<![CDATA[BOL: Related items]]> https://bioinformaticsonline.com/related/41459?offset=790 https://bioinformaticsonline.com/pages/view/33837/awesome-perl-frameworks-libraries-and-software-part-1 Fri, 07 Jul 2017 04:06:59 -0500 https://bioinformaticsonline.com/pages/view/33837/awesome-perl-frameworks-libraries-and-software-part-1 <![CDATA[Awesome perl frameworks, libraries and software - PART 1]]> A curated list of awesome Perl frameworks, libraries and software.

]]> Neel https://bioinformaticsonline.com/bookmarks/view/40969/leaflet-javascript-libraries-for-interactive-maps Mon, 10 Feb 2020 01:35:07 -0600 https://bioinformaticsonline.com/bookmarks/view/40969/leaflet-javascript-libraries-for-interactive-maps <![CDATA[Leaflet: JavaScript libraries for interactive maps]]> Leaflet is one of the most popular open-source JavaScript libraries for interactive maps.

Features

  • Interactive panning/zooming
  • Compose maps using arbitrary combinations of:
    • Map tiles
    • Markers
    • Polygons
    • Lines
    • Popups
    • GeoJSON
  • Create maps right from the R console or RStudio
  • Embed maps in knitr/R Markdown documents and Shiny apps
  • Easily render spatial objects from the sp or sf packages, or data frames with latitude/longitude columns
  • Use map bounds and mouse events to drive Shiny logic
  • Display maps in non spherical mercator projections
  • Augment map features using chosen plugins from leaflet plugins repository

https://rstudio.github.io/leaflet/

Address of the bookmark: https://rstudio.github.io/leaflet/

]]> Jit https://bioinformaticsonline.com/blog/view/44734/data-visualization-in-bioinformatics-useful-and-eye-catching-plots-for-data-analysis Sat, 14 Dec 2024 12:41:53 -0600 https://bioinformaticsonline.com/blog/view/44734/data-visualization-in-bioinformatics-useful-and-eye-catching-plots-for-data-analysis <![CDATA[Data Visualization in Bioinformatics: Useful and Eye-Catching Plots for Data Analysis]]> Data visualization is a cornerstone of bioinformatics, enabling researchers to interpret complex datasets effectively. With a plethora of data types—genomic sequences, expression profiles, protein interactions, and more—the right visualizations can make or break an analysis. This blog highlights some of the most useful and visually compelling plots for bioinformatics data analysis, along with tools to create them.

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.

]]> LEGE https://bioinformaticsonline.com/bookmarks/view/3013/python-and-biopython-tutorial Fri, 23 Aug 2013 06:47:40 -0500 https://bioinformaticsonline.com/bookmarks/view/3013/python-and-biopython-tutorial <![CDATA[Python and BioPython Tutorial]]> A quickstart tutorial that allows to become familiar with the Python language. The exercises expect knowledge of basic concepts of programming. A group of 2nd year computer science students with no previous Python knowledge required 60'-90' to complete the exercises. With about 3 hours time, the exercise is suitable for non-programmers as well.

Address of the bookmark: http://www.biotnet.org/training-materials/python-programmers

]]> Manshi Raghubanshi https://bioinformaticsonline.com/news/view/20331/type-hinting Fri, 09 Jan 2015 22:26:13 -0600 https://bioinformaticsonline.com/news/view/20331/type-hinting <![CDATA[Type Hinting]]> Python creator Guido van Rossum’s proposal for static type-checking annotations is inching closer to reality, and the feature has taken on a new name: type hinting.

image

Back in August, van Rossum published a proposal on the Python mailing list recommending type-checking annotations as a valuable feature for the next version of Python to improve the performance of editors and IDEs, linter capabilities, standard notation, and refactoring. Van Rossum’s latest proposal, posted late last month, outlined plans to publish a Python Enhancement Proposal (PEP) in early January to put the feature now known as type hinting on track for inclusion in Python 3.5, slated for release this September.

Reference

https://quip.com/r69HA9GhGa7J

]]> Pranjali Yadav https://bioinformaticsonline.com/opportunity/view/26627/scientist-computational-genomics-two-positions Sat, 12 Mar 2016 18:07:56 -0600 <![CDATA[Scientist - Computational Genomics (Two Positions)]]> ICRISAT is a non-profit, non-political organization that conducts agricultural research for development in Asia and sub-Saharan Africa with a wide array of partners throughout the world. Covering 6.5 million square kilometers of land in 55 countries, the semi-arid tropics is home to over 2 billion people, with 650 million of these being the poorest of the poor. ICRISAT and its partners help empower those living in the semi-arid tropics, especially smallholder farmers, to overcome poverty, hunger, malnutrition and a degraded environment through more efficient and profitable agriculture.

ICRISAT is headquartered in Patancheru near Hyderabad, India, with two regional hubs and five country offices in sub-Saharan Africa. ICRISAT, established in 1972, is a member of the CGIAR Consortium. For more details, see www.icrisat.org.

