Dataset Information

Comparing vertebrate whole-genome shotgun reads to the human genome.

ABSTRACT: Multi-species sequence comparisons are a very efficient way to reveal conserved genes. Because sequence finishing is expensive and time consuming, many genome sequences are likely to stay incomplete. A challenge is to use these fragmented data for understanding the human genome. Methods for using cross-species whole-genome shotgun sequence (WGS) for genome annotation are described in this paper. About one-half million high-quality rat WGS reads (covering 7.5% of the rat genome) generated at the Baylor College of Medicine Human Genome Sequencing Center were compared with the human genome. Using computer-generated random reads as a negative control, a set of parameters was determined for reliable interpretation of BLAST search results. About 10% of the rat reads contain regions that are conserved in the human genomic sequence and about one-third of these include known gene-coding regions. Mapping the conserved regions to human chromosomes showed a 23-fold enrichment for coding regions compared with noncoding regions. This approach can also be applied to other mammalian genomes for gene finding. These data predicted approximately 42,500 genes in the human, slightly more than reported previously.

SUBMITTER: Chen R

PROVIDER: S-EPMC311156 | biostudies-literature | 2001 Nov

REPOSITORIES: biostudies-literature

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Comparing vertebrate whole-genome shotgun reads to the human genome.

Chen R R Bouck J B JB Weinstock G M GM Gibbs R A RA

Genome research 20011101 11

Multi-species sequence comparisons are a very efficient way to reveal conserved genes. Because sequence finishing is expensive and time consuming, many genome sequences are likely to stay incomplete. A challenge is to use these fragmented data for understanding the human genome. Methods for using cross-species whole-genome shotgun sequence (WGS) for genome annotation are described in this paper. About one-half million high-quality rat WGS reads (covering 7.5% of the rat genome) generated at the ...[more]

PMID: 11691844

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Project description:Accumulating evidence indicates that the gut microbiota affects colorectal cancer development, but previous studies have varied in population, technical methods, and associations with cancer. Understanding these variations is needed for comparisons and for potential pooling across studies. Therefore, we performed whole-genome shotgun sequencing on fecal samples from 52 pre-treatment colorectal cancer cases and 52 matched controls from Washington, DC. We compared findings from a previously published 16S rRNA study to the metagenomics-derived taxonomy within the same population. In addition, metagenome-predicted genes, modules, and pathways in the Washington, DC cases and controls were compared to cases and controls recruited in France whose specimens were processed using the same platform. Associations between the presence of fecal Fusobacteria, Fusobacterium, and Porphyromonas with colorectal cancer detected by 16S rRNA were reproduced by metagenomics, whereas higher relative abundance of Clostridia in cancer cases based on 16S rRNA was merely borderline based on metagenomics. This demonstrated that within the same sample set, most, but not all taxonomic associations were seen with both methods. Considering significant cancer associations with the relative abundance of genes, modules, and pathways in a recently published French metagenomics dataset, statistically significant associations in the Washington, DC population were detected for four out of 10 genes, three out of nine modules, and seven out of 17 pathways. In total, colorectal cancer status in the Washington, DC study was associated with 39% of the metagenome-predicted genes, modules, and pathways identified in the French study. More within and between population comparisons are needed to identify sources of variation and disease associations that can be reproduced despite these variations. Future studies should have larger sample sizes or pool data across studies to have sufficient power to detect associations that are reproducible and significant after correction for multiple testing.

Dataset Information

Comparing vertebrate whole-genome shotgun reads to the human genome.

Publications

Comparing vertebrate whole-genome shotgun reads to the human genome.

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