IsoSplitter: identification and characterization of alternative splicing sites without a reference genome

Abstract

Long-read transcriptome sequencing is designed to sequence full-length RNA molecules and advantageous for identifying alternative splice isoforms; however, in the absence of a reference genome, it is difficult to accurately locate splice sites because of the diversity of patterns of alternative splicing (AS). Based on long-read transcriptome data, we developed a versatile tool, IsoSplitter, to reverse-trace and validate AS gene “split sites” with the following features: (i) IsoSplitter initially invokes a modified SIM4 program to find transcript split sites; (ii) each split site is then quantified, to reveal transcript diversity, and putative isoforms are grouped into gene clusters; (iii) an optional step for aligning short reads is provided, to validate split sites by identifying unique junction reads, and revealing and quantifying tissue-specific alternative splice isoforms. We tested IsoSplitter AS prediction using data sets from multiple model and nonmodel plant species and showed that the IsoSplitter pipeline is efficient to handle different transcriptomes with high accuracy. Furthermore, we evaluated the IsoSplitter pipeline compared with that of the splice junction identification tools, Program to Assemble Spliced Alignments (PASA software needs a reference genome for AS identification) and AStrap, using data from the model plant Arabidopsis thaliana. We found that IsoSplitter determined more than twice as many AS events than AStrap analysis, and 94.13% of the IsoSplitter predicted AS events were also identified by the PASA analysis. Starting from a simple sequence file, IsoSplitter is an assembly-free tool for identification and characterization of AS. IsoSplitter is developed and implemented in Python 3.5 using the Linux platform and is freely available at https://github.com/Hengfu-Yin/IsoSplitter.

Comparative Transcriptome Analysis to Investigate the Immunotoxicity Mechanism Triggered by Dimethomorph on Human Jurkat T Cell Lines

“…To identify the differential expression genes (DEGs) between the two different samples, the expression level of each transcript was calculated according to the fragments per kilobase of exon per million mapped fragments (FPKM) method. RSEM (accessed on 20 March 2020) [ 30 ] was used to quantify gene abundances. A differential expression analysis was performed using DESeq2 [ 31 ] with |log 2 FC| > 1.3, and a Q value ≤ 0.05 was considered to indicate significantly differentially expressed genes.…”

DeepASmRNA: Reference-free prediction of alternative splicing events with a scalable and interpretable deep learning model

“…(2016) https://tensorflow.google.cn AStrap Ji et al. (2019) https://github.com/BMILAB/AStrap Isosplitter Wang et al. (2021) https://github.com/Hengfu-Yin/IsoSplitter DeepASmRNA This study https://github.com/CMB-BNU/DeepASmRNA or http://cmb.bnu.edu.cn/DeepASmRNA/index.php/download …”

“…(2017) utilized the insertions and deletions (indels) flanking perfectly aligned regions from all-versus-all BLAST to identify AS transcripts from transcriptomic data without a reference genome. IsoSplitter used SIM4 ( Florea et al., 1998 ) to align the sequences and identify AS transcripts from the transcriptome ( Wang et al., 2021 ). However, the sensitivities of the methods were not high, and the types of AS events were not further classified.…”

Use of Transcriptomics to Reveal the Joint Immunotoxicity Mechanism Initiated by Difenoconazole and Chlorothalonil in the Human Jurkat T-Cell Line

“…To identify differentially expressed genes (DEGs), the expression level of each transcript was normalized according to the fragments per kilobases per million reads (FPKM). RSEM (http://deweylab.biostat.wisc.edu/rsem/, accessed on 13 March 2022) [26] was applied to quantify gene abundances. Essentially, differential expression analysis was performed using DESeq2 [27], with |log2FC| ≥ 1 and p value ≤ 0.05 regarded to be DEGs.…”