Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

ABSTRACT: RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

PROVIDER: PRJNA574846 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants

Project description:Single nucleotide variants (SNVs) in intronic regions have yet to be systematically investigated for their disease-causing potential. Using known pathogenic and neutral intronic SNVs (iSNVs) as training data, we develop the RegSNPs-intron algorithm based on a random forest classifier that integrates RNA splicing, protein structure and evolutionary conservation features. RegSNPs-intron shows excellent performance in evaluating the pathogenic impacts of iSNVs. Using a high-throughput functional reporter assay called ASSET-seq (ASsay for Splicing using ExonTrap and sequencing), we evaluate the impact of regSNPs-intron predictions on splicing outcome. Together, RegSNPs-intron and ASSET-seq enable effective prioritization of iSNVs for disease pathogenesis.

2019-09-30 | GSE138130 | GEO

Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing

Project description:This SuperSeries is composed of the following subset Series: GSE14694: Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (reproducibility) GSE14695: Computational and Analytical Framework for Small RNA Profiling by High-Throughput Sequencing (standards) Refer to individual Series

2009-06-02 | E-GEOD-14696 | biostudies-arrayexpress

RegSNPs-intron: a computational framework for predicting pathogenic impact of intronic single nucleotide variants.

Project description: Not available

| S-EPMC6883696 | biostudies-literature

Computational frameworks for predicting protein interactions via single-cell proximity sequencing

Project description:Computational frameworks for predicting protein interactions via single-cell proximity sequencing

| PRJNA803449 | ENA

SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators

Project description:SPaRTAN, a computational framework for linking cell-surface receptors to transcriptional regulators

| PRJNA722248 | ENA

Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [RNAseq_THP1_GSK-LSD1]

Project description:Genome-wide transcription factor binding profiles provide valuable insights into hematopoietic development and malignancies. Today, advanced computational methods exploit cis-regulatory information to predict complex gene regulatory networks controlled by transcription factors. However, these prediction methods still require large amounts of reference data or experimental expertise. Here, we present a low-requirement network-based computational framework that exploits bidirectional transcription marked regulatory elements to identify important transcription factors driving hematopoietic decision making. Using CAGE-seq we predicted TF binding and confirmed experimentally validated TF important in cell conversion strategies by exploiting bidirectional regions. Next, we applied our framework to predict driving transcription factors in induced normal erythropoiesis and early myelopoiesis, as well as in acute myeloid leukemia associated MLL-AF9-driven immortalisation. Our approach allowed the identification of experimentally validated as well as thus far unexplored transcription factors in these processes.

2022-11-09 | GSE204710 | GEO

Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_THP1_GSK-LSD1]

2022-11-09 | GSE204708 | GEO

Identification of transcription factors dictating blood cell development using a bidirectional transcription network-based computational framework [CAGEseq_MLLAF9-AML]

2022-11-09 | GSE204707 | GEO

A computational framework for identifying promoter sequences in non-model organisms using RNA-seq datasets

Project description:We developed a computational framework to discover short DNA sequences that confer strong expression in non-model organisms. The framework relies solely on whole genome and RNA sequencing data types, which are easily accessible to a variety of research groups. The framework proceeds in three main stages: 1) identification of a group of highly expressed loci that maintain high transcript counts across a broad range of experimental conditions, 2) extraction of the corresponding upstream candidate promoter regions of these highly expressed loci while minding nearby annotations and avoiding those that may potentially reside in operons, and 3) application of the motif finding algorithm in BioProspector to these upstream regions to predict the location and sequence of the -35 and -10 hexamers that drive the strong expression of these loci. Ultimately, we report sequences of 27-30 bases in length as candidate -35, -10 signals for each of the top loci and create a consensus motif from these predictions. We apply our framework to 80 RNA-seq datasets collected for the methanotroph Methylotuvimicrobium buryatense 5GB1 and validate our predictions computationally and experimentally. The data deposited here represent all RNA-seq data that, until this study, has not previously been published.

2021-05-24 | GSE162089 | GEO

DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics

Project description:This dataset consists of 44 raw MS files, comprising 27 DIA (SWATH) and 15 DDA runs on a TripleTOF 5600 and of two raw mass spectrometry files acquired on a Q Exactive. The composition of the dataset is described in the manuscript by Tsou et al., titled: "DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics", Nature Methods, in press Raw files are deposited here in ProteomeXchange and are associated with the DIA-Umpire processed data. All DIA-Umpire processed results for each sample together with DDA results are deposited in separated folders. Also see the "DataSampleID.xlsx" associated with this Readme file. Internal reference from the Gingras lab ProHits implementation: Project 94, Export version VS2 (Tsou_DIA-Umpire)

2015-01-21 | PXD001587 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data