Ping Qiu

Human members of the eukaryotic protein kinase family

BackgroundEukaryotic protein kinases (EPKs) constitute one of the largest recognized protein families represented in the human genome. EPKs, which are similar to each other in sequence, structure and biochemical properties, are important players in virtually every signaling pathway involved in normal development and disease. Near completion of projects to sequence the human genome and transcriptome provide an opportunity to identify and perform sequence analysis on a nearly complete set of human EPKs.ResultsPublicly available genetic sequence data were searched for human sequences that potentially represent EPK family members. After removal of duplicates, splice variants and pseudogenes, this search yielded 510 sequences with recognizable similarity to the EPK family. Protein sequences of putative EPK catalytic domains identified in the search were aligned, and a phonogram was constructed based on the alignment. Representative sequence records in GenBank were identified, and derived information about gene mapping and nomenclature was summarized.ConclusionsThis work represents a nearly comprehensive census and early bioinformatics overview of the EPKs encoded in the human genome. Evaluation of the sequence relationships between these proteins contributes contextual information that enhances understanding of individual family members. This curation of human EPK sequences provides tools and a framework for the further characterization of this important class of enzymes.

Analysis of a human brain transcriptome map

BackgroundGenome wide transcriptome maps can provide tools to identify candidate genes that are over-expressed or silenced in certain disease tissue and increase our understanding of the structure and organization of the genome. Expressed Sequence Tags (ESTs) from the public dbEST and proprietary Incyte LifeSeq databases were used to derive a transcript map in conjunction with the working draft assembly of the human genome sequence.ResultsExamination of ESTs derived from brain tissues (excluding brain tumor tissues) suggests that these genes are distributed on chromosomes in a non-random fashion. Some regions on the genome are dense with brain-enriched genes while some regions lack brain-enriched genes, suggesting a significant correlation between distribution of genes along the chromosome and tissue type. ESTs from brain tumor tissues have also been mapped to the human genome working draft. We reveal that some regions enriched in brain genes show a significant decrease in gene expression in brain tumors, and, conversely that some regions lacking in brain genes show an increased level of gene expression in brain tumors.ConclusionsThis report demonstrates a novel approach for tissue specific transcriptome mapping using EST-based quantitative assessment.

Hepatitis C virus whole genome position weight matrix and robust primer design

BackgroundThe high degree of sequence heterogeneity found in Hepatitis C virus (HCV) isolates, makes robust nucleic acid-based assays difficult to generate. Polymerase chain reaction based techniques, require efficient and specific sequence recognition. Generation of robust primers capable of recognizing a wide range of isolates is a difficult task.ResultsA position weight matrix (PWM) and a consensus sequence were built for each region of HCV and subsequently assembled into a whole genome consensus sequence and PWM. For each of the 10 regions, the number of occurrences of each base at a given position was compiled. These counts were converted to frequencies that were used to calculate log odds scores. Using over 100 complete and 14,000 partial HCV genomes from GenBank, a consensus HCV genome sequence was generated along with a PWM reflecting heterogeneity at each position. The PWM was used to identify the most conserved regions for primer design.ConclusionsThis approach allows rapid identification of conserved regions for robust primer design and is broadly applicable to sets of genomes with all levels of genetic heterogeneity.

A reference database for tumor-related genes co-expressed with interleukin-8 using genome-scale in silico analysis

BackgroundThe EST database provides a rich resource for gene discovery and in silico expression analysis. We report a novel computational approach to identify co-expressed genes using EST database, and its application to IL-8.ResultsIL-8 is represented in 53 dbEST cDNA libraries. We calculated the frequency of occurrence of all the genes represented in these cDNA libraries, and ranked the candidates based on a Z-score. Additional analysis suggests that most IL-8 related genes are differentially expressed between non-tumor and tumor tissues. To focus on IL-8s function in tumor tissues, we further analyzed and ranked the genes in 16 IL-8 related tumor libraries.ConclusionsThis method generated a reference database for genes co-expressed with IL-8 and could facilitate further characterization of functional association among genes.

Domain-oriented functional analysis based on expression profiling

BackgroundCo-regulation of genes may imply involvement in similar biological processes or related function. Many clusters of co-regulated genes have been identified using microarray experiments. In this study, we examined co-regulated gene families using large-scale cDNA microarray experiments on the human transcriptome.ResultsWe present a simple model, which, for each probe pair, distills expression changes into binary digits and summarizes the expression of multiple members of a gene family as the Family Regulation Ratio. The set of Family Regulation Ratios for each protein family across multiple experiments is called a Family Regulation Profile. We analyzed these Family Regulation Profiles using Pearson Correlation Coefficients and derived a network diagram portraying relationships between the Family Regulation Profiles of gene families that are well represented on the microarrays. Our strategy was cross-validated with two randomly chosen data subsets and was proven to be a reliable approach.ConclusionThis work will help us to understand and identify the functional relationships between gene families and the regulatory pathways in which each family is involved. Concepts presented here may be useful for objective clustering of protein functions and deriving a comprehensive protein interaction map. Functional genomic approaches such as this may also be applicable to the elucidation of complex genetic regulatory networks.