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이 저장소의 skills
SkillsMP는 GPTomics/bioSkills에서 562개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
GPTomics/bioSkills수집된 skill 562개 중 40개를 표시합니다.
Reconstructs single-cell lineage trees and clonal relationships from CRISPR/Cas9 scars, static expressed barcodes (LARRY/CellTag), or somatic mtDNA mutations using Cassiopeia, Startle, and CoSpar. Use when building a phylogeny from barcode scars, choosing a…
원문 언어: 영어
Detect cluster marker genes and assign manual cell type labels in single-cell RNA-seq using Scanpy (Python) and Seurat (R). Use when finding genes that distinguish clusters, ranking markers for annotation, scoring gene signatures, hand-labeling clusters, or…
원문 언어: 영어
Infers metabolite-mediated cell-cell communication from scRNA-seq by scoring enzyme-to-sensor pairs (MEBOCOST), with metabolic flux (scFEA), FBA state (Compass), and neurotransmitter (NeuronChat) alternatives. Use when studying metabolic crosstalk between…
원문 언어: 영어
Integrate multimodal single-cell data (CITE-seq RNA+protein, 10x Multiome RNA+ATAC, unpaired/diagonal RNA+ATAC) and choose the right joint method. Use when classifying an integration task by anchor structure (paired vs unpaired), denoising CITE-seq ADT…
원문 언어: 영어
Analyze Perturb-seq / CROP-seq single-cell CRISPR screens. Use when assigning guides as a mixture problem, removing non-perturbed escaper cells with Mixscape, choosing a calibrated test (SCEPTRE conditional resampling) over naive DE, quantifying effect size…
원문 언어: 영어
Quality control, ambient-RNA handling, normalization, and feature selection for single-cell RNA-seq using Scanpy (Python) and Seurat (R). Use when filtering low-quality cells with MAD-adaptive thresholds, setting tissue-aware mito cutoffs, removing ambient…
원문 언어: 영어
Analyze single-cell ATAC-seq with Signac/ArchR (R) and SnapATAC2 (Python alternative). Use when processing scATAC fragments, choosing a framework, calling consensus peaks, running TF-IDF/LSI while diagnosing the depth component, scoring chromVAR motif…
원문 언어: 영어
Infers developmental trajectories, pseudotime, RNA velocity, and directed fate probabilities from single-cell data using PAGA, Slingshot, Monocle3, DPT, Palantir, scVelo, and CellRank 2. Use when ordering cells along a differentiation continuum, choosing a…
원문 언어: 영어
Process many sequence files in batch (count, merge, split, convert, summarize) with memory-safe streaming and on-disk indexing using Biopython, pysam, or pyfastx. Use when iterating over a directory of FASTA/FASTQ files, merging or splitting datasets,…
원문 언어: 영어
Read, write, and index compressed sequence files (gzip, bzip2, xz, BGZF) with Biopython and bgzip/samtools. Use when working with .gz, .bz2, or .bgz sequence files, when random access into a compressed FASTA/FASTQ is needed, or when SeqIO.index/faidx/tabix…
원문 언어: 영어
Work with FASTQ quality scores using Biopython - access Phred scores, filter and trim by quality, compute per-position profiles, and convert between Sanger/Phred+33, Solexa, and Illumina/Phred+64 encodings. Use when analyzing read quality, filtering or…
원문 언어: 영어
Filter and select sequences by criteria (length, ID, GC content, N content, motifs, patterns, description) using Biopython, streaming so large files never load into RAM. Use when subsetting a FASTA/FASTQ file, removing unwanted or low-quality records, or…
원문 언어: 영어
Convert between sequence file formats (FASTA, FASTQ, GenBank, EMBL, Stockholm) and re-encode FASTQ quality offsets using Biopython Bio.SeqIO. Use when changing a file format for a downstream tool, fixing FASTQ quality encoding (Phred+33 vs Phred+64 vs…
원문 언어: 영어
Handle paired-end FASTQ files (R1/R2) using Biopython while keeping mates synchronized. Use when working with Illumina paired reads, synchronizing pairs, filtering both mates together with orphan routing, interleaving/deinterleaving, or matching mates by read…
원문 언어: 영어
Read biological sequence files (FASTA, FASTQ, GenBank, EMBL, ABI, SFF) with Biopython Bio.SeqIO, choosing between streaming, in-memory, and on-disk-indexed access. Use when parsing sequence files, iterating multi-record files, randomly accessing records by ID…
원문 언어: 영어
Calculate assembly and sequence statistics (N50/L50, auN, NG50/NGA50, length distribution, GC content with ambiguity handling, summary reports) using Biopython. Use when analyzing sequence datasets, generating QC reports, or comparing genome assemblies.
원문 언어: 영어
Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO. Use when saving sequences, creating new sequence files, or outputting modified records.
