Combining tRNA sequencing methods to characterize plant tRNA expression and post-transcriptional modification

ABSTRACTDifferences in tRNA expression have been implicated in a remarkable number of biological processes. There is growing evidence that tRNA genes can play dramatically different roles depending on both expression and post-transcriptional modification, yet sequencing tRNAs to measure abundance and detect modifications remains challenging. Their secondary structure and extensive post-transcriptional modifications interfere with RNA-seq library preparation methods and have limited the utility of high-throughput sequencing technologies. Here, we combine two modifications to standard RNA-seq methods by treating with the demethylating enzyme AlkB and ligating with tRNA-specific adapters in order to sequence tRNAs from four species of flowering plants, a group that has been shown to have some of the most extensive rates of post-transcriptional tRNA modifications. This protocol has the advantage of detecting full-length tRNAs and sequence variants that can be used to infer many post-transcriptional modifications. We used the resulting data to produce a modification index of almost all unique reference tRNAs in Arabidopsis thaliana, which exhibited many anciently conserved similarities with humans but also positions that appear to be “hot spots” for modifications in angiosperm tRNAs. We also found evidence based on northern blot analysis and droplet digital PCR that, even after demethylation treatment, tRNA-seq can produce highly biased estimates of absolute expression levels most likely due to biased reverse transcription. Nevertheless, the generation of full-length tRNA sequences with modification data is still promising for assessing differences in relative tRNA expression across treatments, tissues or subcellular fractions and help elucidate the functional roles of tRNA modifications.

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Status of the Archaeal and Bacterial Census: an Update

mBio ◽

10.1128/mbio.00201-16 ◽

2016 ◽

Vol 7 (3) ◽

Cited By ~ 69

Author(s):

Patrick D. Schloss ◽

Rene A. Girard ◽

Thomas Martin ◽

Joshua Edwards ◽

J. Cameron Thrash

Keyword(s):

16S Rrna ◽

Bacterial Diversity ◽

High Throughput Sequencing ◽

New Technologies ◽

Full Length ◽

16S Rrna Genes ◽

Rrna Genes ◽

Rrna Gene ◽

Short Read ◽

Sequencing Technologies

ABSTRACT A census is typically carried out for people across a range of geographical levels; however, microbial ecologists have implemented a molecular census of bacteria and archaea by sequencing their 16S rRNA genes. We assessed how well the census of full-length 16S rRNA gene sequences is proceeding in the context of recent advances in high-throughput sequencing technologies because full-length sequences are typically used as references for classification of the short sequences generated by newer technologies. Among the 1,411,234 and 53,546 full-length bacterial and archaeal sequences, 94.5% and 95.1% of the bacterial and archaeal sequences, respectively, belonged to operational taxonomic units (OTUs) that have been observed more than once. Although these metrics suggest that the census is approaching completion, 29.2% of the bacterial and 38.5% of the archaeal OTUs have been observed more than once. Thus, there is still considerable diversity to be explored. Unfortunately, the rate of new full-length sequences has been declining, and new sequences are primarily being deposited by a small number of studies. Furthermore, sequences from soil and aquatic environments, which are known to be rich in bacterial diversity, represent only 7.8 and 16.5% of the census, while sequences associated with host-associated environments represent 55.0% of the census. Continued use of traditional approaches and new technologies such as single-cell genomics and short-read assembly are likely to improve our ability to sample rare OTUs if it is possible to overcome this sampling bias. The success of ongoing efforts to use short-read sequencing to characterize archaeal and bacterial communities requires that researchers strive to expand the depth and breadth of this census. IMPORTANCE The biodiversity contained within the bacterial and archaeal domains dwarfs that of the eukaryotes, and the services these organisms provide to the biosphere are critical. Surprisingly, we have done a relatively poor job of formally tracking the quality of the biodiversity as represented in full-length 16S rRNA genes. By understanding how this census is proceeding, it is possible to suggest the best allocation of resources for advancing the census. We found that the ongoing effort has done an excellent job of sampling the most abundant organisms but struggles to sample the rarer organisms. Through the use of new sequencing technologies, we should be able to obtain full-length sequences from these rare organisms. Furthermore, we suggest that by allocating more resources to sampling environments known to have the greatest biodiversity, we will be able to make significant advances in our characterization of archaeal and bacterial diversity.

