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SkillsMP는 mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-에서 810개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-수집된 skill 810개 중 40개를 표시합니다.
Analyze genome collinearity and syntenic blocks using MCScanX, SyRI, and JCVI for comparative genomics. Detect conserved gene order, chromosomal rearrangements, and whole-genome duplications. Use when comparing genome structure between species or identifying…
원문 언어: 영어
Detect and track antimicrobial resistance genes using AMRFinderPlus and ResFinder with epidemiological context. Monitor resistance trends and identify emerging resistance patterns. Use when screening genomes for AMR genes or tracking resistance in…
원문 언어: 영어
Perform multi-locus sequence typing (MLST), core genome MLST, and SNP-based strain typing for bacterial isolate characterization using mlst and chewBBACA. Use when identifying strain types, tracking outbreak clones, or characterizing bacterial isolates.
원문 언어: 영어
Construct time-scaled phylogenies and infer evolutionary dynamics using TreeTime and BEAST2 for outbreak analysis. Estimate divergence times, molecular clock rates, and ancestral states. Use when dating outbreak origins, estimating transmission rates, or…
원문 언어: 영어
Infer pathogen transmission networks and identify likely transmission pairs using TransPhylo and outbreak reconstruction algorithms. Estimate who-infected-whom from genomic and epidemiological data. Use when investigating outbreak transmission chains or…
원문 언어: 영어
Assign pathogen lineages and track variants using Nextclade and pangolin for viral surveillance. Monitor variant prevalence and identify emerging variants of concern. Use when classifying viral sequences, tracking lineage dynamics, or monitoring for variants…
원문 언어: 영어
Compute evolutionary distances and build phylogenetic trees using Biopython Bio.Phylo.TreeConstruction. Use when creating distance matrices from alignments, building NJ/UPGMA trees, or generating bootstrap consensus trees.
원문 언어: 영어
Build maximum likelihood phylogenetic trees using IQ-TREE2 and RAxML-ng. Use when inferring publication-quality trees with model selection, ultrafast bootstrap, or partitioned analyses from sequence alignments.
원문 언어: 영어
Read, write, and convert phylogenetic tree files using Biopython Bio.Phylo. Use when parsing Newick, Nexus, PhyloXML, or NeXML tree formats, converting between formats, or handling multiple trees.
원문 언어: 영어
Modify phylogenetic tree structure using Biopython Bio.Phylo. Use when rooting trees with outgroups or midpoint, pruning taxa, collapsing clades, ladderizing branches, or extracting subtrees.
원문 언어: 영어
Draw and export phylogenetic trees using Biopython Bio.Phylo with matplotlib. Use when creating publication-quality tree figures, customizing colors and labels, or exporting to image formats.
원문 언어: 영어
Genome-wide association studies (GWAS) with PLINK. Perform case-control and quantitative trait association testing using logistic/linear regression with covariates, generate Manhattan and QQ plots for result visualization. Use when running GWAS or association…
원문 언어: 영어
Calculate linkage disequilibrium statistics (r², D'), perform LD pruning for population structure analysis, identify haplotype blocks, and visualize LD patterns using PLINK, scikit-allel, and LDBlockShow. Use when calculating LD or pruning variants.
원문 언어: 영어
PLINK file formats, format conversion, and quality control filtering for population genetics. Convert between VCF, BED/BIM/FAM, and PED/MAP formats, apply MAF, genotyping rate, and HWE filters using PLINK 1.9 and 2.0. Use when working with PLINK format files…
원문 언어: 영어
Analyze population structure using PCA and admixture analysis with PLINK and ADMIXTURE. Identify population clusters, assess ancestry proportions, visualize genetic structure, and choose optimal K for admixture models. Use when analyzing population…
원문 언어: 영어
Python population genetics with scikit-allel. Read VCF files, compute allele frequencies, calculate diversity statistics, perform PCA, and run selection scans using GenotypeArray and HaplotypeArray data structures. Use when analyzing population genetics in…
원문 언어: 영어
Detect signatures of natural selection using Fst, Tajima's D, iHS, XP-EHH, and other selection statistics. Calculate population differentiation, test for departures from neutrality, and identify selective sweeps with scikit-allel and vcftools. Use when…
원문 언어: 영어
Load and parse mass spectrometry data formats including mzML, mzXML, and quantification tool outputs like MaxQuant proteinGroups.txt. Use when starting a proteomics analysis with raw or processed MS data. Handles contaminant filtering and missing value…
원문 언어: 영어
Data-independent acquisition (DIA) proteomics analysis with DIA-NN and other tools. Use when analyzing DIA mass spectrometry data with library-free or library-based workflows for deep proteome profiling.
