Biomedical subjects

Eugene Kolker

Publications and source records attributed to Eugene Kolker.

21 records · Page 2Linked to original sources

PubMed2002

Spectral analysis of distributions: finding periodic components in eukaryotic enzyme length data.

We introduce the spectral analysis of distributions (SAD), a method for detecting and evaluating possible periodicity in experimental data distributions (histograms) of arbitrary shape. SAD determines whether a given empirical distribution contains a periodic component. We also propose a system of probabilistic mixture distributions to model a histogram consisting of a smooth background together with peaks at periodic intervals, with each peak corresponding to a fixed number of subunits added together. This mixture distribution model allows us to estimate the parameters of the data and to test the statistical significance of the estimated peaks. The analysis is applied to the length distribution of eukaryotic enzymes.

Algorithms↗

PubMed2002

Experimental protein mixture for validating tandem mass spectral analysis.

Several methods have been used to identify peptides that correspond to tandem mass spectra. In this work, we describe a data set of low energy tandem mass spectra generated from a control mixture of known protein components that can be used to evaluate the accuracy of these methods. As an example, these spectra were searched by the SEQUEST application against a human peptide sequence database. The numbers of resulting correct and incorrect peptide assignments were then determined. We show how the sensitivity and error rate are affected by the use of various filtering criteria based upon SEQUEST scores and the number of tryptic termini of assigned peptides.

Amino Acid Sequence↗

PubMed2002

Identifying operons and untranslated regions of transcripts using Escherichia coli RNA expression analysis.

Microarrays traditionally have been used to assay the transcript expression of coding regions of genes. Here, we use Escherichia coli oligonucleotide microarrays to assay transcript expression of both open reading frames (ORFs) and intergenic regions. We then use hidden Markov models to analyse this expression data and estimate transcription boundaries of genes. This approach allows us to identify 5' untranslated regions (5' UTRs) of transcripts as well as genes that are likely to be operon members. The operon elements we identify correspond to documented operons with 99% specificity and 63% sensitivity. Similarly we find that our 5' UTR results accurately coincide with experimentally verified promoter regions for most genes.

5' Untranslated Regions↗