Your browser doesn't support javascript.
loading
Top-Down Crawl: a method for the ultra-rapid and motif-free alignment of sequences with associated binding metrics.
Cooper, Brendon H; Chiu, Tsu-Pei; Rohs, Remo.
Afiliação
  • Cooper BH; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.
  • Chiu TP; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.
  • Rohs R; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA.
Bioinformatics ; 38(22): 5121-5123, 2022 11 15.
Article em En | MEDLINE | ID: mdl-36179084
ABSTRACT

SUMMARY:

Several high-throughput protein-DNA binding methods currently available produce highly reproducible measurements of binding affinity at the level of the k-mer. However, understanding where a k-mer is positioned along a binding site sequence depends on alignment. Here, we present Top-Down Crawl (TDC), an ultra-rapid tool designed for the alignment of k-mer level data in a rank-dependent and position weight matrix (PWM)-independent manner. As the framework only depends on the rank of the input, the method can accept input from many types of experiments (protein binding microarray, SELEX-seq, SMiLE-seq, etc.) without the need for specialized parameterization. Measuring the performance of the alignment using multiple linear regression with 5-fold cross-validation, we find TDC to perform as well as or better than computationally expensive PWM-based methods. AVAILABILITY AND IMPLEMENTATION TDC can be run online at https//topdowncrawl.usc.edu or locally as a python package available through pip at https//pypi.org/project/TopDownCrawl. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software Tipo de estudo: Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software Tipo de estudo: Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article