorithm on PXD000001): the posterior probability that each protein, each indistinguishable group and each peptide-spectrum match is present, from the matches' posterior error probabilities under a network in which present proteins emit their peptides.

False discovery rate and q-values for peptide-spectrum matches by target-decoy competition (validated against pyteomics): give each match's score and whether it hit a decoy; returns every match's q-value, the whole set's FDR, and how many targets pass a q-value threshold.

Design and score CRISPR guides on the subject — on/off-target scores, QC flags, and cloning-enzyme conflicts.

Turn the open record's annotated targets into an ORDERABLE sgRNA oligo pool: the best N guides per target, filtered at order time (Pol-III terminator, Graf inactive motif, cloning-enzyme site, minimum on-target score), each composed into the oligo a synthesiser makes — the vector's cloning overhangs around the spacer plus, optionally, one of CRISPOR's eleven subpool barcodes.

Call HITS from a pooled CRISPR screen: per-guide read counts in, a gene-level Bayes factor and a precision/recall curve out.

The complete build package for a knockout or knock-in, as one object: guides inside the target, an off-target census for the chosen one within this molecule, a repair template with real homology arms, a screening PCR whose primers sit OUTSIDE the arms, the protocol in order, the fragments and oligos to buy, and every coordinate ready for the map.

Screen guide spacers the way CHOPCHOP does before it accepts them: GC gate, self-complementary hairpins inside the spacer, and hairpins against the sgRNA backbone.

Search a sequence for sites a guide could also cut, allowing mismatches, and score each one (MIT and CFD) plus the aggregate specificity of the guide WITHIN that sequence.

Count every guide's off-target sites by mismatch class across one sequence, in one pass — the question that comes BEFORE opening any single guide's site table: which of these spacers is worth looking at.

Fold a guide RNA — the spacer alone, and the whole sgRNA on a real published scaffold — and report which bases of the spacer pair with the scaffold.

Every sgRNA scaffold this workbench has sourced, with the nucleases each covers and the publication it comes from.

Find the splice sites a sequence accidentally spells - the check a codon-optimised CDS needs before it goes into a plant or any other eukaryotic host, because a synonymous swap chosen for codon usage or GC can spell a donor or acceptor that was not there before, and the transcript is then spliced somewhere nobody intended.

Find every place a transcription-factor binding motif (a position-frequency matrix, such as a JASPAR matrix's counts) matches a DNA sequence, on both strands, at a threshold set per motif as a fraction of that matrix's own score range.

Find σ70 (housekeeping) promoters in a bacterial sequence and say how strong each one is, using the published La Fleur-Hossain-Salis biophysical model - six interaction motifs (UP element, -35, spacer, extended -10, -10, discriminator) plus the R-loop strength of the first transcribed bases, fitted on 22,132 characterised promoters.

Find rho-independent (intrinsic) transcription terminators in a bacterial sequence: a stem-loop followed by a thymine-rich tail, scored by TransTermHP's published model (Kingsford, Ayanbule & Salzberg 2007).

Score the ribosome-binding site already in front of a start codon: the Shine-Dalgarno hybridisation energy, the mRNA folding that competes with it, the spacing it implies, and the resulting relative initiation strength.

Stack several CDSs into one contiguous operon, designing a fresh ribosome-binding site in front of each one AGAINST ITS REAL UPSTREAM CONTEXT — which is why the RBS for gene 2 is not the RBS design_rbs would give it in isolation.

Codon-usage analysis of an in-frame CDS: CAI / expression stats, GC, and every rare-codon occurrence for the chosen organism (pydna port), plus the two bias measures that need NO reference table — ENC (Wright 1990) and RCBS (Roymondal 2009), which are therefore computable for any organism.

How this region's codon usage compares with the record's OTHER genes (CUFS, Diament, Pinter & Tuller 2014).

Decide whether a sequence is scientifically admissible: protein unchanged, forbidden restriction sites absent, reading frame intact, length/GC/CAI inside stated bounds.

