325 件 ・ 関連度順
概要と使いどころ
AI-agent-powered lead enrichment using Sixtyfour as primary source. Takes an email (+ optional name) and returns comprehensive person + company data with funding, AI/B2B classification, and full error visibility. Higher cost (~$0.20/lead) but simpler architecture.
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。
Find emails, phone numbers, and enrich profiles using ContactOut. LinkedIn enrichment, people search, company enrichment, and batch operations.
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。
Intelligence and enrichment - person and company data, social profiles, events, and funding
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。
Multi-provider waterfall lead enrichment. Takes an email (+ optional name) and returns person + company data by cross-referencing cheap APIs first, using expensive AI agents only as fallback. Cost-efficient (~$0.04-$0.10/lead) with confidence scoring and full error visibility.
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。
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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。
Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.
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。
Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CA-CTCF/CA-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.
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。
Discovers de novo motifs and tests known motif enrichment in ChIP-seq, ATAC-seq, or other peak sequences using HOMER, MEME-ChIP (STREME, CentriMo, TOMTOM, FIMO), monaLisa, and AME. Handles background selection (GC-matched, dinucleotide-shuffled, Markov order-2, peak-flanks), motif databases (JASPAR 2024 CORE PWMs, JASPAR 2026 deep-learning collection, HOCOMOCO v12, HOMER built-in), centrally-enriched motif testing, and differential motif analysis. Use when identifying TF binding motifs in peaks, testing for known TF enrichment, scanning for motif instances, comparing motif content between conditions, or interpreting motifs from deep learning models.
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。
Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.
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。
MEM, Marker Enrichment Modeling, automatically generates and displays quantitative labels for cell populations that have been identified from single-cell data. The input for MEM is a dataset that has pre-clustered or pre-gated populations with cells in rows and features in columns. Labels convey a list of measured features and the features' levels of relative enrichment on each population. MEM can be applied to a wide variety of data types and can compare between MEM labels from flow cytometry,
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。
GSEA and over-representation analysis (ORA) for RNA-seq and proteomics. Wraps Enrichr for ORA against MSigDB, KEGG, GO, and 200+ databases; runs preranked GSEA on ranked DE gene lists. Outputs enrichment tables and running-score plots. Use after DESeq2 or edgeR for pathway-level interpretation.
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。
Guide to KEGG pathway enrichment for DEG results. Covers ORA vs GSEA, mandatory directionality splitting, KEGG organism codes, API failure handling with offline fallbacks, cross-condition comparisons, and answer-first reporting. Consult when running enrichment with clusterProfiler or gseapy.
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。
Enrich CRM records with firmographic and contact data, filling gaps in company and person profiles to improve segmentation, routing, and outreach quality. Use when the user requests crm data enrichment or provides relevant inputs for this workflow.
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。
Enrich any entity — companies, domains, emails, or people — with verified data from multiple sources. Combines Apollo, Strale, LinkedIn, and Akta data to fill gaps in your CRM, lead databases, or research datasets with production-quality enrichment.
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。
Visualize enrichment results using enrichplot package functions. Use when creating publication-quality figures from clusterProfiler results. Covers dotplot, barplot, cnetplot, emapplot, gseaplot2, ridgeplot, and treeplot.
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。
Fix nodes silently disappearing from RFDB when an enricher or post-resolution pass writes new nodes via BatchHandle. Use when: (1) writing a TypeScript enricher that should ADD nodes/edges to existing files, (2) after calling `client.createBatch()` + `batch.addNode({file: 'X', ...})` + `batch.commit()` the original nodes in file X have vanished, (3) tests that load a graph fixture, run an enricher, then query original nodes get empty results, (4) a verification step shows expected nodes pre-enrichment but 0 post-enrichment. Root cause: `BatchHandle.commit()` invokes RFDB's `commit_batch` which AUTO-POPULATES `changed_files` from each added node's `file` field, then the server DELETES all existing nodes whose `file` matches before inserting new ones — file-level upsert semantics. This is correct for the JS/Haskell analyzers (which fully re-emit a file's contents) but catastrophic for enrichers that only ADD to existing files.
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。
Annotate genetic variants (GWAS hits, eQTLs, rare variants) with ENCODE functional data to interpret non-coding variation. Use when the user has variants of interest and wants to understand their regulatory context, identify causal variants from GWAS loci, assess variant impact on regulatory elements, perform enrichment testing of variant sets in tissue-specific annotations, or link variants to target genes through enhancer-gene maps. Handles the full post-GWAS workflow from variant set → tissue mapping → functional annotation → fine-mapping awareness → enrichment → variant-to-gene → prioritization. Use this skill for ANY variant interpretation task involving ENCODE chromatin, accessibility, TF binding, or 3D genome 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。
Guide for annotating ENCODE peaks with genomic features using ChIPseeker and GREAT. Use when users need to assign peaks to genes, determine genomic feature distribution (promoter, intron, intergenic), or perform gene ontology enrichment of peak-associated genes. Trigger on: peak annotation, ChIPseeker, GREAT, peak to gene, genomic feature, promoter enrichment, gene ontology, peak distribution, TSS distance, nearest gene.
