「eq」の検索結果

Validates docked / generated protein-ligand poses using PoseBusters physical-validity tests, strain energy quantification, geometric checks (planarity, vdW overlap, bond/angle distortion), and pose-energy reasonableness. Filters AI-docking outputs (DiffDock, EquiBind, NeuralPLexer) where ~50% of poses fail physical-validity tests. Use when QC-ing docking results, comparing classical vs ML docking outputs, or filtering pose lists before SAR analysis.

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Performs ML-based protein-ligand pose prediction and scoring using DiffDock-L (diffusion-based), Boltz-1 / Boltz-2 (foundation model with affinity), Chai-1, AlphaFold3 ligand, EquiBind, TANKBind, NeuralPLexer, and hybrid workflows (DiffDock pose + GNINA rescore + PoseBusters QC). Explicit handling of when ML beats classical docking, when classical beats ML, the PB-invalid pose problem, and rescoring as the standard production hybrid. Use when modern docking is needed: foundation-model ligand-pose prediction, AI rescoring of classical poses, or scaffold-hopping in cross-docking scenarios.

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Performs gene-level association from GWAS summary statistics via genetically predicted tissue expression using FUSION, PrediXcan, S-PrediXcan, S-MultiXcan, UTMOST, MOSTWAS, kTWAS, EpiXcan, TIGAR-V2, and probabilistic fine-mapping with FOCUS and MA-FOCUS. Use when running TWAS from GWAS sumstats, prioritising candidate causal genes from a GWAS lead locus, picking single-tissue vs cross-tissue models, identifying LD-induced TWAS false positives, choosing ancestry-matched prediction weights, fine-mapping co-regulated TWAS hits, or triangulating TWAS with cis-eQTL Mendelian randomization and colocalization to nominate a causal gene.

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Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO. Supports Clustal, PHYLIP, Stockholm, FASTA, Nexus, and other alignment formats for phylogenetics and conservation analysis. Use when reading, writing, or converting alignment file formats.

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Trim multiple sequence alignments using ClipKIT, trimAl, BMGE, Divvier, or HMMcleaner with mode selection guidance per downstream goal. Use when removing unreliable columns or contaminating residues before phylogenetic inference, HMM building, or selection analysis.

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Detects differential alternative splicing between conditions using rMATS-turbo (binomial LRT on junction counts), leafcutter (Dirichlet-multinomial GLM on intron clusters), MAJIQ V3 deltapsi/HET (Bayesian posterior on LSVs), SUPPA2 (empirical-null on TPM-derived PSI), or Shiba (junction-imbalance-corrected, 2025 SOTA at low coverage). Reports FDR-corrected significance and delta PSI effect sizes. Tools differ in statistical model, annotation dependence, calibration regime, and replicate-count requirements. Use when comparing splicing patterns between treatment groups, tissues, or disease states.

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Detects aberrant splicing in single rare-disease patients vs a control panel using FRASER 2.0 (Bioconductor; Beta-binomial autoencoder on Intron Jaccard Index, default delta cutoff 0.1, q hyperparameter), OUTRIDER (gene-level outlier expression via autoencoder denoising), LeafcutterMD (Dirichlet-multinomial outlier mode of LeafCutter for annotation-free junctions), and DROP (Snakemake pipeline integrating FRASER2 + OUTRIDER + monoallelic expression for clinical diagnostics). The statistical model is fundamentally different from differential splicing — single-sample-vs-cohort outlier detection rather than two-group comparison. Standard tool in EU rare-disease (Solve-RD) and NIH UDN programs. Use when applying RNA-seq to undiagnosed Mendelian disease, validating predicted splice variants in clinical samples, or detecting cryptic splicing in disease tissue.

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Use when users need bioinformatics analysis on the NCPGR cluster, especially when converting analysis requests into local software choices, resource plans, and LSF job scripts.

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Predicts whether a DNA variant alters mRNA splicing using sequence-based deep-learning tools — SpliceAI (10kb context dilated CNN, clinical default), Pangolin (multi-tissue), MMSplice (modular per-region CNN with calibrated ΔPSI), SpliceTransformer/TrASPr (tissue-aware transformers), SpliceVault (empirical 300K-RNA lookup of likely mis-splicing outcomes), CADD-Splice (composite score). Applies the ClinGen SVI 2023 framework for ACMG/AMP variant interpretation (PVS1, PP3, BP4 evidence codes), HGVS splicing nomenclature (c.123+1G>A, c.123-3T>G, r.spl?), extended-window scoring for deep-intronic pseudoexons, tissue-specific predictions, branchpoint variant detection (BPHunter, LaBranchoR), and splice-switching ASO design. Use when interpreting splice impact of clinical variants, prioritizing VUS, identifying deep-intronic pathogenic variants, or designing ASOs.

