The Essential Genome of Escherichia coli K-12
ABSTRACT Transposon-directed insertion site sequencing ( TraDIS ) is a highthroughput method coupling transposon mutagenesis with short-fragment DNA sequencing . 
It is commonly used to identify essential genes . 
Single gene deletion libraries are considered the gold standard for identifying essential genes . 
Currently , the TraDIS method has not been benchmarked against such libraries , and therefore , it remains unclear whether the two methodologies are comparable . 
To address this , a high-density transposon library was constructed in Escherichia coli K-12 . 
Essential genes predicted from sequencing of this library were compared to existing essential gene databases . 
To decrease false-positive identification of essential genes , statistical data analysis included corrections for both gene length and genome length . 
Through this analysis , new essential genes and genes previously incorrectly designated essential were identified . 
We show that manual analysis of TraDIS data reveals novel features that would not have been detected by statistical analysis alone . 
Examples include short essential regions within genes , orientation-dependent effects , and fine-resolution identification of genome and protein features . 
Recognition of these insertion profiles in transposon mutagenesis data sets will assist genome annotation of less well characterized genomes and provides new insights into bacterial physiology and biochemistry . 
IMPORTANCE Incentives to define lists of genes that are essential for bacterial survival include the identification of potential targets for antibacterial drug development , genes required for rapid growth for exploitation in biotechnology , and discovery of new biochemical pathways . 
To identify essential genes in Escherichia coli , we constructed a transposon mutant library of unprecedented density . 
Initial automated analysis of the resulting data revealed many discrepancies compared to the literature . 
We now report more extensive statistical analysis supported by both literature searches and detailed inspection of high-density TraDIS sequencing data for each putative essential gene for the E. coli model laboratory organism . 
This paper is important because it provides a better understanding of the essential genes of E. coli , reveals the limitations of relying on automated analysis alone , and provides a new standard for the analysis of TraDIS data . 
KEYWORDS Escherichia coli, TraDIS, genomics, tn-seq
There are many incentives to define lists of genes that are either essential for bacterial survival or important for normal rates of growth . 
Essential genes of bacterial pathogens may encode components of novel biochemical pathways or potential targets for antibacterial drug development . 
Disruption of genes required for rapid growth results in strains handicapped for exploitation in biotechnology . 
Conversely , normal growth of mutants defective in genes previously expected to be essential could reveal unexpected parallel biochemical pathways for fulfilling the essential function . 
Multiple attempts have been made to generate definitive lists of essential genes , but there are still many discrepancies between studies even for the model bacterium Escherichia coli strain K-12 . 
Two general approaches have been used : targeted deletion of individual genes , as in the Keio collection of mutants ( 1 ) , and random mutagenesis ( 2 , 3 ) . 
Data from several studies using different mutagenesis strategies have yielded inconsistent data and hence conflicting conclusions . 
Transposon-directed insertion site sequencing ( TraDIS ) is one of several high-throughput techniques that combine random transposon mutagenesis with sequencing of the transposon junctions in highdensity mutant libraries ( 4 -- 7 ) . 
Since its inception in 2009 , this high-throughput method has been applied to a range of biological questions ( 4 , 8 -- 15 ) . 
Here , in order to resolve outstanding conflicts , we report the use of this approach to identify the essential genes of E. coli K-12 strain BW25113 , a well-studied model organism for which a complete gene deletion library is available ( 1 ) . 
A confounding factor in determining the `` essentiality '' of a gene is the definition of an essential gene . 
Complete deletion of an essential gene results , by definition , in a strain that can not be isolated following growth . 
However , it is well-known that certain genes are required for growth under specific environmental and nutritional conditions . 
Such genes can be considered conditionally essential . 
For the purposes of this study , we define a gene as essential if the transposon insertion data reveal that the protein coding sequence ( CDS ) , or a portion of the CDS , is required for growth under the conditions tested here . 
To aid our analysis , we developed a statistical model that included corrections for both gene length and genome length in order to decrease false-positive identification of essential genes . 
An additional challenge with defining essentiality in high-throughput studies is an overreliance on automated analysis of the data . 
For example , a consequence of relying only on quantification of the number of unique insertions within a gene is that genes with essential regions will be missed . 
If only part of a gene encodes the essential function , it should be possible to isolate viable mutants with transposon insertions in nonessential regions of the coding sequence ( 2 ) . 
Conversely , reliance on statistical analysis alone can also lead to overestimation of the number of essential genes . 
This is a common result from insertion sequencing analysis ( 16 ) . 
A low number of transposon insertion events within a gene , which fall below the statistical cutoff threshold , can be due to inaccessibility of the gene to transposition because of extreme DNA structure , exclusion by DNA-binding proteins , polarity effects due to insertion in a gene upstream of a cotranscribed essential gene , and location of the gene close to the replication terminus ( 17 ) . 
