Comments (10)
umahsn
commented on August 25, 2024
Hi Liren,
Can you try running NanoCaller with `-nbr_t '0.2,0.6' parameter? Also, is it possible for you to share an IGV plot of what this region 511-2232 looks like?
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rhinempi
commented on August 25, 2024
Hi Mian Umair Ahsan,
thanks for your response.
I have tried the new parameter that you suggested. It seems it removes the SNPs that are close neighbours. But the G genotypes still exist, see below:
##fileformat=VCFv4.2
##FILTER=<ID=PASS,Description="All filters passed">
##contig=<ID=umi1bins>
##FORMAT=<ID=GT,Number=1,Type=String,Description="Genotype">
##FORMAT=<ID=DP,Number=1,Type=Integer,Description="Depth">
##FORMAT=<ID=FQ,Number=1,Type=Float,Description="Alternative Allele Frequency">
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SAMPLE
xxx 511 . T G 33.975 PASS . GT:DP:FQ 1/1:2519:0.2588
xxx 1078 . T G 52.364 PASS . GT:DP:FQ 1/1:2519:0.2557
xxx 2173 . C G 43.977 PASS . GT:DP:FQ 1/1:2517:0.5161
For IGV plot, please see attached.
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pkerbs
commented on August 25, 2024
Dear Developers,
I have noticed the same problem. Is there a solution?
Every variant call outputs a G as the ALT base:
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT ERR4080473
MN908947.3 92 . C G 47.315 PASS vafator_af=0.0003029843963035904;vafator_ac=2;vafator_dp=6601 GT:DP:FQ 1/1:6601:0.0783
MN908947.3 241 . C G 47.345 PASS vafator_af=0.012721490231712857;vafator_ac=84;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.8358
MN908947.3 337 . C G 47.221 PASS vafator_af=0.0013630168105406633;vafator_ac=9;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.0519
MN908947.3 367 . C G 47.336 PASS vafator_af=0.009995456610631531;vafator_ac=66;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.0698
MN908947.3 397 . A G 42.414 PASS vafator_af=0.05088596092685143;vafator_ac=336;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.0509
MN908947.3 432 . T G 44.453 PASS vafator_af=0.004240496743904286;vafator_ac=28;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.0733
MN908947.3 454 . A G 42.383 PASS vafator_af=0.16159321520520975;vafator_ac=1067;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.1616
MN908947.3 456 . T G 44.352 PASS vafator_af=0.005603513554444949;vafator_ac=37;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.0513
MN908947.3 574 . A G 42.236 PASS vafator_af=0.058003937604119336;vafator_ac=383;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.058
MN908947.3 580 . T G 44.345 PASS vafator_af=0.0016659094351052551;vafator_ac=11;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.0721
MN908947.3 610 . A G 42.445 PASS vafator_af=0.0631531122217174;vafator_ac=417;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.0632
MN908947.3 664 . C G 47.232 PASS vafator_af=0.002120248371952143;vafator_ac=14;vafator_dp=6603 GT:DP:FQ 1/1:6603:0.0566
MN908947.3 665 . C G 47.291 PASS vafator_af=0.005719068210350118;vafator_ac=41;vafator_dp=7169 GT:DP:FQ 1/1:7169:0.1121
MN908947.3 672 . A G 42.452 PASS vafator_af=0.05645386116531921;vafator_ac=405;vafator_dp=7174 GT:DP:FQ 1/1:7174:0.0565
MN908947.3 679 . C G 47.325 PASS vafator_af=0.0019504040122596824;vafator_ac=14;vafator_dp=7178 GT:DP:FQ 1/1:7178:0.0755
MN908947.3 700 . A G 42.432 PASS vafator_af=0.05682451253481894;vafator_ac=408;vafator_dp=7180 GT:DP:FQ 1/1:7180:0.0568
MN908947.3 724 . T G 44.388 PASS vafator_af=0.0015318200807686953;vafator_ac=11;vafator_dp=7181 GT:DP:FQ 1/1:7181:0.1106
MN908947.3 726 . A G 42.432 PASS vafator_af=0.05514552290767303;vafator_ac=396;vafator_dp=7181 GT:DP:FQ 1/1:7181:0.0551
MN908947.3 761 . A G 42.394 PASS vafator_af=0.9338440111420613;vafator_ac=6705;vafator_dp=7180 GT:DP:FQ 1/1:7180:0.9338
MN908947.3 776 . C G 47.226 PASS vafator_af=0.0036211699164345403;vafator_ac=26;vafator_dp=7180 GT:DP:FQ 1/1:7180:0.051
MN908947.3 818 . T G 44.452 PASS vafator_af=0.001671541997492687;vafator_ac=12;vafator_dp=7179 GT:DP:FQ 1/1:7179:0.0744
MN908947.3 956 . T G 44.354 PASS vafator_af=0.0008358874338255781;vafator_ac=6;vafator_dp=7178 GT:DP:FQ 1/1:7178:0.0545
I used the following parameters:
--preset ont
--cpu ${params.cpus}
--min_allele_freq 0.05
--ins_threshold 0.05
--del_threshold 0.05
--maxcov 1000
--mincov 10
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umahsn
commented on August 25, 2024
Hi,
Thank you bringing this to our attention and we would like to test this on our end too. Is this data publicly available for us to download and test? If not, would it be possible for you to share a small sample of the data for us to evaluate?