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Impact of human gene annotations on RNA-seq differential expression analysis

10.21203/rs.3.rs-301856/v1 ◽

2021 ◽

Author(s):

Yu Hamaguchi ◽

Chao Zeng ◽

Michiaki Hamada

Keyword(s):

Differential Expression ◽

High Throughput ◽

High Throughput Sequencing ◽

Human Gene ◽

Gene Annotation ◽

Differential Expression Analysis ◽

Rna Seq ◽

Gene Annotations ◽

Sequencing Technologies ◽

The Impact

Abstract Background: Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear.Results: Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically.Conclusions: We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

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Baiting out a full length sequence from unmapped RNA-seq data

BMC Genomics ◽

10.1186/s12864-021-08146-4 ◽

2021 ◽

Vol 22 (1) ◽

Cited By ~ 1

Author(s):

Dongwei Li ◽

Qitong Huang ◽

Lei Huang ◽

Jikai Wen ◽

Jing Luo ◽

...

Keyword(s):

Reverse Transcription ◽

Sanger Sequencing ◽

High Throughput Sequencing ◽

Full Length ◽

Rna Seq ◽

Power Law Distribution ◽

Full Length Sequence ◽

Insertion Mutations ◽

Generation Sequencing ◽

Unmapped Reads

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.

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Index suffix–prefix overlaps by (w, k)-minimizer to generate long contigs for reads compression

Bioinformatics ◽

10.1093/bioinformatics/bty936 ◽

2018 ◽

Vol 35 (12) ◽

pp. 2066-2074 ◽

Cited By ~ 11

Author(s):

Yuansheng Liu ◽

Zuguo Yu ◽

Marcel E Dinger ◽

Jinyan Li

Keyword(s):

High Throughput Sequencing ◽

De Novo ◽

Reference Sequence ◽

Supplementary Information ◽

The Novel ◽

Rna Seq ◽

File Size ◽

Sequencing Technologies ◽

Efficient Storage ◽

Merging Process

Abstract Motivation Advanced high-throughput sequencing technologies have produced massive amount of reads data, and algorithms have been specially designed to contract the size of these datasets for efficient storage and transmission. Reordering reads with regard to their positions in de novo assembled contigs or in explicit reference sequences has been proven to be one of the most effective reads compression approach. As there is usually no good prior knowledge about the reference sequence, current focus is on the novel construction of de novo assembled contigs. Results We introduce a new de novo compression algorithm named minicom. This algorithm uses large k-minimizers to index the reads and subgroup those that have the same minimizer. Within each subgroup, a contig is constructed. Then some pairs of the contigs derived from the subgroups are merged into longer contigs according to a (w, k)-minimizer-indexed suffix–prefix overlap similarity between two contigs. This merging process is repeated after the longer contigs are formed until no pair of contigs can be merged. We compare the performance of minicom with two reference-based methods and four de novo methods on 18 datasets (13 RNA-seq datasets and 5 whole genome sequencing datasets). In the compression of single-end reads, minicom obtained the smallest file size for 22 of 34 cases with significant improvement. In the compression of paired-end reads, minicom achieved 20–80% compression gain over the best state-of-the-art algorithm. Our method also achieved a 10% size reduction of compressed files in comparison with the best algorithm under the reads-order preserving mode. These excellent performances are mainly attributed to the exploit of the redundancy of the repetitive substrings in the long contigs. Availability and implementation https://github.com/yuansliu/minicom Supplementary information Supplementary data are available at Bioinformatics online.

