com.seqbench/workbench

Fold a transcript twice — once freely, once with the aptamer held out of the secondary structure as a bound ligand would hold it — and report what changes at the ribosome binding site. Returns both structures, both free energies, the cost of occupying the aptamer, and whether the site becomes more or less accessible. This is the question a riboswitch design can be checked on before the bench: is the site sequestered in one state and free in the other. Returns NO activation ratio and no fold-change — see this tool's validation note for why that number is not offered. Pair with rbs_occlusion, which finds what can pair over the site in the first place. PREDICTED, NOT MEASURED. None is published for this combination, and none is claimed. The two folds are ViennaRNA MFE structures with its documented parameter set; what is NOT established is that the ligand-bound state is modelled correctly by holding the aptamer out of the secondary structure, which is an approximation of a three-dimensional binding event. No activation ratio is returned, because the only published validation set for predicting one (Espah Borujeni et al., NAR 2016;44(1):1) is CC BY-NC and unusable here. Valid for: single-strand RNA up to 1,000 nt at one temperature, with no pseudoknots and no tertiary structure. A translational riboswitch whose mechanism is sequestering the ribosome binding site — NOT transcriptional attenuators, NOT ribozymes, and NOT any switch whose aptamer overlaps the site it regulates.

Predict an RNA secondary structure by minimum free energy (MFE) using a Zuker dynamic program with Turner 1999 nearest-neighbor stacking energies (no pseudoknots). Returns the dot-bracket structure, the estimated MFE (kcal/mol), and the list of base pairs. A from-scratch, in-browser implementation (there is no usable browser ViennaRNA); the simplified loop energy model makes the MFE a good comparative estimate, not a lab-grade absolute. PREDICTED, NOT MEASURED. No held-out skill statistic is claimed for this implementation. The loop model omits terminal mismatches, dangling ends, coaxial stacking and special hairpins that ViennaRNA and RNAstructure include, so the MFE is a comparative estimate between candidates rather than a lab-grade absolute. Valid for: a single strand up to 600 nt at a fixed 37 °C. No pseudoknots, no two-strand hybridization, and no temperature dependence — a structure predicted here is not a structure at your annealing temperature.

Assemble two or more Sanger reads into a contig WITHOUT a reference sequence — the forward/reverse pair of one insert, or a set of tiling reads. Orientation is worked out from the overlaps, ends are quality-trimmed, and the consensus is quality-weighted. Reports depth and agreement per position, every position where the reads disagree (including a base only one read has), and any read that overlapped nothing. Use sanger_vs_reference instead when you already know what the sequence should be.

Quantify CRISPR editing from a pair of Sanger traces — an unedited control and the edited pool — by decomposing the edited trace onto shifted copies of the control. Returns the indel spectrum (how much of the pool carries each insertion or deletion size), the unedited fraction, and the R² of the decomposition, which is the number that says whether the model fits your traces at all. Non-negative least squares, so no allele is ever assigned a negative share. Does not work for base editing, which makes a mixed base rather than a shift. PREDICTED, NOT MEASURED. Every run reports its own R²: how much of the observed window the shifted-control basis actually explains. That is a measured adequacy of the model on YOUR traces, and a low value means the assumption is wrong here rather than that the edit is weak. On accuracy against a reference method for real samples, none is published for this implementation — the underlying decomposition is TIDE's, whose authors report their own concordance with amplicon sequencing, and that number does not transfer to this code so it is not quoted. This implementation's exact recovery of synthetic mixtures is deliberately not offered as validation either: it tests the arithmetic, not whether the model fits a real trace. Valid for: A pool of alleles that differ from one control read by simple insertions or deletions at a known cut site, where both reads come from the same amplicon and chemistry and both extend well past the cut. NOT valid for substitution-only edits — base editing produces a mixed base, not a shift, and this model cannot see it — nor for a knock-in whose insert is novel sequence rather than a frame shift of the control, nor for any run whose R² comes back low.

