Baiting out a full length sequence from unmapped RNA-seq data.
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ABSTRACT: Background
As a powerful tool, RNA-Seq has been widely used in various studies. Usually, unmapped RNA-seq reads have been considered as useless and been trashed or ignored.Results
We develop a strategy to mining the full length sequence by unmapped reads combining with specific reverse transcription primers design and high throughput sequencing. In this study, we salvage 36 unmapped reads from standard RNA-Seq data and randomly select one 149 bp read as a model. Specific reverse transcription primers are designed to amplify its both ends, followed by next generation sequencing. Then we design a statistical model based on power law distribution to estimate its integrality and significance. Further, we validate it by Sanger sequencing. The result shows that the full length is 1556 bp, with insertion mutations in microsatellite structure.Conclusion
We believe this method would be a useful strategy to extract the sequences information from the unmapped RNA-seq data. Further, it is an alternative way to get the full length sequence of unknown cDNA.
SUBMITTER: Li D
PROVIDER: S-EPMC8626966 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Usually, unmapped reads have been considered as useless and been trashed or ignored. Here, we develop a strategy to mining the full length sequence by unmapped reads combining with specific reverse transcription primers design and high throughput sequencing. In this study, we salvage 36 unmapped reads from standard RNA-Seq data(GSM3188619) and randomly select one 149 bp read as a model(CTGGTGCCATAATTCAGGGAACTGTGTTCTTGATGTACTATCTGAGACATTTGTGCTTCCCCCCATCCAGCTATCAGGCTGTTAGGCAATGCACTTCTAGGAATTAGAATTCTATAAGGAATCTCATGCTGGAAGAACAAAAAGACCCA ). Specific reverse transcription primers(5' end:CTGGTGCCATAATTCAGGGA, 3' end:GGATCTTCACGTAACGGATTGT) are designed to amplify its both ends, followed by next generation sequencing. Then we use a statistical model base on power law distribution to estimate its integrality and significance. Further, we validate it by Sanger sequencing. The result shows that the full length is 1,556 bp, with InDel mutation in microsatellite structure. This would be a useful strategy to extract the sequences information from the unmapped RNA-seq data.
2021-04-22 | GSE172487 | GEO
Project description:Baiting out a full length sequence from unmapped RNA-seq data
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Project description:A widely used approach in transcriptome analysis is the alignment of short reads to a reference genome. However, owing to the deficiencies of specially designed analytical systems, short reads unmapped to the genome sequence are usually ignored, resulting in the loss of significant biological information and insights. To fill this gap, we present Comprehensive Assembly and Functional annotation of Unmapped RNA-Seq data (CAFU), a Galaxy-based framework that can facilitate the large-scale analysis of unmapped RNA sequencing (RNA-Seq) reads from single- and mixed-species samples. By taking advantage of machine learning techniques, CAFU addresses the issue of accurately identifying the species origin of transcripts assembled using unmapped reads from mixed-species samples. CAFU also represents an innovation in that it provides a comprehensive collection of functions required for transcript confidence evaluation, coding potential calculation, sequence and expression characterization and function annotation. These functions and their dependencies have been integrated into a Galaxy framework that provides access to CAFU via a user-friendly interface, dramatically simplifying complex exploration tasks involving unmapped RNA-Seq reads. CAFU has been validated with RNA-Seq data sets from wheat and Zea mays (maize) samples. CAFU is freely available via GitHub: https://github.com/cma2015/CAFU.
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Project description:Massively parallel sequencing of cDNA has enabled deep and efficient probing of transcriptomes. Current approaches for transcript reconstruction from such data often rely on aligning reads to a reference genome, and are thus unsuitable for samples with a partial or missing reference genome. Here we present the Trinity method for de novo assembly of full-length transcripts and evaluate it on samples from fission yeast, mouse and whitefly, whose reference genome is not yet available. By efficiently constructing and analyzing sets of de Bruijn graphs, Trinity fully reconstructs a large fraction of transcripts, including alternatively spliced isoforms and transcripts from recently duplicated genes. Compared with other de novo transcriptome assemblers, Trinity recovers more full-length transcripts across a broad range of expression levels, with a sensitivity similar to methods that rely on genome alignments. Our approach provides a unified solution for transcriptome reconstruction in any sample, especially in the absence of a reference genome.
