spades.py --careful -1 reads_1.fastq.gz -2 reads_2.fastq.gz -o day2_output 2. Monitor Resource Allocation
Using tools like Trimmomatic to clean your raw data before assembly. SPADES-Day2-pc.rar
SPAdes is known for being extremely memory-intensive. A useful practice during "Day 2" exercises is explicitly limiting its resource usage to prevent your PC from crashing. spades
Running the spades.py command on the cleaned "Day 2" datasets. SPADES-Day2-pc.rar
Without it, your final sequence might have small errors that make downstream analysis (like finding genes) more difficult.
Activating the specific environment (e.g., conda activate micro612 ).
It reduces the number of mismatches and short indels (insertions/deletions) by running a post-processing tool called MismatchCorrector.