scholarly journals Analysis of Small RNA Sequencing Data in Plants

2022 ◽  
pp. 497-509
Author(s):  
Vanika Garg ◽  
Rajeev K. Varshney
Keyword(s):  
Next Generation Sequencing ◽  
Rna Sequencing ◽  
Small Rna ◽  
Regulatory Mechanisms ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Meaningful Information ◽  
Expression Studies ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

AbstractOver the past decades, next-generation sequencing (NGS) has been employed extensively for investigating the regulatory mechanisms of small RNAs. Several bioinformatics tools are available for aiding biologists to extract meaningful information from enormous amounts of data generated by NGS platforms. This chapter describes a detailed methodology for analyzing small RNA sequencing data using different open source tools. We elaborate on various steps involved in analysis, from processing the raw sequencing reads to identifying miRNAs, their targets, and differential expression studies.

scholarly journals Paired-end small RNA sequencing reveals a possible overestimation in the isomiR sequence repertoire previously reported from conventional single read data analysis

2021 ◽  
Author(s):  
Jose Francisco Sanchez-Herrero ◽  
Raquel Pluvinet ◽  
Antonio Luna-de Haro ◽  
Lauro Sumoy
Keyword(s):  
Data Analysis ◽  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Technical Noise ◽  
Sequencing Errors ◽  
Abundance And Diversity ◽  
Template Dna ◽  
Generation Sequencing

Abstract Background Next 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. Results We 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. Conclusions We 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.

scholarly journals Paired-end small RNA sequencing reveals a possible overestimation in the isomiR sequence repertoire previously reported from conventional single read data analysis

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Jose Francisco Sanchez Herrero ◽  
Raquel Pluvinet ◽  
Antonio Luna de Haro ◽  
Lauro Sumoy
Keyword(s):  
Data Analysis ◽  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Technical Noise ◽  
Sequencing Errors ◽  
Abundance And Diversity ◽  
Template Dna ◽  
Generation Sequencing

Abstract Background Next 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. Results We 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. Conclusions We 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.

scholarly journals Characterizing the cancer-associated microbiome with small RNA sequencing data

2020 ◽  
Vol 522 (3) ◽  
pp. 776-782
Author(s):  
Wei-Hao Lee ◽  
Kai-Pu Chen ◽  
Kai Wang ◽  
Hsuan-Cheng Huang ◽  
Hsueh-Fen Juan
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Sequencing Data

scholarly journals miRquant 2.0: an Expanded Tool for Accurate Annotation and Quantification of MicroRNAs and their isomiRs from Small RNA-Sequencing Data

2016 ◽  
Vol 13 (5) ◽  
Author(s):  
Matthew Kanke ◽  
Jeanette Baran-Gale ◽  
Jonathan Villanueva ◽  
Praveen Sethupathy
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Cell Types ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Sequencing Technology ◽  
Additional Information ◽  
Disease States ◽  
Non Coding Rnas ◽  
The Rich

SummarySmall non-coding RNAs, in particular microRNAs, are critical for normal physiology and are candidate biomarkers, regulators, and therapeutic targets for a wide variety of diseases. There is an ever-growing interest in the comprehensive and accurate annotation of microRNAs across diverse cell types, conditions, species, and disease states. Highthroughput sequencing technology has emerged as the method of choice for profiling microRNAs. Specialized bioinformatic strategies are required to mine as much meaningful information as possible from the sequencing data to provide a comprehensive view of the microRNA landscape. Here we present miRquant 2.0, an expanded bioinformatics tool for accurate annotation and quantification of microRNAs and their isoforms (termed isomiRs) from small RNA-sequencing data. We anticipate that miRquant 2.0 will be useful for researchers interested not only in quantifying known microRNAs but also mining the rich well of additional information embedded in small RNA-sequencing data.

scholarly journals Evidence for human microRNA-offset RNAs in small RNA sequencing data

