Mycoplasma contamination in the 1000 Genomes Project

Number of mismatches between the 1000 Genomes Project DNA and Mycoplasma

Figure 3 shows that although Bowtie finds matches within one or more Mycoplasma genomes for 75 879 DNA sequences drawn from The 1000 Genomes Project (but does not match them with the human reference genome) the accuracy of the match varies considerably. Many match a Mycoplasma exactly. These are shown on the left of Figure 3. For others, Bowtie reports up to 78 mismatches. Note the long thin tail to the right in Figure 3. Figure 3 also breaks these data down into pair end and single DNA strands and Solexa coding type (normal v. SOLiD colorspace). Colorspace encoding is described on page 48. Although the colorspace encoding represents a small fraction of the whole data, of the DNA measurements which match Mycoplasma only and for which Bowtie reports (on average) three or fewer mismatches, 93% of them are colorspace encoded. Notice however colorspace sequences tend to be much shorter, see Figure 4. On average, if affected, colorspace scans contain many more affected DNA measurements than normally coded Solexa scans. See columns 3–4 of Table 1. Overall ten percent of The 1000 Genomes Project scans contain sequences which match Mycoplasma well (i.e. on average ≤ 3 mismatches) but do not appear in the reference human genome, last figure in Table 1.

Quality of The 1000 Genomes Project DNA measurements

Solexa data, like that from other nextGen scanners, are inherently noisy. Solexa provides an estimate of the signal to noise ratio (expressed as log10) per base position in each DNA sequence. (For example, a quality of 0.5 (S/N=3.16) means the returned base is more likely than the other 3 combined)^b. This can easily mount up to several hundred quality values. To stably condense these into a manageable statistic, we ignore the worse and second to worst base in each DNA sequence and use the third worst. For paired end data, we use worst of the two ends.

If we compare the quality of DNA measurements which match Mycoplasma but which do not occur in the reference human genome (Figure 5) with those which do match GRCh37.p5 we see in both cases measurements with a large numbers of mismatches only occur in low quality data. Figure 6 reports a typical run. Further Figure 5 makes it plain that most of the DNA measurements which match Mycoplasma but which do not occur in the reference human genome contain at least three poor quality values. Nonetheless in our large sample of more than 50 billion DNA measurements drawn randomly from The 1000 Genomes Project, there are 1944 measurements with a quality above 0.5 (which match one or more Mycoplasma genomes with ≤3 mismatches). They occur in 269 scans, this is 7% of our sample, see last number in Table 2.

Entropy of The 1000 Genomes Project DNA matching Mycoplasma

Figure 7 shows that the exactness with which the DNA measurements match Mycoplasma and the entropy (incompressibility) of its sequences appears to be unrelated. For the very much larger volume of sequences which do match the human reference genome, entropy also plays little role. Instead large numbers of mismatches occur only in low entropy sequences. (Figure 8 plots data from a typical 1000 Genomes Project run). Although Bowtie reports a match, in some cases Bowtie must change many (up to 78) individual DNA bases to get an exact match between the measured DNA sequences and one of the published Mycoplasma genomes. Low entropy (compressible) DNA sequences are highly repetitive. Many real genomes have highly repetitive regions. A highly repetitive simple DNA pattern (even if it exactly matches against a genome) is liable to fall in repetitive region of a (published) genome, where coverage is liable to be patchy [9]. See also Figure 9, which concentrates on Mycoplasma only DNA measurements which match Mycoplasma genomes well.

Confirming Bowtie with NCBI BLAST

Implications and ork

Once Mycoplasma is suspected, it may be that individual scans can be clean up relatively easily as cross-species contamination is said to be easily detected ([12], page 2601). Indeed a number of commercial Mycoplasma detection tools are based on looking for Mycoplasma genes [2]. However both current microbiology laboratory [2] and Bioinformatics [1] typically take the robust approach of removing (deleting) all potentially infected materials. Indeed when The 1000 Genomes Project withdraws nextGen data, it withdraws complete scans. That is, it simply discards information on about a billion DNA bases each time a scan is withdrawn.

Raw data from The 1000 Genomes Project are publicly available and are being increasingly widely and diversely used. Whilst noisy data may be acceptable for use by their original owners, who are aware of their limitations, there is an increasing risk of contaminated data being (ab)-used outside the laboratories which initial created them. Indeed with staff-turnover there may be risks associated with using what becomes historical data where their provenance becomes more cloudy. Independent numerical studies could be done. The size of our sample suggests (at least for historical data drawn from the same period) they should yield the same results. However, whilst we have established a lower bound for contamination, future studies should be able to calculate it more precisely. For example, by considering redundant scans and clusters it should be possible to isolate the source and perhaps also provide numerical techniques to mitigate the data [12]. Other studies might also look for other effects and thus extract more scientific knowledge from this valuable resource.

