Dataset Information

IntAPT: integrated assembly of phenotype-specific transcripts from multiple RNA-seq profiles.

ABSTRACT:

Motivation

High-throughput RNA sequencing has revolutionized the scope and depth of transcriptome analysis. Accurate reconstruction of a phenotype-specific transcriptome is challenging due to the noise and variability of RNA-seq data. This requires computational identification of transcripts from multiple samples of the same phenotype, given the underlying consensus transcript structure.

Results

We present a Bayesian method, integrated assembly of phenotype-specific transcripts (IntAPT), that identifies phenotype-specific isoforms from multiple RNA-seq profiles. IntAPT features a novel two-layer Bayesian model to capture the presence of isoforms at the group layer and to quantify the abundance of isoforms at the sample layer. A spike-and-slab prior is used to model the isoform expression and to enforce the sparsity of expressed isoforms. Dependencies between the existence of isoforms and their expression are modeled explicitly to facilitate parameter estimation. Model parameters are estimated iteratively using Gibbs sampling to infer the joint posterior distribution, from which the presence and abundance of isoforms can reliably be determined. Studies using both simulations and real datasets show that IntAPT consistently outperforms existing methods for the IntAPT. Experimental results demonstrate that, despite sequencing errors, IntAPT exhibits a robust performance among multiple samples, resulting in notably improved identification of expressed isoforms of low abundance.

Availability and implementation

The IntAPT package is available at http://github.com/henryxushi/IntAPT.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Shi X

PROVIDER: S-EPMC8097681 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundCis-natural antisense transcripts (cis-NATs) are RNAs transcribed from the antisense strand of a gene locus, and are complementary to the RNA transcribed from the sense strand. Common techniques including microarray approach and analysis of transcriptome databases are the major ways to globally identify cis-NATs in various eukaryotic organisms. Genome-wide in silico analysis has identified a large number of cis-NATs that may generate endogenous short interfering RNAs (nat-siRNAs), which participate in important biogenesis mechanisms for transcriptional and post-transcriptional regulation in rice. However, the transcriptomes are yet to be deeply sequenced to comprehensively investigate cis-NATs.ResultsWe applied high-throughput strand-specific complementary DNA sequencing technology (ssRNA-seq) to deeply sequence mRNA for assessing sense and antisense transcripts that were derived under salt, drought and cold stresses, and normal conditions, in the model plant rice (Oryza sativa). Combined with RAP-DB genome annotation (the Rice Annotation Project Database build-5 data set), 76,013 transcripts corresponding to 45,844 unique gene loci were assembled, in which 4873 gene loci were newly identified. Of 3819 putative rice cis-NATs, 2292 were detected as expressed and giving rise to small RNAs from their overlapping regions through integrated analysis of ssRNA-seq data and small RNA data. Among them, 503 cis-NATs seemed to be associated with specific conditions. The deep sequence data from isolated epidermal cells of rice seedlings further showed that 54.0% of cis-NATs were expressed simultaneously in a population of homogenous cells. Nearly 9.7% of rice transcripts were involved in one-to-one or many-to-many cis-NATs formation. Furthermore, only 17.4-34.7% of 223 many-to-many cis-NAT groups were all expressed and generated nat-siRNAs, indicating that only some cis-NAT groups may be involved in complex regulatory networks.ConclusionsOur study profiles an abundance of cis-NATs and nat-siRNAs in rice. These data are valuable for gaining insight into the complex function of the rice transcriptome.