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。
Guide for de novo and known motif enrichment analysis of ENCODE ChIP-seq and ATAC-seq peaks using HOMER and MEME Suite. Use when users need to discover TF binding motifs in peaks, validate ChIP-seq targets, or find co-binding partners. Trigger on: motif analysis, HOMER, MEME, de novo motif, motif enrichment, findMotifsGenome, AME, MEME-ChIP, known motif, TF binding motif, co-factor, motif discovery.
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 the Skmtc CLI to scaffold projects, install or clone generators from JSR, configure schema sources and enrichments, and produce code artifacts from an OpenAPI v3 or GraphQL SDL schema. Teaches the workspace mental model (`<root>/.skmtc/<project>/`, client.json, bundle, manifest) and the agent contract (strict text / strict JSON modes, exit codes, recipe errors, `agent-context` + `doctor`); the command surface itself is discovered from the binary — `skmtc --help`, `skmtc <cmd> -h` — rather than carried in this skill. Use this skill when the user asks to "run skmtc", "generate code from an OpenAPI schema", "install a skmtc generator", "scaffold a skmtc project", "watch a skmtc project", "configure enrichments", "publish a stack", "deploy to skmtc-hub" (the command is `publish`; there is no `deploy`), "skmtc in CI", or invokes any CLI subcommand. For *authoring* a generator package (Projections, Snippets, transform functions), defer to `skmtc-generator`. When something is broken (no output, wrong output, error messages, a failed bundle build), verify before proposing a fix: read the manifest and the parse issues, and reproduce the failure first.
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。
Aligns and QCs methylated-RNA-immunoprecipitation (MeRIP / m6A-seq) IP and input libraries using STAR or HISAT2 splice-aware mapping, samtools sort/index, IP/input matched-pair tracking, antibody-lot metadata recording, replicate concordance via deepTools multiBamSummary + plotCorrelation, IP enrichment QC via plotFingerprint and per-transcript IP/input ratio distributions, library-complexity saturation curves via PreSeq, and the explicit do-NOT-deduplicate convention for standard non-UMI MeRIP. Use when preparing paired IP and input BAM files for exomePeak2 / MeTPeak / MACS3 peak calling, evaluating MeRIP replicate concordance and IP enrichment, deciding whether to deduplicate (standard MeRIP typically NOT), choosing genome-vs-transcriptome alignment for downstream peak vs m6Anet workflows, recording antibody clone and lot metadata for cross-batch reconciliation, detecting failed IPs via saturation curves and IP/input distribution shape, or generating IP-over-Input bigWig tracks for visualisation.
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。
Calls m6A peaks from MeRIP-seq / m6A-seq paired IP-vs-input data using exomePeak2 (transcript-aware, GC-bias-corrected Poisson GLM), MeTPeak (HMM over sliding windows), MACS3/MACS2 with --nomodel --broad --keep-dup all (genome-wide broad alternative), and DRACH motif enrichment via HOMER or ggseqlogo as a sanity check (NOT a filter). Covers BED12 vs narrowPeak output, exonic vs intronic peak handling, multi-tool reconciliation (intersection vs union), the m6A-vs-m6Am ambiguity at 5'UTR peaks that antibody methods cannot resolve, and orthogonal validation (miCLIP/GLORI/m6A-SAC-seq/m6Anet). Use when calling peaks from paired IP/input genome BAMs, choosing exomePeak2 (transcript-aware default) vs MACS3 (broad genomic) vs MeTPeak (HMM-smoothed low-coverage), confirming DRACH enrichment as a sanity check on the peak set, reconciling differing peak sets across tools, validating MeRIP peaks against single-base methods, interpreting 5' peaks where m6Am contamination is possible, or recommending a consensus strategy.
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。
Annotates ChIP-seq peaks to genomic features, nearest genes, ENCODE candidate cis-regulatory elements (cCREs), and regulatory domains. Uses ChIPseeker (R), HOMER annotatePeaks.pl (CLI), pyranges (Python), GREAT/rGREAT (regulatory domain gene-set enrichment), ChIP-Enrich (locus-length-adjusted), ENCODE SCREEN cCRE classification (PLS/pELS/dELS/CA-CTCF/CA-H3K4me3), and ENCODE-rE2G for cell-type-specific enhancer-gene linking. Handles nearest-TSS vs host-gene ambiguity, promoter window definition, and feature priority. Use when assigning genomic context to peaks, linking enhancer peaks to target genes, classifying peaks against ENCODE cCRE registry, or running gene-set enrichment on peak-associated genes.
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。
Estimates SNP heritability and partitions it across functional annotations, cell types, and loci from GWAS summary statistics or individual-level genotypes. Implements LDSC, stratified LDSC with the baseline-LD model, Finucane 2018 cell-type prioritization, LDAK SumHer, HDL, HESS local heritability, BOLT-REML, GCTA-GREML, graphREML, and Popcorn cross-population genetic correlation. Use when computing total h2_SNP from summary stats, partitioning heritability across functional categories, prioritizing trait-relevant tissues or cell types from ENCODE/Roadmap chromatin marks, reconciling LDSC vs LDAK enrichment estimates, computing local heritability with HESS, estimating genetic correlation between traits, or producing publication-grade enrichment with calibrated sensitivity to model assumptions.
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。