2009 ◽  
Vol 25 (18) ◽  
pp. 2298-2301 ◽  
Author(s):  
D. Langenberger ◽  
C. Bermudez-Santana ◽  
J. Hertel ◽  
S. Hoffmann ◽  
P. Khaitovich ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Sequencing Data

scholarly journals In silico genome wide mining of conserved and novel miRNAs in the brain and pineal gland of Danio rerio using small RNA sequencing data

Genomics Data ◽  
2016 ◽  
Vol 7 ◽  
pp. 46-53 ◽  
Author(s):  
Suyash Agarwal ◽  
Naresh Sahebrao Nagpure ◽  
Prachi Srivastava ◽  
Basdeo Kushwaha ◽  
Ravindra Kumar ◽  
...  
Keyword(s):  
Pineal Gland ◽  
Rna Sequencing ◽  
Danio Rerio ◽  
Small Rna ◽  
In Silico ◽  
Small Rna Sequencing ◽  
Sequencing Data ◽  
Genome Wide ◽  
The Brain

Traces of post-transcriptional RNA modifications in deep sequencing data

2011 ◽  
Vol 392 (4) ◽  
Author(s):  
Sven Findeiß ◽  
David Langenberger ◽  
Peter F. Stadler ◽  
Steve Hoffmann
Keyword(s):  
Rna Sequencing ◽  
Small Rna ◽  
Small Rna Sequencing ◽  
Data Sets ◽  
Sequencing Data ◽  
Rna Modifications ◽  
Deep Sequencing Data ◽  
Complex Processes ◽  
Mature Micrornas ◽  
Rna Maturation

Abstract Many aspects of the RNA maturation leave traces in RNA sequencing data in the form of deviations from the reference genomic DNA. This includes, in particular, genomically non-encoded nucleotides and chemical modifications. The latter leave their signatures in the form of mismatches and conspicuous patterns of sequencing reads. Modified mapping procedures focusing on particular types of deviations can help to unravel post-transcriptional modification, maturation and degradation processes. Here, we focus on small RNA sequencing data that is produced in large quantities aimed at the analysis of microRNA expression. Starting from the recovery of many well known modified sites in tRNAs, we provide evidence that modified nucleotides are a pervasive phenomenon in these data sets. Regarding non-encoded nucleotides we concentrate on CCA tails, which surprisingly can be found in a diverse collection of transcripts including sub-populations of mature microRNAs. Although small RNA sequencing libraries alone are insufficient to obtain a complete picture, they can inform on many aspects of the complex processes of RNA maturation.

scholarly journals Next Generation Sequencing of Pooled Samples: Guideline for Variants’ Filtering

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Santosh Anand ◽  
Eleonora Mangano ◽  
Nadia Barizzone ◽  
Roberta Bordoni ◽  
Melissa Sorosina ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Low Frequency ◽  
Next Generation ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Effective Option ◽  
Sequencing Experiment ◽  
Kolmogorov Smirnov ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

Abstract Sequencing large number of individuals, which is often needed for population genetics studies, is still economically challenging despite falling costs of Next Generation Sequencing (NGS). Pool-seq is an alternative cost- and time-effective option in which DNA from several individuals is pooled for sequencing. However, pooling of DNA creates new problems and challenges for accurate variant call and allele frequency (AF) estimation. In particular, sequencing errors confound with the alleles present at low frequency in the pools possibly giving rise to false positive variants. We sequenced 996 individuals in 83 pools (12 individuals/pool) in a targeted re-sequencing experiment. We show that Pool-seq AFs are robust and reliable by comparing them with public variant databases and in-house SNP-genotyping data of individual subjects of pools. Furthermore, we propose a simple filtering guideline for the removal of spurious variants based on the Kolmogorov-Smirnov statistical test. We experimentally validated our filters by comparing Pool-seq to individual sequencing data showing that the filters remove most of the false variants while retaining majority of true variants. The proposed guideline is fairly generic in nature and could be easily applied in other Pool-seq experiments.

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