WIND (Workflow for pIRNAs aNd beyonD): a strategy for in-depth analysis of small RNA-seq data [version 3; peer review: 2 approved]
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ABSTRACT:
SUBMITTER: Geles K
PROVIDER: S-EPMC8276195 | biostudies-literature | 2021 Jan
REPOSITORIES: biostudies-literature
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Project description:WIND: A Workflow for pIRNAs aNd beyonD, for the evaluation of the performance in piRNAs high expression and low expression conditions, we used Human Testis RNAs (BioChain Institute Inc, Newark, CA, USA) and COLO 205 cell line RNAs (samples are available on ArrayExpress (E-MTAB-8115: Non_treated_Testis_1 and Non_treated_COLO205_1, Non_treated_COLO205_2, Non_treated_COLO205_3).
2020-11-25 | E-MTAB-9782 | biostudies-arrayexpress
Project description:For the evaluation of "WIND: A Workflow for pIRNAs aNd beyonD" performance and the transcriptomic approach on small-RNA identification, and particularly on piRNAs, two mouse Adult Cardiomyocytes (aCMs) were used.
2020-11-28 | E-MTAB-9866 | biostudies-arrayexpress
Project description:RNA sequencing (RNA-seq) is a powerful method of transcript analysis that allows for the sequence identification and quantification of cellular transcripts. RNA-seq has many applications including differential gene expression (DE) analysis, gene fusion detection, allele-specific expression, isoform and splice variant quantification, and identification of novel genes. These applications can be used for downstream systems biology analyses such as gene ontology analysis to provide insights into cellular processes altered between biological conditions. Given the wide range of signaling pathways subject to chaperone activity as well as numerous chaperone functions in RNA metabolism, RNA-seq may provide a valuable tool for the study of chaperone proteins in biology and disease. This chapter outlines an example RNA-seq workflow to determine differentially expressed (DE) genes between two or more sample conditions and provides some considerations for RNA-seq experimental design.
| S-EPMC7336811 | biostudies-literature
Project description:BackgroundCurrent RNA-seq analysis software for RNA-seq data tends to use similar parameters across different species without considering species-specific differences. However, the suitability and accuracy of these tools may vary when analyzing data from different species, such as humans, animals, plants, fungi, and bacteria. For most laboratory researchers lacking a background in information science, determining how to construct an analysis workflow that meets their specific needs from the array of complex analytical tools available poses a significant challenge.ResultsBy utilizing RNA-seq data from plants, animals, and fungi, it was observed that different analytical tools demonstrate some variations in performance when applied to different species. A comprehensive experiment was conducted specifically for analyzing plant pathogenic fungal data, focusing on differential gene analysis as the ultimate goal. In this study, 288 pipelines using different tools were applied to analyze five fungal RNA-seq datasets, and the performance of their results was evaluated based on simulation. This led to the establishment of a relatively universal and superior fungal RNA-seq analysis pipeline that can serve as a reference, and certain standards for selecting analysis tools were derived for reference. Additionally, we compared various tools for alternative splicing analysis. The results based on simulated data indicated that rMATS remained the optimal choice, although consideration could be given to supplementing with tools such as SpliceWiz.ConclusionThe experimental results demonstrate that, in comparison to the default software parameter configurations, the analysis combination results after tuning can provide more accurate biological insights. It is beneficial to carefully select suitable analysis software based on the data, rather than indiscriminately choosing tools, in order to achieve high-quality analysis results more efficiently.
| S-EPMC11197194 | biostudies-literature
Project description:For the evaluation of \\"WIND: A Workflow for pIRNAs aNd beyonD\\" performance and the transcriptomic approach on small-RNA identification, and particularly on piRNAs, a synthetic set of 4 piRNA-like molecules was used. Two non-methylated (SS-22 and SS-28) and two methylated (mSS-22 and mSS-28) of two different lengths (22 nt and 28 nt) were included. Spike-ins were chemically synthesised at Exiqon, the pool of 4 molecules was used at three different concentrations, with a final amount of 0.3 x 10^9 (dil_A), 0.3 x 10^10 (dil_B) and 0.3 x 10^11 (dil_C) molecules/ug of RNA. Library preparation and sequencing were done as previously described in Sellitto et al 2019 (doi: https://doi.org/10.3390/cells8111390)
2020-11-25 | E-MTAB-9772 | biostudies-arrayexpress
Project description:Monitor the efficiency of "WIND: A Workflow for pIRNAs aNd beyonD" for the identification of piRNA molecules in small RNA-seq
| PRJEB41418 | ENA
Project description:SummarySingle-cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression at the individual cell level, unraveling unprecedented insights into cellular heterogeneity. However, the analysis of scRNA-seq data remains a challenging and time-consuming task, often demanding advanced computational expertise, rendering it impractical for high-volume environments and applications. We present CellBridge, an automated workflow designed to simplify the standard procedures entailed in scRNA-seq data analysis, eliminating the need for specialized computational expertise. CellBridge utilizes state-of-the-art computational methods, integrating a range of advanced functionalities, covering the entire process from raw unaligned sequencing reads to cell type annotation. Hence, CellBridge accelerates the pace of discovery by seamlessly enabling insights into vast volumes of scRNA-seq data, without compromising workflow control and reproducibility.Availability and implementationThe source code, detailed documentation, and materials required to reproduce the results are available on GitHub and archived in Zenodo. For the CellBridge pre-processing step (v1.0.0), access the GitHub repository at https://github.com/Sanofi-Public/PMCB-ToBridge and the Zenodo archive at https://zenodo.org/records/10246161. For the CellBridge processing step (v1.0.0), visit the GitHub repository at https://github.com/Sanofi-Public/PMCB-CellBridge and the Zenodo archive at https://zenodo.org/records/10246046.
