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chip-seq profiles generated from one sequencing platform illumina gaii  (Illumina Inc)
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Illumina Inc chip-seq profiles generated from one sequencing platform illumina gaii
Comparison of input DNA profiles obtained by microarray and <t>sequencing</t> technologies . (a) A genome browser view of input DNA profiles of chromosome 2R of D. melanogaster at various developmental stages measured by microarray (INPUT-chip; blue) and sequencing (INPUT-seq; red). (b) A heat map that summarizes the Spearman correlation coefficient between every pair of the nine INPUT-seq and eight INPUT-chip profiles along with genome-wide GC content. The number of mappable reads (in million) is written next to the name of each INPUT-seq profile. (c) The average signal profiles of INPUT-seq and INPUT-chip around transcription start sites (TSSs) and transcription end sites (TESs) are largely consistent, and their variation along these genomic regions generally coincide with GC content variation. (d) We generated 11 additional profiles from one of the INPUT-seq samples (AM) by subsampling the reads at different proportions (90%,80%,...,10%,5%,1%). A heat map summary representation of the Spearman correlation coefficient between every pair of sub-sampled INPUT-seq profiles and GC content is shown here. (e) The relationship between sequencing depth and genomic coverage. The curve shows how sequence read subsampling (i.e., reducing sequencing depth) affects genomic coverage. The genomic coverage of the nine INPUT-seq datasets and our Agilent microarray are also shown in the plot.
Chip Seq Profiles Generated From One Sequencing Platform Illumina Gaii, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Comparison of input DNA profiles obtained by microarray and sequencing technologies . (a) A genome browser view of input DNA profiles of chromosome 2R of D. melanogaster at various developmental stages measured by microarray (INPUT-chip; blue) and sequencing (INPUT-seq; red). (b) A heat map that summarizes the Spearman correlation coefficient between every pair of the nine INPUT-seq and eight INPUT-chip profiles along with genome-wide GC content. The number of mappable reads (in million) is written next to the name of each INPUT-seq profile. (c) The average signal profiles of INPUT-seq and INPUT-chip around transcription start sites (TSSs) and transcription end sites (TESs) are largely consistent, and their variation along these genomic regions generally coincide with GC content variation. (d) We generated 11 additional profiles from one of the INPUT-seq samples (AM) by subsampling the reads at different proportions (90%,80%,...,10%,5%,1%). A heat map summary representation of the Spearman correlation coefficient between every pair of sub-sampled INPUT-seq profiles and GC content is shown here. (e) The relationship between sequencing depth and genomic coverage. The curve shows how sequence read subsampling (i.e., reducing sequencing depth) affects genomic coverage. The genomic coverage of the nine INPUT-seq datasets and our Agilent microarray are also shown in the plot.

Journal: BMC Genomics

Article Title: ChIP-chip versus ChIP-seq: Lessons for experimental design and data analysis

doi: 10.1186/1471-2164-12-134

Figure Lengend Snippet: Comparison of input DNA profiles obtained by microarray and sequencing technologies . (a) A genome browser view of input DNA profiles of chromosome 2R of D. melanogaster at various developmental stages measured by microarray (INPUT-chip; blue) and sequencing (INPUT-seq; red). (b) A heat map that summarizes the Spearman correlation coefficient between every pair of the nine INPUT-seq and eight INPUT-chip profiles along with genome-wide GC content. The number of mappable reads (in million) is written next to the name of each INPUT-seq profile. (c) The average signal profiles of INPUT-seq and INPUT-chip around transcription start sites (TSSs) and transcription end sites (TESs) are largely consistent, and their variation along these genomic regions generally coincide with GC content variation. (d) We generated 11 additional profiles from one of the INPUT-seq samples (AM) by subsampling the reads at different proportions (90%,80%,...,10%,5%,1%). A heat map summary representation of the Spearman correlation coefficient between every pair of sub-sampled INPUT-seq profiles and GC content is shown here. (e) The relationship between sequencing depth and genomic coverage. The curve shows how sequence read subsampling (i.e., reducing sequencing depth) affects genomic coverage. The genomic coverage of the nine INPUT-seq datasets and our Agilent microarray are also shown in the plot.

