Single-cell library · one read, decoded
Cell barcode16 bp
hi, this is Bioinformagician
UMI12 bp
the human behind every tutorial
poly(dT)tail
catches what matters
Read 2 · cDNA insert · the message

Raw reads in. Real understanding out.

maps to → a free YouTube school for bioinformatics your first FASTQ file single-cell variant calling cancer genomics
> channel@meta.data
n_learnersnCount_viewsnFeature_tutorialsfirst_upload
Bioinformagician46.8Ksubscribers2.32Mviews69step-by-step tutorials2021and counting

samplesheet.csv
Sample sheet
sample_id
"Bioinformagician"
investigator
"Khushbu Patel"
assay
"tutorials" · 69 runs
library_size
2,324,686 views
collected
"2021-09-04" → today
mission
"make bioinformatics simple and intuitive"
status: ready · tap the hat
step 1 · cluster

Every tutorial is a cell. Here is the atlas.

The channel's videos, clustered by topic like a single-cell UMAP. Bigger cells have more viewers. Type a tool into FeaturePlot to light up every video that uses it, or tap a cluster to run FindMarkers.

step 2 · order cells in pseudotime

The journey, run as a pipeline.

Ordered by upload date, the tutorials form a trajectory. Each branch point is an era of the channel. Open one and the pipeline runs a full turn on it: sample, alignment to topics, differential expression against the rest of the channel, library growth and the lead transcript.

trajectory › era 2 of 6
step 3 · pick a protocol

Your learning path, written like a lab protocol.

I put these five paths together from my own videos, in the order I'd watch them if I were starting out today. Tick off each step as you go, and this page will remember where you left off.

lab notebook · notes to keep

Notes you can keep.

LinkedIn carousels and PDF guides in one place. Flip through any of them here, then download the PDF.

Dashed covers are placeholders. Each one becomes a real carousel or guide once its PDF is added.
step 4 · annotate the reference

The person behind the pipeline.

Khushbu Patel
LOCUSKHUSHBU_PATEL  69 tutorials  linear  EDU  04-SEP-2021
DEFINITIONBioinformatics scientist working on multi-omics data from pediatric cancers.
ACCESSION@Bioinformagician
KEYWORDSR; Python; Bash; Perl; GATK; STAR; DESeq2; GSEA.
SOURCE[placeholder: where you're based today]
FEATURES 
  mission"Making bioinformatics accessible to all"
  motto"Learning with challenges | Growing with data"
  dataWGS, WES, RNA-Seq, ChIP-Seq, microarray
  hobbiesreading, painting, writing
  origin[placeholder: the moment you decided to start the channel, in your own words]

Khushbu's day job is finding gene and protein targets in pediatric cancer data with statistical and computational methods. The channel takes the same toolkit and opens it up: the tutorial code lives on GitHub so you can rerun the analysis yourself, and the stated goal is a place where bioinformaticians can connect, learn and share ideas.

The intro video, 3 Reasons to create this channel, tells the origin story first-hand.

step 5 · map the interactions

An interactome of collaborators and community.

Like a protein interaction network: the channel at the center, and every partner, platform and community it has worked with around it. Edge weight is views. Tap a node.

step 6 · deposit your results

Add your cell to the atlas.

Every learner is a cell in this dataset. Express a gene below and your cell moves closer to the community. Express all six and it joins the cluster.

DimPlot(community, highlight = "you")46.8K cells + 1
cell_id younFeature 0 / 6unclustered
FeaturePlot(you, features = connect)
  1. SUBSCRIBESubscribe on YouTubeNew tutorials, workflows and Q&As. Join 46.8K learners.
  2. CLONEClone the tutorial codegit clone https://github.com/kpatel427/YouTubeTutorials.gitGitHub ↗
  3. FOLLOWFollow me on LinkedInCarousels, notes and bioinformatics tips in your feed.
  4. COFFEEBuy me a coffeeHelp keep every tutorial free.
  5. COLLABCollaborate on LinkedInPartnerships, talks and guest lectures.
  6. HELLOQuestions, ideas or just hikhushbu_p@hotmail.comEmail ↗