Longer read lengths are required for RNA velocity analysis of single-cell RNA-sequencing with paired immune repertoire and transcriptome profiling
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA717272 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR14071530_1.fastq.gz | Fastqsanger.gz | |||
| SRR14071530_2.fastq.gz | Fastqsanger.gz | |||
| SRR14071531_1.fastq.gz | Fastqsanger.gz | |||
| SRR14071531_2.fastq.gz | Fastqsanger.gz | |||
| SRR14071532_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 60 |
Similar Datasets
Project description:Increasing numbers of studies utilize single cell sequencing (scSeq) technologies that are capable of simultaneously profiling T/B-cell receptor (TCR/BCR) sequences and RNA expression. Here we show that standard sequencing of commonly-used 5’-biased scSeq chemistry precludes the ability to perform RNA velocity analysis; however extended sequencing lengths that allow for paired-end genome alignment alleviates these issues. This study provides important practical implications for paired TCR/BCR and RNA scSeq experiments.
2022-03-25 | GSE169657 | GEO
Project description:Ongoing improvements to next generation sequencing technologies are leading to longer sequencing read lengths, but a thorough understanding of the impact of longer reads on RNA sequencing analyses is lacking. To address this issue, we generated and compared two RNA sequencing datasets of differing read lengths -- 2x75 bp (L75) and 2x262 bp (L262) -- and investigated the impact of read length on various aspects of analysis, including the performance of currently available read-mapping tools, gene and transcript quantification, and detection of allele-specific expression patterns. Our results indicate that, while the scalability of read-mapping tools and the cost-effectiveness of long read protocol is an issue that requires further attention, longer reads enable more accurate quantification of diverse aspects of gene expression, including individual-specific patterns of allele-specific expression and alternative splicing. Two RNA-Seq datasets of differing read lengths (2x262 bp and 2x75 bp)
2014-09-25 | E-GEOD-57862 | biostudies-arrayexpress
Project description:Ongoing improvements to next generation sequencing technologies are leading to longer sequencing read lengths, but a thorough understanding of the impact of longer reads on RNA sequencing analyses is lacking. To address this issue, we generated and compared two RNA sequencing datasets of differing read lengths -- 2x75 bp (L75) and 2x262 bp (L262) -- and investigated the impact of read length on various aspects of analysis, including the performance of currently available read-mapping tools, gene and transcript quantification, and detection of allele-specific expression patterns. Our results indicate that, while the scalability of read-mapping tools and the cost-effectiveness of long read protocol is an issue that requires further attention, longer reads enable more accurate quantification of diverse aspects of gene expression, including individual-specific patterns of allele-specific expression and alternative splicing.
2014-09-25 | GSE57862 | GEO
Project description:We developed a 'Virtual Northern' method, using DNA microarrays for genome-wide systematic analysis of mRNA lengths. We used this method to measure mRNAs corresponding to 84% of the annotated open reading frames (ORFs) in the S. cerevisiae genome, with high precision and accuracy (measurement errors 1 6-7%). We found a close linear relationship between mRNA lengths and the lengths of known or predicted translated sequences; mRNAs were typically around 300 nucleotides longer than the translated sequences. Analysis of genes deviating from that relationship identified ORFs with annotation errors, ORFs that appear not to be bona fide genes, and potentially novel genes. Interestingly, we found that systematic differences in the total length of the untranslated sequences in mRNAs were related to the functions of the encoded proteins. Groups of assays that are related as part of a time series. Keywords: time_series_design
2005-12-30 | GSE3932 | GEO
Project description:Polyadenylation at the 3’ end of eukaryotic messenger RNAs enhances mRNA stability and translational efficiency. Global analysis for poly(A) tail lengths may shed lights on various aspects of gene regulation studies. Two NGS-based methods have been introduced for genome-wide poly(A) profiling, and they have shown human poly(A) profiles with shorter than previously conceived tail lengths. However, both methods are technically challenging and difficult to be repeated or widely adapted. Here we present a more straightforward method for poly(A) profiling. Poly(A)-seq performed on Illumina NextSeq 500 produces single-end 300 nt reads that covers the entirety of poly(A) tails, and poly(A) lengths can be directly calculated from base call data. With Poly(A)-seq we report that the global poly(A) lengths of several human cell lines may be longer than previously reported. We also show that the size selection step during Poly(A)-seq library preparation may greatly affect the sequencing profile, and thus cautions should be taken for comparisons between samples. As a convenient tool, we hope wide applications of Poly(A)-seq helps to bring better understanding of poly(A) tail properties and functions.
