Normalization of whole-genome SNP data from acute monocytic leukemia patients for exome sequencing
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE27193 | GEO | 2011/02/12
SECONDARY ACCESSION(S): PRJNA142067
REPOSITORIES: GEO
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Project description:With the whole genome SNPs array information, we could evaluate the sensitivity and specificity of the point mutation we conclude from the next-generation sequencing data. Furthermore, we could use the true positive mutation as our guidance to exclude the most unreliable single nucleotide variation detected from sequence. After the process, we could promise a very high specificity under minimum loss of sensitivity. To evaluate the sensitivity and specificity of point mutaions detected through the next-generation sequencing data from nine cases of M5 leukemia patients.
2011-02-12 | E-GEOD-27193 | biostudies-arrayexpress
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Project description:DYT1 dystonia is an autosomal-dominantly inherited movement disorder, which is usually caused by a GAG deletion in the TOR1A gene. Due to the reduced penetrance of ~30-40%, the determination of the mutation in a subject is of limited use with regard to actual manifestation of symptoms. In the present study, we used Affymetrix oligonucleotide microarrays to analyze global gene expression in blood samples of 15 manifesting and 15 non-manifesting mutation carriers in order to identify a susceptibility profile beyond the GAG deletion which is associated with the manifestation of symptoms in DYT1 dystonia.We identified a genetic signature which distinguished between asymptomatic mutation carriers and symptomatic DYT1 patients with 86.7% sensitivity and 100% specificity. This genetic signature could correctly predict the disease state in an independent test set with a sensitivity of 87.5% and a specificity of 85.7%.Conclusively, this genetic signature might provide a possibility to distinguish DYT1 patients from asymptomatic mutation carriers. Comparison of whole blood expression profiles of patients with DYT1 dystonia with non manifesting mutation carriers and non mutation carriers
2010-05-26 | E-GEOD-19419 | biostudies-arrayexpress
Project description:Despite the large amounts of transcriptome data generated for many non-model organisms, there has been a much lesser uptake of individual genomic sequencing for detecting SNPs and determining haplotypes in these species. Transcriptome data represents a source of variant information, specifically for coding DNA, however there is little information on the accuracy, coverage and limitations of using transcriptomic data for the identification of single nucleotide polymorphisms (SNPs) in non-model organisms. To investigate this, we generated the first whole exome design for bovine using the Roche Nimblegen Developer system (Roche, USA) and used it to sequence and call SNPs from a lactating dairy cow model with genetically divergent fertility phenotypes (Fert+, n=8; Fert-, n=8). We compared these results to SNPs called from liver and muscle transcriptomes from the same animals. Our exome demonstrated 99.1% coverage of the target design of 56.7MB, whereas the transcriptomes only covered 60 and 54.5% of 44.2 and 42.8MB in liver and muscle respectively. We found that specificity of SNP detection in the transcriptome data is ~75% following basic hard-filtering, but could be increased marginally to ~80% by increasing the minimum threshold of reads covering a SNP, and number of samples in which it was found. Functional annotation of non-synonymous SNPs specific to both the high and low fertility phenotype identified immune response-related genes, supporting previous work that identified this process as key to the phenotype.
2015-10-08 | GSE62160 | GEO
Project description:DYT1 dystonia is an autosomal-dominantly inherited movement disorder, which is usually caused by a GAG deletion in the TOR1A gene. Due to the reduced penetrance of ~30-40%, the determination of the mutation in a subject is of limited use with regard to actual manifestation of symptoms. In the present study, we used Affymetrix oligonucleotide microarrays to analyze global gene expression in blood samples of 15 manifesting and 15 non-manifesting mutation carriers in order to identify a susceptibility profile beyond the GAG deletion which is associated with the manifestation of symptoms in DYT1 dystonia.We identified a genetic signature which distinguished between asymptomatic mutation carriers and symptomatic DYT1 patients with 86.7% sensitivity and 100% specificity. This genetic signature could correctly predict the disease state in an independent test set with a sensitivity of 87.5% and a specificity of 85.7%.Conclusively, this genetic signature might provide a possibility to distinguish DYT1 patients from asymptomatic mutation carriers.
2010-05-27 | GSE19419 | GEO
Project description:Profiling miRNA levels in cells with miRNA-microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. Here, we developed a method to select non-changing miRNAs (“invariants”) and used them to compute linear regression normalization coefficients or Variance Stabilizing Normalization (VSN) parameters. We compared the invariant normalizations to normalization by, scaling, quantile and VSN with default parameters as well as to no normalization using samples with strong differential expression of miRNAs (heart-brain comparison) and samples where only few miRNAs are affected (p53 overexpression in SCC13 cells versus GFP vector control transfected cells). All normalization methods performed better than no normalization. Normalizations procedures based on the set of invariants and quantile were the most robust over all experimental conditions tested. Our method of invariant selection and normalization is not limited to Agilent miRNA arrays and can be applied to other datasets from one color miRNA microarray platforms, focused gene expression arrays and gene expression analysis using quantitative PCR. Keywords: miRNA profiling To assay technical reproducibility, 3 technical replicates from brain and heart RNA (Stratagene) were hybridized on Agilent human miRNA microarrays. To determine sensitivity and specificity, heart and brain RNA were mixed in the following ratios: 50% heart 50% brain, 25% heart 75% brain and 5% heart 95% brain. Each of the dilutions was hybridized in a technical duplicate on Agilent human miRNA arrays. To assay the effect of p53 expression on miRNA levels in the human SCC13 cell line, RNA from 3 biological replicates of p53-expressing or control cells were hybridized in technical duplicates on the microarrays, resulting in a total of 12 hybridizations.
