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Galaxy pipeline for the identification of non-canonical ORFs and their potential biological function

Cristobal Gallardo Alba

2023.07.06

Index of contents

  • 1. Introduction
    1. Galaxy workflow
    1. Preliminary results

Introduction


1. Introduction

  • 1.1. What do we mean by non-canonical ORFs?

  • 1.2. Why study non-canonical ORFs?

  • 1.3. Why have not been yet characterized?

  • 1.4. How identify non-canonical ORFs?

What do we mean by non-canonical ORFs?

What do we mean by non-canonical ORFs?

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Source: Cambridge dictionary

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Mean Ribo-Seq expression and Ribo-Seq expression standard deviation (SD).

Source: Erady, Chaitanya, et al. "Pan-cancer analysis of transcripts encoding novel open-reading frames (nORFs) and their potential biological functions." NPJ Genomic Medicine 6.1 (2021): 4.

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Note: nORFs are uncharacterized and unnanotated open-reading frames with functional relevance.


1. Introduction

  • 1.1. What do we mean by non-canonical ORFs?

  • 1.2. Why study non-canonical ORFs?

  • 1.3. Why have not been yet characterized?

  • 1.4. How identify non-canonical ORFs?

Why study non-canonical ORFs?

Why study non-canonical ORFs?

  • Potentially novel prognostic and diagnostic markers

  • The vast majority have not been investigated

  • Particulary attractive as allosteric celullar regulators

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1. Introduction

  • 1.1. What do we mean by non-canonical ORFs?

  • 1.2. Why study non-canonical ORFs?

  • 1.3. Why have not been yet characterized?

  • 1.4. How identify non-canonical ORFs?

Why have not been characterized?

Why have not been characterized?

  • Arbitrary thresholds on ORF lengths

    • Peptides smaller than 100 aminoacids are usually discarted

  • Frequently annotated as non-coding RNAs

  • Propensity for structural disorder

    • Discarted as intrinsically disordered proteins (IDPs)

Why study small peptides?

Why study small peptides?

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Example: In eukaryotes, small proteins upregulate proteases and chaperones and downregulate protein synthesis when cells encounter ER stress.

Source: Steinberg, R., & Koch, H. G. (2021). The largely unexplored biology of small proteins in pro- and eukaryotes. The FEBS journal, 288(24), 7002-7024.

Why have not been characterized?

  • Arbitrary thresholds on ORF lengths

    • Peptides smaller than 100 aminoacids are usually discarted

  • Frequently annotated as non-coding RNAs

  • Propensity for structural disorder

    • Discarted as intrinsically disordered proteins (IDPs)

Annotated as non-coding RNAs?

Annotated as non-coding RNAs?

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Why have not been characterized?

  • Arbitrary thresholds on ORF lengths

    • Peptides smaller than 100 aminoacids are usually discarted

  • Frequently annotated as non-coding RNAs

  • Propensity for structural disorder

    • Discarted as intrinsically disordered proteins (IDPs)

Why study intrinsically disordered proteins (IDP)?

Why study intrinsically disordered proteins (IDP)?

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Source: Babu, M. M., Kriwacki, R. W., & Pappu, R. V. (2012). Versatility from protein disorder. Science, 337(6101), 1460-1461.

 Disorder-Function Paradigm drawing drawing drawing

Source: Chakrabarti, P., & Chakravarty, D. (2022). Intrinsically disordered proteins/regions and insight into their biomolecular interactions. Biophysical Chemistry, 283, 106769.

1. Introduction

  • 1.1. What do we mean by non-canonical ORFs?

  • 1.2. Why study non-canonical ORFs?

  • 1.3. Why have not been yet characterized?

  • 1.4. How identify non-canonical ORFs?

How identify non-canonical ORFs?

How identify non-canonical ORFs?

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Index of contents

    1. Introduction
  • 2. Galaxy workflow
    1. Preliminary results

Galaxy Workflow

Galaxy Workflow

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Full detailed explanation in the Genome-wide alternative splicing analysis Galaxy training.

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Full detailed explanation in the Genome-wide alternative splicing analysis Galaxy training.

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Full detailed explanation in the Genome-wide alternative splicing analysis Galaxy training.

Initial QC assessment

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Identify potential artifacts that may impact the interpretation of downstream analysis.

Mapping and identication of novel splicing sites with RNASTAR

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Two-pass alignment enables sequence reads to span novel splice junctions by fewer nucleotides, conferring greater read depth and providing significantly more accurate quantification of novel splice junctions.

Post-mapping QC assessment with RSeQC

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RSeQC is a toolkit for generating RNA-seq-specific quality control metrics. The figure corresponds to RSeQC junction saturation of known (A) and novel (B) splicing sites.

Reference-based transcriptome assembly and quantification with StringTie

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StringTie is a fast and highly efficient assembler of RNA-seq alignments into potential transcripts.

Post-assembly QC assessment with rnaQUAST

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rnaQUAST, which will provide us diverse completeness/correctness statistics very useful in order to identify and address potential errors or gaps in the assembly process. The figure is a rnaQUAST cummulative isoform plot.

Isoform switching and functional analysis with IsoformSwitchAnalyzeR

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IsoformSwitchAnalyzieR performs the differential isoform usage analysis by using DEXSeq.

Isoform switching and functional analysis with IsoformSwitchAnalyzeR

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To analyze large-scale patterns in predicted IS consequences, IsoformSwitchAnalyzeR computes all isoform switching events resulting in a gain/loss of a specific consequence (e.g. protein domain gain/loss).

Index of contents

    1. Introduction
    1. Galaxy workflow
  • 3. Preliminary results

Preliminary results

Preliminary results

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The complete analysis can be found in this Galaxy history.

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The complete analysis can be found in this Galaxy history.
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Thanks for you attention!