SilviaDeng/NanoCircle

Rasmus Amund Henriksen, NanoCircle development 2020

NanoCircle

The github reporsitory for the under development tool, NanoCircle 2020. Useful for identifying the coordinates of both simple and chimeric circular molecules, sequenced using long-read sequencing.

Some presteps to perform before running NanoCircle

STEP1 - Trimming and prehandling

adapter and barcode trimming

porechop -i bc05.reads.fastq -b bc05.barcode_trim -t 8

Use fastq-stats to obtain information regarding the sequences

STEP2 - Alignment of sequence reads

creating index

minimap2 -t 6 -x map-ont -d GRCh37.mmi GRCh37.fa

Alignment

minimap2 -t 8 -ax map-ont --secondary=no hg19.25chr.mmi read_file.fastq | samtools sort - > barcode.aln_hg19.bam
# -ax map-ont = Oxford Nanopore genomic reads
# --seconday=no With no reads mapped with SAM flag 0x100 (secondary flag). 
# hg19.25chr.mmi minimizer index for the reference

STEP3 - Identifying representative regions

bedtools genomecov + merge

bedtools genomecov -bg -ibam barcode_hg19.bam | bedtools merge -d 1000 -i stdin | sort -V -k1,1 -k2,2n > barcode_1000_cov.bed

Running NanoCircle to identify the eccDNA coordinates

STEP 4 - Classify the soft-clipped read supporting Simple eccDNA and soft-clipped supporting Chimeric eccDNA

python NanoCircle_arg.py Classify -i barcode_hg19.bam -d temp_reads

Which will be saved in a folder temp_reads containing both simple and complex reads in .bam format.

Create a .bai index for the read .bam

samtools index temp_reads/Simple_reads.bam
samtools index temp_reads/Chimeric_reads.bam

STEP 5 - Identify Simple eccDNA using the coverage file and classified reads

python NanoCircle_arg.py Simple -i barcode_1000_cov.bed -b temp_reads/Simple_reads.bam -q 60 -o barcode_Simple_circles.bed

STEP 6 - Identify Chimeric eccDNA using the coverage file and classified reads

python NanoCircle_arg.py Chimeric -i barcode_1000_cov.bed -b temp_reads/Chimeric_reads.bam -q 60 -o barcode_Chimeric_circles.bed

The output being a bed file with possible configurations of several chimeric eccDNA, since the identification extract reads originating from specific regions.

STEP 7 - Merge Chimeric eccDNA configurations using the coverage file and classified reads

python NanoCircle_arg.py Merge -i barcode_Chimeric_circles.bed -o barcode_Merged_chimeric.bed

The output being a bed file with possible configurations of several chimeric eccDNA, since the identification extract reads originating from specific regions.

Ideas not yet incorporated

STEP 8 - Jaccard Index

calculating jaccard index for each individual circle compared to the estimated region with coverage

bedtools intersect -wao -a barcode_Simple_circles_1000.bed -b barcode_1000_cov.bed | head -10 | awk -v OFS='\t' '{print $1,$2,$3,($4/((($3-$2)+($10-$9))-$4))}'

To check if there might be a small region in between the coordinates without any coverage ? Or just use mean coverage

Different unix command useful for data preparation, analysis and test

#Removing reads aligning to contamination sources, while still keeping the bam format.
samtools view -h BC10.aln_hg19.bam |grep -v '>N'| grep -v '>A' |samtools view -Sbo BC10.bam -
# No reads.
cat BC07.fastq | awk '{print $1}' | grep '@' | sort | uniq | wc –l
# No of mapped
samtools view -F 0x4 BC07/BC07.aln_hg19.bam | cut -f 1 | sort | uniq | wc -l

Will not be updated further since - October 2020