---
title: "Reference-free Isoform Reconstruction with isONpipeline"
canonical: "https://help.biobam.com/space/OED0324/3516760087/Reference-free%20Isoform%20Reconstruction%20with%20isONpipeline"
format: markdown
---
> Macro (toc)

## Introduction

The isONpipeline is composed of 3 tools: isONform, isONclust, and isONcorrect. These three tools are used for the analysis of long-read transcriptomic data. IsONclust clusters long transcriptomic reads where each cluster represents all reads that came from a gene. IsONcorrect amends these clusters. It is only needed for ONT data. Finally, isONform tries to call isoforms from these clusters. Pychopper can be also used to identify, orient, and trim full-length Nanopore cDNA reads.

### Dataset Description

Long-read RNA-seq data from the LRGASP consortium.

- Organism: *Trichechus manatus.*
- Instrument: PacBio Sequel II.
- Layout: LRGASP consortium.

### Publication

Pardo-Palacios, Francisco J., et al. "Systematic assessment of long-read RNA-seq methods for transcript identification and quantification." *bioRxiv* (2021).

<details>
<summary>Abstract</summary>

The Long-read RNA-Seq Genome Annotation Assessment Project (LRGASP) Consortium was formed to evaluate the effectiveness of long-read approaches for transcriptome analysis. The consortium generated over 427 million long-read sequences from cDNA and direct RNA datasets, encompassing human, mouse, and manatee species, using different protocols and sequencing platforms. These data were utilized by developers to address challenges in transcript isoform detection and quantification, as well as *de novo* transcript isoform identification. The study revealed that libraries with longer, more accurate sequences produce more accurate transcripts than those with increased read depth, whereas greater read depth improved quantification accuracy. In well-annotated genomes, tools based on reference sequences demonstrated the best performance. When aiming to detect rare and novel transcripts or when using reference-free approaches, incorporating additional orthogonal data and replicate samples are advised. This collaborative study offers a benchmark for current practices and provides direction for future method development in transcriptome analysis.
</details>

### Original Data

cDNA-ONT Mouse data with Accession ID [ENCFF589LDN](https://www.encodeproject.org/files/ENCFF589LDN/)

## Bioinformatic Analysis

### Application

[isONpipeline](https://biobam.atlassian.net/wiki/spaces/OUM/pages/3463446549)

### Input

[ENCFF589LDN.fastq.gz](https://drive.google.com/file/d/1c1V0zY4scFd089UL4eHDnWNC-8cZSBOn/view?usp=sharing) (1.6 Gb)

### Parameters

#### Long Reads Filtering

- Minimum Read Length: 600
- Maximum Read Length: 5000

#### General Parameters

- Reconstruction Pipeline: PacBio Pipeline
- Isoform Read Support: 10

#### Clustering

- K-mer Size: 15
- Window Size: 50
- Minimum Mapped Fraction: 0.7
- Minimum Aligned Fraction: 0.4
- Minimizers Shared with Cluster: 5

#### Reconstruction

- K-mer: 9
- Window Size: 20
- Maximum difference in 3': 30
- Maximum difference in 5': 50

## Execution Time

3h 26 min

## Output

[transcriptome.fasta](https://drive.google.com/file/d/1SaprzMzqi-BbSIp17Bno5pPaVtEhb-ab/view?usp=sharing): FASTA file with the reconstructed transcriptome

[transcript_reconstruction_report.box](https://drive.google.com/file/d/19Ym8fSg5OoH0Gq3oHQvJmpv1yulWTGds/view?usp=sharing): summary report with transcriptome information and parameters used.

[isoforms_length.box](https://drive.google.com/file/d/1Jf0bAiW1T2ePMSTbSg1Qj4eLSyrUBCdP/view?usp=drive_link): histogram with length distribution of isoforms.

[isoform_support_distribution.box](https://drive.google.com/file/d/1XB93Ijc0ZZVaqc6_TdV1EPIu8w2HHBsr/view?usp=drive_link): histogram distribution of the number of reads that support an isoform.