2020-06-04 | GSE84287 | GEO
Project description:We developed a 'Virtual Northern' method, using DNA microarrays for genome-wide systematic analysis of mRNA lengths. We used this method to measure mRNAs corresponding to 84% of the annotated open reading frames (ORFs) in the S. cerevisiae genome, with high precision and accuracy (measurement errors 1 6-7%). We found a close linear relationship between mRNA lengths and the lengths of known or predicted translated sequences; mRNAs were typically around 300 nucleotides longer than the translated sequences. Analysis of genes deviating from that relationship identified ORFs with annotation errors, ORFs that appear not to be bona fide genes, and potentially novel genes. Interestingly, we found that systematic differences in the total length of the untranslated sequences in mRNAs were related to the functions of the encoded proteins. Groups of assays that are related as part of a time series. Computed
2005-12-29 | E-GEOD-3932 | biostudies-arrayexpress
Project description:Single-cell multi-omic datasets, in which multiple molecular modalities are profiled within the same cell, provide a unique opportunity to discover the interplay between cellular epigenomic and transcriptomic changes. To realize this potential, we developed MultiVelo, a mechanistic model of gene expression that extends the popular RNA velocity framework by incorporating epigenomic data. MultiVelo uses a probabilistic latent variable model to estimate the switch time and rate parameters of gene regulation, providing a quantitative summary of the temporal relationship between epigenomic and transcriptomic changes. Fitting MultiVelo on single-cell multi-omic datasets revealed two distinct mechanisms of regulation by chromatin accessibility, quantified the degree of concordance or discordance between transcriptomic and epigenomic states within each cell, and inferred the lengths of time lags between transcriptomic and epigenomic changes.
2022-08-01 | GSE209878 | GEO
Project description:Across a range of biological processes, cells undergo coordinated changes in gene expression, resulting in transcriptome dynamics that unfold within a low-dimensional manifold. Single-cell RNA-sequencing (scRNA-seq) only measures temporal snapshots of gene expression, yet information on the underlying low-dimensional dynamics can be extracted using RNA velocity, which models unspliced and spliced RNA abundances to estimate the rate of change of gene expression. Available RNA velocity algorithms can be fragile and rely on heuristics that lack statistical control. Moreover, the estimated vector field is not dynamically consistent with the traversed gene expression manifold. Here, we develop a generative model of RNA velocity and a Bayesian inference approach that solves these problems. Our model couples velocity field and manifold estimation in a reformulated, unified framework, so as to coherently identify the parameters of an autonomous dynamical system. Focusing on the cell cycle, we implemented VeloCycle to study gene regulation dynamics on one-dimensional periodic manifolds and validated using live-imaging its ability to infer actual cell cycle periods. We benchmarked RNA velocity inference with sensitivity analyses and demonstrated one- and multiple-sample testing. We also conducted Markov chain Monte Carlo inference on the model, uncovering key relationships between gene-specific kinetics and our gene-independent velocity estimate. Finally, we applied VeloCycle to in vivo samples and in vitro genome-wide Perturb-seq, revealing regionally defined proliferation modes in neural progenitors and the effect of gene knockdowns on cell cycle speed. Ultimately, VeloCycle expands the scRNA-seq analysis toolkit with a modular and statistically rigorous RNA velocity inference framework.
2023-12-18 | GSE250148 | GEO
Project description:BackgroundNext generation sequencing has allowed the discovery of miRNA isoforms, termed isomiRs. Some isomiRs are derived from imprecise processing of pre-miRNA precursors, leading to length variants. Additional variability is introduced by non-templated addition of bases at the ends or editing of internal bases, resulting in base differences relative to the template DNA sequence. We hypothesized that some component of the isomiR variation reported so far could be due to systematic technical noise and not real.ResultsWe have developed the XICRA pipeline to analyze small RNA sequencing data at the isomiR level. We exploited its ability to use single or merged reads to compare isomiR results derived from paired-end (PE) reads with those from single reads (SR) to address whether detectable sequence differences relative to canonical miRNAs found in isomiRs are true biological variations or the result of errors in sequencing. We have detected non-negligible systematic differences between SR and PE data which primarily affect putative internally edited isomiRs, and at a much smaller frequency terminal length changing isomiRs. This is relevant for the identification of true isomiRs in small RNA sequencing datasets.ConclusionsWe conclude that potential artifacts derived from sequencing errors and/or data processing could result in an overestimation of abundance and diversity of miRNA isoforms. Efforts in annotating the isomiRnome should take this into account.
| S-EPMC8077951 | biostudies-literature
Project description:Background We applied the Virtual Northern technique to human brain mRNA to systematically measure human mRNA transcript lengths on a genome-wide scale. Methodology/Principal Findings We used separation by gel electrophoresis followed by hybridization to cDNA microarrays to measure 8,774 mRNA transcript lengths representing at least 6,238 genes at high (>90%) confidence. By comparing these transcript lengths to the Refseq and H-Invitational full-length cDNA databases, we found that nearly half of our measurements appeared to represent novel transcript variants. Comparison of length measurements determined by hybridization to different cDNAs derived from the same gene identified clones that potentially correspond to alternative transcript variants. We observed a close linear relationship between ORF and mRNA lengths in human mRNAs, identical in form to the relationship we had previously identified in yeast. Some functional classes of protein are encoded by mRNAs whose untranslated regions (UTRs) tend to be longer or shorter than average; these functional classes were similar in both human and yeast. Conclusions/Significance Human transcript diversity is extensive and largely unannotated. Our length dataset can be used as a new criterion for judging the completeness of cDNAs and annotating mRNA sequences. Similar relationships between the lengths of the UTRs in human and yeast mRNAs and the functions of the proteins they encode suggest that UTR sequences serve an important regulatory role among eukaryotes.
2013-01-30 | E-SMDB-4088 | biostudies-arrayexpress