The expression-design deliverable: several coding sequences for one protein, each variant following a different published strategy (maximise codon adaptation, match the host's codon usage, harmonise with the native host, or preserve the initiation region), each scored by an engine — CAI, GC, the folding energy of the Kudla -4..+37 initiation window, and rare codons.

Run the DnaChisel optimizer over the subject with real constraints/objectives (GC window, pattern avoidance, translation enforcement, …).

Design a 5'UTR (Shine-Dalgarno + spacer) in front of a CDS to hit a target translation-initiation strength (SC-08 engine, Turner-2004 folding).

Design LAMP (loop-mediated isothermal amplification) primer SETS - four core oligos (F3 · B3 · FIP · BIP) plus optional loop primers, judged against Eiken's own PrimerExplorer V5 criteria.

Design CODEHOP hybrid oligos from a block of aligned PROTEIN - for homologues too diverged for nucleotide-level design to see at all.

Design degenerate primers from a multiple-sequence ALIGNMENT - the 'amplify every homologue in this family' question, which a single-template designer cannot answer because it only ever sees one sequence.

Design BSP or MSP primers for a methylation assay, on the CONVERTED strands: after bisulfite treatment every unmethylated C reads as T, which is a different template from the one every other designer sees.

Design a WALK of sequencing primers that reads a whole region, not one pair: tiled reads with confirmed overlap, on both strands by default, plus the coverage the walk actually achieves and any stretch it cannot reach.

Design a forward + reverse primer pair to amplify the subject (pydna `primer_design`), with Tm, Ta and suggested Taq/Phusion cycling programs.

Multiple-sequence alignment over several sequences, under either of TWO engines that answer different questions and neither of which is a fallback for the other.

Draw a block of an alignment as an SVG figure: the rows named, the residues shaded by how much of their column agrees with them, the column numbers over the block and Easel's Clustal conservation line under it.

Find the rows of an alignment that repeat another: each row's redundancy is its highest percent identity to a row at least as long (ungapped residues), and a row at or above the threshold is redundant, so filtering keeps the longest representative of each near-identical group.

Principal component analysis of the rows of an alignment: the pairwise similarity of every two rows under PID or a substitution matrix (BLOSUM62, PAM250 for protein; Jalview's DNA matrix for nucleotides), decomposed into eigenvectors, and each row's coordinate on the leading components, so rows that cluster sit together.

Read the consensus RNA secondary structure a Stockholm alignment states in its `#=GC SS_cons` line (WUSS, the notation Rfam and Infernal write), and measure how well the aligned rows actually support it.

Assemble fragments by terminal homology (Gibson / NEBuilder / In-Fusion, DnaCauldron).

Open ONE circular backbone at several places and place several inserts in a single step.

Design the PCR primers that CREATE the terminal homology a Gibson/NEBuilder/In-Fusion assembly needs: a Tm-matched pair per fragment, each carrying the 5' overhang that fuses it to its neighbour in the order given (pydna assembly_fragments).

Assemble parts with a Type-IIS enzyme (Golden Gate / MoClo, DnaCauldron). Emits the first construct as a NEW record for downstream nodes.

Check parts BEFORE a Type-IIS assembly: how many recognition sites each carries, which of them are the two deliberate flanking sites and which are internal sites that will mis-cut the part.

Score a Golden Gate junction (fusion-site) set against MEASURED T4 DNA ligase end-joining data.

Decide where to CUT one sequence into Golden Gate fragments, optimising the split positions and the overhangs they create together.

Design a high-fidelity Golden Gate overhang set of a given size against measured ligation data, optionally pinning junctions you cannot change (a fixed vector's own overhangs).

Run a MULTI-LEVEL assembly plan (DnaCauldron `AssemblyPlan`): several assemblies arranged into dependency levels, where each level's products become parts for the next.

DNA melting behaviour of the subject by the Poland–Scheraga model (DECIPHER): the melt curve (mean helicity vs temperature), its Tm50, and the cooperatively-melting domains behind each HRM peak.