원문 언어: 영어
Analyze codon usage and calculate CAI (Codon Adaptation Index), RSCU, and Nc with Biopython, and produce naive max-CAI codon-optimized sequences. Use when scoring a gene's codon bias against a host, optimizing a CDS for heterologous expression, or studying…
원문 언어: 영어
Find sequence motifs, degenerate IUPAC patterns, and transcription-factor binding sites in DNA/RNA using Biopython and regex, including position weight matrix (PWM/PSSM) scoring. Use when locating regulatory elements, counting overlapping motif occurrences,…
원문 언어: 영어
Generate reverse complements and complements of DNA/RNA sequences using Biopython, including IUPAC ambiguity codes, gapped alignments, and minus-strand features. Use when working with the opposite strand, building reverse primers, normalizing strand…
원문 언어: 영어
Calculate nucleotide and protein sequence properties (GC content, GC skew, molecular weight, melting temperature, isoelectric point, instability, hydropathy) with Biopython. Use when analyzing sequence composition, computing primer Tm, estimating DNA or…
원문 언어: 영어
Slice, extract, and concatenate biological sequences and annotated records using Biopython. Use when extracting subsequences by position, splicing exons into a transcript, joining sequences, or carrying a sub-region of an annotated record (with quality scores…
원문 언어: 영어
Tests whether a proposed or predicted RNA secondary structure is supported by evolutionary covariation using R-scape, which scores compensatory substitutions against a phylogeny-aware null and estimates the statistical power of the alignment. Use when…
원문 언어: 영어
Searches for non-coding RNA homologs and classifies RNA families with Infernal covariance models against Rfam, scoring sequence AND secondary-structure conservation jointly. Use when deciding whether a covariance model is the right tool versus BLAST/nhmmer…
원문 언어: 영어
Predicts RNA secondary structure with ViennaRNA, treating the Boltzmann ensemble (partition function, base-pair probabilities, centroid, MEA, stochastic samples) as the object rather than a single MFE fold. Covers consensus folding from alignments…
원문 언어: 영어
Processes experimental RNA structure probing data (SHAPE-MaP, DMS-MaPseq) into per-nucleotide reactivity profiles with ShapeMapper2, then uses them as soft restraints on thermodynamic folding. Covers reagent and readout choice (SHAPE vs DMS,…
원문 언어: 영어
Quantify transcript expression from FASTQ with Salmon (selective alignment) or kallisto (pseudoalignment), bypassing genome mapping. Use when quantifying RNA-seq without alignment, deciding whether a decoy-aware index is required, detecting and verifying…
원문 언어: 영어
Quality control and exploration of RNA-seq count matrices before differential expression. Use when checking library sizes and composition, choosing VST vs rlog for visualization, running PCA and sample correlation, detecting outliers with Cook's distance,…
원문 언어: 영어
Count reads per gene from aligned BAM files using Subread featureCounts. Use when turning STAR/HISAT2 BAMs into a gene-level count matrix for DESeq2/edgeR, deciding library strandedness, handling paired-end fragment counting, choosing how to treat…
원문 언어: 영어
Import transcript-level quantifications from Salmon/kallisto/RSEM into R for gene-level analysis with DESeq2/edgeR using tximport or tximeta. Use when summarizing transcript abundances to gene counts with the correct length offset, choosing a…
원문 언어: 영어
Map translation initiation sites, including non-AUG and alternative starts, from initiation-drug ribosome profiling (TI-seq). Use when locating start codons, detecting near-cognate or upstream initiation, or analyzing harringtonine, lactimidomycin…
원문 언어: 영어
Detect and quantify translated ORFs from Ribo-seq using 3-nucleotide periodicity, including uORFs, internal ORFs, dORFs, and novel ORFs. Use when finding actively translated regions beyond annotated CDS, classifying ORFs by the 2022 community standard,…
원문 언어: 영어
Preprocess ribosome profiling reads with UMI handling, adapter trimming, contaminant/rRNA depletion, and footprint-aware alignment. Use when preparing Ribo-seq FASTQ for periodicity QC, ORF detection, translation efficiency, or stalling analysis, or when…
원문 언어: 영어
Validate Ribo-seq library quality by measuring 3-nucleotide periodicity and calibrating read-length-specific P-site offsets. Use when checking whether footprints capture genuine translation, determining P-site offsets for downstream ORF/TE/stalling analysis,…
원문 언어: 영어
Detect ribosome pausing and stalling at codon resolution from Ribo-seq, using local-relative occupancy metrics and A-site assignment. Use when studying elongation dynamics, codon dwell times, pause motifs, or ribosome collisions, and when judging whether a…
원문 언어: 영어
Quantify translation efficiency (TE) as ribosome occupancy relative to mRNA abundance and test for differential TE between conditions. Use when separating translational from transcriptional regulation, distinguishing genuine translational control from…
원문 언어: 영어
Select restriction enzymes for cloning or diagnostics using Biopython Bio.Restriction. Finds enzymes by cut frequency, overhang type, recognition-site length, commercial availability, compatible ends, and methylation sensitivity, and identifies isoschizomers…
원문 언어: 영어
Predict restriction digest fragment sizes and gel patterns using Biopython Bio.Restriction. Computes fragment lengths and sequences for single and double digests on linear or circular DNA, and interprets them against an agarose gel. Use when predicting the…
원문 언어: 영어
Design and validate Type IIS scarless DNA assembly (Golden Gate, MoClo) using Biopython Bio.Restriction. Screens parts for internal BsaI/BsmBI/BbsI/SapI sites (domestication), previews the fusion overhangs a digest exposes, and validates a fusion-overhang set…
원문 언어: 영어
Build restriction maps showing enzyme cut positions and inter-site distances along DNA using Biopython Bio.Restriction. Produces text or graphical maps for linear and circular molecules, orders sites from single and double digests, and overlays GenBank…
원문 언어: 영어