Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumors and matched controls

ABSTRACT: We prepared small RNA libraries from 29 tumor/normal pairs of human cervical tissue samples. Analysis of the resulting sequences (42 million in total) defined 64 new human microRNA (miRNA) genes. Both arms of the hairpin precursor were observed in twenty-three of the newly identified miRNA candidates. We tested several computational approaches for analysis of class differences between high throughput sequencing datasets, and describe a novel application of log linear model that has provided the most datasets, and describe a novel application of log linear model that has provided the most effective analysis for this data. This method resulted in the identification of 67 miRNAs that were differentially-expressed between the tumor and normal samples at a false discovery rate less than 0.001. A total of 29 tumor/normal pairs of human cervical tissue samples were analyzed. Two samples (G699N_2 and G761T_2) were performed in duplicates. No Fastq files for GSM532871 to GSM532889, GSM532929, and GSM532930. Sequence files are provided as text files for these 22 Sample records in GSE20592_RAW.tar. 38 samples with quality scores are available from SRA as SRP002/SRP002326 (see Supplementary file below).

ORGANISM(S): Homo sapiens

SUBMITTER: Witten D

PROVIDER: S-ECPF-GEOD-20592 | biostudies-other | 2010

REPOSITORIES: biostudies-other

ACCESS DATA

Json Xml

Similar Datasets

Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls.

Project description:BACKGROUND:Ultra-high throughput sequencing technologies provide opportunities both for discovery of novel molecular species and for detailed comparisons of gene expression patterns. Small RNA populations are particularly well suited to this analysis, as many different small RNAs can be completely sequenced in a single instrument run. RESULTS:We prepared small RNA libraries from 29 tumour/normal pairs of human cervical tissue samples. Analysis of the resulting sequences (42 million in total) defined 64 new human microRNA (miRNA) genes. Both arms of the hairpin precursor were observed in twenty-three of the newly identified miRNA candidates. We tested several computational approaches for the analysis of class differences between high throughput sequencing datasets and describe a novel application of a log linear model that has provided the most effective analysis for this data. This method resulted in the identification of 67 miRNAs that were differentially-expressed between the tumour and normal samples at a false discovery rate less than 0.001. CONCLUSIONS:This approach can potentially be applied to any kind of RNA sequencing data for analysing differential sequence representation between biological sample sets.

| S-EPMC2880020 | biostudies-literature

Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumors and matched controls

2010-04-28 | GSE20592 | GEO

Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumors and matched controls

Project description:We prepared small RNA libraries from 29 tumor/normal pairs of human cervical tissue samples. Analysis of the resulting sequences (42 million in total) defined 64 new human microRNA (miRNA) genes. Both arms of the hairpin precursor were observed in twenty-three of the newly identified miRNA candidates. We tested several computational approaches for analysis of class differences between high throughput sequencing datasets, and describe a novel application of log linear model that has provided the most datasets, and describe a novel application of log linear model that has provided the most effective analysis for this data. This method resulted in the identification of 67 miRNAs that were differentially-expressed between the tumor and normal samples at a false discovery rate less than 0.001. A total of 29 tumor/normal pairs of human cervical tissue samples were analyzed. Two samples (G699N_2 and G761T_2) were performed in duplicates. No Fastq files for GSM532871 to GSM532889, GSM532929, and GSM532930. Sequence files are provided as text files for these 22 Sample records in GSE20592_RAW.tar. 38 samples with quality scores are available from SRA as SRP002/SRP002326 (see Supplementary file below).

2010-04-28 | E-GEOD-20592 | biostudies-arrayexpress

Biomarker Discovery and Validation: Statistical Considerations.

Project description:Biomarkers have various applications including disease detection, diagnosis, prognosis, prediction of response to intervention, and disease monitoring. In this era of precision medicine, having validated biomarkers to inform clinical decision making is more important than ever. In this article, we discuss best the practices and potential issues in biomarker discovery and validation. We encourage team science partnerships to bring cutting-edge discovery from bench to bedside, leading to improved patient care and outcomes.

| S-EPMC8012218 | biostudies-literature

Breath Collection from Children for Disease Biomarker Discovery.

Project description:Breath collection and analysis can be used to discover volatile biomarkers in a number of infectious and non-infectious diseases, such as malaria, tuberculosis, lung cancer, and liver disease. This protocol describes a reproducible method for sampling breath in children and then stabilizing breath samples for further analysis with gas chromatography-mass spectrometry (GC-MS). The goal of this method is to establish a standardized protocol for the acquisition of breath samples for further chemical analysis, from children aged 4-15 years. First, breath is sampled using a cardboard mouthpiece attached to a 2-way valve, which is connected to a 3 L bag. Breath analytes are then transferred to a thermal desorption tube and stored at 4-5 °C until analysis. This technique has been previously used to capture breath of children with malaria for successful breath biomarker identification. Subsequently, we have successfully applied this technique to additional pediatric cohorts. The advantage of this method is that it requires minimal cooperation on part of the patient (of particular value in pediatric populations), has a short collection period, does not require trained staff, and can be performed with portable equipment in resource-limited field settings.

| S-EPMC6596991 | biostudies-literature

High-Throughput Transcriptome Profiling in Drug and Biomarker Discovery.