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pkerbs
commented on August 25, 2024
Hi,
thank you for testing this.
I used the publicly available SARS CoV2 sample ERR4080473 which is available here:
ftp.sra.ebi.ac.uk/vol1/fastq/ERR408/003/ERR4080473/ERR4080473_1.fastq.gz
MD5SUM:
312b51c9422663b1c0d322f3f083dcad
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umahsn
commented on August 25, 2024
Thank you, I will get back to you shortly after running some tests.
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umahsn
commented on August 25, 2024
Hi, the error should be fixed now. There was problem with properly normalizing coverage, and also an integer overflow due to very high coverage. Both issues have been fixed and you should get appropriate variant calls now. With the settings you used, I was able to get the following variants:
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT SAMPLE
MN908947.3 30 . A G 40.002 PASS . GT:DP:FQ 1/1:17:0.0588
MN908947.3 241 . C T 388.507 PASS . GT:DP:FQ 1/1:7400:0.8147
MN908947.3 761 . A G 439.735 PASS . GT:DP:FQ 1/1:8015:0.9201
MN908947.3 1059 . C T 312.051 PASS . GT:DP:FQ 1/1:638:0.9404
MN908947.3 3037 . C T 305.081 PASS . GT:DP:FQ 1/1:2630:0.5856
MN908947.3 6825 . A C 440.255 PASS . GT:DP:FQ 1/1:1934:0.8268
MN908947.3 14408 . C T 371.324 PASS . GT:DP:FQ 1/1:7573:0.8575
MN908947.3 23403 . A G 420.27 PASS . GT:DP:FQ 1/1:2616:0.8375
MN908947.3 25563 . G T 407.427 PASS . GT:DP:FQ 1/1:2676:0.8647
Thank you for bringing this to our attention. Let me know if you still have any trouble running NanoCaller.
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pkerbs
commented on August 25, 2024
Hi,
thank you again for the fast response and I will try it out.
I just noticed that the new release here on github is versioned 3.1.0 while in conda the version is 3.0.1?
Best,
Paul
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pkerbs
commented on August 25, 2024
Hi Mian,
I got the same variant calls as you know, except the one at position 30 using the version 3.0.1 from conda.
But it might be due to different pre-processing of the fastq. So I guess its fine :)
Would indel calls also be affected by this issue? Unfortunately, I don't have a sample at hand to test that at the moment.
#CHROM POS ID REF ALT QUAL FILTER INFO FORMAT ERR4080473
MN908947.3 241 . C T 374.199 PASS . GT:DP:FQ 1/1:6603:0.8358
MN908947.3 761 . A G 427.729 PASS . GT:DP:FQ 1/1:7180:0.9338
MN908947.3 1059 . C T 375.889 PASS . GT:DP:FQ 1/1:901:0.9489
MN908947.3 3037 . C T 291.825 PASS . GT:DP:FQ 1/1:2766:0.5983
MN908947.3 6825 . A C 426.855 PASS . GT:DP:FQ 1/1:1768:0.8405
MN908947.3 14408 . C T 404.003 PASS . GT:DP:FQ 1/1:7185:0.8703
MN908947.3 23403 . A G 405.387 PASS . GT:DP:FQ 1/1:2364:0.86
MN908947.3 25563 . G T 381.818 PASS . GT:DP:FQ 1/1:2418:0.8747
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umahsn
commented on August 25, 2024
I don't think the indel calling would be affected, but I will take a look at it to make sure. As for v3.0.1 or 3.1.0, I think there may be an issue with syncing version numbers between github and conda. But there was only one release after v3.0.0 so both 3.0.1 on conda and 3.1 on github should be the same. I will try to fix the version numbers.
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