Some of the best resources to learn Bioinformatics that I refer to frequently.

Hi all, I’m a bioinformatician (PhD in cancer biology, single-cell/multi-omics) and I built The Omics Hub (theomicshub.com), a free tutorial site covering Linux/HPC basics, end-to-end scRNA-seq workflows, and parallel code in both Python (Scanpy) and R (Seurat).

It has 40+ tutorials on QC, normalization, clustering, and UMAP, with the reasoning behind each parameter choice, not just copy-paste code. No sign-up, no paywall.
Website: theomicshub.com

Would love feedback on what to add next. Thinking about spatial transcriptomics or Nextflow/Snakemake modules.

Firestick has compatibility issues for Canadian sports on my iptv, like small skips during live games—feels like a minor hassle when catching Canadian sports in regions like Canada. I adjusted firestick settings, but it didn't fully solve it; tried iptvmeezzy on firestick, and it compatibled steadily in an unpretentious, reliable way, smoothing Canadian sports without many skips. Is this firestick's compatibility or iptv sourcing in areas like Canada? I've also used Ethernet instead of Wi-Fi, which helped a bit. What do you strategize to improve iptv compatibility on firestick for Canadian sports in regions like Canada for your iptv sports watching?

Hello! We are now curating a list of resources. This includes degree requirements at accredited universities, free MOOC courses, web resources, textbooks, and more. Please comment for any suggestions!

Accredited school degree requirement listings

• Johns Hopkins MS in Bioinformatics

• UCSD BS in Bioengineering: Bioinformatics

Tutorials and Courses

General Free Learning Sites

• Coursera

• edX

• BioStars Tutorials

Bioinformatics Courses

• Network Analysis in Systems Biology

• UCSD Bioinformatics series

• University of Toronto Bioinformatics Methods

Bioinformatics Learning Websites

• [BTI Plant Bioinformatics Course - Quick overview of Bioinformatics, Web Tools, Basic, Linux, Basic Databases/SQL and R] (https://btiplantbioinfocourse.wordpress.com/2012-course/core-program/)

• RNA-seq with TopHat and Cufflinks

• OpenHelix - List of free tutorials related to Biotools

• Swiss Institute of Bioinformatics

• Bioinformatics@Becker

• Bioinformatics talks, lectures and videos

• ANGUS - Analyzing Next-Generation Sequencing Data

• Ensembl tutorial videos

• Bioinformatics 2014 workshops

• Microbiome / microgenome data analysis

Foundational Math and Sciences

• Linear Algebra

• Calculus

• Probability

• Chemistry

• Physics with Calculus

Computer Science

• Stanford Introductory Programming - CS50

• [Introduction to Object Oriented Programming with Java] (http://docs.oracle.com/javase/tutorial/java/concepts/)

• UC Berkeley Data Structures

• Introduction to Algorithms with textbook

Biology

• [Fundamentals of Biology] (http://ocw.mit.edu/courses/biology/7-01sc-fundamentals-of-biology-fall-2011/)

• [Molecular Biology of the gene (textbook)] (http://www.amazon.com/Molecular-Biology-Gene-James-Watson/dp/0321762436/ref=sr_1_1?ie=UTF8&qid=1437855792&sr=8-1&keywords=molecular+biology+of+the+gene)

• Structural and Metabolic Biochemistry

Tools and Languages

• R book, Free web tutorial, [R workshop] (http://rcourse.iop.kcl.ac.uk/2011/) or [course] (https://www.coursera.org/course/rprog)

• Linux Command Line, or Free web tutorial

• Explain Shell - Explaining Command Line Terms

• Beginning Perl

• Unix and Perl Primer for Biologists

• Programming Bioinformatics using Python (book)

• [Interactive Python course] (https://www.coursera.org/course/interactivepython1)

• Python Codecademy

Statistics and Data Science

• [Introduction to Data Science] (https://www.coursera.org/course/datasci)

• Data Analysis

• [Data Scientist's Toolbox] (https://www.coursera.org/course/datascitoolbox)

• Mathematical Biostatistics Boot Camp

• Machine Learning

• [Introduction to Statistical Learning (free e-book)(http://www-bcf.usc.edu/~gareth/ISL/)

• The Elements of Statistical Learning (free pdf)

I have been building The Omics Hub as a free educational resource for researchers learning bioinformatics.