The most frequent reason for a low number of insertions is that the product of the disrupted gene is required for normal rates of growth under the conditions tested . 
In the current study , to minimize the possibility of incorrectly designating genes as essential or contributing to fitness , we have supported our statistical analysis with a gene-by-gene inspection of the insertion distribution within each individual gene . 
RESULTS AND DISCUSSION
Sequencing of a mini-Tn5 transposon insertion library in E. coli strain BW25113 . 
We have used a modified method to obtain TraDIS data for a transposon mutant library of E. coli K-12 strain BW25113 ( 4 , 9 ) . 
The BW25113 strain was chosen because it is the parent strain for the Keio collection of deletion mutants and ideal for a direct comparison between data sets . 
A mini-Tn5 transposon with a chloramphenicol resistance cassette was transformed into competent cells and grown overnight on selective medium . 
Individual colonies were pooled to construct the initial library , estimated to consist of approximately 3.7 million mutants . 
An Illumina MiSeq system was used to obtain TraDIS data from two independent DNA extracts of the transposon library ( TL ) , designated TL1 and TL2 ( Table 1 ) . 
Raw data were checked for the presence of an inline index barcode to identify independently processed samples ( Table 1 ) . 
This resulted in 4,818,864 sequence reads from TL1 and 6,189,409 from TL2 . 
After verification of the presence of a transposon sequence and removal of poor-quality data or short sequence reads , 3,891,339 ( 80.75 % ) and 4,387,970 ( 70.89 % ) sequence reads , respectively , were mapped successfully to the E. coli K-12 BW25113 genome ( accession no . 
CP009273 .1 ) ( Table 1 ) . 
The distribution of insertion sites covers the full length of the genome ( Fig. 1A ) . 
There was a high correlation coefficient of 0.96 between the samples ( Fig. 1B ) . 
The data were therefore combined to give a total of 8,279,309 sequences that were mapped to 901,383 unique insertion sites throughout the genome . 
Of the 
8,279,309 mapped sequences , 199,557 were represented by a single read . 
Similar numbers of insertions , 481,360 and 480,072 , were found for both orientations of the transposon . 
The high density of unique insertion sites resulted in an average of one insertion every 5.14 bp and a median distance between insertions of 3 bp . 
An example is shown in Fig. 1D . 
Identification of putative essential genes by TraDIS . 
To determine whether a gene was essential or nonessential , the numbers of insertions per CDS were quantified . 
CDS is defined as the protein coding sequence of a gene , inclusive of the start and stop codons . 
To normalize for gene length , the number of unique insertion points within the CDS was divided by the CDS length in bases . 
This value was termed the insertion index score and has been used previously as a measure of essentiality ( 4 , 8 , 9 , 18 ) , given a sufficiently dense library ( 19 ) . 
The frequency distribution of the insertion index scores was bimodal ( see Fig . 
S1 in the supplemental material ) , as previously shown by others ( 2 ) . 
We assume that genes associated with the left mode ( any data to the left of the trough in Fig . 
S1 ) , which have a low number of transposon insertions , are either essential for survival or genes that , when disrupted , confer a very severe fitness cost ( Fig. 1D ) . 
The second mode is associated with genes with considerably more insertions ; these genes are deemed nonessential ( Fig. 1D ) . 
Based on inspection of the distributions , an exponential distribution model was fitted to the mode that includes essential genes , and a gamma distribution model was fitted to the nonessential mode . 
For a given insertion index score , the probability of belonging to each mode was calculated , and the ratio of these values was termed the log likelihood score . 
A gene was classified as essential if its log likelihood score was less than log2 ( 12 ) and was therefore 12 times more likely ( see Materials and Methods ) to belong to the essential mode than to the nonessential mode . 
Using this approach , sufficient insertions were found in 3,793 genes for them to be classed as nonessential , 162 genes were situated between the two modes and classed as unclear , and 358 genes in the mutant library were identified as essential ( Table S1 ) . 
The 358 putative essential genes identified in the TL data were compared to the essential genes as defined by the Keio collection and the Profiling of the E. coli Chromosome ( PEC ) database ( 1 , 2 ) . 
This comparison revealed 248 genes ( 59.5 % ) that were common to all three data sets ( Fig. 2A and Table S2 ) . 
This agreement between all three data sets strongly supports the hypothesis that these genes are essential so they were not investigated further . 
An additional 169 genes were identified as potentially essential in only one or two of the data sets . 
These genes comprise 16 genes in the Keio and PEC lists that were not identified by our analysis , 25 exclusive to Keio , 18 exclusive to PEC , and 11 and 18 that overlapped between our method and Keio or PEC , respectively ( Fig. 2A ) . 
However , the largest subcategory of 81 genes is unique to our data set . 
Statistical analysis of the transposon insertion density data . 
