Contamination is a common problem, and can be a serious threat to the integrity of sequence data. The conclusions drawn from bad data will be misleading, and can cause fundamental misconceptions in the way we understand HIV biology. Published papers based on contaminated sequences that were missed both by the authors and in the review process have led to scattered erroneous reports regarding virtually all aspects of HIV biology that involve sequences: viral clearance, transmission patterns, rapid and slow progression, drug resistance, central nervous system tropism, immune escape, and variability in populations. Contamination can happen in anyone's laboratory, and is not a sign of sloppy work. It is a fact of life with HIV culture, PCR amplification, and sequencing.
We have selected a few datasets to illustrate the problems that can arise, and how they can be recognized. The sets are anonymous and unrecognizable; the purpose is to show real examples of contamination, not to cast blame on any particular person or group.
|Example 1: a set of C1-C3 sequences containing LAI/HXB2 contamination and sample mix-ups (partial set, published).||View tree||(no alignment)|
|Example 2: in vitro recombination of patient DNA with LAI/HXB2 contamination DNA, especially clear in the alignment (complete set of V3 sequences, published).||View tree||View alignment|
|Example 3: This set was generated to study CTL epitope variation, and consists of partially overlapping sequence fragments of variable length. Phylogenetic analysis was impossible, but a BLAST search and an alignment with the most similar GenBank sequence showed extensive contamination with pNL43.||(no tree)||View alignment|
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