Table 2.
Bioinformatic tools that could be used for genomic, transcriptomic, and epigenetic enrichment and downstream analysis.
| Tool | Description | GitHub Link (If Available) | References |
|---|---|---|---|
| Genomics | |||
| MuTect | Detection of somatic mutations using tumor–normal paired samples obtained from NGS data | https://github.com/broadinstitute/mutect (accessed on 1 July 2023) | [264,265,266] |
| Maftools | Analysis and visualization of mutations in cancer genomics data |
https://github.com/PoisonAlien/maftools (accessed on 1 July 2023) |
[267,268] |
| CopyKit | Preprocessing and analysis of single-cell CNVs |
https://github.com/navinlabcode/copykit (accessed on 1 July 2022) |
[269] |
| HMMcopy | Inference copy number alterations and single-cell CNV analysis |
https://github.com/shahcompbio/hmmcopy_utils (accessed on 1 July 2023) |
[270] |
| CHISEL | Allele-specific and haplotype-specific copy number inference of scDNA-seq data | https://github.com/raphael-group/chisel-data (accessed on 1 July 2023) | [271] |
| Ginkgo | Analysis of scDNA-seq data as well as post-processing steps, such as downstream analysis and phylogenetic trees |
https://www.ginkgobioworks.com/ (accessed on 1 July 2023) |
[270,272] |
| Transcriptomics | |||
| Waddington-OT | Cellular fate determination and differentiation |
https://github.com/zsteve/gWOT (accessed on 1 July 2023) |
[273] |
| Lineage-OT | Lineage tracing and trajectory inference |
https://github.com/aforr/LineageOT (accessed on 1 July 2023) |
[242] |
| Monocle 2 | Cell fate identification through single-cell trajectories |
https://github.com/cole-trapnell-lab/monocle2-rge-paper (accessed on 1 July 2023) |
[274,275] |
| Seurat R package | Single-cell RNA-seq data analysis, including quality control, preprocessing, exploratory analysis, and downstream analysis |
https://github.com/satijalab/seurat (accessed on 1 July 2023) |
[276,277] |
| AddModuleScore function | Biological pathway analysis, gene signatures, or functional modules in individual cells, and for downstream analysis, such as identifying cell states or characterizing cellular heterogeneity based on pathway or module activity. |
https://github.com/satijalab/seurat/blob/master/man/AddModuleScore.Rd (accessed on 1 July 2023) |
[276,278] |
| UCell | Gene signature scores |
https://github.com/carmonalab/UCell (accessed on 1 July 2023) |
[279,280] |
| CytoTRACE | Quantification of cellular trajectories and differentiation of cell states using (scRNA-seq) data |
https://github.com/pinellolab/pyrovelocity/blob/master/pyrovelocity/cytotrace.py (accessed on 1 July 2023) https://cytotrace.stanford.edu (accessed on 1 July 2023) |
[281] |
| Epigenetics | |||
| MACS (Model-based Analysis of ChIP-Seq) | Chromatin immunoprecipitation sequencing (ChIP-seq) data analysis |
https://macs3-project.github.io/MACS/ (accessed on 1 July 2023) |
[282,283,284] |
| SICER (Spatial Clustering for Identification of ChIP-Enriched Regions) | Peak calling in ChIP-seq data |
https://github.com/zanglab/SICER2 (accessed on 1 July 2023) |
[285] |
| ChIPseeker | Annotation and visualization of ChIP-seq data |
https://github.com/YuLab-SMU/ChIPseeker (accessed on 1 July 2023) |
[286] |
| Bismark | Alignment and analysis of DNAme data |
https://github.com/FelixKrueger/Bismark (accessed on 1 July 2023) |
[287] |
| BS Seeker | Alignment of bisulfite-treated reads to the reference genome |
https://github.com/BSSeeker/BSseeker2 (accessed on 1 July 2023) |
[288] |
| MethylKit | Analysis and visualization of DNAme data |
https://github.com/al2na/methylKit (accessed on 1 July 2023) |
[289] |
| Genomation | Visualization, annotation, and analysis of DNAme data |
https://github.com/BIMSBbioinfo/genomation (accessed on 1 July 2023) |
[290] |
| SnapATAC (Single Nucleus Analysis Pipeline for ATAC-seq) | scATAC-seq analysis (alignment of the read to a reference genome, quality control, peak calling, visualization, and clustering) |
https://github.com/r3fang/SnapATAC (accessed on 1 July 2023) |
[291] |
| Cellcano | Inference of cellular hierarchies of scATAC-seq data |
https://marvinquiet.github.io/Cellcano/ (accessed on 1 July 2023) |
[292] |
| Signac | Analysis and visualization of scATAC-seq data (peak calling, quality control, visualization, clustering, and integration with scRNA-seq data) |
https://github.com/stuart-lab/signac (accessed on 1 July 2023) |
[293] |
| EpiAnno | Analysis of scATAC-seq data |
https://github.com/xy-chen16/EpiAnno (accessed on 1 July 2023) |
[294] |
| Enrichment Analysis | |||
| GSEA (gene set enrichment analysis) | Characterization of cellular functions as well as pathway enrichment analysis |
https://www.gsea-msigdb.org/gsea/index.jsp (accessed on 1 July 2023) |
[295] |
| IPA (Ingenuity Pathway Analysis) | Gene set analysis |
https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/ (accessed on 1 July 2023) |
[296] |
| Enrichr | Integrative web-based tool for enrichment analysis |
https://maayanlab.cloud/Enrichr/ (accessed on 19 August 2023) |
[297,298,299] |
| FLAME | Integrative web-based tool for enrichment analysis |
https://github.com/PavlopoulosLab/Flame (accessed on 19 August 2023) |
[300] |