Overestimation of the number of genes that are essential has been noted in studies using transposon insertion sequencing ( 16 ) . 
In previous attempts to use statistical analysis to define an essential gene , a Poissonian model was used to derive a P value for an insertion-free region ( IFR ) of a given length against the null hypothesis that , by chance , no insertions occurred in that region . 
We refined this approach for two reasons . 
First , genomes are sequences of discrete sites : although a continuous Poisson model can provide an approximation to this structure , a naturally discrete picture is more representative of true genome structure . 
Second , unless corrections are applied for gene length or for the genome length , this method risks overestimating the total number of essential genes . 
This problem arises because the method implicitly considers only a single , small genomic region , giving the probability that no insertions will be found in a single region of a given base pair length . 
However , genes and genomes have many such regions that are effectively independent , so the genome-wide probability of observing a `` false-positive '' insertion-free region across the genome will be much higher . 
To avoid this risk of overinterpretation of TraDIS data , we propose a new statistical approach , summarized in Text S1 and Fig . 
S2 . 
First , we replaced the commonly used Poissonian model exp ( x/f ) ( for x consecutive bases without an insert , given inverse insertion density f ; see reference 27 for further discussion of this ) with a geometric model . 
This model gives the probability of seeing k `` failures '' ( insertion-free sites ) then a `` success '' ( insertion event ) in a string of independent trials as P ( k ) ( 1 ) k , where is the probability of a success ( here , an insertion ) . 
The P value associated with a string of L sites being insertion-free is then P kLP k , an easily computable quantity . 
Next , to guard against false-positive results , we need to precisely state the statistic of interest and the corresponding null model . 
Under a null model of random , independent insertions , the three probabilities most pertinent here are those with which ( i ) a single length L region has no insertions ; ( ii ) a gene of length g contains one or more insertion-free regions of length L ; ( iii ) a genome of length G contains one or more insertion-free regions of length L . 
We used stochastic simulations of random insertions with given densities and genome lengths ( Text S1 ) to compute these probabilities . 
These values then give P values for insertion-free region observations , correcting for gene and genome length . 
Specifically , pgene ( L ) is the probability of observing one or more insertion-free regions of at least length L in a model gene ( of length g 1,000 bp ) by chance ( ii ) , and pgenome ( L ) is the probability of observing one or more insertion-free regions of at least length L in a full genome ( of length G 4.6 Mb ) by chance ( iii ) . 
The uncorrected P value ( i ) is that typically reported in other studies . 
Statistical analysis of our current data ( 901,383 inserts in a 4,631,469-bp genome ) gives a corrected pgenome of 0.05 for L 75 bp and pgene of 0.05 for L 36 bp ( pgene of 0.005 for L 47 bp ) . 
In other words , there is a probability of 0.05 that any insertion-free region of length 75 bp could appear anywhere in the genome by chance , and there is a probability of 0.005 that any insertion-free region of length 47 bp will occur anywhere in a gene of length 1,000 bp by chance . 
To our knowledge , this represents the first study with a confident and genome-wide corrected detection resolution ( Fig . 
S2 ) , and the closest yet to approaching the length of the smallest annotated gene in our reference genome ( accession no . 
CP009273 .1 ) , which is 45 bp . 
In checking for uniformity of insertion density across genomic regions , we found that the density of insertions around the terminus ( taken as a region centered around terABCD ) was slightly lower than the genomic average ( a density of 0.142 in the surrounding 500-kb region , or 0.145 in the surrounding 1-Mb region , compared to a 0.195 average ; Fig. 1A ) . 
This density change marginally increases the detection of false-positive essential genes in the vicinity of the terminus but still represents an unprecedented level of coverage . 
Resolution of conflicts between data sets . 
A critical requirement for the validation of a list of essential genes is to explain why the statistical analysis of transposon insertion data failed to identify genes that the Keio library of deletion mutants and the PEC database identified as essential . 
We coupled statistical analysis and manual inspection of the data with literature searches to rationalize conflicting results . 
We find that many of the inconsistencies between data sets can be explained by different methodologies used , definitions of the term `` essential , '' and statistical approaches ( Fig. 2B ) . 
Genes containing transposon-free regions . 
Manual inspection of the data revealed genes with transposon-free regions that were large enough to be identified as significant using the algorithm defined in the previous section . 
These IFRs do not necessarily report that a gene is essential ; rather , they show that the insertions within these genes are sufficiently sparse that the IFR is unlikely to have occurred by chance . 
These genes fall loosely into two groups . 
The first group contains genes for which the 5 = regions are essential and contain no insertions . 
However , there are transposon insertions in the nonessential regions of these genes , such as ftsK ( Fig. 3A and Table S3 ) . 
FtsK is involved in correct segregation of the chromosome during division ( 20 , 21 ) ; the N-terminal domain of FtsK contains four transmembrane passes and is required for localization of FtsK to the septum ( 22 -- 24 ) . 
