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VariantSeq: tutorial for usage with case study.

  1. Página Principal
  2. Nuestros cursos
  3. VariantSeq Tutorial
  4. 4. BIBLIOGRAPHY
  • CONTENTS
  • 1. PRELIMINARY INFORMATION
    • 1.1 - Tutorial objective
    • 1.2 - Tutorial material and case study
    • 1.3 - Experiment design and support
    • 1.4 - Installing and activating VariantSeq and the Server-Side
  • 2. STEP-BY-STEP MODE TUTORIAL
    • 2.1 - Preparing your experiment
    • 2.2 - Quality analysis and preprocessing
    • 2.3 - Mapping
    • 2.4 - Postprocessing
    • 2.5 - Variant calling
    • 2.6 - Variant filtering
    • 2.7 - Annotation
  • 3. PIPELINE MODE TUTORIAL
  • 4. BIBLIOGRAPHY
  • CITE US
  • 4. BIBLIOGRAPHY


    - Anders, S., Pyl, P.T. and Huber, W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 2015;31(2):166-169.

    - Andrews, S. 2016. FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc

    - Bolger, A.M., Lohse, M. and Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014;30(15):2114-2120.

    - Davidson, N.M. and Oshlack, A. Corset: enabling differential gene expression analysis for de novo assembled transcriptomes. Genome Biol 2014;15(7):410.

    - Dobin, A., et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013;29(1):15-21.

    - Goff, L., Trapnell, C. and Kelley, D. 2019. CummeRbund: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data.

    - Kim, D., Langmead, B. and Salzberg, S.L. HISAT: a fast spliced aligner with low memory requirements. Nature methods 2015;12(4):357-360.

    - Kim, D., et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 2013;14(4):R36.

    - Langmead, B. and Salzberg, S.L. Fast gapped-read alignment with Bowtie 2. Nature methods 2012;9(4):357-359.

    - Li, H. and Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 2009;25(14):1754-1760.

    - Love, M.I., Huber, W. and Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 2014;15(12):550.

    - Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 2011;Vol 17(1).

    - Merkel, D. Docker: lightweight Linux containers for consistent development and deployment. Linux Journal 2014;2014:2.

    - Pérez-Sánchez, J. , et al.

    Genome Sequencing and Transcriptome Analysis Reveal Recent Species-Specific Gene Duplications in the Plastic Gilthead Sea Bream (Sparus aurata). Frontiers in Marine Science 2019;6(760).

    - Robinson, M.D., McCarthy, D.J. and Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26(1):139-140.

    - Schmieder, R. and Edwards, R. Quality control and preprocessing of metagenomic datasets. Bioinformatics 2011;27(6):863-864.

    - Trapnell, C., et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat.Protoc. 2012;7(3):562-578.

    - Young, M.D., et al. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11(2):R14.


    ◄CITE US
    • Página Principal
    • Calendario
    • Secciones del curso
      • CONTENTS
      • 1. PRELIMINARY INFORMATION
      • 1.1 - Tutorial objective
      • 1.2 - Tutorial material and case study
      • 1.3 - Experiment design and support
      • 1.4 - Installing and activating Variantseq and the Server-Side
      • 2. STEP-BY-STEP MODE TUTORIAL
      • 2.1 - Preparing your experiment
      • 2.2 - Quality analysis and preprocessing
      • 2.3 - Mapping
      • 2.4 - Postprocessing
      • 2.5 - Variant calling
      • 2.6 - Variant filtering
      • 2.7 - Annotation
      • 3. PIPELINE MODE TUTORIAL
      • 4. BIBLIOGRAPHY
      • Pipeline mode: SNP/Indels results
      • CITE US
      • 4. BIBLIOGRAPHY
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