Measure the rate of a SPECIFIC intended edit from a pair of Sanger traces — an unedited control and the edited pool — by decomposing the edited trace onto three things at once: the wild-type allele, the intended edited allele, and the unintended indels. Serves both readouts that need this: HDR knock-in rate (what fraction of the pool carries the donor's edit, including an insert of novel sequence), and prime editing (the pegRNA's intended substitution, insertion, deletion or replacement as the intended column, and the indel byproducts at the nick as the shift columns). This is what sanger_indel_spectrum cannot do: that tool's basis is indexed by indel LENGTH, so an intended 6 bp knock-in and an accidental 6 bp NHEJ deletion are one column there. Returns knock-in / wild-type / unintended-indel percentages, the byproduct spectrum by shift, and the R² that says whether the model fits your traces at all. Non-negative least squares, so no allele is ever assigned a negative share. For a substitution or replacement the reference allele you name is checked against the control read before anything is fitted; an insertion and a deletion name no reference bases, so there only the position can be range-checked. PREDICTED, NOT MEASURED. Every run reports its own R²: how much of the observed window the basis actually explains, measured on YOUR traces, where a low value means the model is wrong here rather than that the edit is weak. On agreement with a reference method for real samples — amplicon sequencing or clonal genotyping — none is published for this implementation. The underlying decomposition is TIDE/TIDER's, whose authors report their own concordance, and that number does not transfer to this code so it is not quoted. This implementation's near-exact recovery of synthetic mixtures is deliberately not offered as validation: a synthetic mixture is built from the same idealised one-hot peaks the basis assumes, so recovering it tests the arithmetic and cannot test the assum…

Judge a whole plate of Sanger reads against one construct and return one row per clone: PASS, POINT_MUTATION, INDEL, VECTOR_ONLY (the insert is absent), WRONG_INSERT (the backbone matches and the insert does not), LOW_COVERAGE, or AMBIGUOUS. Reads are grouped into clones from their FASTA/FASTQ record names (facility conventions like PlateA_A01_pXY-1_M13F, pXY-1_T7-F, 2026-08-01_pXY_clone3_R), and every read's assignment is reported with a confidence so a grouping can be corrected rather than trusted. Each clone's reads are piled up in reference coordinates, so a difference one read reports where other covering reads read the reference is reported as the sequencing error it is, not as a mutation — and a position no read covered is never PASS. Every verdict cites the positions it rests on. Give insertStart/insertEnd to have clones judged over the insert alone, which is also what VECTOR_ONLY and WRONG_INSERT need.

Align a Sanger ABIF read to a reference and report identity plus every mismatch, insertion and deletion.

Run a registered tool and save its (arguments, result) pair under a short permanent code that anyone with the link can view read-only (/permalink/{code}). Use this to cite or share a specific result (e.g. a verify_construct or verify_assembly check) rather than re-pasting it.

Statistics for a FASTA or FASTQ file: count, length distribution, N50, GC content and (FASTQ) mean quality.

Fetch a public DNA/protein record by accession from NCBI Nucleotide, NCBI Protein, UniProt, or Ensembl (e.g. NM_000546, NP_000537, P04637, ENSG00000141510). Only the accession is sent upstream. Use sequence_search first if you only know a gene/organism name, not an accession. For an Ensembl transcript ID this returns spliced cDNA; for a gene ID it returns the full genomic locus (introns included) — Ensembl's own default for each ID type.

Convert between FASTA and GenBank (whole sequence, CDS or protein), or export to TSV.

One-click DNA analysis: composition, ORFs, restriction-enzyme scan (single cutters) and end-primer Tm composed into a single report with a copyable text block.

Resolve a gene/organism name — or a raw NCBI search term — to candidate accessions, instead of guessing one. Returns up to maxResults hits (accession, title, organism); pass the accession you want to sequence_fetch.

Align raw Sanger or NGS reads (FASTA or FASTQ) back onto a claimed reference sequence using minimap2, and report per-read mapping identity plus exact variant positions (substitutions/insertions/deletions), with a consensus view across reads and a corrected consensus sequence (the reference with every consensus-supported edit applied). Each alignment also reports how much of the READ was used (queryCoveragePct/clippedBases), since identity is measured over the aligned portion only and a partially-used read would otherwise score perfectly. Set circular: true for a plasmid so reads crossing the reference's arbitrary linear start are aligned through the join rather than cut short at it. Also calls STRUCTURAL variants from split alignments — a large deletion, tandem duplication, inversion or backbone rearrangement never appears as a run of mismatches, only as one read aligning at several distant reference positions, so per-base calling reports a perfect clone — and returns a coverage depth profile with the regions no read reached at all, since "never read" is not "correct". On a circular reference one junction cannot always tell an event of length d from one of length referenceLength − d the other way round; where the read's own blocks and the coverage profile settle it they do, and where they do not the call carries an alternateInterpretation with the other reading rather than presenting one as a finding. Set platform (nanopore/pacbio/illumina/sanger) to pick minimap2's preset; the preset used is reported back. Complements verify_construct/verify_assembly: those re-derive what a design SHOULD produce from its own stated inputs; this checks what a real sequencer actually read back.

