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Analysis of a human brain transcriptome map.

Qiu P, Benbow L, Liu S, Greene JR, Wang L - BMC Genomics (2002)

Bottom Line: 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.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.This report demonstrates a novel approach for tissue specific transcriptome mapping using EST-based quantitative assessment.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Group and Human Genomic Research Department, Schering-Plough Research Institute, 2015 Galloping Hill Road, Kenilworth, New Jersey 07033, USA. ping.qiu@spcorp.com

ABSTRACT

Background: Genome 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.

Results: Examination 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.

Conclusions: This report demonstrates a novel approach for tissue specific transcriptome mapping using EST-based quantitative assessment.

No MeSH data available.


Related in: MedlinePlus

Comparison of distribution of TDFNB calculated from data derived from dbEST and Incyte Genomics LifeSeq for chromosome 1. Pearson Correlation Coefficient = 0.658.
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Figure 1: Comparison of distribution of TDFNB calculated from data derived from dbEST and Incyte Genomics LifeSeq for chromosome 1. Pearson Correlation Coefficient = 0.658.

Mentions: We performed digital expression analysis of brain-enriched genes across the human genome with a window size of 5 Mbp and an interval of 1 Mbp. The transcript density factor for normal (non-tumor) brain libraries (TDFNB) was calculated as described in Methods. Figure 1 is an example of the distribution of TDFNB over chromosome 1 using publicly available EST sequences from dbEST and reveals a number of "peaks" that represent transcripts that appear to be preferentially expressed in brain tissues. To check the validity of these peak regions and make sure that the difference is not due to random picking or partial sequencing of cDNA libraries (which is the common random fluctuation caused by digital Northern approach) [17], the analysis was repeated using ESTs and the associated library information from the Incyte Genomics LifeSeq database. The distribution pattern of TDFNB shows an overall correlation coefficient of 0.658 for the whole genome between these two data sources. If we only analyze the region with Z-score >= 2 (i.e. peak regions), the correlation coefficient is 0.935 which suggests that the peak regions resulting from the analysis of public data are most likely not artifacts. Figure 2 shows the comparison of the distributions of TDF on chromosome 1 calculated from ESTs derived from brain tissue libraries vs. ESTs derived from breast tissue libraries. The overall pearson correlation coefficient for these two tissues is 0.113 which suggests that the peak regions observed in Figure 1 are brain specific.


Analysis of a human brain transcriptome map.

Qiu P, Benbow L, Liu S, Greene JR, Wang L - BMC Genomics (2002)

Comparison of distribution of TDFNB calculated from data derived from dbEST and Incyte Genomics LifeSeq for chromosome 1. Pearson Correlation Coefficient = 0.658.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC103672&req=5

Figure 1: Comparison of distribution of TDFNB calculated from data derived from dbEST and Incyte Genomics LifeSeq for chromosome 1. Pearson Correlation Coefficient = 0.658.
Mentions: We performed digital expression analysis of brain-enriched genes across the human genome with a window size of 5 Mbp and an interval of 1 Mbp. The transcript density factor for normal (non-tumor) brain libraries (TDFNB) was calculated as described in Methods. Figure 1 is an example of the distribution of TDFNB over chromosome 1 using publicly available EST sequences from dbEST and reveals a number of "peaks" that represent transcripts that appear to be preferentially expressed in brain tissues. To check the validity of these peak regions and make sure that the difference is not due to random picking or partial sequencing of cDNA libraries (which is the common random fluctuation caused by digital Northern approach) [17], the analysis was repeated using ESTs and the associated library information from the Incyte Genomics LifeSeq database. The distribution pattern of TDFNB shows an overall correlation coefficient of 0.658 for the whole genome between these two data sources. If we only analyze the region with Z-score >= 2 (i.e. peak regions), the correlation coefficient is 0.935 which suggests that the peak regions resulting from the analysis of public data are most likely not artifacts. Figure 2 shows the comparison of the distributions of TDF on chromosome 1 calculated from ESTs derived from brain tissue libraries vs. ESTs derived from breast tissue libraries. The overall pearson correlation coefficient for these two tissues is 0.113 which suggests that the peak regions observed in Figure 1 are brain specific.

Bottom Line: 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.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.This report demonstrates a novel approach for tissue specific transcriptome mapping using EST-based quantitative assessment.

View Article: PubMed Central - HTML - PubMed

Affiliation: Bioinformatics Group and Human Genomic Research Department, Schering-Plough Research Institute, 2015 Galloping Hill Road, Kenilworth, New Jersey 07033, USA. ping.qiu@spcorp.com

ABSTRACT

Background: Genome 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.

Results: Examination 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.

Conclusions: This report demonstrates a novel approach for tissue specific transcriptome mapping using EST-based quantitative assessment.

No MeSH data available.


Related in: MedlinePlus