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Creates sashimi-style plots showing RNA-seq read coverage and splice junction counts using ggsashimi (general-purpose, condition-grouped overlays), rmats2sashimiplot (rMATS-output-aware), MAJIQ-VOILA (LSV posteriors interactive HTML), leafviz (leafcutter clusters Shiny), Jutils (tool-agnostic heatmaps and sashimi for rMATS/leafcutter/SUPPA2/MAJIQ output), or pyGenomeTracks (multi-track publication figures). Tool choice depends on the upstream differential-splicing tool's output format and the publication vs interactive use case. Use when visualizing specific splicing events, validating differential splicing calls, or producing publication-quality figures.

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Test whether two or more traits share a causal variant at a locus using Bayesian colocalization (coloc.abf, coloc.susie, HyPrColoc, moloc, eCAVIAR, SMR/HEIDI, PWCoCo, SharePro). Use when integrating GWAS with eQTL/sQTL/pQTL/mQTL, distinguishing shared causal variants from LD-driven coincidence, handling allelic heterogeneity, choosing between single-causal vs multi-causal methods, picking PP.H4 thresholds, running sensitivity over p12, or harmonising summary statistics for colocalization.

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Generate pileup data for variant calling using samtools mpileup and pysam. Use when preparing data for variant calling, analyzing per-position read data, or calculating allele frequencies.

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Use this skill to understand trailer-specific inspection requirements when a trailer is not coupled to a tractor. Covers annual inspection, DVIR rules, and parking inspections.

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Use this skill when the user asks about oversize / overweight load permits — state-by-state permitting process, escort vehicle (pilot car) requirements, route surveys, federal axle weight limits (Federal Bridge Formula), holiday restrictions, multi-state permits, and how to determine if a load needs a permit. Reference state DOT permitting offices.

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Use when an owner-operator (1099 or LLC/S-corp) asks how to lower their tax bill, what they can deduct, per diem treatment, depreciation choices (Section 179, bonus, MACRS), quarterly estimated payments, S-corp salary vs distributions, retirement plan options (SEP-IRA, Solo 401(k)), or recordkeeping requirements. Covers federal income tax + self-employment tax basics; not state-specific.

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Use this skill when the user asks about workers compensation for trucking — required by state law, classification of drivers, premium calculation (rate × experience modifier × payroll), Common Claim types in trucking, returning injured drivers to work, claims management, premium reduction strategies, and the difference between WC and Occupational Accident insurance for owner-operators. Reference state WC laws + IFTA / fleet insurance.

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Use this skill to understand record retention requirements for currently-employed drivers. Covers DQF, HOS, vehicle records, and D&A specific periods.

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Use this skill when the user asks about passenger-carrying CMVs — buses, motorcoaches, charter operations, school buses, limousines, shuttle services. Covers different HOS rules (10-hour driving / 15-hour on-duty / no 34-restart), Class C CDL with P endorsement, S endorsement for school bus, FMCSA Hours-of-Service for passenger vehicles, $5M insurance minimum, BMC-32 filing, accessibility requirements, and how passenger ops differ from property. Cite 49 CFR 395.5 + 387.33.

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Use when a carrier or driver asks about winter trucking, snow chain requirements, hot-weather precautions, summer tire failures, fuel additive use (anti-gel), idle management in extreme temperatures, weight restrictions in spring thaw, or how seasonal demand patterns affect freight rates and capacity.

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Use this skill when the user asks about refrigerated (reefer) trucking — cold chain compliance, FDA temperature recording requirements, Sanitary Transportation Rule (STR / 21 CFR 1.900-1.934), commodity-specific temperature ranges, refrigeration unit operation, temperature recorder calibration, and how a load is challenged if temperature deviates. Cite 21 CFR Part 1 Subpart Q.

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Use this skill for vehicle brake system inspection requirements. Covers daily DVIR, weekly maintenance, brake adjustment check, and the 'percent of out-of-adjustment' rule.

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Use this skill when the user asks how to design or improve a Preventive Maintenance (PM) program for a fleet — mileage vs time intervals, A/B/C/D inspection levels, ATA standards, common PM tasks, scheduling software, cost tracking, and how PM affects roadside inspections + CSA scores. Reference ATA Equipment Maintenance Council standards.

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Use this skill when the user asks about CMV maintenance program requirements under 49 CFR 396.3 — what records must be kept, retention periods (12-18 months on vehicle file + 1 year after disposal), what counts as systematic inspection/repair/maintenance, preventive maintenance scheduling, brake adjustment records, and what auditors look for in vehicle files. Cite 49 CFR 396.3 + 396.21.

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Use this skill when the user asks about CMV oilfield operations — the 24-hour restart exemption (49 CFR 395.1(d)), oilfield-specific HOS rules, hot oil hauling, specialty equipment (winch trucks, kill trucks, frac sand haulers, water haulers), Texas Railroad Commission permitting, oilfield insurance specifics, and how oilfield operations differ from standard OTR trucking. Cite 49 CFR 395.1(d).

日本語の概要は準備中です。原文の説明を表示しています。