Start a scratch session that holds several named sequences/values (e.g. vector, insert, forward/reverse primer) for use across multiple tool calls via session_run, instead of re-pasting them into every call. Sessions expire after 24 hours.

Fetch named entries from a session. Prefer session_run for actually USING the values — it keeps raw sequences out of your context. Use this mainly to inspect or debug what a session currently holds.

Run any SeqBench tool, resolving selected arguments from a session's named entries instead of pasting them inline, and optionally store selected result fields back into the session by name. This is the main way to chain a multi-part design (vector + insert + primers) across calls without shuttling raw sequences through your own context.

Add or overwrite named entries in an existing session.

Design siRNA duplexes against an mRNA target using the established Reynolds (2004) 8-criteria score and the Ui-Tei (2004) rules, plus the siDirect seed-duplex Tm off-target flag (≥21.5 °C, computed on siDirect's own RNA/RNA scale: Freier 1986 nearest-neighbor parameters, helix initiation A = −10.8, CT = 100 µM, 100 mM Na⁺). Returns ranked candidates with sense/guide oligos (with UU 3' overhangs) and, per candidate, a ready shRNA cassette (sense–loop–antisense–Pol III terminator). Heuristic sequence rules only — no RNA-folding accessibility model and no transcriptome-wide off-target search. PREDICTED, NOT MEASURED. These are rule counts, not a regression, and no skill statistic is claimed for the ranking. Target-site accessibility is not modeled (no folding) and no transcriptome-wide off-target search is performed, so a top-ranked candidate is a starting point for a knockdown panel, not a predicted knockdown level. Valid for: mRNA targets. The seed Tm is computed on siDirect's own RNA/RNA scale (Freier 1986 parameters, helix initiation A = -10.8, CT = 100 µM, 100 mM Na+) and is not comparable with the DNA/DNA Tm reported elsewhere in the toolkit.

Design site-directed mutagenesis primers (QuikChange overlapping or Q5 back-to-back) for a base substitution, an amino-acid codon swap, or an insertion/deletion/delins. The edit can be given as fields or, more simply, by NAME in `mutation`: "E52K", "p.Glu52Lys", "c.155A>G", "c.76_78del", "c.76_77insGGA", "c.76_78dup". A named mutation is checked against the template — if the reference allele it states is not what is actually at that position, the call is refused and the real base or residue is quoted back, because a coordinate belonging to a different transcript or the other strand yields perfectly well-formed primers for the wrong base. `interpretedAs` in the response says which reading was designed.

Measure the sequence features gene-synthesis vendors screen on — repeats (the single largest cause of synthesis failure), GC extremes, GC swings between adjacent windows, homopolymer runs and hairpin-forming inverted repeats — and report each against the threshold vendors publish. Returns measurements and named flags, never a success probability: refitted on the 303 REAL orders its authors publish (scripts/ssc-eval), the published classifier scores F1 0.866 against 0.878 for assuming every order succeeds, and transfers between ordering labs at AUC 0.423 — below chance. Pair with nonrepetitive_parts_design to fix the repeats it finds.

Diagnose a failing Sanger chromatogram: the longest usable window (Mott trimming), whether there is signal above the noise at all, whether more than one molecule is in the tube and from which base, whether quality collapsed at a homopolymer or tandem repeat the polymerase stuttered through, and whether a dye blob or the instrument's own separation is the problem rather than the DNA. Each finding carries the measurement it is based on, what that pattern is usually caused by, and what to do next. Reads .ab1, .abi and .scf.

Read a chromatogram for what the basecaller did not report: positions where a second dye is present under the called base (a heterozygote, or a contaminating template), and bases still visible in the scan past where base calling stopped. Both are arithmetic on the channel intensities already in the file — a ratio, and a peak position extrapolated from the median spacing — not a model. Returns the called sequence rewritten with IUPAC ambiguity codes, the extra bases, and every threshold that produced the answer. Takes the SCAN arrays, because the tail lies past the last peak location.

Translate a nucleotide sequence to protein (single frame or all six frames; standard code).