There is substantial literature reporting the essential function of the N-terminal domain , consistent with our data ( 21 , 22 , 25 ) . 
This is a common observation for insertion data and arises when only the function of the N terminus of the protein is required for viability ( 8 ) . 
Initial analysis of transposon insertion data would lead to these genes being incorrectly classified as nonessential , but attempts to construct a deletion mutant would fail . 
Indeed , previous transposon sequencing experiments failed to identify the essential nature of some of these genes when relying on statistical analysis alone ( 9 ) . 
The second group contains genes with transposon insertion sites throughout the CDS but which have an IFR that passes the significance threshold for essentiality . 
For example , there is a small IFR within the coding sequence of secM of 66 bp ( Fig. 3B and Table S3 ) . 
The secM gene is located upstream of the essential gene secA . 
These genes are cotranscribed and also cotranslated , and secM is known to contain a translational stop sequence that interacts with the ribosomal exit tunnel to halt translation , acting as a translational regulator for secA . 
Specific mutations within the translational stop sequence are lethal unless secA is complemented by expression from a plasmid ( 26 ) . 
The dependence of secA translation on the secM CDS would explain the Keio classification as `` essential . '' 
However , the IFR within secM does not fully correspond with the translation stop sequence , suggesting that there is more to be learned about the translational linkage between the two proteins . 
Other researchers have used different approaches to minimize false classification of essential genes during statistical analysis of the insertion profiles by applying a sliding window , quantifying the mean distance between insertions per gene , or variations of truncating the CDS , such as excluding the 3 = end , analyzing only the first 60 % of the CDS , or analyzing the central 60 % of the CDS ( 18 , 19 , 27 -- 31 ) . 
However , window analysis may overlook genes such as secM and analyzing only the first 60 % of the CDS would overlook genes such as ftsK . 
We suggest that the algorithmic approach used here is a more appropriate method for identifying essential chromosomal regions in a sufficiently dense library . 
However , we see a number of IFRs of 45 bp throughout the genome within nonessential genes , suggesting that our null model of random insertions is not capturing the full structural detail of transposon insertion propensity . 
This suggests our modeling approach is not based on a perfect representation of biological reality and needs further refinement . 
Polar insertions . 
A common feature when creating insertion mutants is the introduction of off-target polar effects where expression of adjacent genes is disrupted by the insertion . 
To mitigate against such polar effects , we designed a cassette that enabled both transcriptional and translational read-through in one direction only . 
To confirm that transcriptional and translational read-through emanates from the transposon , the transposon was cloned in both orientations and in all three reading frames upstream of the lacZ gene in transcription and translation expression vectors pRW224 and pRW225 , derivatives of pRW50 ( 32 , 33 ) . 
Transcriptional read-through was con-firmed for one orientation of the transposon , consistent with transcriptional read-through from the chloramphenicol resistance cassette into the downstream disrupted CDS ( Fig. 4 ) . 
Translational read-through was identified for two of the three open reading frames that coincided with AUG and GUG start codons in the inverted repeat at the end of the transposon . 
More - galactosidase activity was obtained from the construct in which the AUG codon was in frame than when the GUG codon was in frame , confirming that translation was initiated more strongly from the AUG codon . 
Therefore , transcription is initiated from within the transposon , and translation is initiated from within the inverted repeat . 
This allows transcription and translation of downstream essential regions , even from within a CDS . 
Such events can be identified by determining to which DNA strand the sequencing data maps ( Fig. 4C ) . 
Analysis of our data reveals a number of chromosomal regions with insertions in only one orientation . 
Such insertion profiles can offer insight into transcriptional regulation of genes when considered in conjunction with neighboring genes . 
For example , the gene rnc is located in an operon upstream of the essential gene , era . 
Only mutants with transposons that maintain downstream transcription of era are viable ( Fig. 3C ) . 
Baba et al. categorized rnc as essential ( 1 ) . 
However , in the case of the Keio library , construction of an rnc deletion mutant would disrupt the ability of the native promoter to drive downstream expression of the essential era gene , resulting in apparent lethality . 
Similarly , in both the Keio and PEC databases , yceQ is listed as essential , but we observed many insertions in yceQ , but in only one orientation ( Fig. 3D ) . 
The gene is located upstream of the essential gene rne and is divergently transcribed . 
The promoter for rne is positioned within yceQ ( 35 , 36 ) , and deletion of yceQ would remove the promoter for rne , resulting in an apparent lethal effect . 
Our data reveal that while era and rne are essential , rnc and yceQ are not essential . 
Like rnc and yceQ , several of the antitoxin genes are reported to be essential in the Keio library but not in our data set or the PEC database ( Table S3 ) . 
Antitoxins are required only if the corresponding toxin gene is functional . 
