「sequencing」の検索結果

Respond to a brand or executive impersonation incident — deepfaked executives, cloned support lines, fake apps, spoofed domains, or AI-generated scam content wearing your name. Use when a deepfake of a leader is circulating, customers report a fake version of your product or support channel, or to prepare the impersonation playbook before it happens. Produces an incident response: verification protocol, takedown sequencing by platform, customer and public communications, and the hardening plan. For general crisis comms use press-release/pm-crisis skills; for security incidents inside your systems use security-incident-response.

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Use when auditing an AISTATS submission for OpenReview readiness, abstract/full-paper deadlines, the 8-page submission body, double-blind anonymity, supplementary material, reproducibility checklist, dual-submission policy, reviewer volunteer requirements, desk-reject triggers, and final-week submission sequencing.

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Use when deciding which aejmic-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an American Economic Journal: Microeconomics (AEJ: Micro) submission. Routes — it does not replace — the specialized skills.

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Use when deciding which aejmac-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an American Economic Journal: Macroeconomics (AEJ: Macro) submission. Routes — it does not replace — the specialized skills.

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Use when deciding which aeri-* sub-skill to invoke next, or when sequencing work on a short-format manuscript from topic selection through the conditional-accept response for an American Economic Review: Insights (AER: Insights) submission. Routes — it does not replace — the specialized skills.

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Use when deciding which aejpol-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an AEJ: Economic Policy manuscript. Routes — it does not replace — the specialized skills.

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Use when deciding which advmat-* sub-skill to invoke next, or when sequencing manuscript work from scope-fit through revision for an Advanced Materials (Adv. Mater.) manuscript. Routes — does not replace — the specialized skills.

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Use when deciding which aeja-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an American Economic Journal: Applied Economics (AEJ: Applied) submission. Routes — it does not replace — the specialized skills.

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Use when deciding which asq-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an Administrative Science Quarterly (ASQ) manuscript. Routes — it does not replace — the specialized skills.

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Use when auditing an ACM MM (ACM Multimedia) submission for OpenReview readiness — thematic-area choice, the 6-8 page ACM sigconf budget, references-only overflow, double-blind anonymity versus the single-blind Reproducibility/Open-Source/Dataset tracks, supplementary media, dual submission, desk-reject triggers, and last-week sequencing.

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Use when auditing an ACM CCS submission for HotCRP readiness, dual-cycle abstract registration and full-paper deadlines, the 12-page ACM sigconf body, anonymization, the ethics considerations appendix, artifact-availability posture, dual-submission policy, per-cycle submission caps, desk-reject triggers, and final-week sequencing.

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 deciding which acr-* sub-skill to invoke next, or when sequencing manuscript work from fit positioning through rebuttal for 《会计研究》 (Accounting Research). Routes — does not replace — the specialized skills.

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Use when deciding which amr-* sub-skill to invoke next, or when sequencing a theory-building manuscript from theoretical-puzzle framing through developmental-review revision for an Academy of Management Review (AMR) submission. Routes — does not replace — the specialized skills.

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Use when deciding which aos-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an Accounting, Organizations and Society (AOS) manuscript. Routes — it does not replace — the specialized skills.

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Use when deciding which amann-* sub-skill to invoke next, or when sequencing a comprehensive review article from topic selection through revision for an Academy of Management Annals (Annals) submission. Routes — it does not replace — the specialized skills.

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Use when deciding which amj-* sub-skill to invoke next, or when sequencing manuscript work from topic selection through rebuttal for an Academy of Management Journal (AMJ) manuscript. Routes — it does not replace — the specialized skills.

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Basecalls raw Oxford Nanopore signal (POD5/FAST5) into reads with Dorado, choosing the chemistry-matched model and accuracy tier (fast/hac/sup), requesting modified bases (5mCG_5hmCG, 6mA, m6A) at basecall time, and handling duplex, demultiplexing, trimming, and HERRO read correction. Covers why the model+version is an irreversible analysis decision, why methylation cannot be recovered later, and why downstream polish/variant models must match the basecaller. Use when converting POD5/FAST5 to reads, picking a Dorado model for R9/R10 or RNA004, enabling methylation calling, basecalling duplex, demultiplexing barcoded runs, or correcting reads for assembly.

