「vin」の検索結果

Analyzes CRISPR drug-modifier (chemogenomic) screens with drugZ (Li & Hart 2019 Genome Med), a bidirectional Z-score method that identifies synthetic-lethal sensitizing genes and resistance-conferring suppressor genes from vehicle vs drug comparisons. Covers vehicle-anchored design (not Day-0), the bidirectional Z math giving 2-3x sensitivity over MAGeCK / STARS / edgeR / RIGER on drug screens, per-gene sumZ and normZ, synth (sensitizer) vs supp (suppressor) FDR, multi-dose handling, integration with control sgRNAs, and comparison with MAGeCK MLE with dose covariate. Use when running a drug-modifier CRISPR screen, identifying sensitizing or resistance genes for a drug candidate, choosing drugZ vs MAGeCK MLE for chemogenomic analysis, troubleshooting low-effect drug screens where MAGeCK lacks sensitivity, or designing a drug-screen layout (vehicle vs drug arms).

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。

Preprocess CLIP-seq reads (eCLIP, iCLIP, iCLIP2, iCLIP3, irCLIP, PAR-CLIP, FLASH) with protocol-specific UMI extraction, adapter trimming, length filtering, and post-alignment PCR-duplicate collapse. Use when raw CLIP FASTQ must be turned into deduplicated, crosslink-preserving BAM input for peak calling; choosing between two-pass and single-pass adapter trimming; deciding minimum read length; or mapping UMI patterns to specific eCLIP/iCLIP/iCLIP2/iCLIP3 library preps.

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。

Quantify homologous recombination deficiency (HRD) from tumor copy number using the three genomic-scar metrics — loss of heterozygosity (LOH), large-scale state transitions (LST), and telomeric allelic imbalance (TAI) — with scarHRD, and via the whole-genome HRDetect and CHORD models. Covers the genomic instability score, the PARP-inhibitor clinical context, whole-genome-doubling correction, and the scar-versus-state distinction. Use when computing an HRD score for PARP-inhibitor eligibility, deriving LOH/LST/TAI scars from allele-specific copy number, deciding between scar-based and mutational-signature HRD methods, or interpreting an HRD result in a BRCA-reverted or low-purity tumor.

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。

Infer integer allele-specific copy number, tumor purity, and ploidy from tumor sequencing by jointly modeling read depth (logR) and B-allele frequency (BAF) with ASCAT, Sequenza, FACETS, PURPLE, and PureCN (tumor-only). Covers the purity-ploidy identifiability problem, the diploid-baseline (dipLogR) anchor, major/minor copy number, loss of heterozygosity, sunrise/contour fit diagnostics, and reconciliation of conflicting fits. Use when tumor analysis needs absolute copy number rather than relative log2, when estimating purity and ploidy, calling LOH or copy-neutral LOH, resolving whole-genome doubling, running tumor-only allele-specific calling, or choosing among ASCAT, Sequenza, FACETS, and PureCN.

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。

Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.

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Performs structure-based virtual screening using AutoDock Vina, SMINA, GNINA (CNN scoring), and DiffDock-L hybrid workflows with explicit choice rules across rigid vs flexible docking, cross-docking vs self-docking, binding-site detection (P2Rank, fpocket), receptor preparation (PDB2PQR, PROPKA), ligand preparation (meeko, OpenBabel), and ultralarge-library screening (ZINC22, Enamine REAL). Use when screening chemical libraries against a protein target to find candidate binders, ranking docking poses, or selecting a docking workflow for a specific scenario.

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Enumerates virtual chemical libraries via reaction SMARTS transformations using RDKit and Reaction templates, with explicit handling of atom mapping, template extraction (RDKit reaction mining), product validation, RECAP/BRICS fragmentation, R-group decomposition, matched molecular pair analysis (MMPA), and Free-Wilson analysis. Use when generating combinatorial libraries from building blocks, enumerating analog series, deriving structure-activity rules, or extracting transformations from reaction data.

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。

Analyzes chemical libraries by scaffold using Bemis-Murcko scaffolds, generic frameworks, cyclic skeletons, matched molecular pair (MMP) analysis via mmpdb, R-group decomposition, Free-Wilson analysis, scaffold hopping, and chemotype-aware ML train/test splits. Use when identifying chemotype clusters in a library, deriving SAR transformation rules, decomposing series into R-groups, performing scaffold-balanced QSAR splits, or planning analog campaigns.

