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Frequently Asked Questions

  1. What species have reference genomes on EcoOmicsAnalyst?
  2. How is the raw RNA-seq data processed?
  3. What versions of fastp, and kallisto does EOA use?
  4. My species has a reference transcriptome. Should I still consider using Seq2Fun?
  1. What species have reference genomes on EcoOmicsAnalyst?

    EcoOmicsAnalyst currently has reference genomes for dozens of species.

    Species Transcripts ID type Filename
    H. sapiens (human) 199,240 Ensembl transcripts Homo_sapiens.GRCh38.cdna.all.fa.gz
    M. musculus (mouse) 117,486 Ensembl transcripts Mus_musculus.GRCm39.cdna.all.fa.gz
    R. norvegicus (rat) 31,715 Ensembl transcripts Rattus_norvegicus.Rnor_6.0.cdna.all.fa.gz
    C. elegans (roundworm) 36,172 WormBase transcripts Caenorhabditis_elegans.WBcel235.cdna.all.fa.gz
    D. melanogaster (fruitfly) 31,089 Ensembl transcripts Drosophila_melanogaster.BDGP6.32.cdna.all.fa.gz
    D. rerio (zebrafish) 57,775 Ensembl transcripts Danio_rerio.GRCz11.cdna.all.fa.gz
    S. cerevisiae (yeast) 6612 Ensembl transcripts Saccharomyces_cerevisiae.R64-1-1.cdna.all.fa.gz
    A.thaliana (Arabidopsis) 55,854 TAIR transcripts Araport11_genes.201606.cdna.fasta.gz
    B. taurus (cow) 38,030 Ensembl transcripts Bos_taurus.ARS-UCD1.2.cdna.all.fa.gz
    C. japonica (japanese quail) 49,197 NCBI Coturnix_japonica_2.1_cds_from_genomic.fa.gz
    F. candida 42,425 Refseq GCF_002217175.1_ASM221717v1_rna.fna.gz
    G. gallus (chicken) 28,756 Ensembl transcripts Gallus_gallus.GRCg6a.cdna.all.fa.gz
    O. mykiss (rainbow trout; Ensembl) 107,418 Ensembl transcripts Oncorhynchus_mykiss.Omyk_1.0.cdna.all.fa.gz
    O. mykiss (rainbow trout; NCBI) 79,244 RefSeq transcripts GCF_002163495.1_Omyk_1.0_rna.fna.gz
    P. promelas (fathead minnow) 57,299 RefSeq transcripts GCF_016745375.1_EPA_FHM_2.0_rna.fna.gz
    C. dilutus 31,132 De novo assembled transcriptome C.dilutus.contigs.fasta
    D. magna 26,646 Ensembl transcripts Daphnia_magna.daphmag2.4.cdna.all.fa
    L. pipiens (Northern leopard frog) 741,473 De novo assembled transcriptome NLF.transcriptome_jose.fasta.gz
    P. auritus (double crested cormorant) 250,372 De novo assembled transcriptome DCC.transcriptome_jose.fasta.gz
    R. subcapitata 13,383 JGI transcripts Rapsub1_GeneCatalog_transcripts_20191119.nt.fasta
    S. mansoni 14,079 WormBase ParaSite schistosoma_mansoni.PRJEA36577.WBPS17.mRNA_transcripts.fa.gz

  2. How is the raw RNA-seq data processed?

    EcoOmicsAnalyst has 3 main steps to process raw RNA-seq data.

    1. Raw reads quality control, including adopter detection removal, low quality reads and bases removal, error correction. The raw reads will be firstly processed with Fastp to automatically detect and remove adapters, remove low quality and too short reads, trim low quality bases, correct sequencing error of overlapped paired-end reads region.
    2. Reads alignment. The clean reads will be submitted to super-fast, highly accurate pseudoaligners Kallisto for reads alignment and annotation. A gene count table will be generated for each sample.
    3. Summarize results into gene abundance tables, figures. The results generated by aligner will be submitted to R package Tximport to tidy into a gene abundance table (gene X sample) for all samples, which is ready to submitted to ExpressAnalyst for comprehensive downstream analysis and visualization. Besides the gene abundance table, various informative figures and tables are also generated. For example, principle component analysis (PCA) shows similarities among your samples. Rarefaction curve shows the sequence depth and how many genes the sequences can cover. Reads quality plot shows the reads quality before and after quality control. A table summarizes the number of raw and clean reads, number of mapped reads and genes.

    See the Workflow tab for a visual representation.

  3. What versions of fastp and kallisto does EOA use?

    EcoOmicsAnalyst currently uses fastp: v0.21.1, and Kallisto: v0.46.1.

  4. My species has a reference transcriptome. Should I still consider using Seq2Fun?

    This depends on your dataset and your goals. If you are only analyzing data from one species and the reference transcriptome is well-annotated, you likely want to use Kallisto because it includes more features, including non-coding sequences and gene isoforms. However, if you are analyzing data from a species with a sparsely annotated reference, you may want Seq2Fun for better functional analysis. Also, if you are analyzing data from multiple species, even if one or several have references, you may want to use Seq2Fun since this makes integrating the data and comparing results across species very easy.

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