Quick Start¶

Get started with ChimeraLM in under 15 minutes! This tutorial will guide you through your first chimeric read prediction.

Prerequisites¶

• ChimeraLM installed (Installation Guide)
• Basic command-line experience
• A BAM file to analyze (we'll provide sample data)

Step 1: Get Sample Data¶

ChimeraLM includes test data in the repository. If you installed from source:

# Sample data is already available
ls tests/data/mk1c_test.bam

If you installed via pip, download the sample data:

# Download sample BAM file with index
wget https://github.com/ylab-hi/chimera/raw/main/tests/data/mk1c_test.bam
wget https://github.com/ylab-hi/chimera/raw/main/tests/data/mk1c_test.bam.bai

# Or using curl
curl -L -o mk1c_test.bam https://github.com/ylab-hi/chimera/raw/main/tests/data/mk1c_test.bam
curl -L -o mk1c_test.bam.bai https://github.com/ylab-hi/chimera/raw/main/tests/data/mk1c_test.bam.bai

# Verify files downloaded correctly
ls -lh mk1c_test.bam*

Step 2: Run Your First Prediction¶

Run ChimeraLM on the sample data:

chimeralm predict mk1c_test.bam --gpus 0

INFO     [rank: 0] Loading model from Hugging Face
Seed set to 42
GPU available: True (mps), used: False
Generating train split: 75 examples [00:00, 1844.17 examples/s]
Predicting DataLoader 0: 100%|██████████| 4/4 [00:15<00:00, 0.26it/s]

chimeralm predict mk1c_test.bam --gpus 1 --batch-size 24

INFO     [rank: 0] Loading model from Hugging Face
Seed set to 42
GPU available: True (mps), used: True
Predicting DataLoader 0: 100%|██████████| 2/2 [00:03<00:00, 0.66it/s]

Step 3: Understand the Output¶

ChimeraLM creates a predictions file with one line per read:

# View predictions from first batch
head -10 mk1c_test.predictions/0_0.txt

Output format (tab-separated):

read_name<TAB>label
e5f89040-2898-41d9-9ee4-3022168216f0    1
b76512a7-5a74-405b-8ac3-adde6a7ea5e1    0
5b830fb3-6bb7-42a4-ad18-142b9474ed7d    1
edab7cd5-831c-4f51-8ada-c9b4620307c1    0
...

Labels:

• 0: Biological read (keep for analysis)
• 1: Chimeric artifact (remove from analysis)

Step 4: Interpret Results¶

Count how many reads are chimeric:

# Count chimeric reads (label 1)
cat mk1c_test.predictions/*.txt | grep -c "1$"

# Count biological reads (label 0)
cat mk1c_test.predictions/*.txt | grep -c "0$"

Expected results for test data:

• Chimeric artifacts: 55 (73.3%)
• Biological reads: 20 (26.7%)

Typical chimera rates for WGA data:

• MDA (Multiple Displacement Amplification): 10-40%
• PicoPLEX: 5-20%
• Non-WGA data: <1%

Checkpoint: Verify Your Prediction Worked¶

✅ Success indicators:

• Predictions file created
• File contains tab-separated read names and labels
• Labels are 0 or 1
• Number of predictions matches input reads

Next Steps¶

Now that you've completed the basics:

For Analysis¶

Filter your BAM file to remove chimeric reads:

chimeralm filter mk1c_test.bam mk1c_test.predictions

This automatically creates:

• mk1c_test.filtered.bam - Unsorted filtered reads
• mk1c_test.filtered.sorted.bam - Final sorted output (use this!)
• mk1c_test.filtered.sorted.bam.bai - BAM index
• mk1c_test.predictions/predictions.txt - Consolidated predictions

For comprehensive filtering guidance including verification, troubleshooting, and batch processing, see the Filtering BAM Files Tutorial.

For Learning¶

• Optimize performance: See Performance Optimization
• Integrate into pipelines: See Pipeline Integration
• Use the web interface: See Web Command

For Development¶

• Use as a library: See API Reference

Troubleshooting¶

Encountered an issue? Check our Troubleshooting Guide for common problems and solutions.