C. elegans ORFeome version 1.1: experimental verification of the genome annotation and resource for proteome-scale protein expression.

To verify the genome annotation and to create a resource to functionally characterize the proteome, we attempted to Gateway-clone all predicted protein-encoding open reading frames (ORFs), or the 'ORFeome,' of Caenorhabditis elegans. We successfully cloned approximately 12,000 ORFs (ORFeome 1.1), of which roughly 4,000 correspond to genes that are untouched by any cDNA or expressed-sequence tag (EST). More than 50% of predicted genes needed corrections in their intron-exon structures. Notably, approximately 11,000 C. elegans proteins can now be expressed under many conditions and characterized using various high-throughput strategies, including large-scale interactome mapping. We suggest that similar ORFeome projects will be valuable for other organisms, including humans.

EZ-Retrieve: a web-server for batch retrieval of coordinate-specified human DNA sequences and underscoring putative transcription factor-binding sites.

The availability of a draft human genome sequence and ability to monitor the transcription of thousands of genes with DNA microarrays has necessitated the need for new computational tools that can analyze cis-regulatory elements controlling genes that display similar expression patterns. We have developed a tool designated EZ-Retrieve that can: (i) retrieve any particular region of human genome sequence from the NCBI database and (ii) analyze retrieved sequences for putative transcription factor-binding sites (TFBSs) as they appear on the TRANSFAC database. The tool is web-based, user-friendly and offers both batch sequence retrieval and batch TFBS prediction. A major application of EZ-Retrieve is the analysis of co-expressed genes that are highlighted as expression clusters in DNA microarray experiments.

Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer.

BACKGROUND: A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identify potential targets for drug development, and can aid in evaluating treatment efficacy and predicting patient outcome. Microarray technology has enabled marker discovery from human cells by permitting measurement of steady-state mRNA levels derived from thousands of genes. However many challenging and unresolved issues regarding the acquisition and analysis of microarray data remain, such as accounting for both experimental and biological noise, transcripts whose expression profiles are not normally distributed, guidelines for statistical assessment of false positive/negative rates and comparing data derived from different research groups. This study addresses these issues using Affymetrix HG-U95A and HG-U133 GeneChip data derived from different research groups. RESULTS: We present here a simple non parametric approach coupled with noise filtering to identify sets of genes differentially expressed between the normal and cancer states in oral, breast, lung, prostate and ovarian tumors. An important feature of this study is the ability to integrate data from different laboratories, improving the analytical power of the individual results. One of the most interesting findings is the down regulation of genes involved in tissue differentiation. CONCLUSIONS: This study presents the development and application of a noise model that suppresses noise, limits false positives in the results, and allows integration of results from individual studies derived from different research groups.

Comprehensive genome-wide comparison of DNA and RNA level scan using microarray technology for identification of candidate cancer-related genes in the HL-60 cell line.

Genome-wide scans for DNA and RNA changes in the HL-60 cell line relative to normal leukocytes were conducted. Microarray-based comparative genome hybridization (CGH) studies were performed with the Spectral Genomics Human Bacterial Artificial Chromosome (BAC) 3MB system. Transcriptional measurements of approximately 12,500 human genes were monitored using Affymetrix U95A GeneChips. In HL-60, genomic DNA amplification of the 8q24 locus, trisomy 18, and deletions at loci 5q11.2 approximately q31, 6q12, 9p21.3 approximately p22, 10p12 approximately p15, 14q22 approximately q31, 17p12 approximately p13.3, and monosomy X were detected. After obtaining locus information about the RNA transcripts from the Affymetrix database, 4368 genes were stratified both according to status of RNA expression and the DNA copy number of their designated loci. The expression level of 2326 (53.25%) of 4368 transcripts is concordant with DNA copy number. Examples of specific, highly expressed, cancer-associated genes in amplified loci include SERPINB10, MYC, TYMS, HEC, and EPB41L3, while CD14, GZMK, TCF7, FOS, MLH3, CTNNA1, IRF1, VIM, CRK, MAP3K1, STAM, MAX, SFRG5, ENC1, PURA, MNT, RASA1, GLRX, UBE2B, NR3C1, PTENP1, BS69, COPEB, SKIP, PIM2, and MIC2 represent cancer-associated genes in deleted loci with decreased expression. The complementary usage of genome-wide DNA and RNA scans should enhance the identification of candidate genes in the neoplastic process.

Functional cloning of genes encoding dsRNA binding proteins.

Over a dozen human genes code for proteins that specifically bind to double-stranded RNA. These proteins have been implicated in several important cellular processes such as transcriptional activation, inhibition of translational initiation, RNA editing, mRNA localization, signal transduction, and posttranscriptional gene silencing (PTGS or RNAi). The recent discovery that PTGS or RNAi is a dsRNA-mediated pathway has further added to the study of dsRNA binding proteins. A method that enables the cloning of genes encoding dsRNA binding proteins would greatly facilitate the identification and study of these molecules. Here we describe a method for isolating such genes from an expression library using radiolabeled poly(I):poly(C) as a binding substrate.

Functional cloning, sorting, and expression profiling of nucleic acid-binding proteins.

A major challenge in the post-sequencing era is to elucidate the activity and biological function of genes that reside in the human genome. An important subset includes genes that encode proteins that regulate gene expression or maintain the structural integrity of the genome. Using a novel oligonucleotide-binding substrate as bait, we show the feasibility of a modified functional expression-cloning strategy to identify human cDNAs that encode a spectrum of nucleic acid-binding proteins (NBPs). Approximately 170 cDNAs were identified from screening phage libraries derived from a human colorectal adenocarcinoma cell line and from noncancerous fetal lung tissue. Sequence analysis confirmed that virtually every clone contained a known DNA- or RNA-binding motif. We also report on a complementary sorting strategy that, in the absence of subcloning and protein purification, can distinguish different classes of NBPs according to their particular binding properties. To extend our functional annotation of NBPs, we have used GeneChip expression profiling of 14 different breast-derived cell lines to examine the relative transcriptional activity of genes identified in our screen and cluster analysis to discover other genes that have similar expression patterns. Finally, we present strategies to analyze the upstream regulatory region of each gene within a cluster group and select unique combinations of transcription factor binding sites that may be responsible for dictating the observed synexpression.