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Designs covalent inhibitors and warheads targeting cysteine (most common, 98% of covalent drugs), lysine, serine, threonine, tyrosine, and aspartate residues, with explicit handling of warhead reactivity (acrylamide, chloroacetamide, vinyl sulfone, sulfonyl fluoride, fluorosulfate, aldehyde, boronate, nitrile), reversibility (kinact/Ki, t_residence), glutathione (GSH) stability, intrinsic reactivity assays, and covalent docking (DOCKovalent, GOLD, HCovDock). Use when designing covalent inhibitors for targeted covalent inhibition (TCI), KRAS G12C-style approaches, or rationalizing covalent SAR.

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Call accessible chromatin regions from ATAC-seq BAM files using MACS3, MACS2, Genrich, or HMMRATAC. Use when identifying open chromatin from aligned ATAC-seq, choosing between point-source vs HMM peak callers, applying ENCODE-style pseudoreplicate IDR, removing blacklist regions, or fixing 501bp consensus peaks for downstream differential analysis.

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。

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.

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。

Filter alignments by flags, mapping quality, and regions using samtools view and pysam. Use when extracting specific reads, removing low-quality alignments, or subsetting to target regions.

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。

Use this skill when the user asks how to build + manage direct shipper relationships — moving from spot market to dedicated lanes, customer scoring + credit assessment, contract terms, dedicated capacity agreements, KPI reporting, and how to handle customer disputes. Reference standard customer-carrier contract terms.

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Use this skill to evaluate which defensive driving program is best for your fleet. Compares Smith System, CarriersEdge, Compass-native training, and other options.

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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 this skill when the user asks about weigh-station bypass services — Drivewyze, PrePass, BestPass, NORPASS, OOIDA toll savings — how they work, eligibility criteria, costs, and how they tie into CSA scores. Reference each provider's coverage area + pricing.

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。

Use when a carrier asks how to improve MPG, lower fuel cost per mile, manage fuel surcharge, set up a fuel-purchase strategy (in-network vs out-of-network), train drivers on fuel-efficient driving, spec new tractors for fuel economy, or benchmark fleet MPG against industry averages.

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Use this skill when a driver needs to move a CMV within a yard, terminal, or property under § 395.8 yard-move provisions. Covers what counts as yard move, ELD configuration, and where yard move ends and on-duty driving begins.

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Use when a driver has been disqualified or is at risk of disqualification — major offense convictions (DUI, leaving the scene, drug trafficking with CMV), serious traffic violations, RR-grade-crossing violations, imminent-hazard out-of-service, or Clearinghouse positive-test prohibited status. Also use when planning a return-to-duty pathway, SAP process, or reinstatement timeline. Covers 49 CFR 383.51, Part 40 Subpart O, and Part 391.15.

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Use this skill when a driver needs to edit their own ELD record. Covers what edits are allowed, what edits trigger annotations, the unassigned-driving-time procedure, and when carriers must accept or reject driver edits.

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。

Use this skill when an insulin-dependent diabetic driver needs commercial driving certification. Covers the FMCSA insulin variance program, application steps, and continued monitoring.

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。

Use this skill when a driver wants to use a CMV for personal travel after duty hours. Covers what qualifies as legitimate personal conveyance vs. work-related driving, FMCSA guidance on lodging/meals/home travel, and documentation requirements.

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。

Use when a carrier (asset-based, brokerage, or 3PL) asks how to respond to a shipper RFP / RFQ — bid packaging, lane analysis, rate strategy, SOPs/SOWs, qualifications response, insurance compliance, EDI requirements, and how to win competitive bids without leaving margin on the table.

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Turn a large dataset (an existing oversized Coval test set, an export of past conversations, or a CSV/JSON of cases) into a small, high-signal Coval test set by removing duplicates, identifying unique scenarios, and selecting a representative, failure-weighted subset — then bulk-loading it with no row cap. Use when the user says "I have thousands of cases", "dedupe my test set", "my test set is too big", "turn this dataset into a test set", "pick representative scenarios", or "my CSV import only kept 10 / uploaded everything".

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。