81128c8d-99a2-4084-857c-8d0426b92ec4 - samples
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ABSTRACT:
PROVIDER: EGAD00001011158 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:The genomic DNA was extracted from paired tumor and adjacent non-tumor samples of 27 Hispanic HCC patients. The TruSeq Rapid Exome Library Prep kit (Illumina, CA) was used to capture the coding regions of the human genome. The 100 bp paired-end sequencing of every 6-plex whole exome library pool was performed on an Illumina HiSeq 3000 system according to the manufacturer’s recommended protocol to achieve an average coverage of ~100X. The paired-end reads were aligned to the human reference genome (hg19) with decoy sequences (as used in the 1000 Genomes Project) using BWA software. The duplicate reads were marked and removed using Picard and SAMtools, respectively. Local realignment around insertions/deletions (indels) and base quality recalibration were performed using GATK.
| EGAS00001007431 | EGA
Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being “recalibrated” (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration.
2012-03-03 | GSE36217 | GEO
Project description:While the importance of random sequencing errors decreases at higher DNA or RNA sequencing depths, systematic sequencing errors (SSEs) dominate at high sequencing depths and can be difficult to distinguish from biological variants. These SSEs can cause base quality scores to underestimate the probability of error at certain genomic positions, resulting in false positive variant calls, particularly in mixtures such as samples with RNA editing, tumors, circulating tumor cells, bacteria, mitochondrial heteroplasmy, or pooled DNA. Most algorithms proposed for correction of SSEs require a training data set, which is typically either from a part of the data set being M-bM-^@M-^\recalibratedM-bM-^@M-^] (Genome Analysis ToolKit, or GATK) or from a separate data set with special characteristics (SysCall). Here, we combine the advantages of these approaches by adding synthetic RNA spike-in standards to human RNA, and use GATK to recalibrate base quality scores with reads mapped to the spike-in standards. Compared to conventional GATK recalibration that uses reads mapped to the genome, spike-ins improve the accuracy of Illumina base quality scores by a mean of 5 units, and by as much as 13 units M-BM- at CpG sites. In addition, since reads mapping to the genome are not used for recalibration, our method allows run-specific recalibration even for the many species without a comprehensive and accurate SNP database. We also use GATK with the spike-in standards to demonstrate that the Illumina RNA sequencing runs overestimate quality scores for AC, CC, GC, GG, and TC dinucleotides, while SOLiD has less dinucleotide SSEs but more SSEs for certain cycles. We conclude that using these DNA and RNA spike-in standards with GATK improves base quality score recalibration. Four human RNA samples with equimolar ERCC spike-in standards were sequenced on Illumina. Two human brain/liver/muscle RNA mixtures with dynamic range of ERCC spike-in standards were sequenced on SOLiD.
2012-03-03 | E-GEOD-36217 | biostudies-arrayexpress
Project description:Illumina HiSeq sequence data (with >80x coverage) were aligned to the hg19 human reference genome assembly using BWA (Li and Durbin, 2009); duplicate reads were removed from the final BAM file. No realignment or recalibration was performed.
| EGAD00001000699 | EGA
Project description:Illumina HiSeq sequence data (with >30x coverage) were aligned to the hg19 human reference genome assembly using BWA (Li and Durbin, 2009); duplicate reads were removed from the final BAM file. No realignment or recalibration was performed.
| EGAD00001000697 | EGA
Project description:Human nontransformed retinal pigment epithelia RPE-PPM1D-T2 cells carrying a truncating mutation in exon 6 of the PPM1D were exposed to ionising radiation (3 Gy) and subsequently were grown in semisolid media for 8 weeks. Six spheroid clones (RPE-PPM1D-T2-SA clones 1-6) were recovered and then were cultivated in adherent conditions. RNA was isolated from asynchronically growing parental RPE-PPM1D-T2 and transformed RPE-PPM1D-T2-SA-1 to 6 cells and was subjected to whole exome sequencing. RNA sequencing libraries were prepared using KAPA RNA HyperPrep Kit (Roche) and were sequenced on the NovaSeq 6000 system using NovaSeq S1 Reagent Kit v1.5, 200 cycles (Illumina) with mean coverage >120 for RNA samples, respectively. RNA fastq files were mapped to the hg19 reference using Novoalign (novoalign_2.08.03). PCR duplicates were removed from the BAM files using Picard Tools (picard-tools 1.129), and variant calling was performed using GATK HaplotypeCaller (3.8). RNA fastq files were mapped to the hg19 reference using STAR (STAR-2.5.2b). The PCR duplicates were removed using Picard Tools (picard-tools 1.129). All parts of RNAseq data analysis were conducted in R, version 4.3.2. and RNAseq read counts were normalized using R package DESeq2. Fold change (FC) and log2FC were calculated from normalized reads, nontransformed RPE-PPM1D-T1 cells were considered a reference. Significance of differential expression for each gene was evaluated by Fisher's t-test with simulated p-values and Holm's p-value correction for multiple comparisons.