It includes tutorials on:

• Linux and command-line foundations
• HPC and Slurm
• Conda and reproducible environments
• R and Python
• Single-cell RNA-seq and spatial transcriptomics
• Genomics, metagenomics, and phylogenomics
• Workflow management and reproducibility
• Responsible use of AI tools in research

The goal is to explain the biological reasoning behind each step instead of providing commands without context.

I would appreciate constructive feedback from the bioinformatics community:

https://theomicshub.com/

I built a site where you practice bioinformatics instead of reading about it

I've learned a lot of bioinformatics from blogs and tutorials, and I noticed I could follow every one of them and still freeze when I had to write the code myself. Reading feels like progress. Writing the code is where you find out what you actually know.

So I built CtrlDogma, a place where you learn by doing.

The idea

No lectures and no walls of theory. You show what you know and what you can do with your skills, and the work you've done is the proof.

The problem bank

• 50+ coding problems built around real biology: sequences, translation, parsing, statistics and more.

• Grouped into tracks and ordered from foundational to advanced, so you can start easy and work up.

• You write code in the browser and get a verdict on hidden test cases, not just the sample. Hard-coding the sample output won't pass.

• Core problems in every difficulty are free.

Labs

A lab is a small project instead of a single puzzle. Each one is a problem statement split into stages that use skills from the problem bank. In each stage you:

• write code in a notebook that runs in your browser,

• take an optional self-check quiz,

• explain your approach in your own words,

• submit, and the code is graded on the server.

Scores and progress

• Every solved problem adds to your score and moves you up a leaderboard.

• You get a public profile with your stats and activity, so you can show what you've done.

• Finishing a track earns a completion badge.

• Optionally, you can sync your accepted solutions to your own GitHub repo.

Honest notes

• It's a solo project and still growing.

• Most of it is free. Pro unlocks the extra problems, official certificates and unlimited GitHub sync.

• I'd really like to hear: what topics should the next problems and labs cover, and what would make you actually use this?

It's free to start. Would love honest feedback. Link in the comments.

Warning: LONG THREAD!!!

Hey everyone! I'm an E&C engineering graduate who transitioned into the biomedical sciences for my Masters degree. Throughout my program, I struggled to pick up foundational concepts, and it took longer for me to gather the knowledge and understanding required to pick a career path after my program. It took me a while to realize that I was better off doing a Masters in Bioinformatics as my skillset better matched the profile needed for a bioinformatician's role. I've been learning skills to strengthen my profile for a grad school program in bioinformatics. While plenty of resources are available, both on this subreddit and on r/bioinformatics, I've learned that what skills one must focus on depends purely on the end goal one wants to serve. After some research and scouring different threads, I've designed a learning path to help me upskill to build pipelines on nextflow. I believe nextflow programming is a valuable skill set for a bioinformatician, especially one working/pursuing research in genomics. Since I had a tough time collating resources myself, I'm sharing the learning path here. Hopefully, it benefits someone else who's lost in the sea of information that all the well-meaning experts on the bioinformatics threads provide.

