Assembly & Annotation

Protocol with typical commands used for de novo assembly and annotation

• Preprocessing
• Assembly
• Annotation
• Submission
• Visualization

Preprocessing

Quality control: Check quality of your data using FastQC and fastq_stats.py. <source lang='bash'> fastqc ../*.gz </source> Explore the report to do the quality check and identify potential adapters and primers in the sequences.

K-mer analysis: Us the script kmer_analysis.sh to get the genomic properties based on the k-mer distribution. Genomic properties include genome size and percentage of heterozygosity.

Preprocessing/kmer_analysis.sh -m <kmer_size> -c <error-cutoff> -s <hash-size> -t <threads> -i <R1.fastq.gz R2.fastq.gz ...> -o <output_dir>

Trimming: Use Trimmomatic to trim Illumina data. Make sure your fasta file with the adapters corresponds to the adapters found in the FastQC report. Use the following script: preprocessing/run_trimmomatic.sh -t <num_threads> -f <FW_reads.fastq> -r <RV_reads.fastq>

Error correction Lighter is a fast tool to error correct your Illumina data. Use the following script: preprocessing/run_lighter_error_correction.sh -g <genome_size> -c <coverage> -f <FW_reads.fastq> -r <RV_reads.fastq>

Organelle assembly Download a proper reference from the NCBI database. Use the IOGA pipeline to assemble to organellar genome. assembly/run_IOGA.sh -a <assembly> -f <fw_reads.fastq> -r <reverse_reads.fastq> -i <insert_size> -t <num_threads> -n <name_prefix> Map your reads to the newly assembled genome and manually check if it is circular.

Use Pilon to correct remaining errors in the assembly using the mapped reads.

Annotate using MITOS or DOGMA online tools.

Submit here: http://www.ncbi.nlm.nih.gov/LargeDirSubs/dir_submit.cgi