본문으로 건너뛰기
cccskills
무료GitHub에 공개

bioconductor-tfbstools

TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It c

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md5.8 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

TFBSTools

Dependencies & Environment

Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.

  • Version: 1.50.0 · Bioconductor: 3.23 · R: ≥ 4.6
  • Imports: Biobase, Biostrings, pwalign, BiocGenerics, BiocParallel, BSgenome, caTools, DirichletMultinomial, Seqinfo, GenomicRanges, gtools, IRanges, DBI, RSQLite, rtracklayer, seqLogo, S4Vectors, TFMPvalue, XML, XVector
  • Install: BiocManager::install("TFBSTools")

When to Use

  • Matrix Conversion: Converting Position Frequency Matrices (PFMs) to Position Weight Matrices (PWMs) or Information Content Matrices (ICMs) using toPWM and toICM.
  • Motif Scanning: Scanning nucleotide sequences or pairwise alignments for transcription factor binding sites using searchSeq or searchAln.
  • Database Querying: Querying and retrieving matrix data from the JASPAR database using getMatrixSet.
  • Random Profile Generation: Generating random profile matrices via permutation or Dirichlet multinomial mixture models using permuteMatrix and rPWMDmm.
  • Visualization: Visualizing sequence logos for basic PWMs or Transcription Factor Flexible Models (TFFMs) using seqLogo.

When NOT to Use

  • For high-throughput scanning of thousands of large genomic regions, use motifmatchr instead, because searchSeq is optimized for targeted DNAString or DNAStringSet objects rather than massive GRanges peak sets.
  • For de novo motif discovery without external dependencies, use universalmotif because runMEME requires the external MEME suite to be installed on the system.

Data Requirements

  • Input Format: PFMatrix objects, DNAString or DNAStringSet for sequences, and Axt objects for alignments.
  • Structure: 4-row matrices representing A, C, G, T counts or probabilities.
  • Normalization State: Raw position frequency counts for PFMs, which are then converted to log2 probability ratios for PWMs.

Key Parameters

  • type (default): The type of matrix conversion in toPWM (e.g., "log2probratio") or the type of p-value calculation in pvalues (e.g., "TFMPvalue").
  • pseudocounts (default): Numeric value (default 0.8) added to correct small counts or eliminate zero values before log transformation in toPWM.
  • bg (default): A named numeric vector representing background nucleotide frequencies (e.g., c(A=0.25, C=0.25, G=0.25, T=0.25)).
  • min.score (default): Minimum score threshold (e.g., "60%" or "80%") for a sequence match to be reported in searchSeq or searchAln.
  • schneider (default): Logical in toICM indicating whether to apply the Schneider correction.
  • strand (default): Controls which strand is searched in searchSeq (e.g., "*" for both strands).

Best Practices

  • Use pseudocounts=0.8 in toPWM rather than the square root of sequences to avoid overly harsh corrections on zero values.
  • When scanning sequences with searchSeq, use pvalues with type="TFMPvalue" to calculate empirical p-values for the match scores.
  • Use writeGFF3 or writeGFF2 to export SiteSet objects into standard genomic formats for downstream visualization.

Common Pitfalls

  • Pitfall: runMEME fails to execute.
    Fix: Ensure the external MEME software suite is installed and the binary argument correctly points to the executable.
  • Pitfall: searchSeq returns too many false positive hits.
    Fix: Increase the min.score threshold (e.g., from "60%" to "90%") to enforce stricter matching.
  • Pitfall: toPWM throws an error due to zero counts.
    Fix: Ensure pseudocounts is set to a positive value (like 0.8) to eliminate zero values before log transformation.

Alternatives

  • motifmatchr: For faster, parallelized motif scanning across large GRanges objects.
  • universalmotif: For comprehensive motif manipulation and de novo discovery without relying strictly on external binaries like MEME.
  • Biostrings: For basic PWM matching (matchPWM), though it lacks the advanced statistical models and TFFM support of TFBSTools.

Citations

  • Tan G, Lenhard B (2016). TFBSTools: an R/Bioconductor package for transcription factor binding site analysis. Bioinformatics.
  • Mathelier and Wasserman (2013). The next generation of transcription factor binding site, TFFM.

References

<!-- biomate-cta -->

Run this on BioMate

This skill is the knowledge layer — when, why, and how to use tfbstools. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.

▶ Open tfbstools on BioMate →

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bioMate-AI/biomate-bioconductor-kb7932026년 6월 21일 업데이트

bioMate-AI의 스킬 모두 보기

이 스킬의 문제 신고