Nextflow for Bioinformatics: Comprehensive Study Program

Total Duration: 28 weeks (approximately 7 months)

Total Study Hours: 1,120 hours

1. Milestone: Foundations (160 hours)

Program 1: Introduction to Programming (80 hours)

• Book: "Python for Biologists" by Martin Jones

  https://pythonforbiologists.com/

• Online Course: Codecademy's "Learn Python 3"

  https://www.codecademy.com/learn/learn-python-3

• Video Series: MIT OpenCourseWare's "Introduction to Computer Science and Programming in Python"

  https://ocw.mit.edu/courses/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/

Program 2: Linux Basics and Command Line (40 hours)

• Book: "The Linux Command Line" by William Shotts

  https://linuxcommand.org/tlcl.php

• Online Course: edX's "Introduction to Linux"

  https://www.edx.org/course/introduction-to-linux

• Tutorial: Linux Journey

  https://linuxjourney.com/

Program 3: Introduction to Bioinformatics (40 hours)

• Book: "Bioinformatics Data Skills" by Vince Buffalo

  https://www.oreilly.com/library/view/bioinformatics-data-skills/9781449367480/

• Online Course: Coursera's "Introduction to Bioinformatics" by UC San Diego

  https://www.coursera.org/learn/bioinformatics

• Resource: NCBI Handbook

  https://www.ncbi.nlm.nih.gov/books/NBK143764/

2. Milestone: Nextflow Basics (160 hours)

Program 4: Nextflow Fundamentals (80 hours)

• Official Nextflow Documentation

  https://www.nextflow.io/docs/latest/index.html

• Nextflow Training

  https://training.nextflow.io/

• Video: "Getting Started with Nextflow" by Paolo Di Tommaso

  https://www.youtube.com/watch?v=8_i8Tn335X0

Program 5: Nextflow Scripting (80 hours)

• Nextflow Patterns

  https://github.com/nextflow-io/patterns

• Nextflow Examples

  https://github.com/nextflow-io/examples

• Blog: "Nextflow Concepts for Beginners" by Zhuoqing Fang

  https://zhuoqingfang.medium.com/nextflow-concepts-for-beginners-b86ce7c2b06d

3. Milestone: Intermediate Nextflow (240 hours)

Program 6: Advanced Nextflow Concepts (120 hours)

• Nextflow Configuration Documentation

  https://www.nextflow.io/docs/latest/config.html

• Nextflow Error Handling Guide

  https://www.nextflow.io/docs/latest/tracing.html

• Blog: "Nextflow Workflow Patterns" by Phil Ewels

  https://www.nextflow.io/blog/2019/workflow-patterns-in-nextflow.html

Program 7: Nextflow DSL2 (80 hours)

• Nextflow DSL2 Documentation

  https://www.nextflow.io/docs/latest/dsl2.html

• Workshop: "Nextflow DSL2 Workshop" by Seqera Labs

  https://github.com/seqeralabs/nextflow-workshop

• Video: "Introduction to Nextflow DSL2" by Paolo Di Tommaso

  https://www.youtube.com/watch?v=rjkWHehhTXg

Program 8: Version Control with Git (40 hours)

• Book: "Pro Git" by Scott Chacon and Ben Straub

  https://git-scm.com/book/en/v2

• Online Course: Codecademy's "Learn Git"

  https://www.codecademy.com/learn/learn-git

• Interactive Tutorial: Learn Git Branching

  https://learngitbranching.js.org/

4. Milestone: Bioinformatics Applications (320 hours)

Program 9: NGS Data Analysis with Nextflow (160 hours)

• Book: "Bioinformatics Data Skills" by Vince Buffalo (chapters on NGS analysis)

  https://www.oreilly.com/library/view/bioinformatics-data-skills/9781449367480/

• Online Course: Galaxy Training Network's NGS tutorials

  https://training.galaxyproject.org/training-material/topics/sequence-analysis/

• Nextflow Pipelines: nf-core

  https://nf-co.re/

Program 10: Containerization and Reproducibility (80 hours)

• Docker Documentation

  https://docs.docker.com/

• Online Course: edX's "Introduction to Containers w/ Docker, Kubernetes & OpenShift"

  https://www.edx.org/course/introduction-to-containers-w-docker-kubernetes-openshift

• Nextflow Container Documentation

  https://www.nextflow.io/docs/latest/container.html

Program 11: High-Performance Computing with Nextflow (80 hours)

