AntiHunter 2.0: increased speed and sensitivity in searching BLAST output for EST antisense transcripts

Abstract Background Wild rice, including Oryza nivara and Oryza rufipogon, which are considered as the ancestors of Asian cultivated rice (Oryza sativa L.), possess high genetic diversity and serve as a crucial resource for breeding novel cultivars of cultivated rice. Although many rice domestication related traits, such as seed shattering and plant architecture, have been intensively studied at the phenotypic and genomic levels, further investigation is needed to understand the molecular basis of phenotypic differences between cultivated and wild rice. Drought stress is one of the most severe abiotic stresses affecting rice growth and production. Adaptation to drought stress involves a cascade of genes and regulatory factors that form complex networks. Long noncoding natural antisense transcripts (lncNATs), a class of long noncoding RNAs (lncRNAs), regulate the corresponding sense transcripts and play an important role in plant growth and development. However, the contribution of lncNATs to drought stress response in wild rice remains largely unknown. Results Here, we conducted strand-specific RNA sequencing (ssRNA-seq) analysis of Nipponbare (O. sativa ssp. japonica) and two O. nivara accessions (BJ89 and BJ278) to determine the role of lncNATs in drought stress response in wild rice. A total of 1,246 lncRNAs were identified, including 1,091 coding–noncoding NAT pairs, of which 50 were expressed only in Nipponbare, and 77 were expressed only in BJ89 and/or BJ278. Of the 1,091 coding–noncoding NAT pairs, 240 were differentially expressed between control and drought stress conditions. Among these 240 NAT pairs, 12 were detected only in Nipponbare, and 187 were detected uniquely in O. nivara. Furthermore, 10 of the 240 coding–noncoding NAT pairs were correlated with genes previously demonstrated to be involved in stress response; among these, nine pairs were uniquely found in O. nivara, and one pair was shared between O. nivara and Nipponbare. Conclusion We identified lncNATs associated with drought stress response in cultivated rice and O. nivara. These results will improve our understanding of the function of lncNATs in drought tolerance and accelerate rice breeding.

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Decona: From demultiplexing to consensus for Nanopore amplicon data

ARPHA Conference Abstracts ◽

10.3897/aca.4.e65029 ◽

2021 ◽

Vol 4 ◽

Author(s):

Saskia Oosterbroek ◽

Karlijn Doorenspleet ◽

Reindert Nijland ◽

Lara Jansen

Keyword(s):

Sequence Data ◽

Variant Calling ◽

Environmental Dna ◽

Laptop Computer ◽

Consensus Sequences ◽

Sequencing Errors ◽

Blast Output ◽

Command Line Tool ◽

Microbial Symbionts ◽

User Friendly

Sequencing of long amplicons is one of the major benefits of Nanopore technologies, as it allows for reads much longer than Illumina. One of the major challenges for the analysis of these long Nanopore reads is the relatively high error rate. Sequencing errors are generally corrected by consensus generation and polishing. This is still a challenge for mixed samples such as metabarcoding environmental DNA, bulk DNA, mixed amplicon PCR’s and contaminated samples because sequence data would have to be clustered before consensus generation. To this end, we developed Decona (https://github.com/Saskia-Oosterbroek/decona), a command line tool that creates consensus sequences from mixed (metabarcoding) samples using a single command. Decona uses the CD-hit algorithm to cluster reads after demultiplexing (qcat) and filtering (NanoFilt). The sequences in each cluster are subsequently aligned (Minimap2), consensus sequences are generated (Racon) and finally polished (Medaka). Variant calling of the clusters (Medaka) is optional. With the integration of the BLAST+ application Decona does not only generate consensus sequences but also produces BLAST output if desired. The program can be used on a laptop computer making it suitable for use under field conditions. Amplicon data ranging from 300-7500 nucleotides was successfully processed by Decona, creating consensus sequences reaching over 99,9% read identity. This included fish datasets (environmental DNA from filtered water) from a curated aquarium, vertebrate datasets that were contaminated with human sequences and separating sponge sequences from their countless microbial symbionts. Decona considerably simplifies and speeds up post sequencing processes, providing consensus sequences and BLAST output through a single command. Classifying consensus sequences instead of raw sequences improves classification accuracy and drastically decreases the amount of sequences that need to be classified. Overall it is a user friendly option for researchers with limited knowledge of script based data processing.

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