Here, we evaluate the contribution of two major biological processes--DNA replication and transcription--to mutation rate variation in human genomes. Based on analysis of the public human tissue transcriptomics data, ...Here, we evaluate the contribution of two major biological processes--DNA replication and transcription--to mutation rate variation in human genomes. Based on analysis of the public human tissue transcriptomics data, high-resolution replicating map of Hela cells and dbSNP data, we present significant correlations between expres- sion breadth, replication time in local regions and SNP density. SNP density of tissue-specific （TS） genes is sig- nificantly higher than that of housekeeping （HK） genes. TS genes tend to locate in late-replicating genomic re- gions and genes in such regions have a higher SNP density compared to those in early-replication regions. In addi- tion, SNP density is found to be positively correlated with expression level among HK genes. We conclude that the process of DNA replication generates stronger mutational pressure than transcription-associated biological processes do, resulting in an increase of mutation rate in TS genes while having weaker effects on HK genes. In contrast, transcription-associated processes are mainly responsible for the accumulation of mutations in highly-expressed HK genes.展开更多
Myelodysplastic syndromes have increased in frequency and incidence in the American population, but patient prognosis has not significantly improved over the last decade. Such improvements could be realized if biomark...Myelodysplastic syndromes have increased in frequency and incidence in the American population, but patient prognosis has not significantly improved over the last decade. Such improvements could be realized if biomarkers for accurate diagnosis and prognostic stratification were successfully identified. In this study, we propose a method that associates two state-of-the-art array technologies-single nucleotide polymorphism (SNP) array and gene expression array-with gene motifs considered transcription factor -binding sites (TFBS). We are particularly interested in SNP-containing motifs introduced by genetic variation and mutation as TFBS. The potential regulation of SNP-containing motifs affects only when certain mutations occur. These motifs can be identified from a group of co-expressed genes with copy number variation. Then, we used a sliding window to identify motif candidates near SNPs on gene sequences. The candidates were filtered by coarse thresholding and fine statistical testing. Using the regression-based LARS-EN algorithm and a level-wise sequence combination procedure, we identified 28 SNP-containing motifs as candidate TFBS. We confirmed 21 of the 28 motifs with ChIP-chip fragments in the TRANSFAC database. Another six motifs were validated by TRANSFAC via searching binding fragments on coregulated genes. The identified motifs and their location genes can be considered potential biomarkers for myelodysplastic syndromes. Thus, our proposed method, a novel strategy for associating two data categories, is capable of integrating information from different sources to identify reliable candidate regulatory SNP-containing motifs introduced by genetic variation and mutation.展开更多
基金supported by grants from the National Basic Research Program (973 Program 2006CB9-10401 and 2006CB910403) awarded to JY and SH
文摘Here, we evaluate the contribution of two major biological processes--DNA replication and transcription--to mutation rate variation in human genomes. Based on analysis of the public human tissue transcriptomics data, high-resolution replicating map of Hela cells and dbSNP data, we present significant correlations between expres- sion breadth, replication time in local regions and SNP density. SNP density of tissue-specific （TS） genes is sig- nificantly higher than that of housekeeping （HK） genes. TS genes tend to locate in late-replicating genomic re- gions and genes in such regions have a higher SNP density compared to those in early-replication regions. In addi- tion, SNP density is found to be positively correlated with expression level among HK genes. We conclude that the process of DNA replication generates stronger mutational pressure than transcription-associated biological processes do, resulting in an increase of mutation rate in TS genes while having weaker effects on HK genes. In contrast, transcription-associated processes are mainly responsible for the accumulation of mutations in highly-expressed HK genes.
基金supported by grants from NIH(No.1R01LM010185,1U01CA166886,and 1U01HL111560)
文摘Myelodysplastic syndromes have increased in frequency and incidence in the American population, but patient prognosis has not significantly improved over the last decade. Such improvements could be realized if biomarkers for accurate diagnosis and prognostic stratification were successfully identified. In this study, we propose a method that associates two state-of-the-art array technologies-single nucleotide polymorphism (SNP) array and gene expression array-with gene motifs considered transcription factor -binding sites (TFBS). We are particularly interested in SNP-containing motifs introduced by genetic variation and mutation as TFBS. The potential regulation of SNP-containing motifs affects only when certain mutations occur. These motifs can be identified from a group of co-expressed genes with copy number variation. Then, we used a sliding window to identify motif candidates near SNPs on gene sequences. The candidates were filtered by coarse thresholding and fine statistical testing. Using the regression-based LARS-EN algorithm and a level-wise sequence combination procedure, we identified 28 SNP-containing motifs as candidate TFBS. We confirmed 21 of the 28 motifs with ChIP-chip fragments in the TRANSFAC database. Another six motifs were validated by TRANSFAC via searching binding fragments on coregulated genes. The identified motifs and their location genes can be considered potential biomarkers for myelodysplastic syndromes. Thus, our proposed method, a novel strategy for associating two data categories, is capable of integrating information from different sources to identify reliable candidate regulatory SNP-containing motifs introduced by genetic variation and mutation.
