Apr 03, 2024
Vaccinium floribundum Genome Assembly and Annotation Script
- Martina Albuja-Quintana1,
- Gabriela Pozo1,
- Milton Gordillo-Romero1,
- Carolina E. Armijos1,
- María e Lourdes Torres1
- 1Universidad San Francisco de Quito
Protocol Citation: Martina Albuja-Quintana, Gabriela Pozo, Milton Gordillo-Romero, Carolina E. Armijos, María e Lourdes Torres 2024. Vaccinium floribundum Genome Assembly and Annotation Script. protocols.io https://dx.doi.org/10.17504/protocols.io.n92ldmo4nl5b/v1
License: This is an open access protocol distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
We use this protocol and it's working
Created: March 15, 2024
Last Modified: April 03, 2024
Protocol Integer ID: 96735
Keywords: vaccinium floribundum genome assembly, vaccinum floribundum specimen, whole genome of this species, genome, whole genome, genome completeness, illumina short read, sequencing statistics, ab initio gene predictor snap, annotation script oxford nanopore, sequencing, ont long read, short read, long read, oxford nanopore, flye assembly, species, resulting flye assembly, dna
Funders Acknowledgements:
Fondos COCIBA USFQ
Abstract
Oxford Nanopore long reads and Illumina short reads obtained from sequencing the DNA of a Vaccinum floribundum specimen from the Paramo region in Ecuador, were used to assemble and annotate the whole genome of this species. ONT long reads were filtered and trimmed in Nanofilt and Porechop. Sequencing statistics were visualized in Nanoplot. Illumina short reads were evaluated with fastqc. Different assemblers and polishers were used with both long and short reads. The resulting Flye assembly polished with Polca and Medaka was then analyzed for genome completeness and quality with Quast, BUSCO, LAI Index, and Coverage Graph. The assembly was later annotated in Maker in 3 consecutive rounds using the ab initio gene predictor SNAP.
Troubleshooting
Oxford Nanopore Sequencing - Raw Reads Filtering, Trimming, and Statistics
Raw Read Adapter Filtering
Porechop v0.2.4 (RRID:SCR_016967)
porechop -i Mortino_ONT_RawReads.fastq.gz -o Porechop_Mortino_ONT_RawReads.fastq.gz
Raw Read Quality and Length Trimming
Nanofilt v2.8.0 (RRID:SCR_016966)
NanoFilt -q 7 < Porechop_Mortino_ONT_RawReads.fastq > Porechop_Mortino_ONT_q7.fastq
NanoFilt -l 1000 < Porechop_Mortino_ONT_q7.fastq > Porechop_Mortino_ONT_q7_1000.fastq
Raw Read Dataset Statistics
LongQC v1.2.0c
python longQC.py sampleqc -x ont-ligation -o longQC_Mortino_ONT Porechop_Mortino_ONT_q7_1000.fastq
Nanoplot v1.33.0 (RRID:SCR_024128)
NanoPlot --fastq Porechop_Mortino_ONT_q7_1000.fastq --readtype 1D -t 4 --title "Nanoplot_Mortino_ONT" -o Nanoplot_Mortino_ONT
Illumina Sequencing - Raw Reads Statistics
Raw Read Dataset Statistics
FastQC (RRID:SCR_014583)
fastqc FC225MJ3LT3_L1_Mort-Illumina_1.fq.gz FC225MJ3LT3_L1_Mort-Illumina_2.fq.gz -o FastQC_Mortino_Illumina
Genome Size Estimation
k-mer based analysis
Jellyfish v2.3.0 (RRID:SCR_005491)
zcat FC225MJ3LT3_L1_Mort-Illumina_1.fq.gz FC225MJ3LT3_L1_Mort-Illumina_2.fq.gz > Mortino-Illumina_Combined1y2.fq.gz
jellyfish count -t 8 -m 21 -o Mortino_Illumina.jf -c Mortino-Illumina_Combined1y2.fq.gz
