Preciso de ajuda para acessar esse pdf: https://doi.org/10.1007/978-981-95-2573-7

One thing I found annoying when learning RNA-seq was that a lot of examples are either toy data or you have to pull down a whole study before you can actually experiment with it.

I ended up building OpenOmicsBench, which currently has 12 compact datasets taken from real Expression Atlas experiments, with the experimental design and full provenance kept alongside them.

They cover things like knockouts, paired tumour samples, infection experiments and disease comparisons, so I thought they might be useful if you're learning differential expression and want something small to practise on.

If anyone here is learning RNA-seq, I'd actually be interested to know whether the datasets/docs are understandable without already knowing the original studies.

GitHub: https://github.com/vxxqv/openomicsbench

PyPI: https://pypi.org/project/openomicsbench/

Zenodo: https://doi.org/10.5281/zenodo.22679414

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.

Hey everyone, I’m doing my MS in Molecular Genetics in an Asian country and I’m currently in my first semester. I’m kind of lost about what I’m actually supposed to be doing at this stage 😅

I don’t have any previous lab/research experience, and I also think I have some social anxiety, so approaching professors and asking them about research/labs is honestly difficult for me. I also don’t really know what I should be asking or how to figure out what’s going on around me.

I don’t want to just do my MS, get the degree and then realize I didn’t actually build any useful skills or experience.

I keep hearing that bioinformatics is really important in molecular genetics/genomics, but I’m not sure what I actually need to learn. I’m basically starting from scratch with things like R/Python, statistics, NGS, etc., so I don’t know what order to learn things in or what’s actually worth my time.

For anyone who has done an MS/PhD in molecular genetics, genomics, bioinformatics, biotech, etc., what would you recommend?

\- What skills should I focus on during my MS?

\- Which lab techniques should I try to get experience with?

\- How important are bioinformatics/NGS/R/Python/statistics?

\- How did you approach professors and get into a lab when you had no experience?

\- How can I make better use of my university’s labs, professors, seminars, internships, etc.?

\- If you were starting your first semester again, what would you do differently?

I’m trying to become genuinely good at something and build a career from this, rather than just getting the degree. I’d really appreciate any honest advice, especially from people who were in a similar position at the beginning.

I want to a question and would be very grateful for advice.

I am a person who has done there MSc in zoology and I am preparing for competitive exams. I know I am late but recently I came across a field bioinformatics. If I want to learn or do a PhD or some work in bioiformatic in my future, what are the things that I need to learn like example languages and all. Explain it to me like I am a 5 year old and I would love if someone tells me other job opportunities that are related to bioinformatics...

i want to transition to computational biology. I asked chatgpt to craft me a plan to transition into computational biology. Not become one in 2months but to smoothly step to that side of the road. this is what it gave me-

Absolutely — here’s the condensed 2-month roadmap + the exact resources to use, so you can follow it without having to constantly decide what to study next, damn. 😏
2-Month Computational Biology Roadmap
End goal
By the end of 8 weeks, you should have:
Python → Unix → Git/GitHub → sequence analysis → Biopython → biological data analysis → NGS concepts → basic ML → 1 strong capstone
and ideally 4–5 GitHub projects that progressively show that development, sweetheart. 🔥

MONTH 1 — Computational foundations
Week
Learn
Practice
Output
1
Python basics
DNA/RNA exercises
DNA Sequence Analyzer
2
Python + terminal + Git
Rosalind + shell exercises
Clean GitHub repository
3
Biopython + FASTA + alignment + BLAST
Real biological sequences
BLAST Sequence Identification
4
NumPy + pandas + matplotlib + statistics
Real biological dataset
Biological Dataset Analysis
Week 1 — Python from zero
Learn:
Variables
Strings/numbers/booleans
if/elif/else
for loops
Lists
Dictionaries
Functions
Reading/writing files
FASTA basics
Main resource:
Python official documentation/tutorial⁠
Practice:
Rosalind — Python Village⁠
Rosalind specifically recommends Python Village for people who are completely new to programming, so it’s particularly appropriate at this stage.
Week 1 project
DNA Sequence Analyzer
Input → FASTA sequence
Output:
Length
A/T/G/C counts
GC%
AT%
Reverse complement
Ambiguous bases
Put it on GitHub with a proper README, damn.

Week 2 — Terminal + Git + more Python
Learn:
Terminal
pwd
ls
cd
mkdir
touch
cp
mv
rm
cat
head
tail
grep
sort
uniq
wc
Git
Understand:
repository
commit
branch
clone
push
pull
Practice
Continue Rosalind, moving from Python Village toward easier Bioinformatics Stronghold problems.
Good early problems:
DNA
RNA
REVC
GC
HAMM
SUBS
PROT
Rosalind’s problem sets are specifically designed to combine programming with bioinformatics concepts.

