## 5 Materials and Methods
### Library Construction, RNA Sequencing, and Data Processing  
The enriched mRNA was fragmented into short fragments using a fragmentation buffer and reverse-transcribed into cDNA with random primers. Second-strand cDNA was synthesized using DNA polymerase I, RNase H, dNTP, and a buffer. The cDNA fragments were purified using the AMPure XP system (Beckman Coulter, Beverly, MA, USA), end-repaired, poly (A) added, and ligated to Illumina sequencing adapters. Agarose gel electrophoresis was used to a selected size of ligation products, PCR-amplified and sequenced using BGISEQ-500 sequencing technology. After RNA sequencing, the quality control and filtering of the raw data were performed using FastQC ([[https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/)]([https://www.bioinformatics.babraham.ac.uk/projects/fastqc/](https://www.bioinformatics.babraham.ac.uk/projects/fastqc/))). The removal of adapters or low-quality bases from the raw reads was carried out using in-house perl scripts. The clean reads were then assembled into unigenes based on the similarity between the overlapping regions using the Trinity algorithm with min_kmer_cov set to 2 by default and all other parameters set to default. At the same time, the Q20, Q30, and GC content and sequence duplication level of the clean data were calculated. Finally, the transcripts were clustered into unigenes and based on sequence homology, and were searched against public databases. The transcriptome obtained through Trinity splitting was used as the reference sequence (ref), and the clean reads of each sample were mapped on the ref. In this process, the high-quality clean reads were mapped to the full-length transcripts using RSEM v1.2.19 (Madison, WI, USA), and the gene abundances were calculated and normalized to reads per kb per million reads (RPKM). RNA differential expression analysis was performed using DESeq2 v1.0 software ([[https://github.io/DESeq2/v1](https://github.io/DESeq2/v1)]([https://github.io/DESeq2/v1](https://github.io/DESeq2/v1))). The total RNA was first synthesized with a UMI base PCR cDNA Synthesis Kit (BGI) and the first strand of cDNA was amplified using PCR to synthesize the second strand of cDNA. Then, the double-stranded DNA, amplified using secondary PCR, was used for SMRTbell library construction and sequencing. Sequence data processing was performed using the SMRT Link v5.0.1 pipeline (Pacific Biosciences, Menlo Park, CA, USA). The SMRT analysis suite obtained reads of insert (ROI)), read classification (Classify), read clustering and correction (Cluster, Quvier), and, finally, high-quality full-length consistent sequences. Briefly, circular consensus sequence (CCS) reads were extracted from subread BAM files. Then, CCS reads were classified into full-length non-chimeric (FL), non-full-length (nFL), chimeras, and short reads based on cDNA primers and the polyA tail signal. Short reads were discarded. Subsequently, the full-length non-chimeric (FLNC) reads were clustered using iterative clustering for error correction (ICE algorithm). The non-full-length reads were used to polish the above-obtained cluster consensus isoforms using Quiver software v1.0 ([[https://github.com/lukeping/GenomicConsensus](https://github.com/lukeping/GenomicConsensus)]([https://github.com/lukeping/GenomicConsensus](https://github.com/lukeping/GenomicConsensus))) to obtain the FL polished, high-quality consensus sequences (accuracy \(\geq\) 99%). Then, the cd-hit-v4.6.7 software [71] was used to filter final transcriptome isoform sequences by removing the redundant sequences by setting a threshold of 0.99 identities. The Benchmarking Universal Single-Copy Orthologs (BUSCO) (busco.ezlab.org) measure, based on conserved genetics selected from OrthoDB, was used for the assessment of genome and transcriptome integrity. The high-quality full-length sequences of each library were combined for cluster de-redundancy and isoform expression quantification.