One example is yefM . 
We observed a substantial number of insertions in one orientation . 
Unlike rnc and yceQ where insertions maintained downstream expression , in the case of yefM , the opposite is true ; insertions that disrupt expression of the antitoxin but maintain downstream expression of the downstream toxin ( yoeB ) are lethal ( Fig. 3E ) . 
Scrutiny of our data in this manner reveals that these genes are essential . 
Another example of insertion bias is observed in a number of genes at the 3 = end of a transcript , such as rplI ( Fig. 3F ) . 
While rplI is not reported as essential , it is worth noting because insertions restricted exclusively to one orientation within the gene can not be explained by the positional context between an essential gene and promoter . 
One possible explanation for this observation is that transcription promoted from the transposon produces an antisense RNA that inhibits expression of an essential gene . 
Insertion bias , irrespective of the underlying cause , can result in false classification of genes when quantifying insertion index scores , as these genes have half as many insertions relative to the rest of the genome . 
As such , these insertion profiles are to be considered when analyzing data with automated statistical approaches . 
Conditionally essential genes . 
In addition to the scenarios listed above , certain genes present challenges for binary classification of essentiality . 
For example , a gene might code for a protein that is essential at a specific phase of growth , or for growth under certain environmental parameters such as temperature or nutrient availability . 
Our data reveal a range of these conditionally essential genes . 
For instance , the Keio and PEC databases list folK as essential , whereas we detected multiple insertions within folK ( Fig. 3G ) . 
Loss of folK disrupts the ability of the bacterium to produce folate , which is an essential metabolite . 
However , supplementation of the medium with folate abrogates the requirement for folate biosynthesis . 
In addition to folK , the Keio and PEC databases report degS as essential . 
In our data set , degS has a high density of insertions throughout the CDS , suggesting that degS is not essential for growth on an agar plate ( Fig. 3H ) . 
Consistent with this , there is substantial literature showing that degS mutants can be isolated , but they either lyse in the stationary phase of growth or rapidly accumulate suppressor mutations ( 37 -- 40 ) . 
The conditional essentiality of such mutants can be tested by growing the transposon library in liquid broth . 
One would expect that mutants lacking degS will lyse and that folK mutants will be outcompeted as the limited folate available in the medium is depleted . 
To test these scenarios , two independent samples of the transposon library were grown in Luria broth ( LB ) at 37 °C for 5 or 6 generations to an optical density at 600 nm ( OD600 ) of 1.0 and were then sequenced . 
These samples , LB1 and LB2 , resulted in 5,908,163 and 6,403,324 sequences of which 5,201,711 ( 88.04 % ) and 5,382,477 ( 84.06 % ) , respectively , were mapped to the E. coli BW25113 genome ( Table 1 ) . 
Insertion index scores were calculated as before ( Table S4 ) . 
As there was a high correlation coefficient of 0.97 between the gene insertion index scores of each technical replicate ( Fig. 1C ) , the data were combined to give a pool of 10,584,188 sequences . 
Scrutiny of our data revealed substantially fewer degS and folK mutants after growth in LB , supporting our hypothesis that they are conditionally essential ( Fig. 3G and H ) . 
Other genes showing similar fitness costs can be identified in the LB outgrowth data set ( Table S4 ) . 
Errors in library construction . 
The difficulty in classifying a gene as essential through deletion analysis is the dependence on a negative result to inform classification . 
Thus , failure to knock out the gene may result in the false classification of a gene as essential . 
For example , the Keio database originally reported mlaB ( yrbB ) as essential . 
However , our data demonstrate that mlaB is nonessential , and this is supported by the literature ( 41 , 42 ) . 
We have observed similar outcomes for several other genes ( Table S3 ) . 
The reason why knockouts of these genes were not obtained in the construction of the Keio library is unknown . 
In addition to the false-positive outcomes described above , we noted several instances of false-negative results within the Keio library database . 
For example , both our TraDIS data and the PEC database identified 18 genes as essential that are reported as nonessential in the original Keio database ( Table S2 ) . 
Subsequently , Yamamoto et al. ( 34 ) demonstrated that for 13 of these mutants , the target gene was duplicated during construction of the Keio library , resulting in a functional protein ; these genes are almost certainly essential . 
Another difficulty that arises when targeting essential genes for mutagenesis is the potential to select for mutants with a compensatory mutation elsewhere in the genome . 
Our data revealed that hda is an essential gene , but it is classified as nonessential in the Keio database . 
Since the initial description of the Keio library , hda has been reported to be essential , but hda mutants rapidly accumulate suppressor mutations that restore viability ( 43 -- 45 ) . 
We hypothesize that this is an explanation for the observed essentiality of some genes in the TraDIS data set that were described as nonessential by others ( Table S3 ) . 
These effects may arise when creating TraDIS libraries , but the effects are masked by the large number of mutants in the population . 
