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RNA-seq

Tìm thấy 20+ kết quả cho từ khóa "RNA-seq"

RNA-QC-chain: Comprehensive and fast quality control for RNA-Seq data

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Consequently, RNA-QC-Chain has the ability of fast processing massive scale of RNA-Seq data. Comparisons with other QC tools for RNA-Seq data We compared RNA-QC-Chain with two other QC tools for RNA-Seq data, RNA-SeQC and RSeQC, which were developed by different techniques with different features, and have been widely used for RNA-Seq quality checking [17]. RNA-QC-Chain is.

Systematic evaluation of RNA-Seq preparation protocol performance

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Mapping and quantifying mammalian transcriptomes by RNA-Seq. A comprehensive assessment of RNA-seq protocols for degraded and low- quantity samples. Ribosomal RNA depletion for efficient use of RNA-seq capacity. TopHat: discovering splice junctions with RNA-Seq. RSeQC: quality control of RNA-seq experiments.

Improved annotation of the domestic pig genome through integration of Iso-Seq and RNA-seq data

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Full-length transcriptome assembly from RNA-Seq data without a reference genome. Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. TopHat: discovering splice junctions with RNA-Seq. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and cufflinks

A high-throughput SNP discovery strategy for RNA-seq data

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At present, the best assembling and SNP calling soft- ware combination for achieving the most accurate SNP data on RNA-seq is not known. The access to SNP in- formation on RNA-seq data is a formidable task limited by the availability of reliable SNP discovery methods in- cluding assembling and SNP calling pipeline to resolve the problems of genotyping errors and missing data.

Plant stress RNA-seq Nexus: A stress-specific transcriptome database in plant cells

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Plant stress RNA-Seq dataset collection. The annotation for the RNA-Seq datasets regarding plant stress was collected from NCBI GEO [21], and RNA-Seq reads were downloaded from the SRA [22].. Each dataset has several stress-specific subsets that con- tain a group of RNA-Seq samples of plants treated with. We only retained the datasets that had a reference transcriptome for subsequent RNA-Seq analysis. Stress-specific differentially expressed transcripts.

Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

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STAR: ultrafast universal RNA-seq aligner.. ddSeeker: a tool for processing bio-rad ddSEQ single cell RNA-seq data. Normalization and variance stabilization of single- cell RNA-seq data using regularized negative binomial regression

Single-cell RNA-Seq analysis reveals dynamic trajectories during mouse liver development

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Accounting for technical noise in single-cell RNA-seq experiments. Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq

Impact of sequencing depth and technology on de novo RNA-Seq assembly

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Impact of sequencing depth and technology on de novo RNA-Seq assembly

DoGFinder: A software for the discovery and quantification of readthrough transcripts from RNA-seq

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The osmotic stress NIH3T3 RNA-seq data can be found in GEO, accession no.. The hypoxia RNA-seq data was downloaded from GEO, accession no. All authors read and approved the final version of the manuscript.. RSeQC: quality control of RNA-seq experiments.. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Evaluation and comparison of computational tools for RNA-seq isoform quantification

Comparison of RNA isolation methods on RNA-Seq: Implications for differential expression and meta-analyses

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Comparison of RNA isolation methods on RNA-Seq: implications for differential. In this study, we examined the effects of RNA isolation method as a possible source of batch effects in RNA- seq design..

BALLI: Bartlett-adjusted likelihood-based linear model approach for identifying differentially expressed genes with RNA-seq data

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RNA- seq: RNA sequencing. Comparison of RNA-Seq and microarray in transcriptome profiling of activated T cells. Mapping and quantifying mammalian transcriptomes by RNA-Seq. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts.

BaRTv1.0: An improved barley reference transcript dataset to determine accurate changes in the barley transcriptome using RNA-seq

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Selected RNA-seq datasets and data processing. to each RNA-seq tran- scriptome assembly generated. High resolution RT-PCR. Morex was used for HR RT-PCR validation [35]. Comparing HR RT-PCR and RNA-seq alternative splicing proportions.

Discerning novel splice junctions derived from RNA-seq alignment: A deep learning approach

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MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. TrueSight: a new algorithm for splice junction detection using RNA-seq. Human splicing diversity and the extent of unannotated splice junctions across human RNA-seq samples on the sequence read archive. Boosted Categorical Restricted Boltzmann Machine for Computational Prediction of Splice Junctions. DeepSplice: Deep classification of novel splice junctions revealed by RNA-seq.

Stability of methods for differential expression analysis of RNA-seq data

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So, we are particularly interested in the perfor- mance of DE methods on the RNA-seq dataset as the increasing of number of replicates. We also observe that the precisions of these DE methods have sim- ilar patterns as AUCOR and ranks of methods according.

Exploring the changing landscape of cellto-cell variation after CTCF knockdown via single cell RNA-seq

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Accounting for technical noise in single-cell RNA-seq experiments

Systematic identification and validation of the reference genes from 60 RNA-Seq libraries in the scallop Mizuhopecten yessoensis

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Relationship between the Ct of RT-qPCR and the TPM of RNA-Seq. To facilitate an estimation of the Ct of RT-qPCR based on RNA-Seq data, we further evaluated the relationship of the gene expression levels be- tween RNA-Seq and RT-qPCR data using the 10 reference genes, excluding ACT and CYTC . Table 2 The top 10 candidate reference genes in the early development, adult tissues and gonadal development of the scallop M..

Single Cell Explorer, collaboration-driven tools to leverage large-scale single cell RNA-seq data

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The human cell atlas bone marrow single-cell interactive web portal. iS-CellR: a user-friendly tool for analyzing and visualizing single- cell RNA sequencing data. ASAP: a web- based platform for the analysis and interactive visualization of single-cell RNA-seq data. Integrating single-cell transcriptomic data across different conditions, technologies, and species.. BioJupies: automated generation of interactive notebooks for RNA-Seq data analysis in the cloud.

Genome-wide analyses supported by RNA-Seq reveal non-canonical splice sites in plant genomes

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Dedicated split-read aligners like STAR [31, 32] are able to detect non-canonical splice sites during the alignment of RNA-Seq reads to genomic sequences. Nonetheless, the combined number of currently inferred minor non-canonical splice site. combinations is even higher than the number of the major non-canonical AT-AC splice site combinations [30, 34].. We incor- porated RNA-Seq data to differentiate between artifacts and bona fide cases of active non-canonical splice sites..

Transcription profiling of butanol producer Clostridium beijerinckii NRRL B-598 using RNA-Seq

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Additional file 4: Circular plots showing average coverage of the genome by RNA-Seq reads in all six time points. The RNA-Seq sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) under the accession number SRP033480.. Genome sequence and comparative analysis of the solvent-producing bacterium Clostridium acetobutylicum. Complete genome sequence of the nitrogen-fixing and solvent-producing Clostridium pasteurianum DSM 525.

Study on differentially expressed genes related to defoliation traits in two alfalfa varieties based on RNA-Seq

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RNA extraction and RNA-Seq library construction. Total RNA was extracted from three biological replicates of the leaves of each M. Finally, Butterfly analyzed the paths from the de Bruijn graphs that were taken by reads and pairs of reads, and the complete transcripts and unigenes sets were ob- tained from the outputs [54]..