Article Snippet: First, we only compared ChIP-chip profiles generated from one commercial platform (Agilent tiling microarray) with ChIP-seq profiles generated from one sequencing platform (Illumina GAII).

Techniques: Comparison, Microarray, Sequencing, Genome Wide, Generated

Illustration of how variability in an INPUT-seq profile can affect reconstruction of average signal profile at TSS and TES . The top panel shows the average signal profiles at the TSS and TES for the ChIP-chip and ChIP-seq profiles of H3K27Me3 at E-16-20 h. These ChIP-chip and ChIP-seq profiles differ quite substantially, and the ChIP-seq profiles resemble that of the GC content variation (Figure 1c). We subsequently reprocessed the ChIP-seq sample by using the INPUT-seq at AdultFemale as background for normalization since this profile has a strong correlation with GC content variation, which more likely reflect the actual technology-specific biases of our sequencing platform. After this procedure, the average signal profiles of ChIP-chip and ChIP-seq look much more alike, indicating that the original INPUT-seq at E-16-20 h does not appropriately capture the technology-specific variation at these sites.

Journal: BMC Genomics

Article Title: ChIP-chip versus ChIP-seq: Lessons for experimental design and data analysis

doi: 10.1186/1471-2164-12-134

Figure Lengend Snippet: Illustration of how variability in an INPUT-seq profile can affect reconstruction of average signal profile at TSS and TES . The top panel shows the average signal profiles at the TSS and TES for the ChIP-chip and ChIP-seq profiles of H3K27Me3 at E-16-20 h. These ChIP-chip and ChIP-seq profiles differ quite substantially, and the ChIP-seq profiles resemble that of the GC content variation (Figure 1c). We subsequently reprocessed the ChIP-seq sample by using the INPUT-seq at AdultFemale as background for normalization since this profile has a strong correlation with GC content variation, which more likely reflect the actual technology-specific biases of our sequencing platform. After this procedure, the average signal profiles of ChIP-chip and ChIP-seq look much more alike, indicating that the original INPUT-seq at E-16-20 h does not appropriately capture the technology-specific variation at these sites.

Article Snippet: First, we only compared ChIP-chip profiles generated from one commercial platform (Agilent tiling microarray) with ChIP-seq profiles generated from one sequencing platform (Illumina GAII).

Techniques: ChIP-chip, ChIP-sequencing, Sequencing

Effect of normalization with different INPUT-seq on ChIP-seq peak calling . We compared the number of peaks (a) and median peak width (b) of 10 ChIP-seq samples (CBP, H3K9Ac, H3K9Me3, H3K27Ac, H3K27Me3 at E16-20 h and E20-24 h) where each of them was normalization against four different input DNA samples (the input for from the matching time point, AdultFemale, AdultMale, and E-4-8 h). Peak calling was performed with SPP using the same parameters. Clearly peak detection is significantly affected by using different input DNA library as background control. In general, more peaks are identified as statistically significant (FDR < 0.05) when normalized with an INPUT-seq library with higher sequencing depth, although the magnitude of the differences vary across different ChIP datasets.

Journal: BMC Genomics

Article Title: ChIP-chip versus ChIP-seq: Lessons for experimental design and data analysis

doi: 10.1186/1471-2164-12-134

Figure Lengend Snippet: Effect of normalization with different INPUT-seq on ChIP-seq peak calling . We compared the number of peaks (a) and median peak width (b) of 10 ChIP-seq samples (CBP, H3K9Ac, H3K9Me3, H3K27Ac, H3K27Me3 at E16-20 h and E20-24 h) where each of them was normalization against four different input DNA samples (the input for from the matching time point, AdultFemale, AdultMale, and E-4-8 h). Peak calling was performed with SPP using the same parameters. Clearly peak detection is significantly affected by using different input DNA library as background control. In general, more peaks are identified as statistically significant (FDR < 0.05) when normalized with an INPUT-seq library with higher sequencing depth, although the magnitude of the differences vary across different ChIP datasets.

Article Snippet: First, we only compared ChIP-chip profiles generated from one commercial platform (Agilent tiling microarray) with ChIP-seq profiles generated from one sequencing platform (Illumina GAII).

Techniques: ChIP-sequencing, Control, Sequencing