Asteraceae multi-omics information resource
——A multi-omics data platform for Asteraceae plants genetics and breeding research
e.g. FbiAMIRv0_BJCM01000001.1g0000001 or AT1G29280 or WRKY65

About transcriptomics (Download methods)

In the 'Transcriptomics' module, over 3,897 RNA-seq libraries representing 131 diverse organs, tissues, and treatments across 44 species have been processed. The portal includes four functional modules: single/multiple-species gene expression profiles, tissue-specific genes, and comparative transcriptomics. Users can analyze and compare 3,897 gene expression profiles, identify genes specifically expressed in certain tissues, and investigate differentially expressed genes associated with key traits or biological processes using this portal.

The quality of the RNA sequencing reads was assessed using FastQC (v0.11.9) (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Barcode adaptors and low-quality reads (read quality < 70 for paired-end reads) were removed by fastp (v.0.23.0) [1]. The filtered reads were then aligned to the reference genomes separately using Hisat2 (v2.1.0) [2] with default parameters. BAM files containing the aligned reads were processed with StringTie (v1.3.3b) [3] to measure gene expression levels. Gene-level raw count data files were generated using featureCounts (v1.6.4) [4]. The raw count data were imported into Bioconductor package DESeq2 [5] in R to identify the differentially expressed genes(DEGs). Genes with a log2 fold change ≥ 1 or ≤ -1 and an FDR (False Discovery Rate) ≤ were considered DEGs.

Table 1 The original sources of data used in Transcriptomics module.
SpeciesPRJNASRRSample nameTissueTreatmentTreatment groupTreatment timeDescriptionReference
Tutorial video on "Transcriptomics"
Reference
  1. Chen, S., Zhou, Y., Chen, Y. and Gu, J. (2018) fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics, 34, i884-i890.
  2. Kim, D., Langmead, B. and Salzberg, S.L. (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods, 12, 357-360.
  3. Pertea, M., Kim, D., Pertea, G.M., Leek, J.T. and Salzberg, S.L. (2016) Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc, 11, 1650-1667.
  4. Liao, Y., Smyth, G.K. and Shi, W. (2014) featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics, 30, 923-930.
  5. Love, M.I., Huber, W. and Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol, 15, 550.