BOL: Related items

Cracking the Code: A Guide to Bioinformatics Job Hunting

Abhi — Mon, 23 Dec 2024 19:36:41 -0600

Entering the world of bioinformatics is an exciting journey, filled with opportunities to combine biology, data science, and technology to address some of the most pressing scientific challenges. However, securing a position in this competitive field can be daunting, especially for newcomers. Here’s a guide to help you navigate the job-hunting process and land your dream role in bioinformatics.

1. Understand the Landscape

Before diving into applications, take the time to understand the bioinformatics job market. Common roles include:

Bioinformatics Analyst/Scientist: Focused on data analysis and interpretation.
Computational Biologist: Combines computational techniques with biological research.
Data Scientist in Genomics: Applies machine learning and statistical models to genomic data.
Software Developer in Bioinformatics: Designs and develops tools and pipelines for biological research.

Familiarize yourself with the key industries hiring bioinformaticians, such as academia, biotech, pharmaceuticals, healthcare, and agriculture.

2. Build a Strong Foundation

Bioinformatics demands a diverse skill set. Ensure you have a solid foundation in the following areas:

Programming Skills: Proficiency in Python, R, or Perl is often required. Familiarity with tools like Bash scripting and version control systems (e.g., Git) is a plus.
Statistics and Data Analysis: Knowledge of statistical methods, machine learning, and data visualization is crucial.
Biological Knowledge: Understanding genomics, transcriptomics, and proteomics will help you communicate effectively with biologists.
Specialized Tools and Databases: Be comfortable using tools like BLAST, Bowtie, and databases like NCBI and Ensembl.

3. Create a Winning Resume and Portfolio

Highlight your technical skills, biological knowledge, and relevant experience. Tips for a standout application:

Tailor your resume to each job, emphasizing skills mentioned in the job description.
Showcase your experience with real-world datasets by linking to your GitHub profile or online portfolio.
Include details of any publications, presentations, or significant projects.

4. Network Actively

Networking is often the key to discovering opportunities. Here’s how to build connections:

Attend Conferences and Workshops: Events like ISMB or specialized bioinformatics workshops are great for meeting professionals.
Engage Online: Join LinkedIn groups, participate in bioinformatics forums, and follow relevant hashtags on Twitter.
Leverage Alumni Networks: Connect with alumni from your university who are working in the field.

5. Gain Relevant Experience

Experience is a major factor for hiring managers. Ways to enhance your profile include:

Internships: Seek out internships in research labs or biotech companies.
Collaborations: Volunteer to work on projects with professors or peers.
Open Source Contributions: Participate in bioinformatics software development on platforms like GitHub.

6. Prepare for Interviews

Bioinformatics interviews often combine technical and behavioral questions. Prepare by:

Reviewing Key Concepts: Refresh your knowledge of algorithms, sequence analysis, and statistical methods.
Practicing Coding: Be ready to solve coding challenges or discuss code snippets.
Understanding the Organization: Research their recent projects, publications, or products.
Preparing Questions: Demonstrate interest by asking about their tools, workflows, or team structure.

7. Stay Resilient and Persistent

Job hunting can be a long process, but persistence pays off. Tips to keep moving forward:

Keep improving your skills by taking online courses or certifications.
Stay updated with advancements in bioinformatics by following journals and blogs.
Apply to multiple positions and don’t get discouraged by rejections. Each application is a learning experience.

Closing Thoughts

Landing a bioinformatics job requires a mix of technical expertise, networking, and resilience. By understanding the market, showcasing your skills effectively, and continuously learning, you’ll be well on your way to a rewarding career in this dynamic field. Remember, the key to cracking the code is perseverance—stay curious, stay determined, and success will follow.

One page R survival guide !!

Rahul Nayak — Thu, 28 May 2015 21:10:12 -0500

There any many of the documents have been developed and tested by scientist around the world. I found this one really useful. The data used is available for download as data.zip.

Reference@http://www.datasciencecentral.com/profiles/blogs/one-page-r-a-survival-guide-to-data-science-with-r

Templates for the Data Scientist
1. A Template for Preparing Data: *OnePageR - *R
2. A Template for Building Models: *OnePageR - *R
Getting Started as a Data Scientist
1. Getting Started with R and Rattle: *Lecture - *Laboratory
2. Introducing and Interacting with R: *Lecture - *Laboratory
3. BasicR - OnePage(R) - Writing R scripts
Dealing With Data
1. Read Data into R: *OnePageR - *R
2. Explore and Summarise Data: *OnePageR - *R
3. Transform Data: *OnePageR - *R
4. Dealing with Dates and Time: (PDF, R) Dates and Time
5. Visualising Data with GGPlot2: *OnePageR - *R
6. Visualising Data with Maps *OnePageR - *R
7. Spatial (R) Spatial Analysis
8. Handling Big Data *OnePageR - *R
Descriptive Analytics
1. Cluster Analysis: *Lecture - *OnePageR - *R
2. Association Analysis: *Lecture
Predictive Analytics
1. Decision Trees: *Lecture - *OnePageR - *R - *Rattle
2. Ensembles of Decision Trees: *Lecture - *OnePageR - *R
3. SVM (R)
4. KernLab (R)
5. NeuralNetworks (R)
6. NNet (R)
Model Delivery
1. Evaluating Models: *OnePageR - *R
2. Evaluation (R)
3. Scoring (R)
4. PMML (R) Exporting Models for Deployment
Advanced Topics
1. Text Mining: *OnePageR - *R
Advanced R Topics
1. Plots (PDF, R) Miscellaneous Plots
2. Functions (PDF, R) Writing Functions in R
3. Parallel (PDF, R) Parallel Execution
4. Packaging (R) Pulling it Together into a Package
5. Doing R with Style: *OnePageR - *R
6. Literate Data Mining with KnitR: *Lecture - *OnePageR - *

A Brief Bioinformatics Tutorial

Jit — Wed, 21 May 2014 12:50:09 -0500

This is about how to use a computer to find what is known about a gene of interest and also how to get new insights about it.