• Nextflow Executor Documentation

  https://www.nextflow.io/docs/latest/executor.html

• Online Course: FutureLearn's "High-Performance Computing in the Cloud"

  https://www.futurelearn.com/courses/high-performance-computing-cloud

• Tutorial: "Running Nextflow on AWS Batch"

  https://www.nextflow.io/docs/latest/awscloud.html

5. Milestone: Advanced Topics and Projects (240 hours)

Program 12: Nextflow Pipelines and nf-core (80 hours)

• nf-core Documentation

  https://nf-co.re/usage/introduction

• nf-core Tutorials

  https://nf-co.re/usage/tutorials

• Video: "Introduction to nf-core" by Phil Ewels

  https://www.youtube.com/watch?v=gUM9acK25tQ

Program 13: Custom Pipeline Development (120 hours)

• Nextflow Best Practices

  https://www.nextflow.io/docs/latest/best-practices.html

• Case Studies: Nextflow Community Pipelines

  https://github.com/nextflow-io/awesome-nextflow

• Workshop: "Building Reproducible Workflows with Nextflow and nf-core"

  https://github.com/nf-core/nf-core-workshops

Program 14: Best Practices and Optimization (40 hours)

• Nextflow Performance Tuning Guide

  https://www.nextflow.io/docs/latest/perftuning.html

• Blog: "Nextflow Optimization Tips" by Evan Floden

  https://www.nextflow.io/blog/2019/optimize-nextflow-pipelines.html

• Webinar: "Nextflow Optimization and Best Practices" by Seqera Labs

  https://www.youtube.com/watch?v=IjvjlxHVMAo

Note: This program is designed for intensive study, assuming approximately 40 hours per week. Adjust the pace as needed based on your circumstances and learning speed.

PS: I've just started with this and am on Milestone 1 of this journey. If anyone decides to follow this learning path, I'd love to hear about your progress and if this plan benefitted you. For those in the know, if any of these resources are outdated or not recommended, I'm open to critique and will update the plan on the thread.

Thanks for reading if you got this far!

Here's the Full Specialization.

• Introduction to Genomic Technologies

• Genomic Data Science w Galaxy

• Python for Genomic Data Science

• Command Line Tools for Genomic Data Science

• Algorithms for DNA Sequencing

• Bioconductor for Genomic Data Science

• Statistics for Genomic Data Science

It's going to say it costs $49 per course, but that's for the certificate - you can take any one for free.

I'm planning on joining Algorithms for Genomic Sequencing and perhaps that python class. Anyone interested in taking one or two with me?

Feel free to comment below if you're going to take anything, so we can buddy up!

Learning Python becomes much easier when students can see how variables, values, and data structures change while a program runs.

🧬 Consider this simple k-mer indexing example.

Although the code is relatively short, a beginner needs to understand several concepts at once:

• DNA sequence slicing

• k-mer generation

• dictionaries and membership tests

• lists stored as dictionary values

• repeated positions and list mutation

With open-source 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵, students can now easily step through a program and see these concepts in real-time, helping them to more easily get to the right mental model to think about Python code execution.

I'm currently learning bioinformatics and have started building some beginner DNA sequence analysis programs. So far I've implemented things like:

DNA Complement

Reverse Complement

GC Content Calculation

Nucleotide Counting

Basic Sequence Operations

I'll attach screenshots of my code/projects below.

Hey everyone,

I've been working on a self-paced bioinformatics learning platform and wanted to share it with this community: bioskillslab.dev. I'd really appreciate feedback on what's missing. What would make this more useful for beginners? I'm actively adding content.

I'm a biotech student, and I'm really interested in the coding aspects, ai, machine learning, deep learning, and so on.

I'm starting with R and python, and I'm currently taking the CS50P and CS50R course to help me out.

Any advice on how to and what skills to develop in a biotechnolgist's standpoint, from biotechnolgists who are in similar fields? Thanks!

Hi everyone,

I’m looking for some advice from people who’ve been through this or are currently in bioinformatics.

