MTT: a software tool for quality control in sequence assembly.

A large-scale sequencing project requires a tool to control the quality of the input data because a sizable number of trace data may be of low quality. If these data are allowed to enter the sequence assembly pipeline, harm will be done. Hence, it is important to detect such data as soon as possible. MTT (Move-Track-Trim) is a software package analyzing the quality of the lanes. It subjects each lane to a series of tests, and if a lane does not pass all tests, it is flagged as a "bad" lane. The use has a chance to examine both the "good" and the "bad" lanes and reclassify a "bad" lane as "good," or vice versa. Alternatively, the user may decide to retrack the gel or get rid of some lanes altogether. As a by-product of the analysis, MTT performs other useful functions. It trims the lanes and compresses the lane files and moves them to the directories where assembly is carried out. It also generates some useful statistics describing the quality of the gel.

TRAMP: a software package for generating transposon maps.

TRAMP is a software package for generating transposon maps that are used for DNA sequencing. The package provides a variety of automated tools that can always be overridden by the user. The central part of the package is its selection algorithm that finds the most robust map with the smallest number of inserts. TRAMP has been in daily use by the sequencing team at LBL since it was introduced in the Spring of 1994. It is applicable to any sequencing project utilizing the directed strategy.

How to compute the difference between tomographic images.

An algorithm for estimating the difference between two tomographic images is proposed if the two data vectors are obtained in nonoverlapping time intervals. The algorithm takes advantage of statistical independence of the two vectors. The results are significantly different from the traditional approach in which the two images are reconstructed separately and then subtracted.

Results of a clinical receiver operating characteristic study comparing filtered backprojection and maximum likelihood estimator images in FDG PET studies.

The results of a receiver operator characteristic (ROC) study comparing maximum likelihood estimator (MLE) reconstructions of human FDG PET brain scan data to filtered backprojection reconstructions of the same data are reported. The purpose of the study was to determine whether MLE reconstructions would result in higher detectability of small focal lesions introduced artificially into otherwise normal scan data. One physician assisted in defining the location and intensity of the lesions and five physicians read the final images. Data from 90 datasets were used for the study. Of those, 42 were left in their original "normal" condition and 48 were modified by added lesions. All datasets were reconstructed by the two methods and submitted to the five physicians for evaluation. The results show an increase in the area under the ROC curve from approximately 0.65 for filtered backprojection to approximately 0.71 for the maximum likelihood reconstructions for four of the five observers with good statistical significance.

Statistical analysis of maximum likelihood estimator images of human brain FDG PET studies.

The work presented evaluates the statistical characteristics of regional bias and expected error in reconstructions of real positron emission tomography (PET) data of human brain fluoro-deoxiglucose (FDG) studies carried out by the maximum likelihood estimator (MLE) method with a robust stopping rule, and compares them with the results of filtered backprojection (FBP) reconstructions and with the method of sieves. The task of evaluating radioisotope uptake in regions-of-interest (ROIs) is investigated. An assessment of bias and variance in uptake measurements is carried out with simulated data. Then, by using three different transition matrices with different degrees of accuracy and a components of variance model for statistical analysis, it is shown that the characteristics obtained from real human FDG brain data are consistent with the results of the simulation studies.

Feasible images and practical stopping rules for iterative algorithms in emission tomography.

The discussion of the causes of image deterioration in the maximum-likelihood estimator (MLE) method of tomographic image reconstruction, initiated with the publication of a stopping rule for that iterative process (E. Veklerov and J. Llacer, 1987) is continued. The concept of a feasible image is introduced, which is a result of a reconstruction that, if it were a radiation field, could have generated the initial projection data by the Poisson process that governs radioactive decay. From the premise that the result of a reconstruction should be feasible, the shape and characteristics of the region of feasibility in projection space are examined. With a new rule, reconstructions from real data can be tested for feasibility. Results of the tests and reconstructed images for the Hoffman brain phantom are shown. A comparative examination of the current methods of dealing with MLE image deterioration is included.

Stopping Rule for the MLE Algorithm Based on Statistical Hypothesis Testing.

It is known that when the maximum likelihood estimator (MLE) algorithm passes a certain point, it produces images that begin to deteriorate. We propose a quantitative criterion with a simple probabilistic interpretation that allows the user to stop the algorithm just before this effect begins. The MLE algorithm searches for the image that has the maximum probability to generate the projection data. The underlying assumption of the algorithm is a Poisson distribution of the data. Therefore, the best image, according to the MLE algorithm, is the one that results in projection means which are as close to the data as possible. It is shown that this goal conflicts with the assumption that the data are Poisson-distributed. We test a statistical hypothesis whereby the projection data could have been generated by the image produced after each iteration. The acceptance or rejection of the hypothesis is based on a parameter that decreases as the images improve and increases as they deteriorate. We show that the best MLE images, which pass the test, result in somewhat lower noise in regions of high activity than the filtered back-projection results and much improved images in low activity regions. The applicability of the proposed stopping rule to other iterative schemes is discussed.