Responsibilities:Design efficient SQL queries for pulling large sequencing projects.
Serve as a technical adviser to the project leadership and provide computational perspective on product design and deliverability.
Develop and oversee a rapid and incremental software development and release schedule.
Design the software architecture, oversee the implementation and evolution of the design on appropriate hardware platforms.
Working collaboratively in a team environment to design, code, test, debug, and document programs for an integrated genomic analysis pipeline in a rapid and incremental software development and release schedule.
Supervise and review code development and ensure that software products meet project objectives in terms of functionality, scalability, robustness and user experience.
Implement and oversee the QA/QC practices to ensure the development team is adhering to quality standards.
Work closely with the application specialist to integrate feedbacks from teams in each CGIAR center into software customization and improvement.
Assist in training of breeders in the CGIAR centers to use software developed.
Personal Profile:

The applicant should have:

Understanding of genomics data and advanced knowledge of Java, and C/C++ as the programming languages and any of the scripting language like perl and/or Python, SQL
High Performance Computing, data architecture, database platforms and QA/QC practices in software engineering.
She/he should have solid experience in software development projects, preferably as a senior programmer or in the software project management role, and in projects involving big data.
Excellent communication skills are needed to work in this multi-disciplinary, multi-location and multi-cultural team.
Ability to mentor colleagues in quality software development practices is desired.
Educational Qualification : Ph. D or Masters Degree in Computational Biology / Computational Genomics or Equivalent with Research Experience in Mentioned Areas.

More at http://www.icrisat.org/careers/

]]> https://bioinformaticsonline.com/bookmarks/view/36360/dendropy-a-python-library-for-phylogenetic-computing Mon, 23 Apr 2018 05:49:50 -0500 https://bioinformaticsonline.com/bookmarks/view/36360/dendropy-a-python-library-for-phylogenetic-computing <![CDATA[DendroPy: a Python library for phylogenetic computing]]> DendroPy is a Python library for phylogenetic computing. It provides classes and functions for the simulation, processing, and manipulation of phylogenetic trees and character matrices, and supports the reading and writing of phylogenetic data in a range of formats, such as NEXUS, NEWICK, NeXML, Phylip, FASTA, etc. Application scripts for performing some useful phylogenetic operations, such as data conversion and tree posterior distribution summarization, are also distributed and installed as part of the libary. DendroPy can thus function as a stand-alone library for phylogenetics, a component of more complex multi-library phyloinformatic pipelines, or as a scripting “glue” that assembles and drives such pipelines.

The primary home page for DendroPy, with detailed tutorials and documentation, is at:

DendroPy is also hosted in the official Python repository:

Requirements and Installation

DendroPy 4.x runs under Python 3 (all versions > 3.1) and Python 2 (Python 2.7 only).

You can install DendroPy by running:

 

More information is available here:

Address of the bookmark: https://pypi.org/project/DendroPy/

]]> Seema Singh https://bioinformaticsonline.com/blog/view/37586/julia-programming-language-a-python-and-r-rival Sat, 25 Aug 2018 04:46:39 -0500 https://bioinformaticsonline.com/blog/view/37586/julia-programming-language-a-python-and-r-rival <![CDATA[Julia Programming Language, a Python and R rival]]> Big data has grown to become one of the most lucrative fields. In fact, data scientists are some of the most sought people. They are usually hired to analyze, control and parse large chunks of data. Implementing these actions using traditional techniques is not a walk in the park. This is why most data scientists prefer using programming languages such as R and Python. However, there is one more programming language that can do the job. That is Julia programming language.

What Is Julia Language?

Julia is a programming language that came into the limelight in 2012. It is a general-purpose programming language that was designed for solving scientific computations. Julia was meant to be an alternative to Python, R and other programming languages that were mainly used for manipulating data. This is because it has numerous features that can minimize the complexities of numerical computations. 

Julia optimizes on the best features of Python and R while at the same time overlooks their weaknesses. This explains why it is viewed as an alternative to these programming languages. For instance, it utilizes the readability and simplicity of Python then performs faster.

Julia is the most preferred programming language for data scientists and mathematicians. This is because its core features are similar to the ones that are used on most data software. Also, the language is ideal for these two subjects because its syntax is similar to the standard mathematical formulas.

Key Features Of Julia Language
Uses JIT Compilation
Parallelism
Dynamic Typing
Simple Syntax
Allows Metaprogramming
Accessible to Libraries
-1-Array Indexing

Julia Vs Python And R Programming Languages
1. Speed
Julia is faster than both Python and R. This is a very critical aspect that is given special attention in the big data programming. The high speed of Julia is because of JIT compilers. You will need to install external libraries on Python to achieve similar speed.

2. Syntax
Julia has a math-friendly syntax. The syntax of this programming language is similar to the mathematical formulas hence can be used to perform mathematical and scientific computations. This syntax makes it easier to learn than Python.

3. Parallelism
Although both Python and R use parallelism, Julia uses a top-level parallelism. Julia allows the processor to perform to the optimum level than what Python and R can achieve.

4. Versatility
Julia programming language is more versatile than Python and R. It allows a programmer to move from different codes and functions with ease.

