Dec 28, 2025
High-throughput isoform-wide miRNome sequence reconstruction in the TCGA-LUAD cohort using FAS2rDNA
- Marvin De los Santos1
- 1ChordexBio
External link: https://fas2rdna.chordexbio.com/
Protocol Citation: Marvin De los Santos 2025. High-throughput isoform-wide miRNome sequence reconstruction in the TCGA-LUAD cohort using FAS2rDNA. protocols.io https://dx.doi.org/10.17504/protocols.io.rm7vzenqxvx1/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: December 28, 2025
Last Modified: December 28, 2025
Protocol Integer ID: 235950
Keywords: isoform-wide sequence reconstruction, miRNome profiling, miRNA isoforms, TCGA-LUAD, lung adenocarcinoma, genomic coordinate-based reconstruction, FASTA generation, batch genomic workflows, multi-assembly genome support, multi-loci sequence extraction, automated sequence formatting, multi-species genome analysis, machine learning–ready sequences, isoform-level molecular discovery, cancer genomics, scalable bioinformatics pipelines, FAS2rDNA, wide reconstruction of mirna sequence, resolution exploration of mirna isoform diversity, wide mirnome sequence reconstruction, generating mirna isoform, mirna isoform diversity, wide mirnome sequence reconstruction in the tcga, mirna sequence, reconstructed mirnome sequence, mirna isoform, integrative mirna landscape analysis, molecular understanding of lung adenocarcinoma, reconstructing sequence, sequencing dataset, aware nucleotide sequence, sequencing data, scale mirnome study, genomic coordinate, improving molecular understanding, level molecular discovery
Disclaimer
Your usage of different FAS2rDNA versions and other components of the FAS2rDNA suite may be limited. Please refer to the license notice at https://github.com/mahvin92/FAS2rDNA for your review. Note that this protocol was tested using the GDAC-derived TCGA-LUAD miRNA isoform expression data only. Additional validation may be required when applying this workflow to other cohorts or datasets. FAS2rDNA is provided for research use only and has not been validated for clinical or diagnostic use. The software is provided "as is", without warranty of any kind, express or implied. For more information, please visit https://fas2rdna.chordexbio.com/.
Abstract
Large-scale miRNome studies frequently rely on coordinate-based annotations or raw sequencing datasets that are computationally expensive to reprocess and difficult to integrate into sequence-centric analytical workflows. This protocol presents an isoform-wide reconstruction of miRNA sequences from the TCGA-LUAD cohort using FAS2rDNA, enabling direct derivation of strand-aware nucleotide sequences without reanalyzing bulk sequencing data. By reconstructing sequences directly from genomic coordinates, the workflow provides a faster, more scalable, and reproducible alternative for generating miRNA isoform–resolved FASTA datasets.
The reconstructed miRNome sequences generated through this protocol are directly applicable to machine learning–based modeling, isoform-level molecular discovery, and integrative miRNA landscape analysis. Applied to the TCGA-LUAD cohort, this workflow facilitates high-resolution exploration of miRNA isoform diversity with the broader objective of improving molecular understanding of lung adenocarcinoma and supporting data-driven strategies aimed at reducing cancer-related mortality.
Troubleshooting
Protocol Procedure
Acquire or download the TCGA-LUAD miRNome annotation data.
For the purpose of this exercise, the GDAC Broad Institute database will be used to obtain molecular data.
Select 'Lung adenocarcinoma' or LUAD cohort from the list.
Note: You must agree to the TCGA guidelines on the responsible use of data to proceed.
From the LUAD Archive pop-up, navigate to the 'miRSeq' section and select 'illuminahiseq_mirnaseq-miR_isoform_expression'
Wait until the download is finished (the file is normally in the form of tab-delimited .txt file type).
Preprocess and normalize the genomic coordinate annotations.
Open the file using a spreadsheet program.
Inspect the data and filter out any missing values or incorrectly formatted valued.
