title: APART: automated preprocessing for NMR assignments with reduced tedium.
abstract: BACKGROUND
  High-throughput NMR structure determination is a goal that will require progress on many fronts, one of which is rapid resonance assignment. An important rate-limiting step in the resonance assignment process is accurate identification of resonance peaks in the NMR spectra. Peak-picking schemes range from incomplete (which lose essential assignment connectivities) to noisy (which obscure true connectivities with many false ones). We introduce an automated preassignment process that removes false peaks from noisy peak lists by requiring consensus between multiple NMR experiments and exploiting a priori information about NMR spectra. This process is designed to accept multiple input formats and generate multiple output formats, in an effort to be compatible with a variety of user preferences.
  
  
  RESULTS
  Automated preprocessing with APART rapidly identifies and removes false peaks from initial peak lists, reduces the burden of manual data entry, and documents and standardizes the peak filtering process. Successful preprocessing is demonstrated by the increased number of correct assignments obtained when data are submitted to an automated assignment program.
  
  
  BACKGROUND
  APART is available from http://sir.lanl.gov/NMR/APART.htm
  
  
  BACKGROUND
  npawley@lanl.gov; rmichalczyk@lanl.gov
  
  
  BACKGROUND
  Manual pages with installation instructions, procedures and screen shots can also be found at http://sir.lanl.gov/NMR/APART_Manual1.pdf.
authors.affiliation: Los Alamos National Laboratory, Bioscience Division MS G758, Los Alamos, NM 87545, USA. npawley@lanl.gov
authors.forename: Norma H
authors.initials: NH
authors.lastname: Pawley
authors.forename: Jason D
authors.initials: JD
authors.lastname: Gans
authors.forename: Ryszard
authors.initials: R
authors.lastname: Michalczyk
mesh_terms: D000465:Algorithms; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008956:Models, Chemical; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011487:Protein Conformation; D011506:Proteins / Q000032:analysis* / Q000737:chemistry*; D012815:Signal Processing, Computer-Assisted; D012984:Software; D013269:Stochastic Processes
chemical_list: D011506:Proteins