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Bioinformatics: Genomes and Transcriptomes — IQB724

Graduate course, usually offered once a year.

Instructor
Rafael Dias Mesquita (Department of Biochemistry / Institute of Chemistry / UFRJ)
Intended audience
Graduate students with no prior background in bioinformatics. Having taken Basic Bioinformatics is suggested but not required.
Workload
30 hours — lecture and laboratory course (15 hours of lectures and 15 hours of hands-on practice)
Credits
2
Places
12
Prerequisites
None, though previous coursework in biochemistry and molecular biology is desirable. Each student must bring a laptop with working wireless networking.
Required software
Firefox or Chrome, Textpad, BioEdit, the Java Runtime Environment (JRE), R and Expander.

Syllabus

Sequencing (Illumina technology), file formats, sequencing quality control, genome and transcriptome assembly, assembly quality control, gene prediction in genomes, and gene expression analysis based on transcriptome sequencing.

Aim

To teach bioinformatics concepts related to genomics and transcriptomics and to train students in the use of a range of programs for genomic and transcriptomic analyses, mainly within the Galaxy web environment.

Programme

  1. Illumina sequencing: introduction, library preparation (barcodes, single vs. paired end, read length and adapters) and Illumina sequencing technology.
    Practical: logging into Galaxy and learning its basic operations; downloading a (prokaryotic) genome and a (prokaryotic) transcriptome with at least 2 conditions and 2 replicates each, both sequenced on Illumina; converting from SRA to FASTQ format.
  2. Quality control: file formats, sequencing quality metrics, read cleaning and re-assessment of quality.
    Practical: quality control with FastQC, cleaning with Cutadapt and re-running quality control, all within Galaxy.
  3. Transcriptome assembly: introduction, De Bruijn graphs, de novo and genome-guided assembly, the Trinity software, polymorphisms (SNPs), paralogy and alternative splicing.
    Practical: transcriptome assembly with Trinity and Cufflinks, within Galaxy.
  4. Prokaryotic genome assembly: introduction, heterozygosity, error correction, single and paired De Bruijn graphs, the SPAdes software.
    Practical: genome assembly with SPAdes, within Galaxy.
  5. Quality control of genome and transcriptome assemblies: metrics for genomes (coverage, N50, largest contig, length histogram) and for transcriptomes (coverage, N50, longest transcript, length histogram, complete sequences).
    Practical: genome assembly quality control (SPAdes Quality, AssemblyStats and QUAST web); transcriptome quality control (AssemblyStats and BUSCO).
  6. Gene expression analysis: genome- and transcriptome-based alignment, TopHat, Bowtie2, repetitive regions and multi-mapping reads, RPKM and FPKM.
    Practical: aligning the transcriptome reads against the previously assembled transcriptome with Bowtie2; quantifying reads per gene with eXpress.
  7. Statistics for gene expression: identifying differentially expressed genes with EdgeR (coverage, variance, noise, distribution and significance) and clustering expression profiles with Expander.
    Practical: identifying differentially expressed genes with EdgeR; clustering gene expression patterns with Expander.
  8. Gene prediction in genomes: gene regions — start, stop, exons and introns —, promoter regions, transcription termination, operons vs. genes, translation frames, statistics in gene prediction, and HMMs.
    Practical: gene prediction with Prodigal and Glimmer using the previously assembled genome.

Assessment

A seminar presenting the results produced during the course.

Recommended reading

Introduction and Galaxy

  • Baxevanis, A.D. and Ouellette, B.F.F. Bioinformatics — A practical guide to the analysis of genes and proteins. Wiley, 3rd edition, 2005.
  • Galaxy Project: Support and Learn
  • SRA knowledge base: ncbi.nlm.nih.gov
  • Gibas, C. and Jambeck, P. Developing Bioinformatics Computer Skills. O'Reilly, 2001.

Sequencing quality control and read cleaning

Transcriptome assembly

  • Trinity: github.com/trinityrnaseq — Grabherr MG, et al. (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29(7):644-652.
  • Cufflinks: cole-trapnell-lab.github.io — Trapnell C, et al. (2012) Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols 7(3):562-578.
  • Comparisons and reviews: Martin JA, Wang Z (2011) Next-generation transcriptome assembly. Nat Rev Genet 12(10):671-682; Clarke K, et al. (2013) Comparative analysis of de novo transcriptome assembly. Science China Life Sciences 56(2):156-162; Zhao QY, et al. (2011) Optimizing de novo transcriptome assembly from short-read RNA-Seq data. BMC Bioinformatics 12(Suppl 14):S2.

Prokaryotic genome assembly

Assembly quality control

Gene expression

Gene prediction