Additional file 1: of Improved annotation of the domestic pig genome through integration of Iso-Seq and RNA-seq data
Creators: name: H. Beiki
name: H. Liu
name: J. Huang
name: N. Manchanda
name: D. Nonneman
name: T. Smith
name: J. Reecy
name: C. Tuggle
Publisher: name: figshare
Year: 2019
Subjects: subject: Space Science
subject: Medicine
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Molecular Biology
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Developmental Biology
subject: Inorganic Chemistry
subject: FOS: Chemical sciences
subject: FOS: Chemical sciences
subject: Plant Biology

description: Figure S1. (a) Classification of biotypes for detected transcripts; length distribution of transcripts (b), exons (c), and introns (e); (e) distribution of the number of exons per transcript; (f) percentage of nucleotides at donor and acceptor sites. Figure S2. Percentage of PacBio transcript splice junctions supported by short-read Illumina data. Figure S3. Expression analysis of transcripts detected in more than one tissue by Iso-seq data. Figure S4. Number of PacBio transcripts detected in each tissue and their intersections with other tissues using UpSetR [1]. Blue color identifies the proportion of single tissue detected transcripts by PacBio data that were also detected by Illumina reads in at least one other tissue (see the text for more details). Figure S5. (a) Distribution of class “k” transcripts (contains reference) across Ensembl and NCBI annotations, (b) biotypes of transcripts with “k” structure in both Ensembl and NCBI annotations. (c) Expression level of class “k” transcripts across tissues. (d) Classification of class “k” transcripts based on the number of tissues in which they were detected. Figure S6. Biotypes of different transcript types based on Ensembl (a) and NCBI (b) annotations. Figure S7. Classification of class “s” transcripts based on the number of tissues in which they were detected. Figure S8. Example of validation of novel intergenic Iso-seq gene using matched RNA-seq reads and independent liver ChIP-seq (H3K4me3 and H3K36me3) and 3′-RNA-seq experiments. Figure S9. Venn diagram of the number of livers detected Ensembl (a) and NCBI (b) genes with validated extended 3′ end across different samples of an independent liver 3′-RNA-seq experiment. Figure S10. Example of validation of extended 3′ annotation using an independent liver 3′-RNA-seq experiment. Figure S11. Effect of extended annotation on the expression level of Ensembl genes using liver 3′-RNA-seq reads. Genes with same expression in both Iso-seq and Ensembl annotations were marked with red color. Blue line in each graph shows the average of Iso-seq gene expression fold changes over of their matched Ensembl genes in log2 scale that is equal to 0.485 or 40% expression increase. Figure S12. (a) Definition of 5′ candidate region and (b) number of genes with validated candidate 5′ end across different annotations. Figure S13. Example of validation of extended 5′ annotation using an independent Human CAGE data. Table S1. PacBio Iso-seq sequence alignment statistics. Table S2. Illumina sequence alignment statistics. Table S3. Mapping statistics and quality metrics used for the evaluation of ChIP-seq experiment. Table S4. 3′-RNA-seq sequences alignment statistics. Table S5. Functional enrichment analysis of tissue-specific (TS) genes in different porcine tissues. (DOCX 97254 kb)