Screen is like a window manager for your console. It will allow you to keep multiple terminal sessions running and easily switch between them. It also protects you from disconnection, because the screen session doesn’t end when you get disconnected.

You’ll need to make sure that screen is installed on the server you are connecting to. If that server is Ubuntu or Debian, just use this command:

sudo apt-get install screen

Now you can start a new screen session by just typing screen at the command line. You’ll be shown some information about screen. Hit enter, and you’ll be at a normal prompt.

To disconnect (but leave the session running)

Hit Ctrl + A and then Ctrl + D in immediate succession. You will see the message [detached]

To reconnect to an already running session

screen -r

To reconnect to an existing session, or create a new one if none exists

screen -D -r

To create a new window inside of a running screen session

Hit Ctrl + A and then C in immediate succession. You will see a new prompt.

To switch from one screen window to another

Hit Ctrl + A and then Ctrl + A in immediate succession.

To list open screen windows

Hit Ctrl + A and then W in immediate succession

Address of the bookmark: https://github.com/quinlan-lab/applied-computational-genomics

'du' – Check the size of a directory

$ du
This command ( du) gives you a list of directories that exist in the current working directory along with their sizes in kilobytes (default). The last line of the output gives you the total size of the current directory including its subdirectories.

$ du /home/jin1
The above command would give you the directory size of the directory /home/david

$ du -h
The same “du”command with some flag gives you a better output than the default one. The option '-h' stands for human readable format. Therefore, in order to print the sizes of the files / directories in your desire notation use this time suffixed with a 'k' if its kilobytes and 'M' if its Megabytes and 'G' if its Gigabytes.

$ du -ah
If you are interested in checking everything present in a folder use above mentioned command. It gives us not only the directories but also all the files that are present in the current directory. The “-a” flag displays the filenames along with the directory names in the output.

$ du -c
This gives you a grand total as the last line of the output. So if your directory occupies 30MB the last 2 lines of the output would be 30M.

$ du -s
Use this command to displays a summary of the directory size. It is the simplest way to know the total size of the current directory.

$ du -S
This would display the size of the current directory excluding the size of the subdirectories that exist within that directory. So it basically shows you the total size of all the files that exist in the current directory.

$ du --exculde=mp3
Several times it required to exclude some directory in our size calculation. In such cases the above command would display the size of the current directory along with all its subdirectories, but it would exclude all the files having the given pattern present in their filenames.

'df' - finding the disk free space / disk usage

$ df
Hmmm … now “df” command is really useful, and I guess you are going to use it over time. Typing the above command, outputs a table consisting of 6 columns. All the columns are very easy to understand. Remember that the 'Size', 'Used' and 'Avail' columns use kilobytes as the unit. The 'Use%' column shows the usage as a percentage which is also very useful.

$ df -h
Displays the same output as the previous command but the '-h' indicates human readable format. Hence instead of kilobytes as the unit the output would have 'M' for Megabytes and 'G' for Gigabytes.

Example: Linux installed on /dev/hda1
$ df -h | grep /dev/hda1

All right, this is not the only option to check the sizes and free spaces but there are a few more options that can be used with 'du' and 'df' . I will discuss it later.

The fellowship will be for a period of 3 years, or for a period of 4 years, with 25 % compulsory work (e.g. teaching responsibilities at the department) contingent on the qualifications of the candidate and the teaching needs of the department.

Starting date no later than October 1, 2021.

More at https://www.jobbnorge.no/en/available-jobs/job/199984/phd-research-fellow-in-genomics-and-bioinformatics

(details of experience required)
Pay Scale:
INR Rs.12000/-P.M. + HRA
Category:
Science and Research Jobs
How To Apply:
The interested candidates should report for the Interview on 31st
March, 2014 at 10:00 am in the Conference Room of Dean, School of Biotechnology, First floor, Gautam Buddha University, Greater
Noida. Interested candidates may also send their resume to undersigned by post-mail/e-mail shaktis@gbu.ac.in or shaktisahi@gmail.com. No TA and DA will be paid for appearing for the interview
Download Official Notification:

http://www.gbu.ac.in/Recruitment/JRF_advertisement_DSTProject_Shakti_24March14.pdf

More at sandbox.bio

Address of the bookmark: http://sandbox.bio

No. CBSH/MBGE/356

Subject: Advertisement for the award of Junior Research Fellowship.

Applications are invited for award of one Junior Research Fellowship on a consolidated fellowship of Rs. 12,000/- pm in the project “Bioinformatics Sub-DIC ”, under the Coordinatorship Dr. Anil Kumar. The fellowship is purely temporary and may continue till the duration of the project or maximum three years which ever is earlier. The appointment shall be given on six monthly review basis.

ESSENTIAL QUALIFICATION

M.Sc. Bioinformatics having research experience on In silico experimentation.

Candidates possessing the above qualifications may submit their application on
plain paper in the following format to the undersigned latest 18 April, 2014 the interviews will be held on 19 April, 2014 at 11.00 AM in the office of the undersigned. No separate letter for interview will be issued or any TA/DA will be paid for attending the interview.

Advertisement: http://www.gbpuat.ac.in/01042014_18april14_Advertisement%20for%20JRF%20Position,%20BI.pdf

• In a GitHub or GitLab repository
• Free to use
• Written in markdown or similar

NOTE: This list of courses is selected only based on the above criteria.
There are no checks on quality.

https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

Address of the bookmark: https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

Find more at http://www.imtech.res.in/raghava/

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.