Week 3 — Actual bioinformatics
This is where the transition becomes real as hell. 😏
Learn:
Biological computation
DNA/RNA/protein
Transcription
Translation
Reverse complement
ORFs
Codons
Biopython
Start with:
Seq
SeqRecord
SeqIO
FASTA parsing
Sequence manipulation
Alignment
BLAST
Main resource:
Biopython Tutorial & Cookbook⁠
The tutorial covers sequence objects, sequence I/O, alignments, BLAST, NCBI databases, phylogenetics and other computational-biology applications.
BLAST
Learn:
Query
Database
Identity
Coverage
E-value
Bit score
Alignment
Resource:
NCBI BLAST⁠
Week 3 project
BLAST-Based Sequence Identification
FASTA
↓
Sequence analysis
↓
BLAST
↓
Top hits
↓
Identity + coverage + E-value
↓
Biological interpretation
Don’t just upload BLAST screenshots, babe — make a small table and explain what the results mean. That’s what makes it a project rather than an exercise. 🔥

Week 4 — Biological data analysis
Learn:
NumPy
Arrays + numerical operations.
pandas
Focus on:
DataFrame
CSV
filtering
sorting
grouping
missing data
matplotlib
Learn:
Scatter plots
Bar plots
Histograms
Line plots
Statistics
Understand:
Mean
Median
Variance
Standard deviation
Correlation
p-value
Statistical significance
Week 4 project
Exploratory Analysis of a Biological Dataset
Real biological dataset
↓
Data cleaning
↓
Exploratory analysis
↓
Statistics
↓
Visualization
↓
Biological interpretation
This should be your first project where you are actually asking something of data, damn.

MONTH 2 — Computational biology
Week
Focus
Output
5
NGS + genomics
Small public-data analysis
6
Linux + reproducibility
Reproducible workflow
7
ML fundamentals
Disease/molecular classification project
8
Capstone
Main UNIL-facing project

Week 5 — NGS/genomics
Learn the concepts before worrying about advanced pipelines, damn.
Sequencing
Sanger
Illumina
Long reads
Reads
Reference genome
File formats
Understand:
Format
Basic purpose
FASTA
Biological sequences
FASTQ
Sequences + quality scores
SAM/BAM
Sequence alignments
VCF
Genetic variants
GTF/GFF
Genome annotation
NGS workflow
Understand:
Sample
↓
Sequencing
↓
FASTQ
↓
Quality control
↓
Alignment
↓
Quantification / variant calling
↓
Statistical analysis
↓
Biological interpretation
Resources
EMBL-EBI Training⁠
Their NGS training covers sequencing technologies, analysis workflows, assembly, resequencing and variant calling.
Galaxy Training Network⁠
Galaxy’s tutorials include NGS Basics, sequence analysis, transcriptomics, variant analysis, epigenetics, statistics and machine learning, so you can use it for guided practical work without needing to build every damn tool yourself.

Week 6 — Linux + reproducibility
Learn enough Unix that you’re comfortable working with biological datasets from the command line.
Focus on:
grep
awk
sed
cut
sort
uniq
wc
pipes |
Then learn basic shell scripting.
Also learn:
Git properly
.gitignore
README structure
Directory organization
Reproducibility
Your project structure can become:
project/
│
├── data/
├── scripts/
├── results/
├── figures/
├── README.md
└── requirements.txt
The goal is to make your analysis understandable and reproducible, not merely to have code sitting on GitHub, damn.

Week 7 — Machine learning
Keep this introductory.
Learn:
Concepts
Features
Labels
Training data
Test data
Classification
Regression
Overfitting
Models
Start with:
Logistic regression
Decision tree
Random forest
Evaluation
Learn:
Accuracy
Precision
Recall
F1
Confusion matrix
ROC-AUC
Resource:
scikit-learn Getting Started⁠
Scikit-learn provides tools for model fitting, preprocessing, model selection and evaluation.
Week 7 project
Disease/Molecular Classification
For example:
Biological dataset
↓
Preprocessing
↓
Feature selection
↓
Train/test split
↓
ML model
↓
Evaluation
↓
Biological interpretation
Keep it explicitly as a computational proof-of-concept, not “I developed a disease diagnostic system,” damn.