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Discovers, classifies, filters, and quantifies full-length transcript isoforms from PacBio Iso-Seq/Kinnex (HiFi) and Oxford Nanopore (cDNA/direct-RNA) long reads, using the isoseq+pigeon pipeline, SQANTI3, and ONT tools (IsoQuant, FLAIR, Bambu, StringTie2). Covers why a novel isoform is an artifact until proven otherwise (RT template-switching, intra-priming, and 5' degradation manufacture junctions and truncations), the SQANTI3 structural categories and their trust order, the Kinnex skera-split step, orthogonal CAGE/poly-A/short-read-junction validation, and why long-read isoform quantification needs EM. Use when building a full-length isoform catalog, classifying/filtering long-read transcripts, running Iso-Seq or ONT cDNA/dRNA analysis, or judging novel-isoform reliability.

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Assembles microbial-community sequencing into metagenome-assembled genomes (MAGs) with metaFlye (ONT), metaSPAdes/MEGAHIT (Illumina), and hifiasm-meta/metaMDBG (PacBio HiFi), then recovers genomes via multi-binner consolidation (MetaBAT2, MaxBin2, CONCOCT, SemiBin2, VAMB -> DAS_Tool) and QCs them against MIMAG with CheckM2, GUNC, and GTDB-Tk. Covers why a metagenome is not a genome (uneven coverage, micro-diversity, strain collapse to consensus), differential-coverage binning, co-assembly vs per-sample, the rRNA-operon collapse that fails short-read MAGs, and strain resolution with inStrain. Use when reconstructing genomes from a microbiome, soil, ocean, or gut community, recovering MAGs, or resolving strain-level variation.

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Phases small variants, SVs, and methylation from Oxford Nanopore and PacBio long reads (read-backed/physical phasing) with WhatsHap, LongPhase, or HiPhase, and haplotags the BAM (HP/PS tags) for allele-resolved downstream analysis. Covers why phase blocks break at het-sparse gaps (read length x heterozygosity), why phasing the VCF is useless until the BAM is haplotagged, the GT-pipe/PS and read HP/PS tag spec, reporting block N50 with switch error, the diploid-assumption/CNV/haploid-region traps, trio phasing as the gold standard, and the boundary to statistical panel phasing. Use when phasing long-read variants, haplotagging reads for allele-specific methylation/expression or phased SVs, choosing WhatsHap vs LongPhase vs HiPhase, trio phasing, or assessing phasing quality.

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Calls germline small variants (SNPs and indels) from Oxford Nanopore and PacBio HiFi long reads with Clair3, a two-stage (pileup + full-alignment) deep-learning caller, selecting the chemistry- and basecaller-version-matched model, enabling read-based phasing, and benchmarking against GIAB with stratification. Covers why the model string is the experiment (no auto-detection, silent degradation on mismatch), why ONT homopolymer/STR indels are the residual error whole-genome F1 hides, and the somatic/trio/RNA boundary to the ClairS/Clair3-Trio family. Use when calling germline SNVs/indels from ONT or HiFi BAMs, choosing a Clair3 model, phasing variants, or benchmarking long-read calls.

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Estimates tumor fraction (the genome-wide proportion of cfDNA molecules that are tumor-derived, the cfDNA analogue of bulk-tumor purity) from shallow whole-genome sequencing with ichorCNA, an HMM over 1 Mb bins that jointly EM-estimates tumor fraction, ploidy, and subclonal prevalence over a normal/ploidy grid. Encodes the load-bearing reframes: tumor fraction is the quantity that travels across assays and is NOT mutation VAF (clonal-het VAF approximately TF/2), CNA-based estimation has a hard ~3 percent limit-of-detection floor, and near-diploid or copy-neutral-LOH genomes return a falsely low value. Selects the estimator by data type (sWGS to ichorCNA, deep panel to max-VAF, methylation to deconvolution, sub-3 percent to fragmentomics or methylation). Use when quantifying tumor burden from a liquid biopsy, picking a tumor-fraction estimator for a given assay, or reconciling a TF estimate against a panel VAF.

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Decides how to preprocess plasma cfDNA sequencing data so the recoverable signal survives - library-prep-aware fragment expectations (dsDNA vs ssDNA/adaptase prep), UMI/duplex consensus with fgbio (ExtractUmisFromBam, GroupReadsByUmi --strategy paired for duplex, CallMolecularConsensusReads vs CallDuplexConsensusReads, FilterConsensusReads min-reads "total s1 s2"), the align->group->consensus->RE-align ordering, and the cfDNA dedup trap where naive coordinate dedup collapses nucleosome-coincident independent molecules. Covers when single-strand consensus suffices vs when duplex is mandatory, the singleton/sensitivity tax at low input, and reading the insert-size histogram as a pre-analytical QC instrument. Use when processing plasma cfDNA reads before fragmentomics, ctDNA mutation calling, or tumor-fraction estimation.

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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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