Design an in-situ hybridization probe against the subject: tile it into candidate oligos that meet a melting-temperature, G+C and composition specification, screen them for off-target binding against an alignment and for k-mers abundant in a reference, and gather the survivors into the set that images best.

Design padlock probes for in-situ sequencing against the subject: find the two-arm targets whose melting temperature falls in a window, check each against a BLAST search for any other transcript that could be ligated across the junction, pack non-overlapping targets from the 3' end, and assemble each into its padlock with the backbone given.

Design HCR v3.0 split-initiator probe pairs against the subject: tile its reverse complement into 52 nt windows, drop windows with C or G runs, a stable hairpin, G+C or RNA:DNA free energy outside their windows, or (given a bowtie2 alignment of the tiles) more than one genome hit, then pick non-overlapping tiles closest to the target free energy and split each around a 2 nt gap into an odd and an even probe carrying half of the chosen amplifier's initiator.

Design MERFISH encoding probes for the subject transcript: tile it into target regions, keep those whose G+C and melting temperature fall in their windows, that share no k-mer with an rRNA or tRNA, and whose k-mers occur in this gene's transcripts and nowhere else in the transcriptome; attach readout sequences that spell the gene's codeword, add the amplification primers, screen the whole oligo again, select probes that cover every on-bit evenly with least overlap, and drop probes complementary to one another.

Design Xenium custom-panel padlock targets on the subject transcript to 10x Genomics' published rules (CG000683): two 20 nt arms whose primer3 melting temperatures fall in their windows, a ligation-junction dinucleotide the Xenium ligase accepts, no run of five G in the probe, at least four mismatches across both arms to every other transcript in a BLAST search, and probes at least 10 bp apart.

Design a genetic circuit that computes: give it a logic function and it synthesizes a NOR/NOT netlist, assigns real characterized repressor gates from a Cello UCF library, predicts the dynamic range between ON and OFF, and emits the construct's DNA.

Self-similarity or pairwise comparison of the subject as a dot plot — repeats, inversions and shared regions.

All-against-all dot plot over a sequence SET — EMBOSS `polydot`. Every ordered pair is plotted with the exact k-mer engine and laid out as an N×N grid, so shared regions, repeats and inversions across a whole family are visible in one picture.

Build a phylogeny from aligned sequences — distance methods (NJ, BIONJ, FastME, UPGMA/WPGMA), parsimony, maximum likelihood, or Bayesian MCMC under a strict clock and coalescent prior — with branch support from a bootstrap, aLRT or aBayes, or, for the Bayesian method, clade posterior probabilities.

Choose the substitution model for a maximum-likelihood phylogeny by fitting every candidate — model x rate-heterogeneity x invariant-sites — on ONE shared topology and ranking them by AIC, AICc or BIC with Akaike weights.

Edit a tree that is already built — re-root it (at its midpoint, on an outgroup, or remove the root), prune taxa away or down to a set, ladderize it, or collapse branches that are too weakly supported or too short to believe.

Draw a tree as an SVG figure — rectangular, slanted, circular or unrooted, as a phylogram or a cladogram, with tip names, branch support and a scale bar.

Compare trees over the same taxa: the Robinson-Foulds distance between two of them, and the consensus of any number.

Describe a tree without rebuilding it: its shape statistics (tips, height, balance, the gamma statistic), the lineages-through-time curve, and the patristic distance matrix — the path lengths through the tree, which is what to compare against the alignment distances that built it.

Read what a NeXML tree file says about each of its taxa: every annotation on every OTU (an NCBI taxon, a rank, a parent taxon), one row per annotation, plus how many distinct values each property takes.

Convert a tree between the four formats this workbench reads and writes — Newick, NEXUS, PhyloXML and NeXML — sniffing the input format rather than making the caller declare it.

Check an index set you ALREADY have, rather than designing a new one: the minimum distance between any two, how many errors that guarantees correcting, the full distance matrices, and — optionally — the subset that survives pruning to a stricter error budget.

Assign reads to the index each one came from, and say how many could NOT be assigned reliably — a hit further from a barcode than the set's guaranteed correction radius is not that barcode, and counting it as one is how a demultiplexing report hides a failed run.

