Address of the bookmark: https://www.biostarhandbook.com/

https://bioalgorithms.ucsd.edu/research4.html

1. Heatmaps: Exploring Patterns in High-Dimensional Data

Heatmaps are a go-to visualization for representing high-dimensional datasets, such as gene expression or metabolomics data. They use color gradients to display data intensity, making patterns and clusters easily detectable.

• Applications: Gene expression analysis, pathway enrichment, methylation studies.

• Tools: Seaborn (Python), ComplexHeatmap (R), Morpheus (web-based).

Tip: Add dendrograms to visualize clustering of rows and columns for hierarchical relationships.

2. Volcano Plots: Highlighting Differential Features

Volcano plots are indispensable for identifying significantly differentially expressed genes or proteins. They plot the log2 fold change against –log10(p-value), making it easy to spot statistically significant changes.

• Applications: RNA-seq, proteomics, and metabolomics.

• Tools: ggplot2 (R), EnhancedVolcano (R), Plotly (Python).

Tip: Use color to highlight significant features and label key genes or proteins.

3. PCA Plots: Reducing Complexity with Principal Component Analysis

Principal Component Analysis (PCA) plots are used to reduce dimensionality and uncover trends or clusters in data. They provide insights into sample variability and grouping.

• Applications: Transcriptomics, metabolomics, microbiome studies.

• Tools: scikit-learn + Matplotlib (Python), prcomp (R), ClustVis (web-based).

Tip: Annotate clusters with metadata to enhance interpretability.

4. Manhattan Plots: Genome-Wide Association Studies

Manhattan plots visualize p-values across the genome, making it easy to identify significant associations in genome-wide studies. They resemble city skylines, with the highest peaks indicating loci of interest.

• Applications: GWAS, QTL mapping.

• Tools: qqman (R), Matplotlib (Python).

Tip: Use alternating colors for chromosomes and highlight significant SNPs for clarity.

5. Circular Plots (Circos): Visualizing Genomic Relationships

Circular plots are ideal for visualizing relationships across the genome, such as structural variations, gene duplications, or synteny.

• Applications: Comparative genomics, structural variation studies.

• Tools: Circos (standalone), Rcircos (R), pyCircos (Python).

Tip: Keep the plot clean and avoid overcrowding to maintain readability.

6. Sankey Diagrams: Tracking Data Flows

Sankey diagrams visualize flows or relationships between categories, often used to track changes in gene expression or pathway enrichment across conditions.

• Applications: Pathway analysis, gene set enrichment analysis.

• Tools: Plotly (Python), networkD3 (R).

Tip: Use gradients or distinct colors to highlight key transitions.

7. Network Graphs: Mapping Interactions

Network graphs represent relationships between entities, such as protein-protein interactions or gene regulatory networks. Nodes represent entities, and edges represent relationships.

• Applications: Systems biology, interactomics.

• Tools: Cytoscape (standalone), igraph (R), NetworkX (Python).

Tip: Use edge thickness or node size to represent interaction strength or centrality.

8. Violin Plots: Visualizing Data Distribution

Violin plots combine a boxplot with a density plot, showing the distribution and variability of data.

• Applications: Single-cell RNA-seq, quantitative trait analysis.

• Tools: Seaborn (Python), ggplot2 (R).

Tip: Split violins by groups for side-by-side comparisons.

9. Time-Series Plots: Monitoring Changes Over Time

Time-series plots display changes in variables across time points, useful for tracking gene expression dynamics or metabolic fluxes.

• Applications: Time-course experiments, cell cycle studies.

• Tools: Matplotlib (Python), ggplot2 (R).

Tip: Smooth the data to highlight trends while avoiding overfitting.

10. Genome Tracks: Visualizing Genomic Features

Genome tracks display multiple layers of genomic data, such as gene annotations, sequencing coverage, and epigenetic marks.

• Applications: ChIP-seq, ATAC-seq, whole-genome sequencing.

• Tools: IGV (standalone), pyGenomeTracks (Python).

Tip: Stack related tracks for direct comparisons.

11. UpSet Plots: Visualizing Set Intersections

UpSet plots are a powerful alternative to Venn diagrams for visualizing intersections between multiple datasets.

• Applications: Overlap analysis for gene sets, pathways, or variants.

• Tools: UpSetR (R), ComplexUpset (Python).

Tip: Use bar plots to represent the size of each intersection for added clarity.

12. Ridge Plots: Comparing Distributions

Ridge plots visualize the distributions of multiple datasets, stacked for easy comparison.

• Applications: Transcriptomics, single-cell RNA-seq.

• Tools: ggridges (R), Matplotlib (Python).

Tip: Use transparency and consistent scaling for better readability.

13. Chord Diagrams: Visualizing Connections Between Groups

Chord diagrams illustrate relationships between categories, such as shared genes between pathways or overlaps in regulatory elements.

• Applications: Pathway overlap, synteny, co-expression networks.

• Tools: Circlize (R), Holoviews (Python).

Tip: Use distinct colors for each group to emphasize relationships.

14. Treemaps: Hierarchical Data Representation

Treemaps visualize hierarchical data as nested rectangles, with area proportional to data size.

• Applications: Ontology enrichment, pathway analysis.

• Tools: Treemapify (R), Plotly (Python).

Tip: Use colors to represent additional variables, like significance or enrichment scores.

15. T-SNE/UMAP Plots: Dimensionality Reduction for Clustering

T-SNE and UMAP plots are great for visualizing high-dimensional data in two dimensions while preserving local or global structure.

