MyBrain-Seq: A Pipeline for MiRNA-Seq Data Analysis in Neuropsychiatric Disorders
Abstract
:1. Introduction
2. Materials and Methods
2.1. MyBrain-Seq, a Pipeline for miRNA-Seq Analysis
2.2. Quality Control and Adapter Removal
2.3. Alignment to the Reference Genome
2.4. Transcripts Annotation and Quantification
2.5. Differential Expression Analysis
2.6. Hierarchical Clustering
2.7. Functional Analysis
2.8. MiRNA–Protein Interaction Network
2.9. Summarization of the Quality Controls
2.10. MyBrain-Seq Implementation
2.11. Case Study Dataset: Treatment Resistant Schizophrenia
3. Results and Discussion
3.1. MyBrain-Seq Execution
- Creation of the directory tree in the local file system, referred to as “working directory”, shown in Figure 2. The working directory consists of a main directory with two subdirectories: “/input” and “/output”. The input subdirectory is where the parameter files of myBrain-Seq should be placed; the output subdirectory will contain the results after myBrain-Seq execution. This working directory can be initialized using the utilities included in the myBrain-Seq Docker image. This initialization creates a “run.sh” file, used to run the pipeline and templates of the other files required by myBrain-Seq (those inside “/input”). A “README.txt” file is also created with the instructions to fill the template files and run the pipeline.
- The second step is the preparation of the data. In addition to the FASTQ files, myBrain-Seq needs a reference genome (or Bowtie index) and a GFF file with miRNA annotations as biological references to perform the analysis. It is recommended, but not mandatory, to put all these files inside subdirectories under “/input”. Nevertheless, if they are in other locations (e.g., a shared directory to save disk space), the provided “run.sh” will take care of this and create the appropriate Docker volume bindings in a transparent manner for the user.
- The third step is the configuration of the analysis. This comprises the creation of three files, namely: “compi.parameters”, “conditions_file.txt” and “contrast_file.txt”. These files are usually placed into the “input” directory.
- The “compi.parameters” file contains the paths and parameters needed for the analysis, i.e.: path of the working directory, paths to FASTQ files and biological references, paths to “conditions_file.txt” and to “contrast_file.txt” and the adapter sequence. For more information about the optional parameters that can be added, refer to the myBrain-Seq user manual (https://github.com/sing-group/my-brain-seq).
- The “conditions_file.txt” contains the metadata regarding names and conditions of each fastQ file. This file is used by myBrain-Seq to link each sample with a condition and its covariates. Each row of this file contains the name of the FASTQ file, its condition, a user label for that sample and zero or more columns describing the covariates for that sample (e.g., age, sex). All the covariates added in this file will be used in the DEA to adjust the statistical model.
- The “contrast_file.txt” contains the conditions to compare during the analysis and a label for each contrast. Conditions included in this file must be the same as those stated in “contitions_file.txt”. MyBrain-Seq can perform several contrasts in the same pipeline execution if several contrasts are specified in this file, one per line.
- The final step is running myBrain-Seq analysis using the “run.sh” script created during the working directory initialization (step number 1). This script will use “compi.parameters” as reference, mount all the needed Docker volumes (by extracting the path from the Compi parameters file) and create a directory for the log files of the current execution. MyBrain-Seq users do not need to modify this file, as it is ready to use. Thus, users only need to run the script using the path to “compi.parameters” as the unique argument to start the myBrain-Seq analysis.
- Both final and intermediate results are saved in the “/output” directory. Such output files are placed in directories corresponding to the different steps of the workflow, namely: “1_fastqc”, “2_cutadapt”, “3_bowtie”, “4_bam_stats”, “5_feature_counts”, “6_deseq2”, “6_deseq2+edger”, “6_edger” and “7_multiqc”. Results from the hierarchical clustering, functional analysis and network analysis are placed in the directories prefixed with “6_”, according to the data from which they were generated. Files from the same contrast are grouped in subdirectories named with the contrast label.
3.2. MyBrain-Seq Results
- Volcano plot with the results of each DEA; Figure 3A.
- Venn diagram with the DE miRNA coincidences between DESeq2 and EdgeR; Figure 3B.
- Dendrogram with the result of the hierarchical clustering; Figure 3C.
- Heatmap with the result of the hierarchical clustering; Figure 3D.
- HTML file with a miRNA–protein interaction network of the most enriched pathway; Figure 3E.
- Lollipop chart with the word frequency of the enriched terms; Figure 3F.
- Results of the DEA; Figure 4A. Full table in supplementary Table S1.