Write an index set out in the form the next step needs: a CSV or TSV order sheet, FASTA, an IDT bulk-input sheet of the bare tags, an Illumina BCLConvert SampleSheet, or the actual oligos — adapters built on a template, or primers carrying the tag — with the junction warnings that emission produces.

Design a set of NGS sample-index barcodes with a guaranteed minimum distance, and report what that set actually corrects — substitutions, or indels too (DNABarcodes).

Design qPCR assays over the subject — primer pair plus hydrolysis probe, ranked by penalty, with amplicon size/GC, per-oligo Tm and whether both primers bind a single site.

A Scorpion probe's two melting temperatures, side by side: the stem-loop's own hairpin and the duplex the loop makes with its target on the amplicon.

Predict the amplification curve a hydrolysis-probe (TaqMan) qPCR assay will draw, and the Cq read off it, from the assay's own sequences and its thermocycling programme - a forward simulation, not a fit to data.

Amplification curves to Cq, one row per reaction. Fits each raw fluorescence curve to a sigmoid (l4-l7, b4-b7), reads the Cq off it under any of the seven definitions (cpD1, cpD2, maxE, expR, Cy0, CQ, maxRatio) or at a fluorescence threshold you set (the classic Ct), and returns the per-reaction efficiency and the fit-quality block beside it, so a bad fit is visible next to the Cq it produced rather than hidden behind it.

Relative gene expression from a Ct table, by all THREE models the Analyze panel offers: delta-delta-Ct (normalised to a reference gene AND calibrated to a reference group), delta-Ct (calibrated to a reference group only), and the two-step standard-curve model, which fits each gene's own dilution series and converts Ct to amount before taking the ratio.

Which candidate reference gene is actually stable, ranked by all four published algorithms at once plus the RefFinder consensus: geNorm (pairwise variation M), NormFinder (intra/inter-group variance), BestKeeper (SD of raw Cq and correlation to the index) and the comparative delta-Ct method.

Read an instrument's RDML export - the open qPCR exchange standard, v1.0 to v1.4 - and hand back one run's contents as the same tables every other qPCR tool here takes.

Digital PCR (dPCR) partition counts to absolute concentration, with no standard curve.

Two-channel ddPCR droplet-amplitude exports to a template concentration, by CLASSIFYING the raw droplets rather than trusting a count the instrument already made.

ddPCR droplet amplitudes to populations. For TWO targets, a four-way classification — NN, NP, PN, PP — under any of THREE published methods that answer different questions and none of which is a fallback for another.

Melt curves to melting temperature, under any of THREE engines that answer different questions and none of which is a fallback for another.

Uncertainty through any qPCR quantity, three independent ways at once. Give it the expression you actually report - a ratio, a normalised expression, an efficiency-corrected quantity - and a mean and standard deviation for each variable in it (or the replicate observations themselves), and it returns the analytic Taylor propagation, a Monte-Carlo simulation and a permutation resampling side by side, because they disagree in informative ways: the Taylor arm is exact only for a linear expression, and a ratio is not linear.

Resampling error bars on ONE reaction's Cq. A Cq is read off a fitted model, so its uncertainty is how tightly the observations pin that model down - which propagating a measured spread cannot answer.

The MIQE 2.0 checklist for a qPCR paper (Bustin et al. 2025, Clin Chem 71:634, Table 1): every item in its five sections, with your answers.

Power and sample size for a planned experiment: how many replicates a test needs to detect an effect, or what power, detectable effect or significance level a design that is already fixed gives.

The effect a power calculation needs, measured from a pilot instead of guessed: two groups of one measurement in a table, standardized as Hedges' g (default), Cohen's d or Glass's Δ with its noncentral-t confidence interval (effectsize 1.0.3, reproduced), and the two-sample t design sized at the estimate and at the interval limit nearest zero.

Sample size and power for an RNA-seq comparison of two groups, one gene at a time: the biological replicates per group needed to detect a fold change at a given sequencing depth and within-group coefficient of variation, or the power, detectable fold change, CV or α a fixed design gives.