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Transcriptome profiling of mouse samples using nanopore sequencing of cDNA and RNA molecules

10.1101/575142 ◽

2019 ◽

Cited By ~ 2

Author(s):

Camille Sessegolo ◽

Corinne Cruaud ◽

Corinne Da Silva ◽

Audric Cologne ◽

Marion Dubarry ◽

...

Keyword(s):

High Throughput Sequencing ◽

Transcriptome Profiling ◽

Transcript Level ◽

Full Length ◽

Read Length ◽

Rna Seq ◽

Expression Levels ◽

Short Reads ◽

Rna Molecules ◽

Oxford Nanopore

AbstractOur vision of DNA transcription and splicing has changed dramatically with the introduction of short-read sequencing. These high-throughput sequencing technologies promised to unravel the complexity of any transcriptome. Generally gene expression levels are well-captured using these technologies, but there are still remaining caveats due to the limited read length and the fact that RNA molecules had to be reverse transcribed before sequencing. Oxford Nanopore Technologies has recently launched a portable sequencer which offers the possibility of sequencing long reads and most importantly RNA molecules. Here we generated a full mouse transcriptome from brain and liver using the Oxford Nanopore device. As a comparison, we sequenced RNA (RNA-Seq) and cDNA (cDNA-Seq) molecules using both long and short reads technologies and tested the TeloPrime preparation kit, dedicated to the enrichment of full-length transcripts. Using spike-in data, we confirmed that expression levels are efficiently captured by cDNA-Seq using short reads. More importantly, Oxford Nanopore RNA-Seq tends to be more efficient, while cDNA-Seq appears to be more biased. We further show that the cDNA library preparation of the Nanopore protocol induces read truncation for transcripts containing internal runs of T’s. This bias is marked for runs of at least 15 T’s, but is already detectable for runs of at least 9 T’s and therefore concerns more than 20% of expressed transcripts in mouse brain and liver. Finally, we outline that bioinformatics challenges remain ahead for quantifying at the transcript level, especially when reads are not full-length. Accurate quantification of repeat-associated genes such as processed pseudogenes also remains difficult, and we show that current mapping protocols which map reads to the genome largely over-estimate their expression, at the expense of their parent gene. The entire dataset is available from http://www.genoscope.cns.fr/externe/ONT_mouse_RNA.

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Pipeliner: A Nextflow-based framework for the definition of sequencing data processing pipelines

10.1101/476515 ◽

2018 ◽

Cited By ~ 1

Author(s):

Anthony Federico ◽

Tanya Karagiannis ◽

Kritika Karri ◽

Dileep Kishore ◽

Yusuke Koga ◽

...

Keyword(s):

Data Processing ◽

High Throughput Sequencing ◽

Digital Gene Expression ◽

Rna Seq ◽

Sequencing Data ◽

Sequencing Technologies ◽

Computing Environments ◽

Scripting Language ◽

Definition Of ◽

User Friendly

AbstractThe advent of high-throughput sequencing technologies has led to the need for flexible and user-friendly data pre-processing platforms. The Pipeliner framework provides an out-of-the-box solution for processing various types of sequencing data. It combines the Nextflow scripting language and Anaconda package manager to generate modular computational workflows. We have used Pipeliner to create several pipelines for sequencing data processing including bulk RNA-seq, single-cell RNA-seq (scRNA-seq), as well as Digital Gene Expression (DGE) data. This report highlights the design methodology behind Pipeliner which enables the development of highly flexible and reproducible pipelines that are easy to extend and maintain on multiple computing environments. We also provide a quick start user guide demonstrating how to setup and execute available pipelines with toy datasets.

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Transcriptome profiling of mouse samples using nanopore sequencing of cDNA and RNA molecules

Scientific Reports ◽

10.1038/s41598-019-51470-9 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 20

Author(s):

Camille Sessegolo ◽

Corinne Cruaud ◽

Corinne Da Silva ◽

Audric Cologne ◽

Marion Dubarry ◽

...

