707건 · 관련도순
Compares two or more band-tagged frameworks and produces a framework-neutral topic matrix showing coverage and gaps across all inputs, plus an optional reference-centric PLC crosswalk document when a reference framework is supplied.
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A set of tools for working with miRNA affinity models (KdModels), efficiently scanning for miRNA binding sites, and predicting target repression. It supports scanning using miRNA seeds, full miRNA sequences (enabling 3' alignment) and KdModels, and includes the prediction of slicing and TDMD sites. Finally, it includes utility and plotting functions (e.g. for the visual representation of miRNA-target alignment).
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Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working wit
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Build brand partnership strategy — co-branding campaigns, brand collaborations, licensing deals, partner brand alignment, and joint marketing. Use when the user says "brand partnership", "brand collab", "co-branding", "brand collaboration", "partnership marketing", "brand alliance", "co-branded product", "licensing deal", "partner with another brand", "brand collaboration strategy", "how do we partner with brands", "co-marketing partnership", "joint campaign", or wants to build credibility, reach, or revenue through alignment with another brand.
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Splice-aware RNA-seq aligner producing sorted BAM and splice junction tables. Builds genome index, runs two-pass alignment for better junctions. Outputs sorted BAM, junctions (SJ.out.tab), stats (Log.final.out), optional gene counts. Use Salmon for fast pseudoalignment; STAR when a BAM is needed for variant calling, IGV, or ENCODE pipelines.
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CLI toolkit for SAM/BAM/CRAM: sort, index, convert, filter, QC alignments. Core commands: view, sort, index, flagstat, stats, depth, markdup, merge. Required between alignment and variant/peak calling. Use pysam for Python-native BAM access; deeptools for normalized coverage tracks.
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Routes face-alignment installation, import checks, landmark-detection workflows, and detector-backend selection.
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Think and work like an expert Connectomics Scientist. Use when a task calls for Connectomics Scientist judgment. Reasons from vEM acquisition and petascale alignment through FFN/RoboEM segmentation, FlyWire/neuPrint/MICrONS/H01 graphs, and synapse-level QC while treating split/merge errors, alignment tears, false synapses, and release-version drift as first-class failure modes.
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Quantifies CRISPR editing outcomes with CRISPResso2 (Clement 2019 Nat Biotechnol) across Cas9-nuclease (indels, HDR), CBE and ABE base editors (target conversion + bystander), and prime editor (pegRNA-templated) modes. Covers single-amplicon (CRISPResso), multi-sample batch (CRISPRessoBatch), pooled-amplicon (CRISPRessoPooled), WGS off-target (CRISPRessoWGS), and sample-comparison (CRISPRessoCompare) workflows; quantification-window math that controls what is called edited; substitution-vs-indel diagnostic to distinguish BE from Cas9 contamination; MMEJ deletion pattern interpretation; allele-frequency tables; and failure modes from amplicon misalignment or contamination. Use when quantifying editing from amplicon sequencing, choosing CRISPResso mode by design, distinguishing intended edits from bystanders and indel byproducts, debugging low-alignment runs, or generating publication-grade editing reports.
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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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End-to-end ChIP-seq workflow from FASTQ files to annotated peaks. Covers QC, alignment, peak calling with MACS3 (or HOMER), and peak annotation with ChIPseeker. Use when processing ChIP-seq data from alignment through peak annotation.
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Align DNA short reads to reference genomes using bwa-mem2, the faster successor to BWA-MEM. Use when aligning DNA short reads to a reference genome.
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Bisulfite sequencing read alignment using Bismark with bowtie2/hisat2. Handles genome preparation and produces BAM files with methylation information. Use when aligning WGBS, RRBS, or other bisulfite-converted sequencing reads to a reference genome.
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Quantify transcript expression using pseudo-alignment with Salmon or kallisto. Use when quantifying transcripts with Salmon or kallisto.
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Generate alignment statistics using samtools flagstat, stats, depth, coverage, and mosdepth. Use when assessing alignment quality, calculating coverage, or generating QC reports.
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Sort alignment files by coordinate or read name using samtools and pysam. Use when preparing BAM files for indexing, variant calling, or paired-end analysis.
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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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Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam. Use when enabling random access to alignment files or fetching specific genomic regions.
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Workflow for gene and transcript quantification from RNA-seq reads using alignment-based or alignment-free tools.
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Design engineering principles for making TUI interfaces feel polished. Use when building Textual widgets, reviewing TUI code, implementing transitions, focus states, alignment, spacing, or any visual detail work. Triggers on TUI polish, design details, "make it feel better", "feels off", layout alignment, color consistency, responsive sizing.
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Create stakeholder alignment artifacts including responsibility matrices, decision frameworks, and communication plans.
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Compares two or more band-tagged frameworks and produces a framework-neutral topic matrix showing coverage and gaps across all inputs, plus an optional reference-centric PLC crosswalk document when a reference framework is supplied.
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Create alignment around your idea before starting the project
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Implement affective valence as directional derivative of interoceptive energy landscapes for AI alignment. Use when building alignment-aware RL agents, validating GF(3) conservation in reward signals, training Langevin-based policies, or analyzing fold-change detection signals in POMDP environments.
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