How many biological replicates an RNA-seq experiment needs when the question is about the WHOLE experiment rather than one gene: the exact negative-binomial test with the per-gene α derived from a target false discovery rate across m genes of which m1 are expected to move (RnaSeqSampleSize 2.23.0, Zhao et al. 2018; the test is Robinson & Smyth 2008's, edgeR's exactTest), or the AVERAGE power across genes whose effects differ, with the FDR controlled (ssizeRNA 1.3.3, Bi & Liu 2016).

Power and FDR for a two-group RNA-seq experiment by SIMULATION (PROPER 1.44.0): whole experiments drawn from a real pilot (Cheung, Gilad, Bottomly, MAQC, or a sequencing depth), each run through edgeR's exact test at every sample size.

Which candidates in a table are not beaten on every objective at once: Pareto (non-dominated) sorting of rows scored on several columns, each to maximize or minimize.

Solve C₁V₁ = C₂V₂ for whichever of the four quantities is left empty (stock concentration, stock volume, final concentration, final volume), each in its own unit: molar (M to fM), mass per volume (mg/mL to ng/mL), fold (X) or percent.

Plan a serial dilution: the same fold dilution repeated tube after tube from a stock, each tube made to one volume.

Convert a mass of a molecule to an amount in moles and a number of molecules, or an amount back to a mass.

Scale one PCR reaction into a master mix for N reactions plus overage. Starts from a vendor's own published reaction setup (scaled to any reaction volume in proportion to the vendor's columns, so every final concentration is the vendor's, with water making up the volume and the template added per tube), or from your own component list.

Will the sequencer SEE these indexes? Per-cycle channel occupancy for a set of index sequences under an instrument's own SBS chemistry, checked against Illumina's rules (no dark cycle, both channels preferred, no GG in the first two cycles), each violation citing the vendor article it comes from — and the same set scored across EVERY chemistry generation at once, because XLEAP moved the dual-color base from A to C and a set balanced on a NovaSeq 6000 is not balanced on a NovaSeq X.

Generate INDEL-correcting barcodes — FREE divergence codes (Hawkins et al., PNAS 2018) — by reserving each codeword's error sphere over the whole 4^k space, so the guarantee survives an insertion or deletion rather than only a substitution.

Turn index sequences into the oligos a synthesiser makes: each index spliced between 5'/3' adapter arms, or onto a PCR primer in either orientation with an optional spacer.

Will this pool of sample indexes actually sequence? One verdict over the indexes a lab is about to load: duplicates, uniform length, the distance the set really guarantees against the distance it claims, per-cycle color balance on the chosen instrument's own chemistry, the index-2 orientation that instrument reads, and index-hopping exposure — how many i7 x i5 combinations exist against how many are in use, and which swaps would turn one sample's read into another's undetectably.

Plan the cycling programme for an amplicon on a NAMED enzyme, and check the reaction against that manufacturer's own published limits.

Recombination between molecules — Gateway BP/LR (att sites), Cre/loxP, a serine integrase whose two half-sites you supply, plain homologous recombination through shared arms, or a CRISPR knock-in that keeps only products whose repair region contains every guide's cut.

Compare EVERY assembly chemistry this engine has against one set of parts, and say what each one costs and risks.

Ligate fragments by their overhangs (pydna ligation_assembly) — sticky, optionally blunt.

Cut and ligate in ONE POT (pydna restriction_ligation_assembly): every input is digested by the same enzymes inside the reaction and the pieces are joined as they appear, so a junction that re-forms a recognition site can be cut again.

Join fragments without a ligase by one of the five named methods — LIC, SLIC, In-Fusion, CPEC or USER (pydna).

Design the PCR primers a ligation-independent reaction needs — LIC, SLIC, In-Fusion, CPEC or USER: a Tm-matched pair per amplified fragment, in the order given.

Overlap-extension (fusion) PCR — join fragments that share terminal homology into one product, carrying their features across (pydna).

Design a combinatorial library in one step: SLOTS, each offering a bin of interchangeable parts, plus the CONSTANTS every member carries.