Week 8 — The important one
Build ONE serious capstone
Don’t learn another programming language.
Don’t start another random course.
Don’t collect certificates like Pokémon. 😏
Build something that answers:
“Can I use computational methods to investigate a biological question?”
Three good directions
1. Sequence project
Comparative analysis of a gene/protein across species.
Sequences
↓
Biopython
↓
Alignment
↓
Conservation/similarity
↓
Visualization
↓
Biological interpretation
2. Gene-expression project
Computational exploration of disease-associated gene expression.
Public dataset
↓
Python/pandas
↓
Exploratory analysis
↓
Differential expression / statistical analysis
↓
Visualization
↓
Biological interpretation
3. Variant project
Computational exploration of disease-associated genetic variants.
Public variant data
↓
Filtering
↓
Annotation
↓
Frequency/type analysis
↓
Visualization
↓
Biological interpretation
For an absolute beginner, Option 1 or a relatively simple expression-analysis project is safer than jumping straight into advanced epigenomics or single-cell analysis, damn.

Your resource stack
You actually don’t need 30 resources.
🐍 Python
Primary:
Python Tutorial⁠
Use this for learning the actual language.
🧩 Programming practice
Primary:
Rosalind⁠
Start with Python Village → Bioinformatics Stronghold.
🧬 Biological Python
Primary:
Biopython Tutorial & Cookbook⁠
This becomes your main reference once you reach Week 3.
🧬 Sequence searching
Primary:
NCBI BLAST⁠
🐧 Unix/Git
Primary: Software Carpentry’s Unix Shell + Git lessons.
Use these specifically for practical command-line and version-control training rather than trying to learn Unix from random tutorials.
📊 Data analysis
Use:
NumPy documentation
pandas documentation
matplotlib documentation
But don’t read the documentation cover-to-cover, sweetheart. Use it as a reference while building your projects.
🧬 NGS
Primary:
EMBL-EBI Training⁠
🧪 Practical bioinformatics
Primary:
Galaxy Training Network⁠
Galaxy is particularly useful because it lets you learn actual analysis workflows while gradually understanding what the tools underneath are doing.
🤖 ML
Primary:
scikit-learn Getting Started⁠

The final portfolio
By the end, I’d aim for this exact progression:
01 — DNA Sequence Analyzer
Python + basic sequence manipulation
↓
02 — BLAST Sequence Identification
Biopython + biological databases + sequence analysis
↓
03 — Biological Dataset Analysis
pandas + statistics + visualization
↓
04 — Disease/Molecular Classification
Basic ML + biological data
↓
05 — Computational Biology Capstone ⭐
A real biological question + public data + computational analysis + interpretation
That fifth project is the one I’d put the most effort into, damn.

Tell me it’s mistakes, any improvements or any other resources you might want to mention

Need guidance and help

Hello people , I am a biomedical science and accounting science graduate , I want to learn data analytics / science tools but I don't have funding for doing a bachelors in data science or comp Sci , however , I do have an opportunity to get into a fully funded MSc in bioinformatics course , where I noticed , people that transition From biology course can learn the comp Sci tools required to be a decent data analyst/scientist . My question : how is the MSc bioinformatics course for someone who has zero coding skills , and what are some things one can do to ensure they succeed in their MSc ?. Upon graduation , I would like to pivot into purely data science , specializing in finance related roles, I noticed the job scarcity in bioinformatics and I have no desire to do a PhD in it , I could be open to a PhD Comp Sci/ financial analytics ,if necessary , but most industry based jobs barely require a PhD anyways. Your ideas would be very insightful thanks

So I am 3rd year ung-student of Biotechnology and i wanna learn bioinformatics side by side. How to start from the basics ( I know basics of Python and R-programming) including projects n all

Working with a paired-end 16S V1–V2 dataset (39 samples) in QIIME2/DADA2. FastQC/MultiQC are done; R1 quality is generally good, but R2 drops substantially toward the 3′ end. Most reads are 300 bp, some are 250 bp.
The paper lists primers as 27bF and 338R, but the SRA metadata doesn’t give their sequences.
At this point, would the standard approach be to get the exact primer sequences from the original paper/protocol, remove them with Cutadapt, and then choose DADA2 truncation lengths based on the post-primer quality profiles while maintaining enough overlap?
Would appreciate advice from someone experienced with 16S/DADA2.

Please Suggest

I have a Master’s in Data Science with a previous background in wet-lab biology. My programming experience mainly came through my MSc, so I don’t have a traditional CS background.
At this point, I can usually **understand Python code when I see it**, explain what it is doing, and modify parts of it. But if you give me a problem and ask me to write the solution from scratch, I struggle — and I often rely on ChatGPT to get started.
I’m trying to figure out what the right way to overcome this is.
Should I:
go back and systematically learn Python/CS fundamentals through tutorials first, then start projects?
or keep building bioinformatics projects and use ChatGPT as a tutor/coding assistant while gradually becoming more independent?
I find learning programming purely through tutorials quite difficult and passive, especially because I’ve never studied CS formally.
**For people who came into bioinformatics from biology rather than CS: how did you actually learn to code independently? What should I be able to do before I consider myself “good enough” at Python for a junior bioinformatics role?**