Novel 2019 coronavirus genome

IVDC-HB-01/2019 has been cell cultured with one round of passaging. This should be considered the most reliable. It may could have cell adaptations but it is identical to WIV04/2019 which is direct sequenced so if independent, suggests there are no cell adaptations.

The first genome WH-Human_1 has one SNP difference from all the others which may mean it is real. However it is not known if this genome is from a sample from one of the same patients as the other 5.

IVDC-HB-04/2020 is also suspect - it has 5 non-synonymous mutations and 3 synonymous

The nextstrain tool-tip is misleading here. The reference used has over-lapping annotations ORF1a and ORF1ab. There is a total of 3 mutations inferred for this branch. C1023T, C1025T, A18460G
The first two change the aa sequence of ORF1a in coding 253 and 245 (and these are the same as the mutations listed in ORF1ab).

I was mistaken. This is wrong:

The last mutation is synonymous also in ORF1ab after the slippage site.
So: 2 adjacent non-synonymous, 1 synonymous.

The last mutation at A18460G is also non-synonymous. All three mutations are non-synonymous

HB-04 has a bunch of indels.

Use Base-By-Base to view the alignment (visual sumary shows all diffs)

Thanks for the feedback Andrew and Richard. I’ve updated to split ORF1a and ORF1b. This makes it clearer how nucleotide mutations map to amino acid substitutions.

Ignoring the divergent BetaCoV/Wuhan/IVDC-HB-05/2019 sequence and masking the initial 11 bases of the alignment, we have the following 5 strains and their mutations relative to the base of the outbreak clade:

  • WIV04/2019 - no mutations
  • IVDC-HB-01/2019 - no mutations
  • IPBCAMS-WH-01/2019 - 3 nucleotide mutations / 2 AA changes
  • IVDC-HB-04/2020 - 3 nucleotide mutations / 3 AA changes (includes C1023T and C1025T which are suspect being so close together)
  • WH-Human_1 - 2 nucleotide mutations / 1 AA change

This alignment was stripped to map to reference and so lacks indels.

The single basepair gaps are in homopolymeric runs suggesting a sequencing platform that maybe has problems with those. There are 2 larger deletions which I assume are missing read coverage.

I get slightly different stats on the # mutations - HB-04 has some indels that need corrections. Keeping HB-01 as the reference (should maybe be WH-01 though, as that’s the oldest sequence):

IVDC-HB-01/2019: [ref]
IPBCAMS-WH-01/2019: 3 mutations (2 non-syn / 1 syn)
WIV04/2019: 0 mutations
Hu-1/2019: 1 mutation (1 non-syn)
IVDC-HB-04/2020: 2 mutations (2 non-syn) (however, I don’t believe these, so I think this should also be 0 mutations)

I agree with Trevor that the mutations in HB-04 are suspect - right next to each other, non-synonymous, close to a poly-T stretch, and this sequence also needed some manual editing for indels. I think these are probably not correct and that sequence would then also be identical.

As for IVDC-HB-05, I agree with everybody that this sequence is definitely wrong (clustering of mutations, wacky ts/tv ratio, etc). If I do my very best to eliminate sequencing errors that I have commonly observed over the years, then I get a maximum of 7 mutations in this sequence, 4 of which are non-synonymous. These 7 can’t be excluded as likely errors (unlike the other 46 mutations in this sequence), but I think they still represent a (substantial) over-estimation.

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Taking what I said above at face value and making WH-01 the reference (being the earliest sequence), we have three substitutions early in the tree (2 non-synonymous, 1 synonymous), followed by one additional substitution in Hu-1 (non-synonymous).

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Thanks for checking this Kristian. I agree with you that the SNP at the end of Hu-1/2019 is spurious as is the SNP at 18460 in BetaCoV/Wuhan/IVDC-HB-04/2020 that appears directly adjacent to a long stretch of ambiguous bases. I’ve masked site 18460 as well as both ends of the alignment. With these changes our counts agree. Additionally, I thought it prudent to drop IVDC-HB-05 from the tree as its divergence is almost certainly spurious.

I’ve updated accordingly.

Or poor assembly.
Think the coverage is so low that regions are missing?

I think it would be unlikely that you would get zero coverage just for 1 basepair. The fact that they are in homopolymeric runs suggests systematic run-length errors. This is probably not Illumina data.

Several sequences including Thailand cases has been added to GISAID today.
Although GISAID announced their whole genome analysis result, I am wondering if you would update your analysis, since your analysis provide much more information including diversities.
Sincerely. has been updated with all genomes currently in GISAID.

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The Zhejiang Provincial Center for Disease Control and Prevention has shared two new genomes via We’ve updated to include them in our analysis bringing total up to 15 highly related samples.

Four more genomes were released, bringing the total to 19. Note that a couple of these look suspicious:

EPI_ISL_403928: A lot of mutations - can’t be trusted at this stage
EPI_ISL_403931: Mutations in the 5’ end that are wrong

Still not a lot of diversity.

Based on this dataset I count 17 SNPs that appear to be real and 35 that do not (this is not including indels in 402120). All SNPs are private - none of them transmitted.

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Thanks @Kristian_Andersen. We’ve updated accordingly, but have also left out Wuhan/IPBCAMS-WH-05/2020 / EPI_ISL_403928 due to appearance of spurious mutations.

However, I’d note that if there’s been multiple spillover events from the animal reservoir, I would really expect to be seeing clusters of genetically distinct human cases. So, a divergent sequence by itself is an expected thing. What threw me here however, was strange clustering of mutations in Wuhan/IPBCAMS-WH-05/2020.

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Yeah, we can’t a priori assume that a divergent sequence is wrong, but for all sequences in this set where there were multiple mutations, many (all) of them were clearly the result of sequencing errors. Even after taking out the most ‘offensive’ sequences, I’m still fairly certain ~50% of SNPs are sequencing errors.

Doing some quick calculations on a SARS alignment from the 2002 epidemic, I get an N/S rate of ~0.5 and a Ts/Tv of ~ 3.5 - meaning that while synonymous and/or transitions are more frequent, but maybe not as much as we would typically see with other RNA viruses (e.g., Ts/Tv typically ~8). Just as a reference for trying to weed out sequencing errors and bad sequences.

1 Like has been updated with 6 new genomes sampled from Guangdong shared by the Guangdong Provincial Center for Diseases Control and 3 new genomes sampled from Wuhan shared by Institute of Pathogen Biology, Chinese Academy of Medical Sciences.

We now observe clustering of related infections. These are a cluster of two infections in Zhuhai (Guangdong/20SF028/2020 and Guangdong/20SF040/2020) and a cluster of three infections in Shenzhen (Guangdong/20SF013/2020, Guangdong/20SF025/2020, Guangdong/20SF012/2020). These are noted in GISAID as “family cluster infection” and reflect two separate family clusters ( This almost certainly represents human-to-human transmission.

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Hi all,

Hopefully this isn’t too off-topic but I thought I’d put it here in case others are also interested in this application of sequences - we are looking into starting animal model work and are not sure what viruses we could get or whether we’d need to try and make our own via reverse genetics from synthetic gene constructs. Based on what we know now, is there a sense of which sequence or sequences would be best to use?



Hey Dave - they’re basically all identical except for a handful of SNPs, so you can pretty much pick any. Take a look at Andrew’s overall analysis here: Phylogenetic analysis of 23 nCoV-2019 genomes, 2020-01-23.

I might suggest Wuhan-Hu-1, which is on Genbank:

Great, many thanks Kristian.