Use when summarizing an RNA folding landscape by counting how many structures fall into each energy band, rather than enumerating individual folds one by one.
원문 언어: 영어
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이 저장소의 skills
SkillsMP는 vimalinx/bio-agent에서 417개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
vimalinx/bio-agent수집된 skill 417개 중 40개를 표시합니다.
Use when summarizing an RNA folding landscape by counting how many structures fall into each energy band, rather than enumerating individual folds one by one.
원문 언어: 영어
Use when computing optimal and suboptimal secondary structures for hybridization of two RNA strands, such as probe-target binding predictions.
원문 언어: 영어
Use when evaluating the free energy (kcal/mol) of an RNA secondary structure, calculating co-folding energies for two RNA strands, or analyzing consensus structures from multiple sequence alignments.
원문 언어: 영어
Use when predicting RNA secondary structures, calculating minimum free energy (MFE) folds, or computing partition functions and base pairing probabilities for RNA sequences.
원문 언어: 영어
Use when comparing, aligning, or computing similarity/distance between RNA secondary structures, or when generating multiple structure alignments with consensus prediction.
원문 언어: 영어
Use when computing RNA specific heat profiles from sequence data to analyze melting behavior and thermal stability across temperature ranges.
원문 언어: 영어
Use when searching for RNA sequences that fold into a predefined secondary structure, inverting RNA folding predictions to find sequences matching target bracket notation structures.
원문 언어: 영어
Use when predicting locally stable secondary structures from multiple sequence alignments of RNA
원문 언어: 영어
Use when computing locally stable RNA secondary structures with a maximal base pair span, scanning large genomes for short RNA structures, or predicting local RNA folding with Z-score filtering.
원문 언어: 영어
Use when analyzing RNA secondary structure landscapes to find local minima via gradient walks, generate barrier trees, or compute rates for kinetic modeling with treekin.
원문 언어: 영어
Use when predicting secondary structures and base pairing probabilities for multiple interacting RNA molecules
원문 언어: 영어
Use when performing pairwise structural alignments of RNA sequences that incorporate both sequence and structure information through base pair propensity vectors.
원문 언어: 영어
Use when converting legacy ViennaRNA 1.8.4 energy parameter files to the 2.0+ format used by modern ViennaRNA tools.
원문 언어: 영어
Use when calculating structure distances between thermodynamic ensembles of RNA secondary structures from sequence input.
원문 언어: 영어
Use when searching an RNA sequence for pseudoknot-forming interactions by combining local accessibility with interaction energy, especially when ordinary pseudoknot-free folding is insufficient.
원문 언어: 영어
Use when screening a small query RNA against longer target RNA sequences for inter-molecular hybridization sites, especially when optional RNAplfold accessibility profiles should influence the ranking.
원문 언어: 영어
Use when computing local RNA secondary structure pair probabilities, scanning large genomes for short stable RNA structures, or analyzing unpaired region probabilities across sliding windows.
원문 언어: 영어
Use when visualizing RNA secondary structures from dot-bracket notation or Stockholm alignments, generating structure diagrams, or creating annotated consensus structure plots.
원문 언어: 영어
Use when working with RNA soft constraints and need to compute pairing probabilities with position-specific perturbation minimization from the ViennaRNA package.
원문 언어: 영어
Use when building or reviewing an end-to-end RNA-seq workflow from raw reads through quantification, differential expression, and basic interpretation.
원문 언어: 영어
Use when searching target RNAs for interactions with a query H/ACA snoRNA, especially when the search should respect H/ACA-specific structural constraints and optionally use accessibility profiles.
원문 언어: 영어
Use when computing suboptimal RNA secondary structures within an energy range above the minimum free energy, or when sampling structures from the Boltzmann ensemble.
원문 언어: 영어
Use when calculating thermodynamics of RNA-RNA interactions, including accessibility and binding energy predictions for RNA duplex formation.
원문 언어: 영어
Use when turning `bcftools roh` output plus a VCF/BCF into an interactive HTML visualization of ROH segments and homozygosity rates.
원문 언어: 영어
Use when searching protein sequences against conserved domain databases like CDD using reverse position-specific BLAST
원문 언어: 영어
Use when searching nucleotide sequences against protein domain profile databases (PSSMs) to detect conserved domains via position-specific scoring.
원문 언어: 영어
Use when launching an NCBI converter binary through the `run-ncbi-converter` wrapper that downloads and caches the platform-specific executable on demand.
원문 언어: 영어
Use when batch-running `bcftools roh` across a directory of VCF, VCF.GZ, or BCF files and merging the resulting ROH calls across samples.
원문 언어: 영어
Use when wrapping a command in NCBI-style file locking so only one worker for a given lock base runs at a time.
원문 언어: 영어
Use when converting old `samtools pileup -c` output into VCF and filtering for SNP-only or indel-only calls.
원문 언어: 영어
Use when working with samtools.pl, a Perl CLI utility installed by the bioconda samtools package.
원문 언어: 영어
Use when working with SAM, BAM, or CRAM alignment files to sort, index, view, convert, or compute statistics.
원문 언어: 영어
Use when converting SCN-format records into XML for downstream EDirect or XML-based processing.
원문 언어: 영어
Use when identifying and masking low-complexity regions in protein sequences with the SEG algorithm before BLAST or other downstream analyses.
원문 언어: 영어
Use when populating an htslib/CRAM `REF_CACHE` directory from FASTA input or by scanning a directory tree for FASTA files.
원문 언어: 영어
Use when working with FASTA or FASTQ files for statistics, filtering, transformation, format conversion, searching, or set operations.
원문 언어: 영어
Use when doing lightweight FASTA/FASTQ transformations such as conversion, subsampling, subsequence extraction, trimming, or quick QC with seqtk.
원문 언어: 영어
Use when routing DNA, RNA, or protein sequence tasks to the core sequence-analysis commands that are actually installed in this workspace.
원문 언어: 영어
Use when you need to shift genomic intervals in BED/GFF/VCF files by a specified number of base pairs, either uniformly or strand-specifically.
원문 언어: 영어
Use when you need to randomly permute feature locations across a genome for statistical testing or generating null distributions.
원문 언어: 영어