jellyfish histo -t 10 Mortino_Illumina.jf > Mortino_df.histo
k-mer profile visualization
GenomeScope v2.0 (RRID:SCR_017014)
De novo Genome Assembly and Polishing
Assembly
SMARTdenovo v1.0.0 (RRID:SCR_017622)
smartdenovo.pl -p SdN_Assembly_Mortino -c 1 Porechop_Mortino_ONT_q7_1000.fastq > SdN_Assembly_Mortino.mak
make -f SdN_Assembly_Mortino.mak
Flye v2.9.2 (RRID:SCR_017016)
flye --nano-raw Porechop_Mortino_ONT_q7_1000.fastq --out-dir Flye_Mortino_Assembly -g 0.5g
MaSuRCA v.4.1.0 (RRID:SCR_010691)
masurca -i FC225MJ3LT3_L1_Mort-Illumina_1.fq.gz,FC225MJ3LT3_L1_Mort-Illumina_2.fq.gz -r Porechop_Mortino_ONT_q7_1000.fastq -t 5
Polishing
Medaka v1.11.1
medaka_consensus -i Assembly.fasta -o Medaka_Assembly -t 4 -m r941_min_fast_g507
POLCA (MaSuRCA, v4.1.0 RRID:SCR_010691)
polca.sh -a Assembly.fasta -r FC225MJ3LT3_L1_Mort-Illumina_1.fq.gz -r FC225MJ3LT3_L1_Mort-Illumina_2.fq.gz -t 5
Genome assembly quality, continuity, and completeness assessment
Quast v5.2.0 (RRID:SCR_001228)
quast.py Assembly.fasta -r Vaccinium_myrtillus_GCA_016920895.1_VacMyr_v1.0_genomic.fna.gz' -o Quast_ref_Vmyrtillus
BUSCO v5.4.7 (RRID:SCR_015008)
busco -i Assembly.fasta -l eudicots_odb10 -o BUSCO_Assembly -m genome
Long Terminal Repeat (LTR) Assembly Index (LAI)
LTRharvest v1.6.2 (RRID:SCR_018970)
1. Create an "Enhanced Suffix Array"
gt suffixerator -db Assembly.fasta -indexname Assembly_indextable.fsa -tis -suf -lcp -des -ssp -sds -dna
2. Run LTRHARVEST
gt ltrharvest -index Assembly_indextable.fsa -minlenltr 100 -maxlenltr 7000 -mintsd 4 -maxtsd 6 -motif TGCA -motifmis 1 -similar 85 -vic 10 -seed 20 -seqids yes > Assembly.fa.harvest.scn
LTR_FINDER v1.0.7 (RRID:SCR_015247)
ltr_finder assembly.fasta > Assembly_LTRfinder.txt
Combine both documents from LTRFinder and LTRHarvest
cat Assembly_LTRfinder.txt Assembly.fa.harvest.scn > Assembly.fa.rawLTR.scn
LTR_retriever v2.8.7 (RRID:SCR_017623)
LTR_retriever -genome Assembly.fasta -inharvest Assembly.fa.rawLTR.scn -threads 10
Coverage Graphs of ONT and Illumina Reads
1. Change contig names from assembly file
awk '/^>/{print ">Mortino" ++i; next}{print}' < Assembly.fasta > Assembly_nom.fasta
2. Identify longest contig
cat Assembly_nom.fasta | bioawk -c fastx '{ print length($seq), $name }' | sort -k1,1rn | head -1
3. Extract the longest contig from the assembly
Samtools package v1.18 (RRID:SCR_002105)
samtools faidx Assembly_nom.fasta Name_Longest_Contig > Longest_contig.fasta
4. Extract 10% of ONT and Illumina reads (total reads were obtained from previously run Nanoplot)
BWA (RRID:SCR_010910)
reformat.sh in1=FC225MJ3LT3_L1_Mort-Illumina_1.fq.gz in2=FC225MJ3LT3_L1_Mort-Illumina_2.fq.gz out1=FC225MJ3LT3_L1_Mort-Illumina_1_subset_10.fq.gz out2=FC225MJ3LT3_L1_Mort-Illumina_2_subset_10.fq.gz reads=# of total reads samplereadstarget=# of 10% reads
5. Map ONT reads and illumina reads, seperately, to the assembly
ONT reads: minimap2 v2.26 (RRID:SCR_018550)
minimap2 -ax map-ont Longest_contig.fa Porechop_Mortino_ONT_q7_1000.fastq > ONT_Assembly.sam
Illumina reads: BWA (RRID:SCR_010910)
bwa index Longest_contig.fa
bwa mem Longest_contig.fa FC225MJ3LT3_L1_Mort-Illumina_1_subset_10.fq FC225MJ3LT3_L1_Mort-Illumina_2_subset_10.fq > Illumina_assembly.sam