Keyword(s):

High Throughput Sequencing ◽

Transcriptome Profiling ◽

Transcript Level ◽

Full Length ◽

Read Length ◽

Rna Seq ◽

Expression Levels ◽

Short Reads ◽

Rna Molecules ◽

Oxford Nanopore

Abstract Our vision of DNA transcription and splicing has changed dramatically with the introduction of short-read sequencing. These high-throughput sequencing technologies promised to unravel the complexity of any transcriptome. Generally gene expression levels are well-captured using these technologies, but there are still remaining caveats due to the limited read length and the fact that RNA molecules had to be reverse transcribed before sequencing. Oxford Nanopore Technologies has recently launched a portable sequencer which offers the possibility of sequencing long reads and most importantly RNA molecules. Here we generated a full mouse transcriptome from brain and liver using the Oxford Nanopore device. As a comparison, we sequenced RNA (RNA-Seq) and cDNA (cDNA-Seq) molecules using both long and short reads technologies and tested the TeloPrime preparation kit, dedicated to the enrichment of full-length transcripts. Using spike-in data, we confirmed that expression levels are efficiently captured by cDNA-Seq using short reads. More importantly, Oxford Nanopore RNA-Seq tends to be more efficient, while cDNA-Seq appears to be more biased. We further show that the cDNA library preparation of the Nanopore protocol induces read truncation for transcripts containing internal runs of T’s. This bias is marked for runs of at least 15 T’s, but is already detectable for runs of at least 9 T’s and therefore concerns more than 20% of expressed transcripts in mouse brain and liver. Finally, we outline that bioinformatics challenges remain ahead for quantifying at the transcript level, especially when reads are not full-length. Accurate quantification of repeat-associated genes such as processed pseudogenes also remains difficult, and we show that current mapping protocols which map reads to the genome largely over-estimate their expression, at the expense of their parent gene.

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Impact of human gene annotations on RNA-seq differential expression analysis

BMC Genomics ◽

10.1186/s12864-021-08038-7 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Yu Hamaguchi ◽

Chao Zeng ◽

Michiaki Hamada

Keyword(s):

Differential Expression ◽

High Throughput ◽

High Throughput Sequencing ◽

Human Gene ◽

Gene Annotation ◽

Differential Expression Analysis ◽

Rna Seq ◽

Gene Annotations ◽

Sequencing Technologies ◽

The Impact

Abstract Background Differential expression (DE) analysis of RNA-seq data typically depends on gene annotations. Different sets of gene annotations are available for the human genome and are continually updated–a process complicated with the development and application of high-throughput sequencing technologies. However, the impact of the complexity of gene annotations on DE analysis remains unclear. Results Using “mappability”, a metric of the complexity of gene annotation, we compared three distinct human gene annotations, GENCODE, RefSeq, and NONCODE, and evaluated how mappability affected DE analysis. We found that mappability was significantly different among the human gene annotations. We also found that increasing mappability improved the performance of DE analysis, and the impact of mappability mainly evident in the quantification step and propagated downstream of DE analysis systematically. Conclusions We assessed how the complexity of gene annotations affects DE analysis using mappability. Our findings indicate that the growth and complexity of gene annotations negatively impact the performance of DE analysis, suggesting that an approach that excludes unnecessary gene models from gene annotations improves the performance of DE analysis.

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Quantitative RNA-seq analysis of the Campylobacter jejuni transcriptome

Microbiology ◽

10.1099/mic.0.050278-0 ◽

2011 ◽

Vol 157 (10) ◽

pp. 2922-2932 ◽

Cited By ~ 33

Author(s):

Roy R. Chaudhuri ◽

Lu Yu ◽

Alpa Kanji ◽

Timothy T. Perkins ◽

Paul P. Gardner ◽

...