Ensure that the 'isoform_coords' header of your data follow the FAS2rDNA-required format:
Standard format:
assembly:chromosome:start-end:strand
Example data:
hg19:9:106938220-106938244:+
Once quality check is done, rename the 'SampleId', 'miRNA_ID', 'isoform_coords', and 'read_count' headers to 'sample_id', 'gene_id', 'seq_loc', and 'description', respectively. This will ensure the data is properly formatted for FAS2rDNA.
Perform isoform-wide sequence reconstruction using FAS2rDNA.
For the purpose of this exercise, the FAS2rDNA-CLI version will be used (available at https://fas2rdna.chordexbio.com/)
Install the required python dependencies.
pip install pandas pyfaidx tqdm
apt-get update -qq
apt-get install -y samtools
Install or download the FAS2rDNA-CLI script.
Note that the technical documentation of FAS2rDNA can be accessed publicly on GitHub (available at https://github.com/mahvin92/FAS2rDNA)
Run FAS2rDNA using the the format below, specifying the location of the TCGA-LUAD miRNA annotation text file:
python3 fas2rdna.py \
--input-dir /Users/Desktop/FAS2rDNA
or build a custom FASTA header by running the following line:
python3 fas2rdna.py \
--input-dir /Users/Desktop/FAS2rDNA \
--header "{sample_id}|{gene_id}|{seq_loc}|{description}"
After FAS2rDNA finishes running, inspect the resulting .fasta ouput files. FAS2rDNA will generate the individual .fasta file from multiple text files and the combined .fasta file, compiling all multi-FASTA sequences in one file. The result map is in the following structure:
/Users/Desktop/FAS2rDNA/
├── LUAD.txt
├── test.txt
└── fas2rdna_output/
├── genomes/
├── fasta/
│ ├── LUAD.fasta
│ └── test.fasta
└── All_sequences.fasta
Cross-reference the generated sequences with the data input, ensuring all samples were represented, the sequences are in FASTA-format and the .fasta files are not empty.
Sample LUAD data:
sample_id gene_id seq_loc description
TCGA-05-4390-01A-02T-1754-13 hsa-let-7a-1 hg19:9:96938243-96938263:+ 1
TCGA-05-4390-01A-02T-1754-13 hsa-let-7a-1 hg19:9:96938243-96938264:+ 4
TCGA-05-4390-01A-02T-1754-13 hsa-let-7a-1 hg19:9:96938243-96938265:+ 1
TCGA-05-4390-01A-02T-1754-13 hsa-let-7a-1 hg19:9:96938243-96938266:+ 4
TCGA-05-4390-01A-02T-1754-13 hsa-let-7a-1 hg19:9:96938244-96938263:+ 13
...
Sample reconstructed LUAD miRNome FASTA-formatted sequences (FAS2rDNA result):
>TCGA-05-4390-01A-02T-1754-13_hsa-let-7a-1
ATGAGGTAGTAGGTTGTATAG
>TCGA-05-4390-01A-02T-1754-13_hsa-let-7a-1
ATGAGGTAGTAGGTTGTATAGT
>TCGA-05-4390-01A-02T-1754-13_hsa-let-7a-1
ATGAGGTAGTAGGTTGTATAGTT
>TCGA-05-4390-01A-02T-1754-13_hsa-let-7a-1
ATGAGGTAGTAGGTTGTATAGTTT
>TCGA-05-4390-01A-02T-1754-13_hsa-let-7a-1
TGAGGTAGTAGGTTGTATAG
...
Additional note
FAS2rDNA operates in batch (multiple experiments), multi-assembly (detects different assembly versions like hg17, hg18, hg19, hg38 in humans), multi-loci (can reconstruct sequence from different DNA locations), auotomated formatting (compile multi-FASTA-formatted results, ready for downstream analyses), and multi-species (supports assemblies from humans to yeast genomes) workflows, giving users the speed, scalability, confidence and convenience needed to analyze and perform experiments using large genomic data.