A QC gate. Compares a wired value against a threshold and PASSES or FAILS the pipeline there — every node downstream of a failed gate is skipped, so a run stops at the first check that does not hold.

Keep only the rows of a table-shaped output that meet a condition — the bands above a size, the guides over a score, the assays under a penalty.

The region selected on the canvas, as its own record. This is what makes a highlighted stretch a pipeline INPUT: wire its record into any node's subject and that step runs on the selection rather than on the whole molecule.

Rejoin two branches: takes the outputs of several conditional steps and emits the first one that actually ran.

A QC gate over a TABLE: assert something about every row at once (every band inside a size window, no guide above an off-target score, at least three assays under a penalty).

Reduce a whole TABLE column to one number — the largest band, the median guide score, the spread of a set of melting temperatures.

Screen a primer POOL against one or more templates: every predicted product, per-primer off-target binding, the pairs that tell templates apart on a gel, and the all-against-all primer-dimer matrix a pool actually lives or dies by (pydna primer_screen + primer3 thal).

Read a structure file — PDB, PDBx/mmCIF, or a core CIF small-molecule crystal structure — and report what is in it: the chains and what kind each is, their sequences, the ligands and ordered solvent, the disulfides, the residues the depositor declared modified, the temperature-factor range, the unit cell and space group, the resolution and the experiment.

Find antibody and T-cell-receptor variable domains in a protein sequence, number them under IMGT, Kabat, Chothia, Martin, AHo or Wolfguy, call chain type and species, and assign the closest V and J germline with percent identity.

Scan antibody and TCR variable domains for sequence liabilities (Asn deamidation, Asp isomerization, N-glycosylation sequons, Asp-Pro cleavage, Met and Trp oxidation, unpaired or missing conserved cysteines), each placed in its IMGT region so CDR hits can be told from framework ones.

Humanize an antibody variable domain with Sapiens, the antibody language model behind BioPhi: score every position against human repertoires, take the highest-scoring residue in the frameworks, and keep the parental CDRs (and optionally the Kabat Vernier zone).

Humanize an antibody variable domain by CDR grafting: find the closest human germline V and J, merge them into an acceptor framework, and put the parental CDRs onto it.

Name the CONSTANT region of an antibody chain: which immunoglobulin gene and allele it carries (IGHG1, IGHA2, IGKC, IGLC2 …), and where each IMGT domain sits on it — CH1, the hinge, CH2, CH3, the secretory tail and the membrane exons, or the single C region of a light chain.

Properties of an assembled antibody from its heavy and light chains: molecular weight, isoelectric point, net charge at a pH, and the extinction coefficient and A280 with cysteines reduced and with every pair formed into a cystine, for a Fab (1 heavy + 1 light) or an IgG (2 + 2).

Type the ANTIBODY-ANTIGEN INTERFACE of a deposited complex: which residue of each antibody chain touches which residue of the antigen, how close, and what kind of interaction each pair makes - ionic, polar, weak polar, hydrophobic, aromatic, carbonyl, or a disulfide.

Several antibodies side by side: each record numbered by ANARCI, its heavy or its light variable domains aligned column by column by their numbering (a column is one scheme position, so nothing is re-aligned and an insertion is its own column), the CDR of every column under the chosen definition, how many columns agree, and the same properties read off each V domain: length, mass, isoelectric point, net charge, GRAVY, ε280 and the net charge of its CDRs.

Assign each CDR of an antibody heavy, kappa or lambda variable domain to its canonical structure class: SCALOP's length-independent clusters of known loop conformations (CDR-H1, H2, L1, L2, L3; H3 has no canonical classes).

How common each residue of an antibody variable domain is at its IMGT position, and which residues are rarer there than a cut (abYsis's 1% "unusual residue" by default).

Per-site and per-sample genotype statistics of a call set, for all samples or one population: allele counts and frequencies, called and missing calls, heterozygous and homozygous counts, observed and expected heterozygosity and the inbreeding coefficient F (scikit-allel), and the exact Hardy–Weinberg test with its excess-heterozygosity tail (bcftools +fill-tags).