원문 언어: 영어
Statistical testing for differentially abundant proteins between conditions. Covers limma and MSstats workflows with multiple testing correction. Use when identifying proteins with significant abundance changes between experimental groups.
원문 언어: 영어
Peptide-spectrum matching and protein identification from MS/MS data. Use when identifying peptides from tandem mass spectra. Covers database searching, spectral library matching, and FDR estimation using target-decoy approaches.
원문 언어: 영어
Protein grouping and inference from peptide identifications. Use when resolving protein ambiguity from shared peptides. Handles protein groups and protein-level FDR control using parsimony and probabilistic approaches.
원문 언어: 영어
Quality control and assessment for proteomics data. Use when evaluating proteomics data quality before downstream analysis. Covers sample metrics, missing value patterns, replicate correlation, batch effects, and intensity distributions.
원문 언어: 영어
Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Covers site localization, motif analysis, and quantitative PTM analysis. Use when analyzing phosphoproteomic data or other modification-enriched samples.
원문 언어: 영어
Protein quantification from mass spectrometry data including label-free (LFQ, intensity-based), isobaric labeling (TMT, iTRAQ), and metabolic labeling (SILAC) approaches. Use when extracting protein abundances from MS data for differential analysis.
원문 언어: 영어
Build, manage, and search spectral libraries for proteomics. Use when creating or working with spectral libraries for DIA analysis. Covers DDA-based library generation, predicted libraries (Prosit, DeepLC), and library formats.
원문 언어: 영어
Download large datasets from NCBI efficiently using history server, batching, and rate limiting. Use when performing bulk sequence downloads, handling large query results, or production-scale data retrieval.
원문 언어: 영어
Run remote BLAST searches against NCBI databases using Biopython Bio.Blast. Use when identifying unknown sequences, finding homologs, or searching for sequence similarity against NCBI's nr/nt databases.
원문 언어: 영어
Retrieve records from NCBI databases using Biopython Bio.Entrez. Use when downloading sequences, fetching GenBank records, getting document summaries, or parsing NCBI data into Biopython objects.
원문 언어: 영어
Find cross-references between NCBI databases using Biopython Bio.Entrez. Use when navigating from genes to proteins, sequences to publications, finding related records, or discovering database relationships.
원문 언어: 영어
Search NCBI databases using Biopython Bio.Entrez. Use when finding records by keyword, building complex search queries, discovering database structure, or getting global query counts across databases.
원문 언어: 영어
Query NCBI Gene Expression Omnibus (GEO) for expression datasets using Biopython Bio.Entrez. Use when finding microarray/RNA-seq datasets, downloading expression data, or linking GEO series to SRA runs.
원문 언어: 영어
Run local BLAST searches using BLAST+ command-line tools. Use when running fast unlimited searches, building custom databases, performing large-scale analysis, or when NCBI servers are slow or unavailable.
원문 언어: 영어
Find homologous sequences using iterative BLAST (PSI-BLAST), profile HMMs (HMMER), and reciprocal best hit analysis. Use when identifying orthologs, distant homologs, or protein family members where standard BLAST is not sensitive enough.
원문 언어: 영어
Download sequencing data from NCBI SRA using the SRA toolkit. Use when downloading FASTQ files from SRA accessions, prefetching large datasets, or validating SRA downloads.
원문 언어: 영어
Access UniProt protein database for sequences, annotations, and functional information. Use when retrieving protein data, GO terms, domain annotations, or protein-protein interactions.
원문 언어: 영어
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.
원문 언어: 영어
Gene Ontology over-representation analysis using clusterProfiler enrichGO. Use when identifying biological functions enriched in a gene list from differential expression or other analyses. Supports all three ontologies (BP, MF, CC), multiple ID types, and…
원문 언어: 영어
Gene Set Enrichment Analysis using clusterProfiler gseGO and gseKEGG. Use when analyzing ranked gene lists to find coordinated expression changes in gene sets without arbitrary significance cutoffs. Detects subtle but coordinated expression changes.
원문 언어: 영어
KEGG pathway and module enrichment analysis using clusterProfiler enrichKEGG and enrichMKEGG. Use when identifying metabolic and signaling pathways over-represented in a gene list. Supports 4000+ organisms via KEGG online database.
원문 언어: 영어