Similarly , in the PEC library , where insertion density is low , essential genes with an insertion in a nonessential region of the gene will be falsely classified as nonessential when relying on single insertion mutants to inform essentiality . 
An example of this false-negative classification in the PEC database is tadA ( Table S3 ) . 
The TadA protein is a tRNA-specific deaminase , and its essentiality is reported in the Keio database and our data set and is supported by the literature ( 46 ) . 
The PEC database reports a single insertion site within the extreme 3 = end of the tadA gene . 
We have identified a range of underlying causes behind data set discrepancies and highlight that there are numerous possible insertion profiles for an `` essential '' gene . 
As such , it is important to note that no single statistical method , to our knowledge , would fully identify every essential gene and that manual inspection of data is crucial . 
Genes identified as essential only by TraDIS . 
There are 81 genes identified as essential using our insertion index data , which are not reported as essential in the Keio or PEC database ( Table 2 ) . 
These genes fall into two groups , those with no insertions and the remainder with insertions in the CDS . 
The first group is most likely to be essential . 
For example , rpsU is essential in our data and has been described as essential by others ( Fig. 5A ) ( 47 ) . 
However , in the Keio library , there is a duplication event , which gives rise to a mutant that produces a functional protein ( 34 ) . 
Scrutiny of our data for the remaining genes reveals that there are additional essential genes with a low frequency of insertions . 
For instance , holD has been described in the literature as an essential gene ( 48 ) . 
Our data support that finding ( Fig. 5B and Table S1 ) . 
However , holD mutants are available in the Keio collection . 
The demonstration by Durand et al. and others ( 48 -- 50 ) that holD mutants accumulate extragenic suppressor mutations at high frequency may explain why these mutants are considered nonessential in the Keio database and why we observe a low frequency of insertions in our experiments . 
A number of the genes unique to our analysis were not identified as essential in the Keio collection or PEC database simply because they are not included in either of these data sets . 
This is in part because the Keio collection of knockout mutants was based on available annotation data at the time ( 51 ) . 
For example , the identification and location of ynbG , yobI , and yqcG were published only in 2008 ( 52 ) . 
These genes show very sparse or no transposon disruption in our data , and consequently , these genes are potentially essential ( Fig. 5C , D , and E ) . 
Further validation studies would be required to confirm this . 
As mentioned previously , overreporting of essential genes may occur when nones-sential genes have low insertion index scores . 
Such low insertion index scores may arise due to attenuated growth . 
An example of gene misclassification because mutation results in a fitness cost and attenuated growth is guaA . 
The low insertion index score results in guaA being classed as essential despite having many insertions . 
The fitness effect was confirmed by growing the library in LB , as such mutants are outcompeted ( Fig. 5F ) , and the literature supports the fact that this gene is not essential and has an altered growth rate ( 53 ) . 
High-resolution features within a TraDIS data set . 
Manual inspection of a TraDIS data set can reveal additional information that might go unnoticed in a highthroughput analysis pipeline . 
A common observation from this and previous detailed analysis of data from saturated transposon libraries is the ability to determine , at the base pair level of resolution , the boundaries of essential regions within a gene . 
An example of an essential gene with a dispensable 3 = end is yejM ( pbgA in Salmonella enterica serotype Typhimurium ) . 
Only the 5 = end of the CDS is essential , up to and including codon 189 , which corresponds with five transmembrane helices of the protein structure ; the C terminus of the protein is a periplasmic domain that is dispensable for viability ( Fig. 6A ) ( 54 -- 56 ) . 
Our TraDIS data revealed insertions in codons 
186 and 189 . 
Analysis of the transposon orientation at these points revealed that they corresponded with the same transposon insertion location but , due to the 9-bp duplication introduced by the transposon , in different transposon orientations . 
The introduced transposon sequence maintains codon 189 , completely consistent with previously reported results ( 54 , 56 ) . 
In addition , as a result of our transposon design , a further feature of our TraDIS data is the identification of genes with dispensable 5 = ends . 
An example of this is yrfF , which encodes an inhibitor of the Rcs stress response ( Fig. 6B ) ( 57 , 58 ) . 
This phenomenon , while less well covered in the literature , is not surprising , given that Zhang et al. report equal likelihood of a required intragenic region residing at the 5 = or 3 = end of a gene , albeit in Mycobacterium tuberculosis ( 31 ) . 
These mutants will be viable only if the remaining CDS can be translated into a functional product , and one would expect to find an orientation bias where the transposon drives downstream transcription and translation of the essential region . 
Interestingly , inspection of our data revealed essential genes with isolated insertions within the coding sequence . 
An example of this is grpE . 
The grpE gene codes for the essential nucleotide exchange factor that forms a dimer and interacts with the DnaK/J complex ( 59 ) . 