2024-04-05 | E-MTAB-13933 | biostudies-arrayexpress
Project description:This experiment was conducted to generate targeted resequencing data covering a region associated with osteosarcoma in greyhounds. 8 greyhounds diagnosed with osteosarcoma and 7 greyhounds without tumors were sequenced. DNA from the 15 dogs was used to prepare libraries and hybrid capture performed to enrich the region of interest prior to paired-end sequencing using Illumina Genome Analyzer II. The reads were aligned to the dog-genome CanFam2.0 using bwa and pre-processed using Picard and GATK. Variant discovery was performed using GATK. The resulting list of variants were used in the study to finemap the associated region and look for causal variants. We submit the preprocessed BAM-files that still have all reads although some reads are flagged. We also submit the resulting vcf-file with called and filtered variants in all individuals.
2013-11-16 | E-MTAB-1984 | biostudies-arrayexpress
Project description:Human nontransformed retinal pigment epithelia RPE-PPM1D-T2 cells carrying a truncating mutation in exon 6 of the PPM1D were exposed to ionising radiation (3 Gy) and subsequently were grown in semisolid media for 8 weeks. Six spheroid clones (RPE-PPM1D-T2-SA clones 1-6) were recovered and then were cultivated in adherent conditions. DNA was isolated from asynchronically growing parental RPE-PPM1D-T2 and transformed RPE-PPM1D-T2-SA-1 to 6 cells and was subjected to whole exome sequencing. DNA sequencing libraries were prepared using KAPA EvoPlus Kit (Roche) and were sequenced on the NovaSeq 6000 system using NovaSeq S1 Reagent Kit v1.5, 200 cycles (Illumina) with mean coverage >35 DNA samples, respectively. DNA fastq files were mapped to the hg19 reference using Novoalign (novoalign_2.08.03). PCR duplicates were removed from the BAM files using Picard Tools (picard-tools 1.129), and variant calling was performed using GATK HaplotypeCaller (3.8). Copy number variations (CNV) were analyzed using CNVkit version 0.7.4. Areas with median coverage >20 were included in the analysis. RNA fastq files were mapped to the hg19 reference using STAR (STAR-2.5.2b). The PCR duplicates were removed using Picard Tools (picard-tools 1.129).
2024-04-05 | E-MTAB-13923 | biostudies-arrayexpress
Project description:Purpose: Development of specific and precise RNA base editing toolkit. Methods: The transcriptome-wide off-target sites identification, annotation, and transcriptome-wide genes regulation of each editor were analyzed by RNA-seq data. The raw reads (.fastq) were first processed using Trim Glore (v0.6.7) to remove adapter sequences with default parameters. Then the trimmed data were used to remove rRNA data by Bowtie2 (v2.5.0) with key parameters –no-unal -I 1 -X 1000. The cleaned reads data were then aligned to the indexed human GRCh38 reference genome by STAR (v2.7.1a) with key parameters --outSAMstrandField intronMotif --outFilterIntronMotifs RemoveNoncanonical. The aligned BAMs were added RG tag of sample information, sorted, indexed, stated by Samtools (v1.15.1) with default parameters. Then the processed BAMs were removed duplications by the Picard Markduplicates tool (v2.23.9) with key parameters ASO=coordinate REMOVE_DUPLICATES=true. Then remove duplicate BAMs were used to filter reads containing clips by Samtools with default parameters. All the cleaned BAMs were downsampled to an equivalent level for subsequent analysis to ensure a fair comparison of transcriptome-wide off-targets. The uniquely mapped reads in final BAMs were used to calculate gene expression level by Cuffdiff (v2.2.1) with default parameters. Finally, we filtered the differential expression genes using cutoff |Log2FoldChange| > 1 and P-value < 0.05, based on the Cuffdiff quantification results. Results: Our new programmable RNA base editing toolkits have high editing efficiency, flexible editing scope and high transcriptome-wide specificity. Conclusions: We deleveped verstaile RNA editing toolkits enabling presice, specific and versatile RNA base editing.
2024-10-31 | GSE230140 | GEO
Project description:Bam files from deep exome sequencing of blood DNA samples from five TET2 mutation carriers (Ly1, Ly2, Ly9, Ly11, Ly14) and three wild-type family members (Ly8, Ly10, Ly13) extracted at multiple time points. Library preparations were performed with SeqCap EZ Exome v3 (Roche, Switzerland) using six different index primers per sample for which paired-end Illumina sequencing was done with 75bp read length and HiSeq4000 sequencer. After alignment (bwa version 0.7.12), base recalibration (GATK 3.5), realignment around indels (GATK 3.5) and duplicate removal (MarkDuplicates; Picard Tools version 1.79), data from libraries with six different indexes were merged.
| EGAD00001004780 | EGA