- List of DE miRNAs; Figure 4B.
- Enriched pathways; Figure 4C. Full table in supplementary Table S2.
- miRNA–protein interaction network; Figure 4D. Full table in supplementary Table S3.
- Adapter-trimmed FASTQ files.
- BAM and SAM files resulting from the alignment.
- A TXT file with the counts of miRNA per sample.
- A summary of the quantification results.
- A file per contrast with a subset of counts for that contrast.
- A TSV file with the expression per sample of each DE miRNA, used for the hierarchical clustering.
3.3. Case Study
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Dependencies | Version | Dependencies | Version |
---|---|---|---|
pegi3s/r_deseq2 | 1.32.0 | pegi3s/samtools_bcftools | 1.10 |
pegi3s/r_edger | 3.36.0 | pegi3s/r_data-analysis | 4.1.1_v2 |
pegi3s/r_enhanced-volcano | 1.12.0 | pegi3s/r_venn-diagram | 1.7.0 |
pegi3s/cutadapt | 1.16 | pegi3s/r_network | 4.1.1_v2_v3 |
pegi3s/fastqc | 0.11.9 | pegi3s/multiqc | 1.14.0 |
pegi3s/bowtie1 | 1.2.3 | python3 | 3.8.5 |
pegi3s/feature-counts | 2.0.0 | DIANA Tarbase annotations | 8 |
pegi3s/samtools_bcftools | 1.9 | Reactome annotations | 83 |
Pérez-Rodríguez et al. 2023 [8] | MyBrain-Seq | ||||
---|---|---|---|---|---|
Pathway | p-Value | q-Value | Pathway | p-Value | q-Value |
Metabolism of proteins | 2.32 × 10−55 | 5.03 × 10−52 | HuR (ELAVL1) binds and stabilizes mRNA | 4.22 × 10−8 | 1.22 × 10−5 |
Gene expression (Transcription) | 1.47 × 10−54 | 1.6 × 10−51 | Activation of anterior HOX genes in hindbrain development during early embryogenesis | 4.80 × 10−8 | 1.22 × 10−5 |
Cellular responses to stress | 3.18 × 10−47 | 1.73 × 10−44 | Transcriptional regulation by small RNAs | 5.22 × 10−8 | 1.22 × 10−5 |
Disease | 1.08 × 10−44 | 3.89 × 10−42 | Cyclin E associated events during G1/S transition | 5.31 × 10−8 | 1.22 × 10−5 |
Metabolism of RNA | 7.31 × 10−43 | 1.99 × 10−40 | MAPK6/MAPK4 signaling | 5.52 × 10−8 | 1.22 × 10−5 |
Cell Cycle | 1.33 × 10−42 | 3.22 × 10−40 | PPARA activates gene expression | 5.67 × 10−8 | 1.22 × 10−5 |
Developmental Biology | 8.61 × 10−34 | 1.7 × 10−31 | Cyclin A:Cdk2 associated events at S phase entry | 5.68 × 10−8 | 1.22 × 10−5 |
Transcriptional Regulation by TP53 | 5.17 × 10−33 | 9.37 × 10−31 | Potential therapeutics for SARS | 5.99 × 10−8 | 1.22 × 10−5 |
DNA Repair | 2.5 × 10−32 | 4.18 × 10−30 | Assembly of the pre-replicative complex | 7.16 × 10−8 | 1.22 × 10−5 |
Innate Immune System | 1.94 × 10−31 | 3.01 × 10−29 | SUMOylation of ubiquitinylation proteins | 8.15 × 10−8 | 1.22 × 10−5 |
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Pérez-Rodríguez, D.; Agís-Balboa, R.C.; López-Fernández, H. MyBrain-Seq: A Pipeline for MiRNA-Seq Data Analysis in Neuropsychiatric Disorders. Biomedicines 2023, 11, 1230. https://doi.org/10.3390/biomedicines11041230
Pérez-Rodríguez D, Agís-Balboa RC, López-Fernández H. MyBrain-Seq: A Pipeline for MiRNA-Seq Data Analysis in Neuropsychiatric Disorders. Biomedicines. 2023; 11(4):1230. https://doi.org/10.3390/biomedicines11041230
Chicago/Turabian StylePérez-Rodríguez, Daniel, Roberto Carlos Agís-Balboa, and Hugo López-Fernández. 2023. "MyBrain-Seq: A Pipeline for MiRNA-Seq Data Analysis in Neuropsychiatric Disorders" Biomedicines 11, no. 4: 1230. https://doi.org/10.3390/biomedicines11041230