Floating-point arithmetic is considered as esoteric subject by many people. This is rather surprising, because floating-point is ubiquitous in computer systems: Almost every language has a floating-point datatype; computers from PCs to supercomputers have floating-point accelerators; most compilers will be called upon to compile floating-point algorithms from time to time; and virtually every operating system must respond to floating-point exceptions such as overflow. This paper presents a tutorial on the aspects of floating-point that have a direct impact on designers of computer systems. It begins with background on floating-point representation and rounding error, continues with a discussion of the IEEE floating point standard, and concludes with examples of how computer system builders can better support floating point.
A powerful technique for peptide and protein identification is tandem mass spectrometry followed by database search using a program such as SEQUEST or Mascot. These programs, however, become slow and lose sensitivity when allowing nonspecific cleavages or peptide modifications. De novo sequencing and hybrid methods such as sequence tagging offer speed and robustness for wider searches, yet these approaches require better spectra with more complete and consecutive fragmentation and, hence, are less sensitive to low-abundance peptides. Here we describe a new hybrid method that retains the sensitivity of pure database search. The method uses a small amount of de novo analysis to identify likely b- and y-ion peaks--"lookup peaks"--that can then be used to extract candidate peptides from the database, with the number of candidates tunable to fit a computing budget. We describe a program called ByOnic that implements this method, and we benchmark ByOnic on several data sets, including one of mouse blood plasma spiked with low concentrations of recombinant human proteins. We demonstrate that ByOnic is more sensitive than sequence tagging and, indeed, more sensitive than the three most popular pure database search tools--SEQUEST, Mascot, and X!Tandem--on both the peptide and protein levels. On the mouse plasma samples, ByOnic consistently found spiked proteins missed by the other tools.
We report on two different approaches to assessing spectral quality prior to identification: binary classification, which predicts whether or not SEQUEST will be able to make an identification, and statistical regression, which predicts a more universal quality metric involving the number of b- and y-ion peaks. The best of our binary classifiers can eliminate over 75% of the unidentifiable spectra while losing only 10% of the identifiable spectra. Statistical regression can pick out spectra of modified peptides that can be identified by a de novo program but not by SEQUEST. In a section of independent interest, we discuss intensity normalization of mass spectra.
Matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) is the pre-eminent technique for mass mapping of glycans. In order to make this technique practical for high-throughput screening, reliable automatic methods of annotating peaks must be devised. We describe an algorithm called Cartoonist that labels peaks in MALDI spectra of permethylated N-glycans with cartoons which represent the most plausible glycans consistent with the peak masses and the types of glycans being analyzed. There are three main parts to Cartoonist. (i) It selects annotations from a library of biosynthetically plausible cartoons. The library we currently use has about 2800 cartoons, but was constructed using only about 300 archetype cartoons entered by hand. (ii) It determines the precision and calibration of the machine used to generate the spectrum. It does this automatically based on the spectrum itself. (iii) It assigns a confidence score to each annotation. In particular, rather than making a binary yes/no decision when annotating a peak, it makes all plausible annotations and associates them with scores indicating the probability that they are correct.
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