「upstream」の検索結果

Review data retention, deletion, backup residency, and cross-border data transfer.

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。

Ensure data model/DB designs cover constraints, integrity, indexes, migrations, rollback, and operational impacts.

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。

Ensure designs define data flow, state ownership, consistency boundaries, and cross-boundary writes to prevent drift and incidents.

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。

新規・変更 skill の fixture/eval と、WITH/WITHOUT の paired ablation による限界寄与の評価可能性を確認し、activation と effectiveness を分離して案内する。

Ensure architecture diagrams are readable, consistent with text, and clear on scope, boundaries, and data flow.

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。

Detect missing validation plans (how to verify the design is correct) in design documents and ADRs.

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。

Ensure design changes stay consistent across ADRs, diagrams, and specs; decisions are traceable; and drift is explicitly managed.

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。

Ensure API/contract changes specify versioning strategy, backward compatibility, deprecation plan, and migration guidance.

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。

Ensure architecture/design docs define clear boundaries, ownership, dependency direction, and change impact to avoid tight coupling.

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。

Detect undefined or inconsistent cache strategies (layers, consistency, invalidation, TTL, failure handling) in design documents.

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。

Ensure architecture docs capture availability targets, failover strategy, capacity headroom, and resilience trade-offs for critical services.

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。

Ensure design docs explicitly capture risks, assumptions, and open questions with owners, deadlines, and mitigation plans.

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。

Ensure architecture/design docs state traffic assumptions, performance budgets, resource limits, and cost risks for critical paths.

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。

Ensure API design follows RESTful naming and consistent conventions.

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。

Checks whether AI-assisted work defines review criteria, accessible context, explicit review loop, human judgment boundary, and feedback capture before delegating to an agent.

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。

Ensure ADRs capture context, decision, alternatives, tradeoffs, and follow-ups in a way that prevents future drift.

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。

Ensure architecture updates document affected stakeholders, notification plan, and deprecation/retirement signals to keep knowledge aligned.

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。

Ensure architecture docs define bounded contexts, ownership, and a consistent ubiquitous language to prevent domain drift and coupling.

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。

Parses, queries, converts, and extracts from GTF and GFF3 gene-model annotation files - walking the gene/transcript/exon/CDS hierarchy with gffutils (queryable SQLite DB), converting formats and extracting transcript/CDS/protein FASTA with gffread, slurping to dataframes with gtfparse/pyranges, and sanitizing malformed files with AGAT. Covers the 1-based-inclusive vs 0-based BED coordinate conversion (start-1 only), deriving implicit features (introns/UTRs/TSS), phase-not-frame, the stop-codon-in-or-out-of-CDS convention, and the chr1-vs-1 seqid and gene-ID-version mismatches that silently produce all-zero count matrices and dropped joins. Use when extracting features or sequences from an annotation, converting GTF<->GFF3 or GTF->BED, traversing the gene tree, or diagnosing a coordinate/provenance mismatch upstream of counting or DE.

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。

Detects m6A modifications from Oxford Nanopore direct-RNA-sequencing (ONT DRS) signal data using m6Anet (Hendra 2022 *Nat Methods* 19:1590; multiple-instance-learning neural network over DRACH 5-mer signal). Covers the required upstream pipeline (Dorado / Guppy basecalling -> minimap2 transcriptome alignment with `-ax map-ont -uf -k14 --secondary=no` -> nanopolish eventalign with `--scale-events --signal-index` (m6Anet-required) plus `--summary` / `--threads` housekeeping -> m6anet dataprep -> m6anet inference), per-site vs per-read probability interpretation including the `mod_ratio` per-site stoichiometry column, the DRACH-only modeling constraint, minimum-coverage thresholds (20-50 reads per site for stable probability estimates), multi-condition comparison via xPore (Pratanwanich 2021 *Nat Biotechnol* 39:1394), Nanocompore (Leger 2021 *Nat Commun* 12:7198), ELIGOS (Jenjaroenpun 2021 *NAR* 49:e7), and Dorado native modification calling (RNA004 chemistry, 2024+), reference-transcriptome version pinning, the cDNA-vs-DRS chemistry distinction (cDNA-Nanopore CANNOT be used for modification detection), and orthogonal validation against MeRIP / GLORI. Use when calling m6A from ONT DRS without immunoprecipitation, choosing m6Anet vs xPore vs Nanocompore vs ELIGOS vs Dorado native, interpreting per-site `probability_modified` vs `mod_ratio` vs per-read modification probabilities, comparing methylation between conditions from ONT data, deciding between m6Anet for known DRACH sites and Dorado/Remora for genome-wide screening, pinning RNA002 vs RNA004 chemistry and basecaller model versions, or troubleshooting eventalign / dataprep failures.

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。

Pull pre-computed ortholog calls from public databases (OrthoDB, Ensembl Compara, OMA browser, eggNOG, PANTHER, KEGG Orthology, HomoloGene) via their REST APIs. Use when orthologs are already curated upstream, when the question is "what is the X ortholog of Y" rather than "how to infer orthology de novo", when batch-mapping gene IDs across species, or when comparing the resources for consensus calls. Encodes confidence-level semantics, 1:1 vs 1:many vs many:many, HomoloGene deprecation, and when to defect to de novo computation.

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。

Detects allele-specific transcription factor or histone modification binding from heterozygous-variant ChIP-seq using WASP (reference-bias filter; mandatory upstream), RASQUAL (joint QTL + bias-corrected testing), BaalChIP (Bayesian beta-binomial with copy-number-aware overdispersion), and AlleleSeq (personalized diploid genome). Handles imprinted-locus awareness, X-inactivation artifacts, cancer copy-number imbalance, and integration with downstream caQTL / bQTL mapping. Use when identifying variants with allelic effects on TF binding, fine-mapping causal regulatory variants, validating deep-learning variant predictions, or characterizing cis-acting regulatory effects.

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。

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.

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。

Integrate Convex static self hosting into existing apps using the latest upstream instructions from get-convex/self-hosting every time. Use when setting up upload APIs, HTTP routes, deployment scripts, migration from external hosting, or troubleshooting static deploy issues across React, Vite, Next.js, and other frontends.

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。