One-box variant lookup against MyVariant.info: accepts an rsID, chrom:pos:ref:alt (colon- or hyphen-separated), genomic HGVS ("chr17:g.7676154G>C"), or transcript HGVS c. ("NM_000546.6:c.215C>G" / "TP53:c.215C>G", bridged via the hgvs_convert tool). Returns a ClinVar significance summary, gnomAD exome/genome allele frequencies, and CADD/SIFT/PolyPhen2/REVEL pathogenicity predictor scores — each section explicitly null when that source has no data, never silently omitted. See the result's own "caveats" for real data-freshness limits (frozen gnomAD/CADD snapshots, periodic ClinVar snapshot).

Align a query to a reference and call variants (substitutions, insertions, deletions) in HGVS g. notation, with optional coding effects.

Turn one variant into one buildable plan: verify the reference allele actually sits where the coordinate says, apply the edit, design site-directed mutagenesis primers to install it, design KASP/ARMS allele-specific primers to genotype it afterwards, and consolidate everything into a single oligo order table. Takes either a construct sequence with a 1-based position and ref/alt alleles (offline, deterministic), or an HGVS "c." description resolved through the MANE crosswalk and a live Ensembl exon map. A mismatched reference allele is refused with the bases that were actually found there, because a coordinate that is right for another isoform yields a perfectly valid primer set for the wrong base. Bases shared by both alleles are trimmed first, so a VCF-anchored pair is designed as the substitution or indel it actually is. Mutagenesis covers every class (a substitution, an insertion, a deletion and a multi-base replacement are all one interval replacement); KASP needs a single-base substitution's 3'-terminal base, so for an indel the genotyping half comes back as a named omission with the reason and the readout that does work, never as an empty list.

Return one vector from the library: its GenBank accession and version, length, topology, organism/definition, complete sequence, and the full annotated feature table (type, label, 1-based inclusive start/end, strand, spliced length, and the location descriptor as the record wrote it). Accepts the library id, the vector name, or the accession. An unrecognized id is an error carrying the closest names — never an empty result.

Browse a curated library of publicly deposited, feature-annotated cloning and expression vectors — by name, category (E. coli cloning/expression, yeast, mammalian, plant binary, BAC/fosmid, recombineering, phage/M13), length window, or annotated feature (e.g. 'T7 promoter', 'ori', 'AmpR'). Each hit reports the vector's accession, length, topology and feature count; vector_library_get returns the sequence and the full feature table. A curated public-record set, NOT a vendor catalog — see the gate's notChecked.

Deterministic self-check: given the same method/parts cloning_simulate would use (restriction-ligation, Gibson, Golden Gate, LIC, SLIC or In-Fusion/CPEC — optionally deriving a part by in-silico PCR first), re-derive the expected WHOLE product and diff it against a claimed final sequence. Returns pass/fail plus the exact position and nature of any discrepancy — not an opinion, the same deterministic simulation SeqBench already runs, run a second time as a check. A recipe that can give more than one molecule is checked against ALL of them and `matchedCandidate` names the one the claim matched: a non-directional ligation really does put the insert in both ways round (half the plate carries each), a vector cut more than twice offers more than one backbone, and a Gibson junction whose fragments already share terminal sequence has two honest readings (one homology arm, or a tandem repeat present twice). See verify_construct for a narrower, insert-only check that doesn't require declaring the vector/enzymes/method.

Re-derive a construct's insert from the PCR (template + primers) claimed to have produced it, then check — independently of that claim — whether the expected insert actually appears (either orientation) in the claimed final construct, at what identity, and with exact mismatch positions if not. Optionally also checks for a premature stop in a declared reading frame. Primers may carry a non-templated 5' tail (a restriction site, a Gibson arm, a tag): a construct missing ONLY tail bases still passes, since that is exactly what digesting a tailed amplicon removes before ligation — see match.templateCoveragePct and match.unalignedIsTailOnly, and note the pass does not establish that the right enzyme made the cut. This re-derives from the claim's own stated inputs; it does not review the claim's prose.

Predict restriction-digest fragment sizes and their gel migration positions against a chosen DNA ladder.

Validate a differential-expression table (gene, log2 fold-change, p-value/FDR) and compute -log10(p) plus up/down/non-significant counts at conventional default thresholds (|log2FC|>=1, p<=0.05), for the Volcano Plot visualization. Invalid rows (non-finite log2FC, or p-value outside (0,1]) are dropped and reported rather than failing the whole batch.

Search the live web (via Tavily) for information not covered by SeqBench's own tools — recent literature, protocols, vendor/reagent info, general facts. Returns a short synthesized answer (if available) plus ranked source snippets with URLs. This does not run any bioinformatics calculation itself; use the dedicated tools for that.

Run an ordered multi-tool pipeline over many records. One sequence value can be chained between steps.