This repository provides a complete computational framework to identify lncRNAs and putative regulatory interactions between lncRNAs and protein-coding genes by integrating:
- Two lncRNA prediction tools (FEELnc and CPC2) to increase confidence in results
- Two differential expression analysis tools (DESeq2 and edgeR)
- Co-expression network analysis (WGCNA)
├── 01_preprocessing/
│ ├── run01_fastp_trimming.py
│ ├── run02_hisat_mapping.py
│ ├── run03_stringtie_assembly.sh
│ └── run04_gffcompare.sh
├── 02_lncRNA_identification/
│ └── run05_FEELnc_CPC2_lncRNAprediction.txt
├── 03_differential_expression_analysis/
│ ├── run06_featurecounts.sh
│ └── run07_EdgeR_DESEQ2_DEanalysis.ipynb
├── 04_coexpression_WGCNA_analysis/
│ └── run08_WGCNA_coexpanalysis.ipynb
├── 05_network_filtering/
│ ├── run09_coexp_data_filter.ipynb
│ ├── run10_genepairs_net_construction.py
│ ├── run11_quantilnetwork_to_cytoscape.py
│ ├── run12_edges_attributes1_pairsxtrait.py
│ ├── run13_edges_attributes2_to_cytoscape.ipynb
│ ├── run14_nodes_attributes_to_cytoscape.ipynb
│ └── run15_annotation_nodes_of_interest.ipynb
└── README.md
Note: The modules must be executed in order.
Input: - Raw paired-end reads (forward and reverse)
Example:
your_path/sample1_1.fastq.gz your_path/sample1_2.fastq.gz
Output: - Trimmed reads
Input: - Trimmed reads - Reference genome index
Example:
../sample1_paired1.fq.gz ../sample1_paired2.fq.gz
Build index:
hisat2-build reference_genome.fasta index_output
Output: - .bam files
Input: - .bam - ref_genome.gtf
Output: - stringtie_merged.gtf
Input: - ref_genome.gtf - stringtie_merged.gtf
Output: - Comparison data
Input: - ref_genome.gtf - stringtie_merged.gtf
Output: - Common lncRNAs
Input: - .bam
Output: - count_matrix.tsv
Input: - count_matrix.tsv - metadata.txt
Output: - DE genes, PCA, heatmaps
Input: - TMM.tsv - metadata.txt - binary_traits.tsv
Output: - Network files and plots
Input: - geneModuleMembership.csv - PvalueModuleMembership.csv - geneTraitSignificance_resistant.csv - GeneSignificancePvalue_resistant.csv
Output: - filtered_mm.tsv
Input: - list_genes_of_interest.txt
Output: - gene_list_pairs.txt
Input: - bigNet_edges.txt
Output: - Filtered networks
Input: - filtered_network.txt - gene_list_pairs.txt
Output: - Weighted gene pairs
Input: - bignet_075_resistant.txt - bignet_075_resistant24hpi.txt - bignet_075_resistant60hpi.txt - bignet_075_resistant84hpi.txt - pairs genes with weight info.
Output: - edges_attributes.tsv
Input: - filtered_mm.tsv - genes_DE_run07.txt - any list of genes attributes
Output: - node_attributes.tsv
Input: - annotation_file.tsv - list_nodes.txt - list_edges.txt
Output: - annotated_genes.tsv
- R v4.5.1.
- Python v3.10.
- FastQC v0.11.9 [1]
- MultiQC v.1.23 [2]
- Fastp v0.23.2 [3]
- Hisat2 v2.2.165 [4]
- Stringtie v2.2.2 [5]
- Gffcompare v0.11.2 [6]
- CPC2 v1.0.1 [7]
- FEELnc v3 [8]
- FeatureCounts v1.22.2 [9]
- edgeR v4.6.3 [10]
- DESeq2 v1.48.1 [11]
- WGCNA package v.1.73 [12]
- Andrews S. FastQC: a quality control tool for high throughput sequence data. Available Online Httpwwwbioinformaticsbabrahamacukprojectsfastqc. 2010.
- Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–8. https://doi.org/10.1093/bioinformatics/btw354.
- Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. iMeta. 2023;2. https://doi.org/10.1002/imt2.107.
- Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol. 2019;37:907–15. https://doi.org/10.1038/s41587-019-0201-4.
- Pertea M, Pertea GM, Antonescu CM, Chang TC, Mendell JT, Salzberg SL. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads. Nat Biotechnol. 2015;33:290–5. https://doi.org/10.1038/nbt.3122.
- Pertea M, Pertea G. GFF Utilities: GffRead and GffCompare. F1000Research. 2020;9. https://doi.org/10.12688/f1000research.23297.1.
- Kang YJ, Yang DC, Kong L, Hou M, Meng YQ, Wei L, et al. CPC2: A fast and accurate coding potential calculator based on sequence intrinsic features. Nucleic Acids Res. 2017;45:W12–6. https://doi.org/10.1093/nar/gkx428.
- Wucher V, Legeai F, Hédan B, Rizk G, Lagoutte L, Leeb T, et al. FEELnc: A tool for long non-coding RNA annotation and its application to the dog transcriptome. Nucleic Acids Res. 2017;45. https://doi.org/10.1093/nar/gkw1306.
- Liao Y, Smyth GK, Shi W. FeatureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30. https://doi.org/10.1093/bioinformatics/btt656.
- Robinson MD, McCarthy DJ, Smyth GK. edgeR: A Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2009;26:139–40. https://doi.org/10.1093/bioinformatics/btp616.
- Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. https://doi.org/10.1186/s13059-014-0550-8.
- Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics. 2008;9. https://doi.org/10.1186/1471-2105-9-559.