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/

Here is a list of what is changing:

1. All databases at the root level will be dbV5.
2. The dbV5 file naming,  “_v5” will be removed. Databases with  no “_vX” descriptor will be dbV5.
3. dbV4 tarballs will be renamed with "_v4", files included in tarball will not be renamed.
4. dbV4 databases will be moved to a v4 subdirectory.
5. As of 1/13/20 the Cloud directory will be frozen with no more new entries.
6. The will be no more updates to dbV4 databases.
7. The FASTA directory will contain nr, nt, swissprot, and pdbaa files.

If you have any questions or concerns, please contact blast-help@ncbi.nlm.nih.gov

The enzyme portal integrates many resources, most of them hosted by EBI and also external ones such as BioPortal. Its main goal is to provide information about enzymes in a suitable format, with a usable interface designed for intended users. Instead of reinventing the wheel, it makes use of available and reliable resources to that end.

Related Literature:

http://nar.oxfordjournals.org/content/41/D1/D773.full

http://www.biomedcentral.com/1471-2105/14/103

Address of the bookmark: http://www.ebi.ac.uk/enzymeportal/

https://link.springer.com/epdf/10.1186/s12864-018-5330-5?author_access_token=ICASEpyMAQaxLlKw--fyCG_BpE1tBhCbnbw3BuzI2RMA57KLmXk5bZabRUiDQzRFHXd6hjm4kWSiLV3mU5XVMitqXUwFMSo4x5vbfty0EDQ9PW1sd1h923_TYXkvJ5niSwAyZ7BklJ0ujFAFhcKtjw%3D%3D

Address of the bookmark: https://webs.iiitd.edu.in/raghava/humcfs/

More at https://software.broadinstitute.org/gatk/download/bundle

ftp://ftp.ncbi.nlm.nih.gov/snp/organisms/human_9606/VCF/GATK/.

ftp://ftp.broadinstitute.org/bundle/hg38/hg38bundle/

Address of the bookmark: https://console.cloud.google.com/storage/browser/genomics-public-data/resources/broad/hg38/v0?pli=1

More recently, we've been enthused about using k-mer based similarity measures and computing and searching k-mer-based sketch search databases for all the things.

But I haven't spent too much talking about using k-mers for taxonomy, although that has become an ahem area of interest recently, if you read into our papers a bit.

In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!

Address of the bookmark: http://ivory.idyll.org/blog/2017-something-about-kmers.html

General information.

Field terminator is "\t|\t"

Row terminator is "\t|\n"

nodes.dmp file consists of taxonomy nodes. The description for each node includes the following

fields:

tax_id -- node id in GenBank taxonomy database

  parent tax_id -- parent node id in GenBank taxonomy database

  rank -- rank of this node (superkingdom, kingdom, ...) 

  embl code -- locus-name prefix; not unique

  division id -- see division.dmp file

  inherited div flag  (1 or 0) -- 1 if node inherits division from parent

  genetic code id -- see gencode.dmp file

  inherited GC  flag  (1 or 0) -- 1 if node inherits genetic code from parent

  mitochondrial genetic code id -- see gencode.dmp file

  inherited MGC flag  (1 or 0) -- 1 if node inherits mitochondrial gencode from parent

  GenBank hidden flag (1 or 0)            -- 1 if name is suppressed in GenBank entry lineage

  hidden subtree root flag (1 or 0)       -- 1 if this subtree has no sequence data yet

  comments -- free-text comments and citations

Taxonomy names file (names.dmp):

tax_id -- the id of node associated with this name

name_txt -- name itself

unique name -- the unique variant of this name if name not unique

name class -- (synonym, common name, ...)

Divisions file (division.dmp):

division id -- taxonomy database division id

division cde -- GenBank division code (three characters)

division name -- e.g. BCT, PLN, VRT, MAM, PRI...

comments

Genetic codes file (gencode.dmp):

genetic code id -- GenBank genetic code id

abbreviation -- genetic code name abbreviation

name -- genetic code name

cde -- translation table for this genetic code

starts -- start codons for this genetic code

Deleted nodes file (delnodes.dmp):

tax_id -- deleted node id

Merged nodes file (merged.dmp):

old_tax_id                              -- id of nodes which has been merged

new_tax_id                              -- id of nodes which is result of merging

Citations file (citations.dmp):

cit_id -- the unique id of citation

cit_key -- citation key

pubmed_id -- unique id in PubMed database (0 if not in PubMed)

medline_id -- unique id in MedLine database (0 if not in MedLine)

url -- URL associated with citation

text -- any text (usually article name and authors).

-- The following characters are escaped in this text by a backslash:

-- newline (appear as "\n"),

-- tab character ("\t"),

-- double quotes ('\"'),

-- backslash character ("\\").

taxid_list -- list of node ids separated by a single space