2009-02-16 | E-GEOD-12085 | biostudies-arrayexpress
Project description:High-throughput sequencing of DNA coding regions has become a common way of assaying genomic variation in the study of human diseases. Copy number variation (CNV) is an important type of genomic variation, but detecting and characterizing CNV from exome sequencing is challenging due to the high level of biases and artifacts. We propose CODEX, a normalization and CNV calling procedure for whole exome sequencing data. The Poisson latent factor model in CODEX includes terms that specifically remove biases due to GC content, exon capture and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. CODEX is compared to existing methods on a population analysis of HapMap samples from the 1000 Genomes Project, and shown to be more accurate on three microarray-based validation data sets. We further evaluate performance on 222 neuroblastoma samples with matched normals and focus on a well-studied rare somatic CNV within the ATRX gene. We show that the cross-sample normalization procedure of CODEX removes more noise than normalizing the tumor against the matched normal and that the segmentation procedure performs well in detecting CNVs with nested structures.
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Project description:Next generation sequencing platforms have become essential tools for understanding DNA in a wide range of contexts. Their success heavily relies on the accuracy, sensitivity and specificity of methods used to discern differences between the reference genome and genomes under investigation. Here we compare the relative performances of five popular single nucleotide variant callers with and without their associated recommended hard filtering criteria. We compare: FreeBayes; the Genome Analysis Toolkit’s Haplotype Caller and Unified Genotyper; SAMtools; and VarScan. We tailor this comparison to suit smaller projects with modest sample numbers (n = 10) and coverage (~10X) to fill a current gap in the literature. Other comparison studies are generally applicable only to larger projects in model species, where there is access to large amounts of sequencing data and curated callsets for base and variant quality score recalibration. We estimated the accuracy, sensitivity and specificity of each pipeline according to the genotype concordance rate and number with the “truth” dataset for 10 canine samples. The truth dataset was defined as genotypes obtained from the CanineHD BeadChip array. Whole genome sequencing data was performed on the Illumina HiSeq2000 or HiSeq2500 platform as 100-101 base pair, paired end reads to an average sample coverage of 10.3X. Apart from GATK Haplotype Caller, applying recommended hard filters did not improve the performance of genotyping concordance at the tested levels of minimum coverage. The default VarScan pipeline with no additional filters applied (VarScan uses SAMtools mpileup, without base alignment quality computation) generally outperformed other callers in terms of accuracy, sensitivity and specificity. The results of this study demonstrate that hard filtering of variant calls from low-powered genome studies can impair accuracy, sensitivity and specificity of callsets and provides some benchmark performance metrics on a range of low coverage levels.
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Project description:BackgroundMyeloid Sarcoma with monocytic differentiation is rare and quite likely is missed by surgical pathologists. However it is frequently misdiagnosed because of its non-specific imaging and histological pattern.Case presentationWe report the case of a 64-year-old woman with gastric primary myeloid sarcoma with monocytic differentiatio. Upper endoscopy revealed a neoplastic growth at the junction of the lesser curvature and gastric antrum. Except for a slightly increased peripheral monocyte count, no abnormalities were found on hematological and bone-marrow examination. Gastroscopic biopsy showed poorly differentiated atypical large cells with visible nucleoli and nuclear fission. Immunohistochemistry showed positive CD34, CD4, CD43, and CD56 expression, and weakly positive lysozyme expression. Immune markers for poorly differentiated adenocarcinoma, malignant melanoma, and lymphohematopoietic-system tumors were negative. The final diagnosis was myeloid sarcoma with monocytic differentiation. Chemotherapy did not shrink the tumor, so, radical surgery was performed. Although the tumor morphology did not change postoperatively, the immunophenotype did. CD68 and lysozyme expression (tumor tissue markers) changed from negative and weakly positive to strongly positive, AE1/3 expression (epithelial marker) changed from negative to positive, and CD34, CD4, CD43, and CD56 expression (common in naive hematopoietic cell-derived tumors) was greatly attenuated. Exome sequencing revealed missense mutations in FLT3 and PTPRB, which are associated with myeloid sarcoma, and in TP53, CD44, CD19, LTK, NOTCH2, and CNTN2, which are associated with lymphohematopoietic tumors and poorly differentiated cancers.ConclusionWe diagnosed myeloid sarcoma with monocytic differentiation after excluding poorly differentiated adenocarcinoma, common lymphohematopoietic-system tumors, epithelioid sarcoma, and malignant melanoma. We identified that the immunophenotypic of patient had alterations after chemotherapy, and FLT3 gene mutations. We hope that the above results will improve our understanding of this rare tumor.
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