The isolated insertion occurs only in the orientation that maintains expression of the remaining CDS ( Fig. 6C ) . 
Mapping of the site of transposon insertion onto the previously determined protein structure of GrpE indicated that the insertion occurred within the part of the gene encoding a flexible linker between two - helices 
MATERIALS AND METHODS
Strains and plasmids . 
E. coli K-12 strain BW25113 , the parent strain of the Keio library , was used for construction of a transposon library . 
The strain has the following genotype : rrnB3 ΔlacZ4787 hsdR514 Δ ( araBAD ) 567 Δ ( rhaBAD ) 568 rph-1 ( 65 ) . 
The transposon mutant library was constructed by collaborators from Discuva Ltd. , Cambridge , United Kingdom , following a method described for Salmonella Typhi ( 4 ) . 
The main differences were that a mini-Tn5 transposon coding for a chloramphenicol resistance cassette was used . 
This was amplified by PCR from the cat gene of the plasmid vector pACYC184 ( 66 ) using oligonucleotide primers incorporating the Tn5 transposon mosaic ends . 
Transposomes were prepared using Tn5 transposase ( Epicentre , Madison , WI , USA ) , and these were introduced into E. coli K-12 strain BW25113 by electrotransformation . 
Transposon mutants were selected by growth on LB agar supplemented with chloramphenicol . 
Approximately 5.6 million colonies representing an estimated 3.7 million mutants were pooled and stored in 15 % glycerol at 80 °C . 
Media and growth conditions . 
DNA was extracted from two samples of the transposon library glycerol stock to generate TraDIS data referred to as TL1 and TL2 in the text . 
In addition , DNA was extracted from two independent cultures , LB1 and LB2 , of the library grown in Luria broth ( LB ) ( 10 g tryptone , 5 g yeast extract , 10 g NaCl ) and grown for generations at 37 °C with shaking until the culture reached an optical density at 600 nm ( OD600 ) of 1.0 . 
- Galactosidase assay . 
- Galactosidase assays were used to measure the activity of transposon : : lacZ fusions . 
The transposon was cloned in each orientation , for all three open reading frames , into transcription and translation assay vectors pRW224 and pRW225 ( 33 ) . 
Strains carrying the transposon : : lacZ fusions were grown overnight at 37 °C with aeration in LB supplemented with 35 g/ml tetracycline ( Sigma ) . 
The density of the overnight culture was determined by measuring OD650 and then used to subculture into 5 ml LB and incubated at 37 °C with aeration until the mid-exponential phase of growth ( OD650 of 0.3 to 0.5 ) . 
Each culture was lysed by adding 100 l each of toluene and 1 % sodium deoxycholate , mixed by vortexing for 15 s and aerating for 20 min at 37 °C . 
The - galactosidase activity of each culture was assayed by the addition of 100 l of each culture lysate for three technical replicates to 2.5 ml Z buffer ( 10 mM KCl , 1 mM MgSO4 · 7H2O , 60 mM Na2HPO4 , 30 mM NaH2PO4 · 2H2O supplemented with 2.7 ml - mercaptoethanol per liter of distilled water , adjusted to pH 7 ) supplemented with 13 mM 2-nitrophenyl - - D-galactopyranoside ( ONPG ) ( Sigma ) . 
The reaction mixture was incubated at 37 °C until a yellow color had developed , after which the reaction was stopped by adding 1 ml of 1 M sodium carbonate . 
The absorbance of the reaction at OD420 was measured , and - galactosidase activity was calculated in Miller units . 
TraDIS sequencing . 
Harvested cells were prepared for sequencing following an amended TraDIS protocol ( 4 , 8 , 9 ) . 
Genomic DNA was isolated using a Qiagen QIAamp DNA blood minikit , according to the manufacturer 's specifications . 
DNA was quantified and mechanically sheared by ultrasonication . 
Sheared DNA fragments were processed for sequencing using NEB Next Ultra I kit . 
Following adaptor ligation , a PCR step was introduced to enrich for transposon-containing fragments , using a forward primer specific for the transposon 3 = end and a reverse primer specific for the adaptor . 
After PCR purification , an additional PCR prepared DNA for sequencing through the addition of Illumina-specific flow cell adaptor sequences and custom inline index barcodes of variable length in the forward primers . 
The purpose of this was to increase indexing capacity while staggering introduction of the transposon sequence to increase base diversity during sequencing . 
Samples were sequenced using Illumina MiSeq 150 cycle v3 cartridges , aiming for an optimal cluster density of 800 clusters per mm2 . 
Sequencing analysis . 
Raw data were collected and analyzed using a series of custom scripts . 
The Fastx barcode splitter and trimmer tools , of the Fastx toolkit , were used to assess and trim the sequences ( 67 ) . 
Sequence reads were first filtered by their inline indexes , allowing no mismatches . 