• Applications: Single-cell transcriptomics, clustering analyses.

• Tools: scikit-learn (Python), Seurat (R).

Tip: Combine with metadata annotations for better cluster interpretation.

Bringing It All Together

The choice of visualization can significantly impact the insights gained from bioinformatics data. By selecting plots tailored to your data type and analysis goals, you can effectively communicate your findings and make your research more impactful. Whether you’re a seasoned bioinformatician or a beginner, mastering these visualizations will elevate your analyses and presentations.

The manual translation of many, frequently rather complex SPSS scripts often presents itself as a tedious and error-prone task, and represents a rather large obstacle for many analysts and companies to migrate to a modern, open source data management and analysis tool like R. With translate2R this hurdle will be diminished substantially.

Find at https://service.eoda.de/translater/?lang=en

Pine Biotech, Inc., a US-based startup working with the Tauber Bioinformatics Research Center is offering a full curriculum online preparing students without any technical background for real-life challenges with large scale biomedical data. Workshops on processing, analysis and biomedical interpretation of Next Generation Sequencing data cover important up-to-date algorithms and machine learning approaches. The most important thing is that there are virtually no pre-requisites such as coding, biostatistics or advanced medical skills. If you know what gene is and how the genes are expressed, you are ready to take the courses or join our workshops. Learn more: https://edu.t-bio.info/workshops/

1. P.Pollastro, S.Rampone (2002). HS3D, a Dataset of Homo Sapiens Splice Regions, and its Extraction Procedure from a Major Public Database , International Journal of Modern Physics C, 13(8), 1105-1117. (please cite this paper)

2. P.Pollastro, S.Rampone (2003). HS3D: Homo Sapiens Splice Site Data Set , Nucleic Acids Research, 2003 Annual Database Issue.

Address of the bookmark: http://www.sci.unisannio.it/docenti/rampone/

What is HiBC?

The Human Intestinal Bacteria Collection (HiBC) is a comprehensive, high-quality reference repository of bacterial isolates derived from human fecal samples. It focuses on anaerobic and facultative anaerobic bacteria that play pivotal roles in digestion, immune modulation, vitamin synthesis, and pathogen resistance. The collection includes both culturable strains and genomic data from unculturable taxa, bridging the gap between culture-dependent and -independent microbiome studies.

Why is HiBC Important?

1. Understanding Microbiome-Host Interactions
   HiBC enables deeper insight into the functions of specific bacterial taxa in the gut. With well-characterized isolates, researchers can conduct mechanistic studies to explore how certain bacteria influence metabolism, inflammation, or mental health.

2. Precision Probiotics and Therapeutics
   By providing access to native human gut microbes, HiBC supports the development of next-generation probiotics, live biotherapeutic products (LBPs), and fecal microbiota transplantation (FMT) alternatives.

3. Standardization and Reproducibility
   With standardized cultivation and genomic protocols, HiBC ensures consistency across microbiome research studies, improving reproducibility and comparability of findings.

4. Antimicrobial Resistance (AMR) Surveillance
   HiBC includes metadata on antibiotic resistance genes (ARGs), helping track the spread of AMR in commensal gut bacteria and understanding its implications for human health.

Key Features of HiBC

• Culturable Bacteria Repository: A living collection of anaerobic and facultative strains isolated from healthy and diseased individuals worldwide.

• Metadata-rich Entries: Each isolate is annotated with host details (age, health status, diet), geographical origin, phenotypic traits, and antibiotic susceptibility profiles.

• Whole Genome Sequencing (WGS): High-quality genome assemblies for most strains to support functional and comparative genomics.

• Interactive Database Access: User-friendly search and filtering options for strain selection based on taxonomy, function, or clinical relevance.

• Cross-linking with Other Databases: Integration with NCBI, GOLD, and Human Microbiome Project (HMP) data for broader context and validation.

Applications of HiBC

• Microbiome-based diagnostics and biomarker discovery

• Host-microbe interaction studies in gnotobiotic mouse models

• Gut microbiome modulation through diet, drugs, or engineered bacteria

• Longitudinal studies of gut flora across age, geography, and lifestyle

• Environmental and evolutionary microbiology of human-associated bacteria

Accessing HiBC

Researchers and interested parties can explore the HiBC database through its official website: https://www.hibc.rwth-aachen.de/. The platform offers comprehensive information on bacterial isolates, including taxonomy, cultivation conditions, and genomic data, facilitating advanced research in human gut microbiome studies.

Final Thoughts

The HiBC is a cornerstone resource in the rapidly evolving field of microbiome research. As science moves toward personalized medicine and microbial therapeutics, having a reliable and diverse collection of human gut bacteria is not just useful — it's essential. Whether you're a microbiologist, clinician, computational biologist, or biotechnologist, HiBC offers tools to accelerate discovery and innovation in gut microbiome science.

if you map reads to GRCh38 or hg38, use the following:

ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/001/405/GCA_000001405.15_GRCh38/seqs_for_alignment_pipelines.ucsc_ids/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.gz

There are several other versions of GRCh37/GRCh38. What’s wrong with them? Here are a collection of potential issues:

More at http://lh3.github.io/2017/11/13/which-human-reference-genome-to-use

Address of the bookmark: http://lh3.github.io/2017/11/13/which-human-reference-genome-to-use

Address of the bookmark: https://doc-openbio.readthedocs.io/projects/seqmule/en/latest/