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RNA-seq data

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

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Therefore, even when the RNA-Seq data has a single end and a very. 3 Contamination identification for a semi-simulated RNA-Seq data (Dataset 2) using RNA-QC-Chain.

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

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Improved annotation of the domestic pig genome through integration of Iso-Seq and RNA-seq data. On average, 90% of the splice junctions were supported by RNA-seq within tissue. We validated a large proportion of these extensions by independent pig poly(A) selected 3 ′ -RNA- seq data, or human FANTOM5 Cap Analysis of Gene Expression data. Indeed, Iso-seq data has been used for genome annotation of different species from Maize to Human [24–26].

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

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Conclusions: Through comparison of authentic SNPs obtained by PCR cloning strategy and putative SNPs predicted from different combinations of five assemblers, two SNP callers, and two paired-end read lengths, we provided a reliable and efficient strategy, Trinity-GATK with 150 bp paired-end read length, for SNP discovery from RNA-seq data. Keywords: Single nucleotide polymorphism (SNP), RNA-seq, Paired-end read length, Trinity, GATK.

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

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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.

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

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BioJupies: automated generation of interactive notebooks for RNA-Seq data analysis in the cloud. SCANPY: large-scale single-cell gene expression data analysis. VASC: dimension reduction and visualization of single-cell RNA-seq data by deep variational autoencoder. Single-cell reconstruction of the early maternal-fetal interface in humans. Bias, robustness and scalability in single-cell differential expression analysis

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

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In conclusion, we developed a metric to measure the stability of DE methods for differential expression analy- ses of RNA-seq data. Overall, the metric could rank DE methods according to the stability levels. On one hand, we summarize stability performance of 6 popular DE methods based on our study (Table 1).

Characteristics of allelic gene expression in human brain cells from single-cell RNA-seq data analysis

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To be highly conservative while keeping a reasonable number of hetSNPs for ana- lysis, we decided to use the cutoff of 20 for each of the two alleles in pooled RNA-seq data, yielding a positive predictive value of 99.46% on average (Fig. b Summary of the mouse scRNA-seq dataset from 34 embryos at different developmental stages.

Single cell RNA-seq data clustering using TF-IDF based methods

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Single cell RNA-seq data clustering using TF-IDF based methods. Conclusions: Empirical experimental results show that TF-IDF methods consistently outperform commonly used scRNA-Seq clustering approaches.. Keywords: Single cell RNA-Seq, Clustering, TF-IDF. Empirical evaluation on simulated and real cell mix- tures of FACS sorted cells with different levels of com- plexity suggests that the TF-IDF methods consistently outperform existing scRNA-Seq clustering methods.

Revealing transcription factor and histone modification co-localization and dynamics across cell lines by integrating ChIP-seq and RNA-seq data

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To this end, we extracted signal peaks from the ChIP-seq data for 55 TFs and 11 HMs and the gene expression level from the RNA-Seq data in human GM12878 and K562 cell lines (Additional file 1: Table S2). The localization of 55 TFs and 11 HMs were analyzed in the upstream and downstream region of transcription start sites in the two cell lines. Then, we compared the overlap ratio and the average overlap ratio of TFs’ binding or HMs in two cell lines.

Identification and characterization of SSR, SNP and InDel molecular markers from RNA-Seq data of guar (Cyamopsis tetragonoloba, L. Taub.) roots

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Identification and characterization of SSR, SNP and InDel molecular markers from RNA-Seq data of guar (Cyamopsis. Hence, the present work was done to enrich the molecular markers resource of guar by identifying high quality SSR, SNP and InDel markers from the RNA-Seq data of the roots of two guar varieties.. Results: We carried out RNA-Seq analysis of the roots of two guar varieties, namely, RGC-1066 and M-83.

Cloud accelerated alignment and assembly of full-length single-cell RNA-seq data using Falco

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Comparison of Falco alignment-only mode with rail-RNA As part of the evaluation of the alignment-only analysis using the Falco framework, the performance of Falco was also compared against Rail-RNA, a previously published tool designed for scalable alignment of RNA-seq data developed using the MapReduce programming paradigm.. Rail-RNA was able to perform alignment of the human brain dataset in about 6 h using a 40 node cluster, increas- ing to 16 h using a 10 node cluster.

Indel detection from DNA and RNA sequencing data with transIndel

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Surprisingly, we found only 12 coding indels called from WES data that overlapped with coding indels called from RNA-seq data (Fig. there were more coding indels called from RNA-seq data.

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

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

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The funding body had no role in the design of the study, collection, analysis, and interpretation of data, or in writing the manuscript.. 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..

Comparison of RNA-seq and microarray platforms for splice event detection using a cross-platform algorithm

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Prediction and quantification of splice events from RNA-Seq data

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

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Our single-cell RNA-Seq data showed that Epcam expression was de- tected in 1 of 2 cells of E11.5 hepatoblasts and 5 of 43 cells of E11.5 mesenchymal cells, not detected in E12.5. This temporal change of Epcam expression indicated our single-cell RNA-seq data was consistent with the previous observation via immunofluorescence assay or flow cytometric analysis [7].

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.

SCReadCounts: Estimation of cell-level SNVs expression from scRNA-seq data

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Single cell RNA sequencing (scRNA-seq) brings major ad- vantages over bulk RNA-seq analyses, especially the ability to distinguish cell populations and to assess cell-type spe- cific phenotypes [1].

Choice of library size normalization and statistical methods for differential gene expression analysis in balanced two-group comparisons for RNA-seq studies

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RNA-seq: High-throughput RNA sequencing. RSEM: RNA-seq by expectation- maximization. RNA-Seq: a revolutionary tool for transcriptomics. RNA-seq: from technology to biology. De novo assembly and analysis of RNA-seq data. New gene models and alternative splicing in the maize pathogen Colletotrichum graminicola revealed by RNA-Seq analysis. SNP discovery in the bovine milk transcriptome using RNA-Seq technology.. Reliable identification of genomic variants from RNA-seq data.

Integrating RNA-Seq with GWAS reveals novel insights into the molecular mechanism underpinning ketosis in cattle

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RNA-seq: impact of RNA degradation on transcript quantification. Impact of RNA degradation on fusion detection by RNA-seq. A survey of best practices for RNA-seq data analysis. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2