Project description:The development of new drugs is multidisciplinary and systematic work. High-throughput techniques based on "-omics" have driven the discovery of biomarkers in diseases and therapeutic targets of drugs. A transcriptome is the complete set of all RNAs transcribed by certain tissues or cells at a specific stage of development or physiological condition. Transcriptome research can demonstrate gene functions and structures from the whole level and reveal the molecular mechanism of specific biological processes in diseases. Currently, gene expression microarray and high-throughput RNA-sequencing have been widely used in biological, medical, clinical, and drug research. The former has been applied in drug screening and biomarker detection of drugs due to its high throughput, fast detection speed, simple analysis, and relatively low price. With the further development of detection technology and the improvement of analytical methods, the detection flux of RNA-seq is much higher but the price is lower, hence it has powerful advantages in detecting biomarkers and drug discovery. Compared with the traditional RNA-seq, scRNA-seq has higher accuracy and efficiency, especially the single-cell level of gene expression pattern analysis can provide more information for drug and biomarker discovery. Therefore, (sc)RNA-seq has broader application prospects, especially in the field of drug discovery. In this overview, we will review the application of these technologies in drug, especially in natural drug and biomarker discovery and development. Emerging applications of scRNA-seq and the third generation RNA-sequencing tools are also discussed.

| S-EPMC7013098 | biostudies-literature

Biomarker discovery in MALDI-TOF serum protein profiles using discrete wavelet transformation.

Project description:MotivationAutomatic classification of high-resolution mass spectrometry proteomic data has increasing potential in the early diagnosis of cancer. We propose a new procedure of biomarker discovery in serum protein profiles based on: (i) discrete wavelet transformation of the spectra; (ii) selection of discriminative wavelet coefficients by a statistical test and (iii) building and evaluating a support vector machine classifier by double cross-validation with attention to the generalizability of the results. In addition to the evaluation results (total recognition rate, sensitivity and specificity), the procedure provides the biomarker patterns, i.e. the parts of spectra which discriminate cancer and control individuals. The evaluation was performed on matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) serum protein profiles of 66 colorectal cancer patients and 50 controls.ResultsOur procedure provided a high recognition rate (97.3%), sensitivity (98.4%) and specificity (95.8%). The extracted biomarker patterns mostly represent the peaks expressing mean differences between the cancer and control spectra. However, we showed that the discriminative power of a peak is not simply expressed by its mean height and cannot be derived by comparison of the mean spectra. The obtained classifiers have high generalization power as measured by the number of support vectors. This prevents overfitting and contributes to the reproducibility of the results, which is required to find biomarkers differentiating cancer patients from healthy individuals.AvailabilityThe data and scripts used in this study are available at http://www.math.uni-bremen.de/~theodore/MALDIDWT.

| S-EPMC2647828 | biostudies-literature

A statistical framework for biomarker discovery in metabolomic time course data.

Project description:MotivationMetabolomics is the study of the complement of small molecule metabolites in cells, biofluids and tissues. Many metabolomic experiments are designed to compare changes observed over time under two experimental conditions or groups (e.g. a control and drug-treated group) with the goal of identifying discriminatory metabolites or biomarkers that characterize each condition. A common study design consists of repeated measurements taken on each experimental unit thus producing time courses of all metabolites. We describe a statistical framework for estimating time-varying metabolic profiles and their within-group variability and for detecting between-group differences. Specifically, we propose (i) a smoothing splines mixed effects (SME) model that treats each longitudinal measurement as a smooth function of time and (ii) an associated functional test statistic. Statistical significance is assessed by a non-parametric bootstrap procedure.ResultsThe methodology has been extensively evaluated using simulated data and has been applied to real nuclear magnetic resonance spectroscopy data collected in a preclinical toxicology study as part of a larger project lead by the COMET (Consortium for Metabonomic Toxicology). Our findings are compatible with the previously published studies.AvailabilityAn R script is freely available for download at http://www2.imperial.ac.uk/~gmontana/sme.htm.

| S-EPMC3129523 | biostudies-literature

An integrated genomics approach for novel biomarker discovery in squamous cell cervical carcinoma

Project description:Detection of copy number dependent gene expression profiles in epithelial tumours has lead to the discovery of genes significant in diagnosis, prognosis and of therapeutic value. We sought to apply the technologies of gene expression profiling and copy number aberration detection by high density oligonucleotide microarray analysis to detect genes with expression profiles regulated by copy number. We surveyed copy number aberrations in 27 squamous cell carcinomas tissues and ten cervical carcinoma cell lines by Agilent 244k aCGH, and in a companion study of 77 tissues we obtained transcriptional profiles of a broader ranger of histologies. Briefly, for normal (NAD) and squamous intraepithelial lesions (SILs), DNA and RNA were extracted from approximately 5 - 25 x 5 mm thin sections of fresh frozen cervical tissue, respectively. Epithelial were microdissected, pooled and processed as follows. For RNA extraction, tissue was collected in Trizol (Invitrogen, UK) and subject to homogenization (Polytron), total RNA was recovered following standard processes, and quality assessed by BioAnalyzer (Agilent). DNA was recovered following Proteinase K digestion, phenol chloroform extraction, and ethanol precipitation. Contaminating RNA was removed by RNAse A treatment, and a second round of phenol chloroform extraction and ethanol precipitation. For SCCs, RNA and DNA was extracted from whole tumour (>85%), following similar strategies as previously described. This submission describes the copy number aberrations of 27 cervical SCC tissues and 10 common cervical SCC cell lines

2011-03-05 | E-GEOD-27673 | biostudies-arrayexpress

An integrated genomics approach for novel biomarker discovery in squamous cell cervical carcinoma

2011-03-05 | GSE27673 | GEO