AGA algorithm
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB29576 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:One of the main disadvantages of using DNA microarrays for miRNA expression profiling is inability of adequate comparison of expression values across different miRNAs. This leads to a large amount of miRNAs with high scores which are actually not expressed in examined samples, i.e. false positives. We propose a post-processing algorithm which performs scoring of miRNAs in the results of microarray analysis based on expression values, time of discovery of miRNA represented by MIMAT accession number and correlation level between the expressions of miRNA and corresponding pre-miRNA in considered samples. The algorithm was successfully validated by the comparison of the results of its application to miRNA microarray breast tumor samples with publicly available miRNA-seq breast tumor data.
2019-10-04 | GSE138411 | GEO
Project description:Interventions: None.
Primary outcome(s): The aim of the study is to create a serum CEA-measurement based algorithm of CEA rising. If the algorithm proves higher sensitivity of detecting recurrence, and therefore higher curable treatment, it can result in an application which helps professionals in health care, mainly general practitioners, with decision making. One should think of decisions like closer monitoring of CEA measurements, additional imaging based on serum CEA measurements (CEA-triggered imaging”) or referring to a specialist
Study Design: Non-randomized controlled trial, Open (masking not used), N/A , unknown, Other
| 2442417 | ecrin-mdr-crc
Project description:We developed a novel algorithm, smORFer, detecting smORFs (e.g. <50 codons) which performs with higher accuracy in prokaryotic organisms. smORFer considers structural features of genetic sequence along with in-register translation and using Fourier transform converts them into a measurable score to faithfully select smORFs. The algorithm is executed in a modular way and dependent on the data available different modules can be tested.
2021-05-12 | GSE150601 | GEO
Project description:A post-processing algorithm for miRNA microarray data
| PRJNA575757 | ENA
Project description:Klebsiella pneumoniae poses a significant global health threat primarily attributable to its pronounced resistance. Here, we report an in vitro acquired resistance analyses of K. pneumoniae to the combination of amikacin and polymyxin B. We found some differentially expressed genes associated with the resistome of K. pneumoniae. The main differences were found in the genes aphA, asmA, phoP, and in the arn operon. Once these genes are related to modification in lipopolysaccharides, aminoglycosides and in the membrane structure, the mechanisms associated with them can justify the resistance acquisition to amikacin and polymyxin b.
2024-04-26 | GSE264574 | GEO
Project description:Copy number variants (CNVs) are currently defined as genomic sequences that are polymorphic in copy number and range in length from 1,000 to several million base pairs. Among current array-based CNV detection platforms, long-oligonucleotide arrays promise the highest resolution. However, the performance of currently available analytical tools suffers when applied to these data because of the lower signal:noise ratio inherent in oligonucleotide-based hybridization assays. We have developed wuHMM, an algorithm for mapping CNVs from array comparative genomic hybridization (aCGH) platforms comprised of 385,000 to more than 3 million probes. wuHMM is unique in that it can utilize sequence divergence information to reduce the false positive rate (FPR). We apply wuHMM to 385K-aCGH, 2.1M-aCGH, and 3.1M-aCGH experiments comparing the 129X1/SvJ and C57BL/6J inbred mouse genomes. We assess wuHMM’s performance on the 385K platform by comparison to the higher resolution platforms and we independently validate 10 CNVs. The method requires no training data and is robust with respect to changes in algorithm parameters. At a FPR of less than 10%, the algorithm can detect CNVs with five probes on the 385K platform and three on the 2.1M and 3.1M platforms, resulting in effective resolutions of 24 kb, 2-5 kb, and 1 kb, respectively. Keywords: CNV detection algorithm development and assessment
2008-03-12 | GSE10511 | GEO
Project description:For this study, we created three highly representational different sized genomic libraries of P. aeruginosa PAO1 within the vector pBTB-1. Vector pBTB-1 is a low copy number broad host range plasmid containing a Ã-lactamase resistance marker, a pBAD promoter upstream of the cloning site, as well as transcriptional terminators flanking the cloning site to aid in insert stability. These libraries were transformed into the recombination-deficient P. aeruginosa PAO1 mutant, PAO2003, pooled, and parallel selections performed to identify via traditional sequencing or SCALEs the genomic regions capable of conferring increased tolerance to amikacin, gentamicin, or tobramycin. These antibiotics are all structurally related but have differing substitution patterns on their aminoglycoside backbones. These differences in structure and substitution patterns impact the activity of each antibiotic. We chose to study three different aminoglycosides in attempts to identify genomic regions capable of conferring resistance to not only a specific aminoglycoside, but also to the more general class of aminoglycosides.
2009-12-19 | E-MEXP-2396 | biostudies-arrayexpress
Project description:These data, combined with other cohorts (GSE6532, GSE12093, and qRT-PCR based cohorts), was used to construct the EP algorithm, which predicts the likelihood of developing of a distant recurrence of early stage breast cancer under endocrine treatment. In addition, EPclin, a combination of the EP score, the nodal status and the tumor size, was constructed.
2011-10-11 | GSE26971 | GEO
Project description:Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts, and developed a Random-Forest based algorithm to integrate histone modification profiles for identification of enhancers.As a training set, we used histone modification profiles at genome-wide binding sites of p300 in the two cell types identified using ChIP-seq. We show that this algorithm not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify an optimal set of three chromatin marks for enhancer prediction. ChIP-Seq Analysis of p300 in hESC H1 and IMR90 cells. Sequencing was done on the Illumina Genome Analyzer II platform for the H1 data and Illumina HiSeq for IMR90.Data was mapped to hg18 using Bowtie.
2012-05-09 | E-GEOD-37858 | biostudies-arrayexpress
Project description:Transcriptional enhancers play critical roles in regulation of gene expression, but their identification has remained a challenge. Recently, it was shown that enhancers in the mammalian genome are associated with characteristic histone modification patterns, which have been increasingly exploited for enhancer identification. However, only a limited number of histone modifications have previously been investigated for this purpose, leaving the questions answered whether there exist an optimal set of histone modifications that could improve the enhancer prediction. Here, we address this issue by exploring a rich dataset produced by the human Epigenome Roadmap Project. Specifically, we examined genome-wide profiles of 24 histone modifications in human embryonic stem cells and fibroblasts, and developed a Random-Forest based algorithm to integrate histone modification profiles for identification of enhancers.As a training set, we used histone modification profiles at genome-wide binding sites of p300 in the two cell types identified using ChIP-seq. We show that this algorithm not only leads to more accurate and precise prediction of enhancers than previous methods, but also helps identify an optimal set of three chromatin marks for enhancer prediction.
2012-05-10 | GSE37858 | GEO