Skip to content

pipeline_workflow.md

Latest commit

 

History

History
174 lines (142 loc) · 8.07 KB

pipeline_workflow.md

File metadata and controls

174 lines (142 loc) · 8.07 KB

DNA Barcoding Pipeline - Visual Workflow

Overview: From Chromatograms to Species Identification

┌─────────────────────────────────────────────────────────────────────┐
│                    DNA BARCODING WORKFLOW                            │
│                   (5 Steps to Species ID)                           │
└─────────────────────────────────────────────────────────────────────┘

YOUR SAMPLES                          WHAT HAPPENS
(from UC Genomics)                    (This pipeline does it)
─────────────────                     ──────────────────────


📊 Raw Data
─────────────
.ab1 files
(chromatograms)                       ┌──────────────────────┐
  • AT-HV1F.ab1 ────┐                │  STEP 1: QC          │
  • AT-HV1R.ab1 ────┼──────────────> │  Quality Control     │
  • AT-HV3F.ab1 ────┤                └──────────────────────┘
  • AT-HV3R.ab1 ────┘                         │
  • (30 total files)                          │ Checks quality & length
                                              ▼
                                       ✓ PASSED: 12 sequences
                                       ✗ FAILED: 18 sequences
                                              │
                                              ▼
🧬 Passed Sequences
──────────────────
Individual F & R reads                ┌──────────────────────┐
>AT-HV1F                             │  STEP 2: CONSENSUS   │
ATCGATCGATCG...       ──────────────> │  Combine F+R pairs   │
>AT-HV1R                             └──────────────────────┘
ATCGATCGATCG...                              │
>AT-HV3F                                     │ Pairs F+R reads
ATCGATCGATCG...                              │ Creates consensus
>AT-HV3R                                     ▼
ATCGATCGATCG...                  ✓ 4 consensus sequences created
                                 ✗ 4 samples missing F or R
                                              │
                                              ▼
🎯 Consensus Sequences
─────────────────────
Best sequence from F+R            ┌──────────────────────────┐
>AT-HV1 (consensus)               │  STEP 3: COMBINE         │
ATCGATCGATCG...       ────────────>│  Add reference sequences │
>AT-HV3 (consensus)               └──────────────────────────┘
ATCGATCGATCG...                            │
>AT-JM2 (consensus)                        │ Adds 52 known
ATCGATCGATCG...                            │ SoCal mosquitoes
>AT-WL2 (consensus)                        ▼
ATCGATCGATCG...               56 sequences (4 yours + 52 refs)
                                           │
                                           ▼
📐 Combined Dataset
──────────────────
Your samples + references         ┌──────────────────────┐
(56 sequences total)              │  STEP 4: ALIGN+TREE  │
                  ──────────────> │  Compare all         │
                                  └──────────────────────┘
                                           │
                                           │ MAFFT aligns
                                           │ IQ-TREE builds tree
                                           ▼
                                  Tree shows where YOUR
                                  samples cluster with
                                  known species!
                                           │
                                           ▼
🌳 Phylogenetic Tree                ┌──────────────────────┐
────────────────────               │  STEP 5: BLAST       │
Your 4 consensus     ──────────────>│  Identify species    │
sequences                          └──────────────────────┘
                                           │
                                           │ Compares to
                                           │ GenBank database
                                           ▼
🏷️  Species Identified!
────────────────────
AT-HV1  → Aedes albopictus (99.55%) - Asian tiger mosquito
AT-HV3  → Culex pipiens (98.12%)     - Northern house mosquito
AT-JM2  → Culex pipiens (99.25%)     - Northern house mosquito
AT-WL2  → Culex pipiens (98.67%)     - Northern house mosquito


📊 Final Results: HTML Reports + Tree Figures + Summary Table

What You'll Learn at Each Step

Step 1: Quality Control (5 minutes)

QUESTION: Are my sequences good enough?

YOU LEARN:

  • What makes a good Sanger sequence
  • Why some sequences fail (low quality, too short)
  • How to read chromatograms
  • Why you need BOTH forward and reverse reads

YOU DO:

  • Run one simple command
  • Look at HTML report with chromatogram visualizations
  • Count how many F and R reads passed

KEY CONCEPT: Not all sequences from the core are usable - that's normal! We need high quality data for accurate species ID.

Step 2: Consensus Sequences (3 minutes)

QUESTION: How do we combine forward and reverse reads?

YOU LEARN:

  • Why forward and reverse reads are sequenced
  • How consensus sequences improve accuracy
  • What happens if only F or only R passes QC

YOU DO:

  • Run one simple command with --pairs-only flag
  • View HTML report showing which samples have complete pairs
  • See how many samples make it to the next step

KEY CONCEPT: Consensus from F+R is more accurate than a single read! Only complete pairs are used.

Step 3: Alignment & Tree (10 minutes)

QUESTION: How do my samples compare to known mosquito species?

YOU LEARN:

  • What "alignment" means (lining up DNA sequences)
  • How phylogenetic trees show evolutionary relationships
  • How to interpret your samples clustering with references

YOU DO:

  • Run alignment + tree commands (combined with 52 reference sequences)
  • View tree showing where YOUR 4 samples cluster
  • Identify which reference species are closest to yours

KEY CONCEPT: The tree shows you which known species your samples are most similar to!

Step 4: Species Identification (5 minutes)

QUESTION: What species are my samples?

YOU LEARN:

  • How BLAST works (compares to GenBank database)
  • What % identity means (>98% = same species)
  • How to interpret top hits and write scientific names correctly

YOU DO:

  • Run BLAST command on your consensus sequences
  • Read HTML report with top matches
  • Fill in summary table with species names and % identity

KEY CONCEPT: BLAST confirms what the tree suggests! >98% match = probably the same species.

Time Investment

First time (with tutorial): ~30 minutes

  • 10 min learning workflow
  • 20 min running test data

Your real data: ~10 minutes

  • Just run the commands
  • Focus on interpreting results

Writing up results: ~30 minutes

  • Answer assignment questions
  • Include figures in report