CiteULike: nedwards's Lee

Coexpression analysis of human genes across many microarray data sets.

HK Lee — 2006-09-08T15:43:38-00:00

Genome Res, Vol. 14, No. 6. (June 2004), pp. 1085-1094.

We present a large-scale analysis of mRNA coexpression based on 60 large human data sets containing a total of 3924 microarrays. We sought pairs of genes that were reliably coexpressed (based on the correlation of their expression profiles) in multiple data sets, establishing a high-confidence network of 8805 genes connected by 220,649 "coexpression links" that are observed in at least three data sets. Confirmed positive correlations between genes were much more common than confirmed negative correlations. We show that confirmation of coexpression in multiple data sets is correlated with functional relatedness, and show how cluster analysis of the network can reveal functionally coherent groups of genes. Our findings demonstrate how the large body of accumulated microarray data can be exploited to increase the reliability of inferences about gene function.

CGI: a new approach for prioritizing genes by combining gene expression and proteinprotein interaction data

Ma — 2007-01-26T03:04:35-00:00

Bioinformatics, Vol. 23, No. 2. (15 January 2007), pp. 215-221.

Fast and accurate probe selection algorithm for large genomes

WK Sung — 2007-09-19T15:58:59-00:00

Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE (2003), pp. 65-74.

The oligo microarray (DNA chip) technology in recent years has a significant impact on genomic study. Many fields such as gene discovery, drug discovery, toxicological research and disease diagnosis, will certainly benefit from its use. A microarray is an orderly arrangement of thousands of DNA fragments where each DNA fragment is a probe (or a fingerprint) of a gene/cDNA. It is important that each probe must uniquely associate with a particular gene/cDNA. Otherwise, the performance of the microarray will be affected. Existing algorithms usually select probes using the criteria of homogeneity, sensitivity, and specificity. Moreover, they improve efficiency employing some heuristics. Such approaches reduce the accuracy. Instead, we make use of some smart filtering techniques to avoid redundant computation while maintaining the accuracy. Based on the new algorithm, optimal short (20 bases) or long (50 or 70 bases) probes can be computed efficiently for large genomes.

Comprehensive yeast proteome analysis using a capillary isoelectric focusing-based multidimensional separation platform coupled with ESI-MS/MS

Weijie Wang — 2007-09-13T19:29:24-00:00

PROTEOMICS, Vol. 7, No. 8. (2007), pp. 1178-1187.

As demonstrated in this study, a CIEF-based multidimensional separation platform not only is compatible with the detergent-based membrane protein preparation protocol, but also achieves both the largest yeast membrane proteome coverage and the most comprehensive analysis of the yeast proteome to date. By using a 1% false discovery rate for total peptide identifications, a total of 2513 distinct yeast proteins are identified from the SDS-solubilized fraction with an average of 5.4 peptides leading to each protein identification. Among proteins identified from the SDS-solubilized fraction, 407 proteins are predicted to contain at least two or more transmembrane domains using TMHMM (www.cbs.dtu.dk/services/TMHMM-2.0/), corresponding to 46% yeast membrane proteome coverage. Only four additional membrane proteins are identified in the soluble and urea-solubilized fractions, affirming the utility of SDS extraction for enriching the membrane proteome. By combining proteome results obtained from the soluble, urea-solubilized, and SDS-solubilized fractions, a single yeast proteome analysis yields the identification of 3632 distinct yeast proteins, corresponding to 55% theoretical yeast proteome coverage or 70% of proteins predicted to be expressed during log-phase growth in rich media.

Comparative Evaluation of Tandem MS Search Algorithms Using a Target-Decoy Search Strategy

Brian Balgley — 2007-09-13T19:16:39-00:00

Mol Cell Proteomics, Vol. 6, No. 9. (1 September 2007), pp. 1599-1608.

Peptide identification of tandem mass spectra by a variety of available search algorithms forms the foundation for much of modern day mass spectrometry-based proteomics. Despite the critical importance of proper evaluation and interpretation of the results generated by these algorithms there is still little consistency in their application or understanding of their similarities and differences. A survey was conducted of four tandem mass spectrometry peptide identification search algorithms, including Mascot, Open Mass Spectrometry Search Algorithm, Sequest, and X! Tandem. The same input data, search parameters, and sequence library were used for the searches. Comparisons were based on commonly used scoring methodologies for each algorithm and on the results of a target-decoy approach to sequence library searching. The results indicated that there is little difference in the output of the algorithms so long as consistent scoring procedures are applied. The results showed that some commonly used scoring procedures may lead to excessive false discovery rates. Finally an alternative method for the determination of an optimal cutoff threshold is proposed. 10.1074/mcp.M600469-MCP200

Qscore: an algorithm for evaluating SEQUEST database search results.