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Project description:BackgroundFull-length isoform quantification from RNA-Seq is a key goal in transcriptomics analyses and has been an area of active development since the beginning. The fundamental difficulty stems from the fact that RNA transcripts are long, while RNA-Seq reads are short.ResultsHere we use simulated benchmarking data that reflects many properties of real data, including polymorphisms, intron signal and non-uniform coverage, allowing for systematic comparative analyses of isoform quantification accuracy and its impact on differential expression analysis. Genome, transcriptome and pseudo alignment-based methods are included; and a simple approach is included as a baseline control.ConclusionsSalmon, kallisto, RSEM, and Cufflinks exhibit the highest accuracy on idealized data, while on more realistic data they do not perform dramatically better than the simple approach. We determine the structural parameters with the greatest impact on quantification accuracy to be length and sequence compression complexity and not so much the number of isoforms. The effect of incomplete annotation on performance is also investigated. Overall, the tested methods show sufficient divergence from the truth to suggest that full-length isoform quantification and isoform level DE should still be employed selectively.
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Project description:BackgroundEarly single-cell RNA-seq (scRNA-seq) studies suggested that it was unusual to see more than one isoform being produced from a gene in a single cell, even when multiple isoforms were detected in matched bulk RNA-seq samples. However, these studies generally did not consider the impact of dropouts or isoform quantification errors, potentially confounding the results of these analyses.ResultsIn this study, we take a simulation based approach in which we explicitly account for dropouts and isoform quantification errors. We use our simulations to ask to what extent it is possible to study alternative splicing using scRNA-seq. Additionally, we ask what limitations must be overcome to make splicing analysis feasible. We find that the high rate of dropouts associated with scRNA-seq is a major obstacle to studying alternative splicing. In mice and other well-established model organisms, the relatively low rate of isoform quantification errors poses a lesser obstacle to splicing analysis. We find that different models of isoform choice meaningfully change our simulation results.ConclusionsTo accurately study alternative splicing with single-cell RNA-seq, a better understanding of isoform choice and the errors associated with scRNA-seq is required. An increase in the capture efficiency of scRNA-seq would also be beneficial. Until some or all of the above are achieved, we do not recommend attempting to resolve isoforms in individual cells using scRNA-seq.
| S-EPMC7087381 | biostudies-literature
Project description:BackgroundIn 2014, a novel tick-borne virus of the Flaviviridae family was first reported in the Mogiana region of Brazil and named the Mogiana tick virus (MGTV). Thereafter, the Jingmen tick virus (JMTV), Kindia tick virus (KITV), and Guangxi tick virus (GXTV)-evolutionarily related to MGTV-were reported.ResultsIn the present study, we used small RNA sequencing (sRNA-seq) to detect viruses in ticks and discovered a new MGTV strain in Amblyomma testudinarium ticks collected in China's Yunnan Province in 2016. We obtained the full-length genome sequence of this MGTV strain Yunnan2016 (GenBank: MT080097, MT080098, MT080099 and MT080100) and recommended it for its inclusion in the NCBI RefSeq database for future studies on MGTV, JMTV, KITV and GXTV. Phylogenetic analysis showed that MGTV, JMTV, KITV and GXTV are monophyletic and belong to a MGTV group. Furthermore, this MGTV group of viruses may be phylogenetically related to geographical regions that were formerly part of the supercontinents Gondwana and Laurasia.ConclusionsTo the best of our knowledge, this is the first study in which 5' and 3' sRNAs were used to generate full-length genome sequences of, but not limited to, RNA viruses. We also demonstrated the feasibility of using the sRNA-seq based method for the detection of viruses in pooled two and even possible one small ticks. MGTV may preserve the characteristic of ancient RNA viruses, which can be used to study the origin and evolution of RNA viruses. In addition, MGTV can be used as novel species for studies in phylogeography.
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Project description:MotivationBecause of the advantages of RNA sequencing (RNA-Seq) over microarrays, it is gaining widespread popularity for highly parallel gene expression analysis. For example, RNA-Seq is expected to be able to provide accurate identification and quantification of full-length splice forms. A number of informatics packages have been developed for this purpose, but short reads make it a difficult problem in principle. Sequencing error and polymorphisms add further complications. It has become necessary to perform studies to determine which algorithms perform best and which if any algorithms perform adequately. However, there is a dearth of independent and unbiased benchmarking studies. Here we take an approach using both simulated and experimental benchmark data to evaluate their accuracy.ResultsWe conclude that most methods are inaccurate even using idealized data, and that no method is highly accurate once multiple splice forms, polymorphisms, intron signal, sequencing errors, alignment errors, annotation errors and other complicating factors are present. These results point to the pressing need for further algorithm development.Availability and implementationSimulated datasets and other supporting information can be found at http://bioinf.itmat.upenn.edu/BEERS/bp2.Supplementary informationSupplementary data are available at Bioinformatics online.
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