The tutorial is divided in three main parts:

In the Sequence part, you will see how to look efficiently for a particular protein sequence, how to blast it against the database of your choice to find homologues, how to perform a multiple alignment of the homologues you've selected and how to edit this alignment.
The Structure part is about molecular visualization, homology modeling and structural domain prediction.
In the Function part, you will be introduced to you 3 useful servers to investigate the function of a protein. i.e. finding interactors, co-expressed genes, see a phylogenetic profile, easily access papers citing your gene etc ...

During all the three parts, we will use the S. cerevisiae VPS36 protein as an example.

Address of the bookmark: http://www.mrc-lmb.cam.ac.uk/rlw/text/bioinfo_tuto/introduction.html

Bioinformatics algorithms tutorials

John Parker — Tue, 24 Jun 2014 00:10:45 -0500

Useful bioinformatics tutorial, such as

De Bruijn Graphs for NGS Assembly
Algorithms for PacBio Reads
Software and Hardware Concepts for Bioinformatics
Finding us in Homolog.us (Search Algorithms)
NGS Genome and RNAseq Assembly - a Hands on Primer
Introduction to PERL, Python, R and C/C++ for Bioinformatics

Address of the bookmark: http://www.homolog.us/Tutorials/

R for Microsoft Excel

Jitendra Narayan — Wed, 18 Feb 2015 00:43:27 -0600

If you currently use a spreadsheet like Microsoft Excel for data analysis, you might be interested in taking a look at this tutorial on how to transition from Excel to R by Tony Ojeda. The tutorial explains how to use R functions in place of Excel formulas, including tools like =AVERAGE and =VLOOKUP. For the most part, it uses modern R packages to keep the R code clear and concise.

You'll likely still be using Excel as a data source, though, so you'll also want to check out this guide to importing data from Excel to R from MilanoR.

Reference http://www.r-bloggers.com/an-r-tutorial-for-microsoft-excel-users/

A guide for complete R beginners :- R Syntax

Archana Malhotra — Fri, 20 Feb 2015 23:41:03 -0600

R is a functional based language, the inputs to a function, including options, are in brackets. Note that all dat and options are separated by a comma

Function(data, options)

Even quit is a function

So is help

help(read.table)

Provides the help page for the FUNCTION ‘read.table’

help.search(“t test”)

Searches for help pages that might relate to the phrase ‘t test’

NOTE: quotes are needed for search strings, they are not needed when referring to data objects or function names.

There is a short cut for help,

? shows the help page on a function name, same as help(function)

?read.table

?? searches for help pages on functions, same as help.search(‘phrase’)

??“t test”

Information is usually returned from a function, by default this is printed to screen

read.table(‘data.tsv’)

This can always be stored, we call what it is stored in an ‘object’

mydata

here mydata is an object of type dataframe

Reminder:

Vector: a list of numbers, equivalent to a column in a table
Data Frame = a collection of vectors. Equivalent to a table

Hint:

Up/Down arrow keys can be use to cycle through previous commands

OpenCPU

Rahul Nayak — Sun, 05 Jul 2015 18:34:46 -0500

OpenCPU is a system for embedded scientific computing and reproducible research. The OpenCPU server provides a reliable and interoperable HTTP API for data analysis based on R.

The OpenCPU JavaScript client library provides the most seamless integration of R and JavaScript available today.

OpenCPU uses standard R packaging to develop, ship and deploy web applications. Several open source example apps are available from Github.

Installing your own OpenCPU server is super easy and only takes a few minutes.

More at https://www.opencpu.org/

Interactive Bioinformatics Resources !

Jit — Thu, 12 Aug 2021 00:09:00 -0500

Learn how to use bioinformatics tools right from your browser.
Everything runs in a sandbox, so you can experiment all you want.

More at sandbox.bio

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

SciLifeLab tutorial for bioinformatics analysis !

Jit — Tue, 17 Apr 2018 04:33:00 -0500

SciLifeLab is a national center for molecular biosciences with focus on health and environmental research.

Courses

Old courses (2012-2014)

Machine learning training and courses in bioinformatics !

Rahul Nayak — Tue, 31 Dec 2019 19:33:07 -0600

Machine learning techniques have been successful in analyzing biological data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. In this class, we will learn basics about probabilistic models and machine learning techniques. We will focus on probabilistic models (Markov models, Hidden Markov models, and Bayesian networks) for biological sequence analysis and systems biology. Other machine learning techniques, such as Naive bayes, neural networks and SVMs will only be covered briefly.

More at http://homes.sice.indiana.edu/yye/lab/teaching/spring2017-I529/