Transcript-Level Analysis

Introduction

Transcriptome Analysis of Monilinia fructigena.

Dataset Description

Transcriptomes of Monilinia fructicola, Monilinia laxa, and Monilinia fructigena, the causal agents of brown rot of stone and pome fruits. For this tutorial, only the data of Monilinia laxa is used. This dataset comprises paired-end reads that were corresponding to mycelium grown in the dark for 4 days, mycelium grown in the dark for 2 days, and then exposed to light for 2 days, as well as in germinating conidia (2 replicates per each condition).

  • Organism: Monilinia laxa

  • Instrument: Illumina HiScanSQ

  • Layout: Paired-end

Publication

De Miccolis Angelini RM, Abate D, Rotolo C, Gerin D, Pollastro S, Faretra F. De novo assembly and comparative transcriptome analysis of Monilinia fructicola, Monilinia laxa and Monilinia fructigena, the causal agents of brown rot on stone fruits. BMC Genomics. 2018 Jun 5;19(1):436. doi: 10.1186/s12864-018-4817-4. PMID: 29866047; PMCID: PMC5987419.Monilinia fructicola, Monilinia laxa and Monilinia fructigena, the causal agents of brown rot on stone fruits.

 Abstract

Brown rots are important fungal diseases of stone and pome fruits. They are caused by several Monilinia species but M. fructicola, M. laxa, and M. fructigena are the most common all over the world. Although they have been intensively studied, the availability of genomic and transcriptomic data in public databases is still scant. We sequenced, assembled, and annotated the transcriptomes of the three pathogens using mRNA from germinating conidia and actively growing mycelia of two isolates of opposite mating types per species for comparative transcriptome analyses.

Original Data

Bioinformatic Analysis

1- Expression Quantification

Application

Create Count Table, Transcript-level Quantification (Transcriptomics).

Input

Parameters

  • Input Reads: [Paired-end] SRR6312174, SRR6312175, SRR6312181, SRR6312182, SRR6312187, and SRR6312190 FASTQ files

  • Upstream Files Pattern: _1

  • Downstream Files Pattern: _2

  • Input FASTA: cds.box

  • Gene-level Estimations: false

  • Estimate RSPD: true

  • Append Poly(A) Tails: false

  • Strand Specificity: Non-Strand Specific

  • Provide Fragment Length Distribution: false

  • Generate Alignment Files: false

Execution Time

30-40 minutes.

Output

2- Differential Expression Analysis

Application

Pairwise Differential Expression Analysis (Transcriptomics).

Input

Parameters

  • CPM Filter: 0.0

  • Samples reaching CPM Filter: 1

  • Normalization Method: TMM (Trimmed mean of M values)

  • Design Type: Simple Design

  • Primary Experimental Factor: Condition

  • Primary Contrast Condition: dark_4_days

  • Primary Reference Condition: getminating_conidia

  • Select a Statistical Test: Exact Test

  • Robust: True

Execution Time

5-10 minutes.

Output

3- Enrichment (GSEA)

Application

Gene Set Enrichment Analysis (Functional Analysis).

Input

Parameters

  • Reference Annotation: protein.box

  • Number of Permutations: 1000

  • Enrichment Statistic: Classic

  • Number of Detailed Results: 50

  • Detailed Results for All GOs: false

  • GO Categories: biological_process,molecular_function,cellular_component

  • Gene Sets Max Size: 500

  • Gene Sets Min Size: 15

  • Do Not Filter: false

  • Filter Mode: FDR

  • Filter Value: 0.25

Execution Time

5 minutes.

Output

4- Enrichment (Fisher’s Exact Test)

Application

Fisher’s Exact Test (Functional Analysis).

Input

Parameters

  • Test-Set Genes: Up-regulated genes

  • Reference Annotation: protein.annot

  • Do Not Filter: false

  • Filter Value: 0.05

  • Two Tailed: false

  • Remove Double IDs: false

  • Filter Mode: FDR

  • Annotations: GO IDs

  • GO Categories: biological_process,molecular_function,cellular_component

Execution Time

5 minutes.

Output

Workflow

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