I’m finishing my MSc in Computer Science in a couple of months (my focus is a subfield of Machine Learning), and I’m considering applying for a PhD in Bioinformatics. The catch is that I have little to no formal biology background.

From what I’ve seen, many people in bioinformatics come from the opposite direction: strong biology/biomedical knowledge, but less depth in computer science or ML. My situation feels inverted. My idea would be to apply state-of-the-art ML techniques to biological/medical data, in a way that has relevance for both academia and industry. I already have a research topic in mind that seems like a good fit for a Bioinformatics program at a top university in my country (here, you usually should present a research topic/plan when applying to a PhD position).

Besides my MSc, I also work in a fairly standard software engineering / data science role, so I’m comfortable with production ML, data pipelines, etc. What worries me is whether the biology gap will be a serious bottleneck, especially during the early PhD years.

My motivation for pursuing a PhD is primarily career-oriented: I’m interested in improving my prospects as an ML researcher/developer, and I don’t plan to stay solely in academia.

So I have a few questions:

• Does it make sense to pursue a PhD in Bioinformatics with a CS-heavy background, or would a CS PhD with biological applications be a safer route?

• How steep is the biology learning curve in practice?

• Are there specific areas of bioinformatics where a strong ML background is particularly valued?

• What would you recommend reading or studying to build solid foundations in biology (from a CS/ML perspective)?

Any experiences, regrets, or success stories would be really appreciated. Thanks!

P.S.: I’m not based in the US, so my decision isn’t affected by the current funding cuts or science policy changes under the US government.

Hey everyone!

I'm currently doing my BSc and have recently developed a strong interest in Bioinformatics. The idea of combining biology with data and programming really fascinates me, and I want to build a solid foundation in this field.

I'm starting completely from scratch and don't have proper guidance on where to begin what skills to learn first, which programming language to start with, or any good beginner-friendly resources.

If anyone here could share a roadmap, free resources, or general advice for someone just starting out, I'd really appreciate it A

Thanks in advance!

I have this error and don't how to solve it

Hi, I'm a student and I made a game to practice designing RNA-seq experiments.

Each case is a researcher with a question and a budget. You choose replicates, library type, sequencing depth, batches and the analysis. Then the experiment is simulated and "Reviewer 2" reviews your design. Each comment cites a paper.

It's free and open source. It runs in the terminal on Windows, macOS and Linux. It's in English and Spanish.

It's still in development, so I'd like feedback, especially if something is scientifically wrong or a case isn't realistic.

https://github.com/leomorgzzz/reviewer2

Programming courses often focus heavily on understanding code, while paying far less attention to understanding the program state. But code does not exist in isolation. Its main goal is to change the program state, before ultimately producing some output.

To develop an accurate mental model of program execution, students need to understand both:

• the instructions being executed

• the values, references, and data structures those instructions create and modify

Reading code alone does not always reveal how the program state changes during execution. That is why I created 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵: a tool that visualizes the state of a Python program as it changes, step by step.

It can help explain a wide range of introductory Python topics. Here are just a few examples:

• Loops, Lists and Dictionaries

• Python Data Model

• Function Calls

• Recursion

• Algorithms

• Classes

• Custom Data Structures

Instead of reconstructing the program state from print statements, students can now watch it change as each line executes. This makes unfamiliar concepts easier to understand and bugs easier to fix.

Help your students learn Python programming more thoroughly and easily.

See: more examples

Bioinformatics: There's just SO much to learn!

Alright, so I posted this yesterday inquiring the folks at /r/bioinformatics how they deal with the overwhelming amounts of material you need to learn. How does one go about learning an interdisciplinary subject as complex as bioinformatics?

Takeaways

Here are some take-aways I got from the thread:

• Chocolate, beer and chocolate beer is helpful.

• It is OKAY to cry sometimes.

• Time, and lots of it. Be patient. Take one day at a time.

• Remember that it's not hard, there's just a lot to it.

• Stay focused on the topic at hand.

• Pick a specialization to narrow your field.