| S-EPMC10751228 | biostudies-literature
Project description:BackgroundRNA-Sequencing analysis methods are rapidly evolving, and the tool choice for each step of one common workflow, differential expression analysis, which includes read alignment, expression modeling, and differentially expressed gene identification, has a dramatic impact on performance characteristics. Although a number of workflows are emerging as high performers that are robust to diverse input types, the relative performance characteristics of these workflows when either read depth or sample number is limited-a common occurrence in real-world practice-remain unexplored.ResultsHere, we evaluate the impact of varying read depth and sample number on the performance of differential gene expression identification workflows, as measured by precision, or the fraction of genes correctly identified as differentially expressed, and by recall, or the fraction of differentially expressed genes identified. We focus our analysis on 30 high-performing workflows, systematically varying the read depth and number of biological replicates of patient monocyte samples provided as input. We find that, in general for most workflows, read depth has little effect on workflow performance when held above two million reads per sample, with reduced workflow performance below this threshold. The greatest impact of decreased sample number is seen below seven samples per group, when more heterogeneity in workflow performance is observed. The choice of differential expression identification tool, in particular, has a large impact on the response to limited inputs.ConclusionsAmong the tested workflows, the recall/precision balance remains relatively stable at a range of read depths and sample numbers, although some workflows are more sensitive to input restriction. At ranges typically recommended for biological studies, performance is more greatly impacted by the number of biological replicates than by read depth. Caution should be used when selecting analysis workflows and interpreting results from low sample number experiments, as all workflows exhibit poorer performance at lower sample numbers near typically reported values, with variable impact on recall versus precision. These analyses highlight the performance characteristics of common differential gene expression workflows at varying read depths and sample numbers, and provide empirical guidance in experimental and analytical design.
| S-EPMC6234607 | biostudies-literature
Project description:BackgroundSingle nucleotide polymorphisms (SNP) have been applied as important molecular markers in genetics and breeding studies. The rapid advance of next generation sequencing (NGS) provides a high-throughput means of SNP discovery. However, SNP development is limited by the availability of reliable SNP discovery methods. Especially, the optimum assembler and SNP caller for accurate SNP prediction from next generation sequencing data are not known.ResultsHerein we performed SNP prediction based on RNA-seq data of peach and mandarin peel tissue under a comprehensive comparison of two paired-end read lengths (125 bp and 150 bp), five assemblers (Trinity, IDBA, oases, SOAPdenovo, Trans-abyss) and two SNP callers (GATK and GBS). The predicted SNPs were compared with the authentic SNPs identified via PCR amplification followed by gene cloning and sequencing procedures. A total of 40 and 240 authentic SNPs were presented in five anthocyanin biosynthesis related genes in peach and in nine carotenogenic genes in mandarin. Putative SNPs predicted from the same RNA-seq data with different strategies led to quite divergent results. The rate of false positive SNPs was significantly lower when the paired-end read length was 150 bp compared with 125 bp. Trinity was superior to the other four assemblers and GATK was substantially superior to GBS due to a low rate of missing authentic SNPs. The combination of assembler Trinity, SNP caller GATK, and the paired-end read length 150 bp had the best performance in SNP discovery with 100% accuracy both in peach and in mandarin cases. This strategy was applied to the characterization of SNPs in peach and mandarin transcriptomes.ConclusionsThrough 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. This strategy discovered SNP at 100% accuracy in peach and mandarin cases and might be applicable to a wide range of plants and other organisms.
| S-EPMC6391812 | biostudies-literature
Project description:Long noncoding RNAs (lncRNAs) are increasingly being recognized as modulators in various biological processes. However, due to their low expression, their systematic characterization is difficult to determine. Here, we performed transcript annotation by a newly developed computational pipeline, termed RNA-seq and small RNA-seq combined strategy (RSCS), in a wide variety of cellular contexts. Thousands of high-confidence potential novel transcripts were identified by the RSCS, and the reliability of the transcriptome was verified by analysis of transcript structure, base composition, and sequence complexity. Evidenced by the length comparison, the frequency of the core promoter and the polyadenylation signal motifs, and the locations of transcription start and end sites, the transcripts appear to be full length. Furthermore, taking advantage of our strategy, we identified a large number of endogenous retrovirus-associated lncRNAs, and a novel endogenous retrovirus-lncRNA that was functionally involved in control of Yap1 expression and essential for early embryogenesis was identified. In summary, the RSCS can generate a more complete and precise transcriptome, and our findings greatly expanded the transcriptome annotation for the mammalian community.
| S-EPMC10498003 | biostudies-literature