/D.-antillarum-transcriptome-analysis

De novo transcriptome assembly and gene expression analysis of D. antillarum exposed to three different pH treatments

D.-antillarum-transcriptome-analysis

De novo transcriptome assembly and gene expression analysis of D. antillarum exposed to three different pH treatments

Summary

The sea urchin Diadema antillarum used to be a common species in the Caribbean, but has faced serious decline in recent years. While its genome has never been fully sequenced, other similar species have been sequenced and can be used to compare and identify functional genomic groups. By combining our knowledge of other urchin species with RNA-seq of D. antillarum, we can identify what genes are important to stress response. RNA-seq is the best choice to analyze changes in gene expression over different treatments.

Data

Adult and larvae D. antillarum were exposedto high (7.9), medium (7.6), and low (7.2) pH conditions before RNA extractions. Samples were sequenced using Illumina HiSeq 4000 100bp SR (single-end reads). Read sets will be transferred to the URI kitt server for analysis.

Analysis Methods

  1. Asses quality of reads using FastQC
  2. Trim reads for quality and trim adapters using fastp
  3. de novo transcriptome assembly using Trinity
  4. Identify candidate coding regions using TransDecoder
  5. Cluster sequences and reduce data CDHit
  6. Annotate reads using Trinnotate
  7. Find orthologs using Orthofinder
  8. Use DESeq2 to identify differential gene expression

Future Analysis

Compare D. antillarum to two other urchin species (other species being analyzed by other investigators)

References

FastQC

https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

MultiQC

MultiQC: Summarize analysis results for multiple tools and samples in a single report Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller Bioinformatics (2016) doi: 10.1093/bioinformatics/btw354 PMID: 27312411

Fastp

Shifu Chen, Yanqing Zhou, Yaru Chen, Jia Gu; fastp: an ultra-fast all-in-one FASTQ preprocessor, Bioinformatics, Volume 34, Issue 17, 1 September 2018, Pages i884–i890, https://doi.org/10.1093/bioinformatics/bty560

Trinity

Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, Chen Z, Mauceli E, Hacohen N, Gnirke A, Rhind N, di Palma F, Birren BW, Nusbaum C, Lindblad-Toh K, Friedman N, Regev A. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat Biotechnol. 2011 May 15;29(7):644-52. doi: 10.1038/nbt.1883. PubMed PMID: 21572440.

TransDecoder

Haas & Papanicolaou et al., manuscript in prep. http://transdecoder.github.io

CDHit

Weizhong Li, Lukasz Jaroszewski & Adam Godzik. Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics (2001) 17:282-283, PDF, Pubmed

Trimmomatic

Bolger, A. M., Lohse, M., & Usadel, B. (2014). Trimmomatic: A flexible trimmer for Illumina Sequence Data. Bioinformatics, btu170.

DESeq2

Love, M.I., Huber, W., Anders, S. (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15:550. 10.1186/s13059-014-0550-8