Jean — Molecular Biology & Genomics

SALI activity cliffs (Guha & Van Drie 2008) in a list of small molecules with one activity each: every pair scored as the activity difference over one minus the Tanimoto similarity of RDKit path fingerprints, steepest first; identical fingerprints with different activities score Infinity.

Put a drawing that is on screen into the lab notebook as a numbered figure: a chart, gel, map or any other picture a tool drew on the stage.

Replace a Sanger read's own base calls with TraceTuner's, on the trace that is open, so the rest of the editor works from the re-called read: its sequence, its consensus, its verification against a reference, and every export.

Read an alignment window and say how well its reads agree with the reference underneath them, the way the pileup's own bar does: the compared and differing base counts, plus the RefSeq gene models and known dbSNP variants over the same window.

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.

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.

Read one window of a BAM or SAM in this project: its reads, the depth across it, and what the pairs say about the library — the same window, filters and record cap the pileup draws.

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

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.

A flow cytometry list-mode file (FCS 2.0, 3.0 or 3.1) from the project, end to end: read and linearize every event ($PnE anti-log, $PnG gain, $TIMESTEP), compensate it with the spillover matrix the file declares, apply a Gating-ML 2.0 gating strategy from the project, and report every population with its event count, percent of parent and percent of all events, the mean, median, SD and CV of every channel in every population, a binned two-channel density for a plot, and a density-valley threshold on any channel (flowDensity deGate).

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

Say what a genomic region holds in the reference tracks: the genes and RefSeq transcripts it overlaps, which complete coding sequences it CONTAINS (the only case where a residue-level design is honest), the DGV structural variants seen there in control populations with their frequencies, and how much of it lies in segmental duplications and how similar the best paralogue is.

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.

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).

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.

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.

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).

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).

Put the features from a GTF in this project onto the open record. A GTF lists features and their positions and carries no sequence, so this is how one reaches a molecule.

Substitute one called ALT allele into a sequence and return the edited bases, the range the edit occupies and the length change.

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.

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

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.

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

Assess a small molecule given as SMILES or a molfile: RDKit's descriptors, medchem's drug-likeness rules (Lipinski, Veber, Ghose, Egan, REOS, rule of three, CNS and more) and the papers' own readings where medchem departs from them, with every bound and this molecule's value, a structural-alert screen (PAINS, Brenk, NIH, ZINC and ChEMBL's seven curated sets) with the matched atoms, the molecular formula with its masses and natural-abundance isotope pattern, QED drug-likeness under all three weightings, ESOL aqueous solubility, synthetic accessibility and natural-product likeness, the Bemis–Murcko scaffold and generic framework, the BOILED-Egg's gut-absorption and brain-penetration verdicts, and per-atom logP, polar-surface and charge contributions.

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.

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.

Find and name known features on the subject — vector parts, origins, resistance markers and tags — by exact match, by local alignment (which finds a diverged copy and reports the identity and coverage that earned it), or by both, on either strand and across the origin, plus Cre/loxP and Gateway att recognition sites.

openCyto 2.24's automated gates on two channels of an FCS file: a cut on each axis (gate_mindensity, the minimum of the KDE between its two highest peaks; gate_mindensity2, features of a smoothing spline over the KDE; or gate_quantile), the sequential quadrant gate (gate_quad_sequential), and the singlet gate on area × height (gate_singlet, a Huber regression and its prediction band).

Protein → DNA: the coding sequence that would encode a protein, weighted by a real codon-usage table.

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.

Search a sequence against THIS PROJECT'S OWN records with blastn or blastp, on this machine: the local database is built from every project record of the program's alphabet, and every E-value is computed from that database's own size.

Search a sequence against NCBI BLAST+ at EMBL-EBI (blastn, blastp, blastx, tblastn or tblastx) over ENA, UniProtKB, PDB and the vector-screening sets.

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.

Read one window of a genome the way a genome browser shows it: the cytogenetic bands it spans, the transcripts in the chosen RefSeq set, which residues of each protein the window encodes (translated from the assembly’s own bases), how many dbSNP 155 variants fall in it and how many ClinVar calls pathogenic, and every CRISPR site for the chosen nuclease (SpCas9 unless told otherwise) with its strand, cut position and on-target scores.

Fill Addgene's own deposit spreadsheet from a record: one row per plasmid with its name and sequence, plus your answers for every plasmid (purpose, plasmid type, cloning method, resistance, growth strain and the other drop-downs).

Build DnaCauldron's assembly report from an assembly result: the per-construct summary table, the constructs and errors spreadsheets as pandas-compatible CSV, and the Outcome / Number of assemblies stats.

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

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.

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.

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.

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.

Call the bases of a Sanger trace from the signal, discarding the instrument's calls and deriving the bases, their positions and their quality from the peaks.

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.

Call the bases of a Sanger trace AND ask at every position whether a second allele is present, writing a two-base IUB code (R, Y, S, W, K, M) where it is.

Withdraw a research run's pending hypotheses (all of them, or the ones named) so no resume runs them, and return the sealed run file.

A small molecule's tautomers and the canonical one, as RDKit's TautomerEnumerator finds and scores them: 37 hydrogen-shift transforms (after MolVS and Sitzmann 2010), scored by aromatic rings, twelve weighted groups and hydrogens on P, S, Se and Te, ties to the smaller SMILES.

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.

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.

Acquisition quality over time for an FCS file, by flowAI (flow rate, signal acquisition and dynamic range checks), PeacoQC (peak trajectories, isolation tree and MAD) or flowCut (deviant and low-density segments).

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.

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.

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

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.

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

Check primer pairs for products off their template, the way NCBI Primer-BLAST does, and rank them by it: each distinct primer is searched against a sequence database at EMBL-EBI with short-query BLAST, hits are paired into products by Primer-BLAST's own mismatch rule, and the template is searched too so the record identical to it is recognized as the intended target.

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).

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.

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.