Keyword(s):

High Throughput Sequencing ◽

Research Community ◽

Genomic Research ◽

Functional Genomic ◽

Rna Seq ◽

Sequencing Technologies ◽

Physiology And Biochemistry ◽

Transcriptional Units ◽

Basic Biology ◽

Developed World

C ampylobacter jejuni is the most common bacterial cause of foodborne disease in the developed world. Its general physiology and biochemistry, as well as the mechanisms enabling it to colonize and cause disease in various hosts, are not well understood, and new approaches are required to understand its basic biology. High-throughput sequencing technologies provide unprecedented opportunities for functional genomic research. Recent studies have shown that direct Illumina sequencing of cDNA (RNA-seq) is a useful technique for the quantitative and qualitative examination of transcriptomes. In this study we report RNA-seq analyses of the transcriptomes of C. jejuni (NCTC11168) and its rpoN mutant. This has allowed the identification of hitherto unknown transcriptional units, and further defines the regulon that is dependent on rpoN for expression. The analysis of the NCTC11168 transcriptome was supplemented by additional proteomic analysis using liquid chromatography-MS. The transcriptomic and proteomic datasets represent an important resource for the Campylobacter research community.

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Host-virus chimeric events in SARS-CoV2 infected cells are infrequent and artifactual

10.1101/2021.02.17.431704 ◽

2021 ◽

Author(s):

Bingyu Yan ◽

Srishti Chakravorty ◽

Carmen Mirabelli ◽

Luopin Wang ◽

Jorge L. Trujillo-Ochoa ◽

...

Keyword(s):

Genome Rearrangement ◽

High Throughput Sequencing ◽

Rna Virus ◽

Fruit Fly ◽

Template Switching ◽

Rna Seq ◽

Sequencing Technologies ◽

Cellular Genes ◽

Infected Cells ◽

Alignment Errors

AbstractPathogenic mechanisms underlying severe SARS-CoV2 infection remain largely unelucidated. High throughput sequencing technologies that capture genome and transcriptome information are key approaches to gain detailed mechanistic insights from infected cells. These techniques readily detect both pathogen and host-derived sequences, providing a means of studying host-pathogen interactions. Recent studies have reported the presence of host-virus chimeric (HVC) RNA in RNA-seq data from SARS-CoV2 infected cells and interpreted these findings as evidence of viral integration in the human genome as a potential pathogenic mechanism. Since SARS-CoV2 is a positive sense RNA virus that replicates in the cytoplasm it does not have a nuclear phase in its life cycle, it is biologically unlikely to be in a location where splicing events could result in genome integration. Here, we investigated the biological authenticity of HVC events. In contrast to true biological events such as mRNA splicing and genome rearrangement events, which generate reproducible chimeric sequencing fragments across different biological isolates, we found that HVC events across >100 RNA-seq libraries from patients with COVID-19 and infected cell lines, were highly irreproducible. RNA-seq library preparation is inherently error-prone due to random template switching during reverse transcription of RNA to cDNA. By counting chimeric events observed when constructing an RNA-seq library from human RNA and spike-in RNA from an unrelated species, such as fruit-fly, we estimated that ~1% of RNA-seq reads are artifactually chimeric. In SARS-CoV2 RNA-seq we found that the frequency of HVC events was, in fact, not greater than this background “noise”. Finally, we developed a novel experimental approach to enrich SARS-CoV2 sequences from bulk RNA of infected cells. This method enriched viral sequences but did not enrich for HVC events, suggesting that the majority of HVC events are, in all likelihood, artifacts of library construction. In conclusion, our findings indicate that HVC events observed in RNA-sequencing libraries from SARS-CoV2 infected cells are extremely rare and are likely artifacts arising from either random template switching of reverse-transcriptase and/or sequence alignment errors. Therefore, the observed HVC events do not support SARS-CoV2 fusion to cellular genes and/or integration into human genomes.

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