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HIV consensus sequences are used in various bioinformatic, evolutionary, and vaccine related research. Since the previous HIV-1 subtype and CRF consensus sequences were constructed in 2002, the number of publicly available HIV-1 sequences have grown exponentially, especially from non-EU and US countries. Here, we reconstruct 90 new HIV-1 subtype and CRF consensus sequences from 3,470 high-quality, representative, full genome sequences in the LANL HIV database. While subtypes and CRFs are unevenly spread across the world, in total 89 countries were represented. For consensus sequences that were based on at least 20 genomes, we found that on average 2.3% (range 0.8–10%) of the consensus genome site states changed from 2002 to 2021, of which about half were nucleotide state differences and the rest insertions and deletions. Interestingly, the 2021 consensus sequences were shorter than in 2002, and compared to 4,674 HIV-1 worldwide genome sequences, the 2021 consensuses were somewhat closer to the worldwide genome sequences, i.e., showing on average fewer nucleotide state differences. Some subtypes/CRFs have had limited geographical spread, and thus sampling of subtypes/CRFs is uneven, at least in part, due to the epidemiological dynamics. Thus, taken as a whole, the 2021 consensus sequences likely are good representations of the typical subtype/CRF genome nucleotide states. The new consensus sequences are available at the LANL HIV database.

HIV consensus sequences are used in various bioinformatic, evolutionary, and vaccine related research. Since the previous HIV-1 subtype and CRF consensus sequences were constructed in 2002, the number of publicly available HIV-1 sequences have grown exponentially, especially from non-EU and US countries. Here, we reconstruct 90 new HIV-1 subtype and CRF consensus sequences from 3,470 high-quality, representative, full genome sequences in the LANL HIV database. While subtypes and CRFs are unevenly spread across the world, in total 89 countries were represented. For consensus sequences that were based on at least 20 genomes, we found that on average 2.3% (range 0.8–10%) of the consensus genome site states changed from 2002 to 2021, of which about half were nucleotide state differences and the rest insertions and deletions. Interestingly, the 2021 consensus sequences were shorter than in 2002, and compared to 4,674 HIV-1 worldwide genome sequences, the 2021 consensuses were somewhat closer to the worldwide genome sequences, i.e., showing on average fewer nucleotide state differences. Some subtypes/CRFs have had limited geographical spread, and thus sampling of subtypes/CRFs is uneven, at least in part, due to the epidemiological dynamics. Thus, taken as a whole, the 2021 consensus sequences likely are good representations of the typical subtype/CRF genome nucleotide states. The new consensus sequences are available at the LANL HIV database. [ABSTRACT FROM AUTHOR]

The reference sequences play an essential role in genome assembly, like type specimens in taxonomy. Those references are also samples obtained at some time and location with a specific method. How can we evaluate or discriminate uncertainties of the reference itself and assembly methods? Here we bootstrapped 50 random read data sets from a small circular genome of a Escherichia coli bacteriophage, phiX174, and tried to reconstruct the reference with 14 free assembly programs. Nine out of 14 assembly programs were capable of circular genome reconstruction. Unicycler correctly reconstructed the reference for 44 out of 50 data sets, but each reconstructed contig of the failed six data sets had minor defects. The other assembly software could reconstruct the reference with minor defects. The defect regions differed among the assembly programs, and the defect locations were far from randomly distributed in the reference genome. All contigs of Trinity included one, but Minia had two perfect copies other than an imperfect reference copy. The centroid of contigs for assembly programs except Unicycler differed from the reference with 75bases at most. Nonmetric multidimensional scaling (NMDS) plots of the centroids indicated that even the reference sequence was located slightly off from the estimated location of the true reference. We propose that the combination of bootstrapping a reference, making consensus contigs as centroids in an edit distance, and NMDS plotting will provide an evidential statistic way of genetic assembly for non-fragmented base sequences.