RE Moore — 2007-08-30T15:27:40-00:00

J Am Soc Mass Spectrom, Vol. 13, No. 4. (April 2002), pp. 378-386.

A scoring procedure is described for measuring the quality of the results for protein identifications obtained from spectral matching of MS/MS data using the Sequest database search program. The scoring system is essentially probabilistic and operates by estimating the probability that a protein identification has come about by chance. The probability is based on the number of identified peptides from the protein, the total number of identified peptides, and the fraction of distinct tryptic peptides from the database that are present in the identified protein. The score is not strictly a probability, as it also incorporates information about the quality of the individual peptide matches. The result of using Qscore on a large test set of data was similar to that achieved using approaches that validate individual spectral matches, with only a narrow overlap in scores between identified proteins and false positive matches. In direct comparison with a published method of evaluating Sequest results, Qscore was able to identify an equivalent number of proteins without any identifiable false positive assignments. Qscore greatly reduces the number of Sequest protein identifications that have to be validated manually.

Computational prediction of proteotypic peptides for quantitative proteomics

Parag Mallick — 2007-01-09T19:30:00-00:00

Nature Biotechnology, Vol. 25, No. 1. (31 December 2006), pp. 125-131.

Assessing bias in experiment design for large-scale mass spectrometry-based quantitative proteomics.

Brian Piening — 2007-07-13T14:06:54-00:00

Mol Cell Proteomics (7 July 2007)

Mass spectrometry-based proteomics holds great promise as a discovery tool for biomarker candidates in the early detection of diseases. Recently, much emphasis has been placed upon producing highly reliable data for quantitative profiling, for which highly reproducible methodologies are indispensable. The main problems that affect experimental reproducibility stem from variations introduced by sample collection, preparation and storage protocols, and liquid chromatography-mass spectrometry (LC-MS) settings and conditions. On the basis of a formally precise and quantitative definition of similarity between LC-MS experiments, we have developed Chaorder, a fully automatic software tool that can assess experimental reproducibility of sets of large-scale LC-MS experiments. By visualizing the similarity relationships within a set of experiments, this tool can form the basis of quality control and thus help assess the comparability of mass spectrometry data over time, across different laboratories, and between instruments. Applying Chaorder to data from multiple laboratories and a range of instruments, experimental protocols, and sample complexities revealed biases introduced by the sample processing steps, experimental protocols and instrument choices. Moreover, we show that reducing bias by correcting for just a few steps, for example randomizing the run order, does not provide much gain in statistical power for biomarker discovery.

Analysis of the S. cerevisiae proteome with PeptideAtlas

Nichole King — 2006-11-14T06:25:27-00:00

Genome Biology, Vol. 7 (13 November 2006), R106.

We present the Saccharomyces cerevisiae PeptideAtlas composed from 47 diverse experiments and 4.9 million tandem mass spectra. The observed peptides align to 61% of Saccharomyces Genome Database (SGD) open reading frames (ORFs), 49% of the uncharacterized SGD ORFs, 54% of S. cerevisiae ORFs with a Gene Ontology annotation of 'molecular function unknown', and 76% of ORFs with Gene names. We highlight the use of this resource for data mining, construction of high quality lists for targeted proteomics, validation of proteins, and software development.

Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry

Frank Desiere — 2007-08-08T12:40:21-00:00

Genome Biology, Vol. 6, No. 1. (2004)

A crucial aim upon the completion of the human genome is the verification and functional annotation of all predicted genes and their protein products. Here we describe the mapping of peptides derived from accurate interpretations of protein tandem mass spectrometry (MS) data to eukaryotic genomes and the generation of an expandable resource for integration of data from many diverse proteomics experiments. Furthermore, we demonstrate that peptide identifications obtained from high-throughput proteomics can be integrated on a large scale with the human genome. This resource could serve as an expandable repository for MS-derived proteome information.