Hi all,
I am newbie in NGS data analysis and first task in it come to me is de novo assembly. I have plant mitochondrial genome to assemble for which I have almost 24GB of data with R1 and R2 each.
However, I have through with QC analysis and velvet output. The output with velvet I have got is contigs.fa files for multiple kmers, as 55, 95, 10. The read length is 101.
I got statistics for kmer 85 contig.fa using quast which is as follows:
Assembly contigs
# contigs (>= 0 bp) 2933
# contigs (>= 1000 bp) 274
Total length (>= 0 bp) 2145071
Total length (>= 1000 bp) 1433182
# contigs 441
Largest contig 62822
Total length 1548880
GC (%) 45.35
N50 7528
N75 3479
L50 51
L75 129
# N's per 100 kbp 0.00
But I am stuck here now, since I am not getting idea how to say this is good to proceed with or bad to go with something else. Also, it would be of great help if anyone suggest me further steps to be taken to arrive at well assembled genome.
Regards,
Mandar
I am newbie in NGS data analysis and first task in it come to me is de novo assembly. I have plant mitochondrial genome to assemble for which I have almost 24GB of data with R1 and R2 each.
However, I have through with QC analysis and velvet output. The output with velvet I have got is contigs.fa files for multiple kmers, as 55, 95, 10. The read length is 101.
I got statistics for kmer 85 contig.fa using quast which is as follows:
Assembly contigs
# contigs (>= 0 bp) 2933
# contigs (>= 1000 bp) 274
Total length (>= 0 bp) 2145071
Total length (>= 1000 bp) 1433182
# contigs 441
Largest contig 62822
Total length 1548880
GC (%) 45.35
N50 7528
N75 3479
L50 51
L75 129
# N's per 100 kbp 0.00
But I am stuck here now, since I am not getting idea how to say this is good to proceed with or bad to go with something else. Also, it would be of great help if anyone suggest me further steps to be taken to arrive at well assembled genome.
Regards,
Mandar
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