• You don't need to know all of it to start. If a small part in a tutorial goes about one thing, don't get sidetracked to learn it.

• Also don't get upset that you don't know everything, cuz you never will. :'(

Resolution!

So I've decided that I'm going to post here, every Tuesday, Wednesday and Friday opening up a thread on one relevant subject. For each thread, I encourage you guys to go out and learn just one thing related to the subject of the day.

• Tuesdays: Computer science - Data structures or algorithms (doesn't have to be bioinformatics related).

• Wednesdays: Biology/Biochemistry/Chemistry - Including sequencing chemistry.

• Fridays: Statistics/Data Science

• (Monday and Thursdays are paper/problem discussions)

For example, on Tuesday (Computer Science day), I'll take 15 minutes to learn about suffix tries, which I was always curious about. I'll go do some quick googling, reading and then write a 3-5 sentence summarizing what I've learned. Diagrams are always helpful.

I'm hoping that participating in this will help solidify what you guys learned and also help everyone else, as they'll read your posts.

And most importantly, what you write about doesn't have to be advanced - it can be something super simple and easy! The thread will be for learning, not judging.

Thanks! I'm looking forward to this.

I have developed two bioinformatics projects concerning breast cancer by using data from the public GEO datasets—would be interested to know your feedback!

Hi everyone!

I am an 18-year-old student from India who is studying bioinformatics and has recently developed two separate projects in which she looked at the gene expression associated with breast cancer; I should be most grateful for any technical comments that the community can provide.

### OncoMap v1 — the Breast Cancer Bioinformatics Explorer

**GitHub:** https://github.com/sakshamdbis987-cmyk/OncoMap/

**Live dashboard:** https://sakshamdbis987-cmyk.github.io/OncoMap/

**Dataset:** [GSE42568](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE42568)

The study looked at 104 samples of breast cancer and 17 normal samples, with a focus on eight genes: BRCA1, BRCA2, TP53, ERBB2, PIK3CA, ESR1, PTEN and EGFR.

The output contains the differential-expression results, volcano plots, an expression heatmap and an interactive gene explorer.

### 2. OncoMap v2 — Validation of the Method on an Independent Cohort and Investigation into Subtypes

**Dataset:** [GSE65194](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE65194)

The project is an extension of v1 since it involves a separate cohort. It comprises:

– The quality control of 178 microarray arrays.

– An investigation was carried out on 23 pairs of technical replicates.

- A matrix at the level of individual patients consisting of 130 patients with tumours and 11 healthy controls.

- Comparisons between tumour and healthy cases as well as those among the four subtypes.

A comparison between Welch's t-test and limma.

A cross-cohort comparison was carried out for the 38 target probes.

In the comparison of tumour versus healthy tissue, all 38 of the probes had the same expression directions when analysed by Welch and limma; among the different cohorts, 24 out of the 38 probes showed concordant directions.

The v2 report and interactive dashboard have been made ready locally, and the public release will come next.

### A little about me

I am still getting to know Python, together with R, pandas, NumPy, matplotlib and limma, and I'm using AI coding assistance.

They are computational projects which are exploratory, not clinical or diagnostic ones.

I should like to get some feedback regarding my replicate handling, the multiple-testing corrections, my validation strategy and the biological interpretation.

Constructive criticism is always appreciated. Thanks!

I recently made a tutorial video on the basics of DNA sequencing, and how to make sense of VCF files (the file type produced by DNA sequencing, with information about genetic variants).

Here's the video if anyone's interested.

It's specifically meant for people who are beginners in the field of bioinformatics / genomics, including people who had their DNA sequenced and are interested in exploring their own data.

Bit of background - I work as a bioinformatics scientist at a biotech company for my day job, and my main hobby is making educational videos about this field. I'm also interested in biohacking and decentralized science more broadly, and try to contribute to that with my videos. Basically encouraging people to self-educate on these topics, and trying to get over the myth that you need to be a genius or have some fancy credential to explore biology.

Anyway, thought some people in this community might be interested!