6. Convert sam files to bam
samtools view -bS ONT_Assembly.sam > ONT_Assembly.bam
samtools view -bS Illumina_Assembly.sam > Ilumina_Assembly.bam
7. Sort bam files
samtools sort ONT_Assembly.bam > ONT_Assembly_sorted.bam
samtools sort Illumina_Assembly.bam > Illumina_Assembly_sorted.bam
8. Coverage depth
Samtools package v1.18 (RRID:SCR_002105)
samtools depth ONT_Assembly_sorted.bam > depth_Assembly_ONT
samtools depth Illumina_Assembly_sorted.bam > depth_Assembly_Illumina
5. Graphs: script developed by López et al., 2023
python 3
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('depth_Assembly_sorted', sep="\t", names=["Sequence", "Base_position", "Depth"], index_col=0)
fig=df.plot(x='Base_position', y='Depth', kind='line', figsize=(16, 4)).get_figure()
fig.savefig("Coverage_plot.pdf")
Genome scaffolding
ntLink v1.3.9
ntLink scaffold gap_fill target=Assembly.fasta reads=Porechop_Mortino_ONT_q7_1000.fast overlap=true
Removing Foreign Contamination
python3 ./fcs.py screen Assembly.fasta --fasta ./fcsgx_test.fa.gz --out-dir ./gx_out/ --gx-db "$GXDB_LOC/gxdb" --tax-id 6973
Genome Annotation
RepeatModeler v2.0.3 (RRID:SCR_015027)
BuildDatabase -name Assembly.fasta RepeatModeler -threads 32 -database Mortino_genome -LTRStruct >& repeatmodeler.log
Change Contig Names in Assembly File
awk '/^>/{print ">Mortino" ++i; next}{print}' Assembly_nom.fasta
Generate Executable Files
maker -CTL
1. Modify Maker_opts.ctl file
2. Run 10 iterations of the first run of maker
maker
3. Run and train SNAP script
sh Snap_Pult_Creator.sh
4. Run 5 iterations of the second run of maker
maker
5. Rerun and train SNAP script
sh Snap_Pult_Creator.sh
4. Run 5 iterations of the third run of maker
maker
Generating and Filtering Final Files
1. Generate single gff and fasta protein and transcript files from all 3 maker rounds
gff3_merge -d Assembly_master_datastore_index.log -o Mortino_All.gff
fasta_merge -d Assembly_master_datastore_index.log -o Mortino_All
2. Identify conserved protein regions in predicted gene models
InterProScan v5.64-9.60 (RRID:SCR_005829)
interproscan.sh -appl PfamA -iprlookup -goterms -f tsv -i Mortino.all.maker.proteins.fasta
3. Modify the original gff3 file by identifying gene models with conserved protein domains
ipr_update_gff Mortino_All.gff Mortino.all.maker.proteins.fasta.tsv > Mortino_genomic_updated.all.gff
4. Eliminate gene models with AED <1
quality_filter -s Mortino_updated.all.gff > Mortino_FINAL.all.gff
5. Calculate annotation statistics with AGAT
AGAT (Another Gff Analysis Toolkit) v1.2.0
agat_sp_statistics.pl –gff Mortino_FINAL.all.gff -o Mortino_Stats
6. Filter out gene models with no conserved protein regions and AED <1 from protein and transcript fasta files
perl -lne 'print $1 if /\tmRNA\t.+ID=([^;]+).+_AED=(.+?);/' Mortino_FINAL.all.gff > genes_from_gff.aed-1.0.ids
fastaqual_select.pl -f Mortino.all.maker.proteins.fasta -inc genes_from_gff.aed-1.0.ids > Mortino_Proteins_Final.fasta
fastaqual_select.pl -f Mono_Anotado_All.all.maker.transcripts.fasta -inc genes_from_gff.aed-1.0.ids > Mono_Anotado_All_Transcripts_Final.fasta