Transposon similarity matching was done by identifying the first 35 bp of the sequenced transposon in two parts : 25 bases ( 5 = to 3 = , corresponding to the PCR2 primer binding site ) were matched , allowing for three mismatches , trimmed , and then the remaining 10 bases ( corresponding to the sequenced transposon ) matched , allowing for one mismatch , and trimmed . 
Sequences less than 20 bases long were removed using Trimmomatic ( 68 ) . 
Trimmed , filtered sequences were then aligned to the reference genome E. coli BW25113 ( accession no . 
CP009273 .1 ) , obtained from the NCBI genome repository ( 69 ) . 
Where gene names differed between databases , the BW25113 annotation was used . 
The aligner bwa was used , with the mem algorithm ( 0.7.8-r455 [ 75 ] ) . 
Aligned reads were filtered to remove any soft clipped reads . 
The subsequent steps of conversion from SAM ( sequence alignment/map ) files to BAM ( binary version of SAM ) files , and the requisite sorting and indexing , were done using SAMtools ( 0.1.19-44428cd [ 70 ] ) . 
The BEDTools suite was used to create BED ( browser extensible data ) files which were intersected against the coding sequence boundaries defined in general feature format ( . 
gff ) files obtained from the NCBI ( 71 ) . 
Custom python scripts were used to quantify insertion sites within the annotated CDS boundaries . 
Data were inspected manually using the Artemis genome browser ( 72 ) . 
Essential gene prediction . 
The frequency of insertion index scores was plotted in a histogram using the Freedman-Diaconis rule for choice of bin widths ( see Fig . 
S1 in the supplemental material ) . 
Using the R MASS library ( http://www.r-project.org ) , an exponential distribution ( red line ) was fitted to the left , `` essential '' mode ( i.e. , any data to the left of the trough in Fig . 
S1 ) ; a gamma distribution ( blue line ) was fitted to the right , `` nonessential '' mode ( i.e. , any data to the right of the trough ) . 
The probability of a gene belonging to each mode was calculated , and the ratio of these values was used to calculate a log likelihood score . 
Using a 12-fold likelihood threshold , based on the log likelihood scores , genes were assigned as `` essential '' if they were 12 times more likely to be in the left mode than in the right mode , and `` nonessential '' if they were 12 times more likely to be in the right mode ( 9 ) . 
Genes with log likelihood scores between the upper and lower log2 12 threshold values of 3.6 and 3.6 , respectively , were deemed 
`` unclear . '' 
A threshold cutoff of log2 ( 12 ) was chosen , as it is more stringent than log2 ( 4 ) ( used by Langridge et al. [ 4 ] ) , and consistent with analysis used by Phan et al. ( 9 ) . 
Essential gene lists . 
The Keio essential gene list is composed of the original essential genes minus three open reading frames ( ORFs ) , JW5190 , JW5193 , and JW5379 , as they are not annotated within strain MG1655 and are thought to be spurious , giving a final list of 300 genes ( 1 , 73 ) . 
The PEC data set is composed of the 300 genes listed as essential for strain W3110 ( 2 ) . 
The lists of essential genes were compared using BioVenn ( 74 ) . 
Statistical analysis . 
For details of the statistical analysis , see Text S1 , Fig . 
S1 , and Fig . 
S2 in the supplemental material . 
Accession number ( s ) . 
TraDIS sequencing data are available from the European Nucleotide Archive under accession no . 
PRJEB24436 . 
SUPPLEMENTAL MATERIAL
Supplemental material for this article may be found at https://doi.org/10.1128/mBio .02096 -17 . 
TEXT S1 , DOCX file , 0.1 MB . 
FIG S1 , PDF file , 0.02 MB . 
FIG S2 , TIF file , 0.1 MB . 
TABLE S1 , XLSX file , 0.2 MB . 
TABLE S2 , PDF file , 0.04 MB . 
TABLE S3 , PDF file , 0.03 MB . 
TABLE S4 , XLSX file , 0.3 MB . 
ACKNOWLEDGMENTS
We thank Discuva Ltd. for providing some of their large transposon mutant library . 
We thank N. Loman and J. Quick for help with optimization of our MiSeq protocol . 
We thank the authors of Langridge et al. ( 2009 ) for kindly supplying their R code for essential gene prediction . 
We thank Tony Hitchcock and Steve Williams for their support . 
Last , we thank M. J. Collingwood and R. W. Meek for their generous help with drawing figures . 
This research has been supported by the Midlands Integrative Biosciences Training Partnership ( MIBTP , BBSRC ) Ph.D. program , and the University of Birmingham Elite Ph.D. . 
Scholarship to I.R.H. Cobrabio contributed to the University of Birmingham Elite Ph.D. studentship . 
I.G.J. is supported by a Birmingham Fellowship . 
S.J. is supported by the BBSRC and MRC .