Since Mycoplasma are rampant in modern microbiology laboratories [2] it is no surprise to find some in parts of data from The 1000 Genomes Project. We have identified some samples which have a higher than average chance of being contaminated by Mycoplasma. In silico studies should be reinforced by checking the source of the data. We urge each member of The 1000 Genomes Project Consortium (as some are apparently doing [12]), particularly those using single ended colorspace scanners (cf. Table 1) to re-check their procedures. Drexler and Uphoff [2] suggest using at least two detection techniques when checking samples for Mycoplasma.

Estimating entropy

In statistical mechanics, entropy is the degree of disorder in a system [14]. In information theory this translates to the degree or randomness or incompressibility of data, particularly in transmission of messages [15]. $\text{entropy}=-\sum plogp$, where p is the probability of a sequence of symbols and we sum over all possible symbols. For replicability, the remainder of this section details how we approximate entropy using actual DNA base counts in finite sequences.

In order to have entropy expressed in bits we use log2.

A reasonable estimate of the compressibility of variable length DNA sequences can be made by considering all loss-less coding schemes of up to four bases. The most efficient coding scheme gives the most compressible output. For example, a long sequence of adenine (AAAAAAAAAAAAAAAAAAAA...) can be recoded as a shorter sequence of 00000..., where 0 is one of the new 256 codes needed to represent AAAA–TTTT. Since the coding is loss-less, the encoded sequence contains the same information and so it has the same entropy.

We approximate probability p by the actual ratio of each symbol to the number of symbols in the string, p=i/l, so $\text{entropy}=-{\sum }_{\text{all symbols}}(i/l){log}_2(i/l)$. Where l is the length of the encoded string and i is the number of each symbol in it. To get the best estimate, we would have to consider all codings. By using the minimum of all 10 possible codings of length up to four DNA bases, we get a reasonable estimate that can deal exactly with not only runs of single bases up to runs of four repeated bases, but gives reasonable estimates with larger repeating sequences. DNA bases which are unknown (i.e. coded as N) are ignored. We use $\text{entropy}=\underset{\text{all codings}}{min}\left(-\sum (i/l){log}_2(i/l)\right)$. Thus the sequence ACGTACGTACGTACGTACGT, which is highly compressible, has an entropy of −(5/5) log2(5/5)=0. Whereas a simple count of number of bases would show A C G and T each occur 5 times (are present in equal numbers) and so incorrectly would say the string has maximal entropy $-{\sum }_{\mathrm{i}=\mathrm{A},\mathrm{C},\mathrm{G},\mathrm{T}}(5/20){log}_2(5/20)=2$. More sophisticated calculations might consider longer potential coding sequences but then the coding tables would be much larger and eventually their information content could no longer be ignored.

Two base colorspace encoding

Some next generation DNA scanners use a technology which instead of reading DNA sequences one base at a time they use multiple fluorescent dyes to read adjacent (overlapping) pairs of bases. Reduced noise is claimed, since as the pairs overlap, each base is read twice. Data are presented as the initial base followed by transitions from one base type to the next in the sequence (hence needing 4 colours). A potential downside is if an error does occur, the rest of the sequence will be nonsense. Whereas in direct encoding only the erroneous base is effected. It is possible to convert between the two encodings. However because of the different noise characteristics it is usually recommended, as we did, to use tools like Bowtie which can deal with colorspace encoded data directly.

Selecting a high quality sample to confirm with NCBI BLAST

We used NCBI’s Blast [10] program to confirm our Bowtie results. (We used the default parameters provided by the EBI web interface except we request the first 1000 matches, rather than the first 50 matches). Using BLAST on each of the sequences in Table 3 shows each of the seven high quality DNA measurements (see page 39) do, as expected, match one or more species of Mycoplasma and none matches the reference human genome. In a few cases the second pair matches “Homo sapiens clones”, rather than the human reference sequence. Often these are draft sequences and only in one case (ERR013159.14600701) do both ends of DNA pair match the clone. The final column of Table 3 reports an example of one of the Mycoplasma genes which BLAST finds which match the DNA sequence. In the case of paired end DNA measurements, BLAST has been run separately on both end. The reported gene is matched by both ends. (In three cases an example gene has not been chosen because BLAST matches the whole of, a number of, Mycoplasma genomes). Noting the example gene’s similarity, it is tempting to ascribe some biological meaning to the gene, however BLAST effectively searches all the published DNA sequences and so the similarity may well simply reflect a bias in the published sequences. Ribosomal DNA is highly conserved and has been heavily studied as a tree of life phylogenetic marker of evolutionary inheritance, which makes it one of the more frequent genes in today’s DNA sequence databanks.

We take BLAST’s matches and the lack of BLAST matches against the official human reference genome as confirming our Bowtie results. That is, Table 3 suggests samples ERR009050, ERR002459, ERR013159 and ERR022473 appear to have been contaminated with Mycoplasma. However, of these four, only in one (ERR009050) are there more than a few score DNA measurements which Bowtie matches against Mycoplasma.