title: A novel approach for transcription factor analysis using SELEX with high-throughput sequencing (TFAST).
abstract: BACKGROUND
  In previous work, we designed a modified aptamer-free SELEX-seq protocol (afSELEX-seq) for the discovery of transcription factor binding sites. Here, we present original software, TFAST, designed to analyze afSELEX-seq data, validated against our previously generated afSELEX-seq dataset and a model dataset. TFAST is designed with a simple graphical interface (Java) so that it can be installed and executed without extensive expertise in bioinformatics. TFAST completes analysis within minutes on most personal computers.
  
  
  METHODS
  Once afSELEX-seq data are aligned to a target genome, TFAST identifies peaks and, uniquely, compares peak characteristics between cycles. TFAST generates a hierarchical report of graded peaks, their associated genomic sequences, binding site length predictions, and dummy sequences.
  
  
  RESULTS
  Including additional cycles of afSELEX-seq improved TFAST's ability to selectively identify peaks, leading to 7,274, 4,255, and 2,628 peaks identified in two-, three-, and four-cycle afSELEX-seq. Inter-round analysis by TFAST identified 457 peaks as the strongest candidates for true binding sites. Separating peaks by TFAST into classes of worst, second-best and best candidate peaks revealed a trend of increasing significance (e-values 4.5 × 10(12), 2.9 × 10(-46), and 1.2 × 10(-73)) and informational content (11.0, 11.9, and 12.5 bits over 15 bp) of discovered motifs within each respective class. TFAST also predicted a binding site length (28 bp) consistent with non-computational experimentally derived results for the transcription factor PapX (22 to 29 bp).
  
  
  CONCLUSIONS
  TFAST offers a novel and intuitive approach for determining DNA binding sites of proteins subjected to afSELEX-seq. Here, we demonstrate that TFAST, using afSELEX-seq data, rapidly and accurately predicted sequence length and motif for a putative transcription factor's binding site.
authors.affiliation: Department of Microbiology and Immunology, University of Michigan, Ann Arbor, Michigan, United States of America.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Reiss
authors.forename: Frederick M
authors.initials: FM
authors.lastname: Howard
authors.forename: Harry L T
authors.initials: HL
authors.lastname: Mobley
mesh_terms: D000465:Algorithms; D001483:Base Sequence; D001665:Binding Sites; D016384:Consensus Sequence; D003627:Data Interpretation, Statistical; D059014:High-Throughput Nucleotide Sequencing; D008390:Markov Chains; D008958:Models, Molecular; D011485:Protein Binding; D052156:SELEX Aptamer Technique; D012984:Software; D014157:Transcription Factors / Q000737:chemistry*
chemical_list: D014157:Transcription Factors