The only area that Python and R are superior to Julia is in terms of community. Given that Julia is a new programming language, it has a small community as compared to others which have been around for years.

In overall Julia programming language is a better alternative that you can use to handle Big data projects. Despite having a small community, it is one of those programming languages that you can easily learn.

]]> Radha Agarkar https://bioinformaticsonline.com/bookmarks/view/40298/environment-for-tree-exploration-ete-is-a-python-programming-toolkit-that-assists-in-the-recontruction-manipulation-analysis-and-visualization-of-phylogenetic-trees Wed, 27 Nov 2019 05:32:33 -0600 https://bioinformaticsonline.com/bookmarks/view/40298/environment-for-tree-exploration-ete-is-a-python-programming-toolkit-that-assists-in-the-recontruction-manipulation-analysis-and-visualization-of-phylogenetic-trees <![CDATA[Environment for Tree Exploration (ETE) is a Python programming toolkit that assists in the recontruction, manipulation, analysis and visualization of phylogenetic trees]]> The Environment for Tree Exploration (ETE) is a Python programming toolkit that assists in the recontruction, manipulation, analysis and visualization of phylogenetic trees (although clustering trees or any other tree-like data structure are also supported).

Other tools

https://github.com/shenwei356/taxonkit

 

Address of the bookmark: https://pypi.org/project/ete3/3.1.1/

]]> Rahul Nayak https://bioinformaticsonline.com/blog/view/44633/learn-python-with-example Tue, 06 Aug 2024 23:51:51 -0500 https://bioinformaticsonline.com/blog/view/44633/learn-python-with-example <![CDATA[Learn python with example]]>

There are over 21 unique Python project walkthroughs in this content that range from beginner to advanced. See below for the timestamps for these projects:

00:00:00 | How To Navigate These Projects
---
00:01:46 | #1 - Quiz Game (Easy)
Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/quiz_game.py 
---
00:22:00 | #2 - Number Guessing Game (Easy)
Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/number_guesser.py 
---
00:39:49 | #3 - Rock, Paper, Scissors (Easy)
Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/rock_paper_scissors.py 
---
00:54:40 | #4 - Choose Your Own Adventure Game (Easy)
Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/choose_your_own_adventure.py 
---
01:06:47 | #5 - Password Manager (Medium)
Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/ 
Fernet Cryptography Documentation: https://cryptography.io/en/latest/fernet/ 
---
01:37:37 | #6 - PIG (Medium)
Code: https://github.com/techwithtim/3-Mini-Python-Projects/blob/main/project1.py 
---
01:59:07 | #7 - Madlibs Generator (Medium)
Code: https://github.com/techwithtim/3-Mini-Python-Projects/blob/main/project2.py 
---
02:15:04 | #8 - Timed Math Challenge (Medium)
Code: https://github.com/techwithtim/3-Mini-Python-Projects/blob/main/project3.py 
---
02:28:02 | #9 - Slot Machine (Medium)
Code: https://github.com/techwithtim/Python-Slot-Machine 
---
03:20:43 | #10 - Turtle Racing (Medium)
Code: https://github.com/techwithtim/Turtle-Racing-V2 
Turtle Docs: https://docs.python.org/3/library/turtle.html 
---
04:13:09 | #11 - WPM Typing Test (Medium)
Code: https://github.com/techwithtim/WPM_Typing_Test 
Curses Docs: https://docs.python.org/3/howto/curses.html 
05:09:43 | #12 - Alarm Clock (Easy)
Python Project Idea Blog: https://hackr.io/blog/python-projects 
Sound Effects: https://www.fesliyanstudios.com/royalty-free-sound-effects-download/alarm-203 
---
05:22:07 | #13 - Password Generator (Easy)
Python Project Idea Blog: https://hackr.io/blog/python-projects 
---
05:39:16 | #14 - Shortest Path Finder (Advanced)
Code: https://github.com/techwithtim/3-Mini-Python-Projects-For-Intermediates/blob/main/path-finder.py 
---
06:14:53 | #15 - NBA Stats & Current Scores (Medium)
Code: https://github.com/techwithtim/3-Mini-Python-Projects-For-Intermediates/blob/main/nba-scores.py 
---
06:38:22 | #16 - Currency Converter (Medium)
Code: https://github.com/techwithtim/3-Mini-Python-Projects-For-Intermediates/blob/main/currency-converter.py 
API: https://free.currencyconverterapi.com/
---
06:58:51 | #17 - YouTube Video Downloader (Medium)
Code:  https://github.com/techwithtim/Python-Beginner-Automation-Projects/blob/main/youtube.py 
---
07:09:50 | #18 - Automated File Backup (Medium)
Code: https://github.com/techwithtim/Python-Beginner-Automation-Projects/blob/main/backup.py 
---
07:21:18 | #19 - Mastermind/4 Color Match (Advanced)
---
07:48:20 | #20 - Aim Trainer (Advanced)
Code: https://github.com/techwithtim/Python-Aim-Trainer 
---
08:39:20 | #21 - Advanced Python Scripting (Advanced)
Code: https://github.com/techwithtim/Python-Scripting-Project 

]]> BioStar