Linkage disequilibrium between every pair of biallelic sites of a call set: the dosage correlation r of Rogers and Huff (scikit-allel), or haplotype r² and D′ from the maximum-likelihood two-locus haplotype frequencies (PLINK 1.9 --r2 dprime-signed), plus LD pruning by sliding windows (scikit-allel locate_unlinked).

Differentiation between populations of a call set: Weir and Cockerham's θ over two or more populations, or Hudson's or Patterson's Fst between pairs, per site and averaged with a block-jackknife standard error (scikit-allel).

Principal components of a call set's genotypes, the population-structure picture: biallelic sites above a minor-allele count, LD-pruned by sliding windows, scaled per site (Patterson, standard or centring) and decomposed; every sample's coordinates with its population, and the variance each component explains (scikit-allel pca).

Individual ancestry coefficients, the ADMIXTURE-style picture: each sample as a mixture of K ancestral populations, fitted by sparse non-negative matrix factorisation for every K in a range, with K scored by the cross-entropy of predicting genotypes held out at random (LEA snmf, Frichot et al. 2014).

Differential expression from raw RNA-seq read counts, by one of the three standard count models.

Over-representation analysis (ORA): which gene sets hold more of a gene list than chance, by the one-sided hypergeometric test against a background, as clusterProfiler's enricher() runs it (enrichit 0.2.6).

Preranked gene set enrichment analysis (GSEA; Subramanian et al. 2005) with fgsea's multilevel p-values (Korotkevich et al.), either as clusterProfiler's GSEA() runs it (enrichit 0.2.6, with q-values; or its fixed-count or adaptive sample and permute tests) or as fgsea 1.38 itself runs it (fgseaMultilevel, or fgseaSimple with a permutation count): weighted running-sum enrichment score, normalized ES against size-matched permutations, multilevel Monte Carlo p-values down to eps, adjusted p, log2err, rank at maximum, leading-edge genes.

Competitive gene set test that accounts for inter-gene correlation (camera; Wu & Smyth 2012), as limma 3.68.5 runs it: is a set more differentially expressed than the genes outside it, with the variance of its mean statistic inflated by the correlation between its genes instead of treating them as independent.

Sweep a set of sequences against every exact recognition site in the NEB catalog and report the sites each one carries far less (avoided) or far more (enriched) often than a stated null predicts, with the false discovery rate controlled across every (sequence, site) test.

Score every coding sequence of a genome against the genome's own codon usage and report the genes whose synonymous codon choice that average does not explain, with the false discovery rate controlled across the genes.

Ask whether some loci of a multi-locus alignment have a different history from the rest, by the incongruence length difference test (Farris et al. 1994).

Ask whether a reference-gene stability ranking holds across conditions, and which candidate genes rank well everywhere rather than in one tissue or treatment.

Test a hypothesis that a gene drives a phenotype ("gene G regulates IL-2 production") the way POPPER does: each evidence table is one falsification test, an implication of the hypothesis that a gene-level score could contradict (expression in the relevant tissue, association with related disease, interaction with genes annotated to the process).

Measure how often a corpus of maps carries a cryptic σ70 promoter, a cryptic splice site or a synthesis-breaking repeat MORE EXTREME THAN CHANCE, rather than how many it carries.

Find the genes whose own alignment rejects the species tree. Each named alignment file is one gene, its records the taxa, named as the species tree's tips.

Check a published PCR or sequencing assay against the sequences that are circulating now: for each oligo, where its best binding site is in each genome, how many positions mismatch, and whether a mismatch sits in the 3′ end a polymerase has to extend through.

Check every annotated CDS of one or more GenBank maps against its own bases and genetic code: a frame that does not close (frameshift), a stop inside the reading frame, a first codon the code cannot start with, a last codon that is not a stop, and a /translation the bases do not give.

Put a hypothesis of the form "gene G is involved in trait T" through POPPER-style sequential falsification on two public tables, each test fixed in advance and run in a fixed order.