Skip to main content
Browse Subject Areas

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

Automatic identification of optimal marker genes for phenotypic and taxonomic groups of microorganisms

  • Elad Segev ,

    Contributed equally to this work with: Elad Segev, Zohar Pasternak, Tom Ben Sasson

    Roles Conceptualization, Formal analysis, Methodology, Software, Supervision, Validation, Writing – original draft, Writing – review & editing

    Affiliation Department of Mathematics, Holon Institute of Technology, Holon, Israel

  • Zohar Pasternak ,

    Contributed equally to this work with: Elad Segev, Zohar Pasternak, Tom Ben Sasson

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Validation, Writing – original draft, Writing – review & editing

    Affiliation Department of Plant Pathology and Microbiology, Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel

  • Tom Ben Sasson ,

    Contributed equally to this work with: Elad Segev, Zohar Pasternak, Tom Ben Sasson

    Roles Conceptualization, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

    Affiliation Department of Mathematics and Computer Science, The Open University of Israel, Raanana, Israel

  • Edouard Jurkevitch,

    Roles Conceptualization, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Department of Plant Pathology and Microbiology, Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot, Israel

  • Mira Gonen

    Roles Conceptualization, Methodology, Software, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Department of Computer Science, Ariel University, Ariel, Israel


Finding optimal markers for microorganisms important in the medical, agricultural, environmental or ecological fields is of great importance. Thousands of complete microbial genomes now available allow us, for the first time, to exhaustively identify marker proteins for groups of microbial organisms. In this work, we model the biological task as the well-known mathematical “hitting set” problem, solving it based on both greedy and randomized approximation algorithms. We identify unique markers for 17 phenotypic and taxonomic microbial groups, including proteins related to the nitrite reductase enzyme as markers for the non-anammox nitrifying bacteria group, and two transcription regulation proteins, nusG and yhiF, as markers for the Archaea and Escherichia/Shigella taxonomic groups, respectively. Additionally, we identify marker proteins for three subtypes of pathogenic E. coli, which previously had no known optimal markers. Practically, depending on the completeness of the database this algorithm can be used for identification of marker genes for any microbial group, these marker genes may be prime candidates for the understanding of the genetic basis of the group's phenotype or to help discover novel functions which are uniquely shared among a group of microbes. We show that our method is both theoretically and practically efficient, while establishing an upper bound on its time complexity and approximation ratio; thus, it promises to remain efficient and permit the identification of marker proteins that are specific to phenotypic or taxonomic groups, even as more and more bacterial genomes are being sequenced.


The first complete bacterial genome sequence was published in 1995 [1]. Since then, sequencing technology has developed rapidly, causing a dramatic reduction in the cost of sequencing, which made bacterial genome sequencing affordable to a great number of labs [2]. The Ensembl database of whole genomes of bacteria has grown from about 9,000 to more than 40,000 in a few short years [3,4], and this number continues to increase exponentially each year [5]. This large number of bacterial genomes enabled us, for the first time, to identify marker genes for specific groups of microbial organisms based on the full complement of genes in each genome [6]. Currently, molecular typing of specific microbial groups is mostly done using either multi-locus sequence typing (MLST) or core genome MLST (cgMLST). In MLST, 5–16 (usually 7–8) housekeeping genes are selected as molecular markers and their sequence is compared between isolates [7]. However, the limited repertoire of highly conserved genes, as well as their sequence conservation, may sometime limit the discriminative power of this method, as evident in the typing of Enterococcus faecium [8]. The solution is usually to increase the number of genes, so in cgMLST, between 1500–3000 marker genes are used, which increases the discriminative power but forces any new isolate to be fully sequenced before it can be typed, thus requiring complex genomic analysis. A different way to improve MLST is by discarding the usage of housekeeping genes in favor of small groups of genes that are unique to specific taxonomic or phenotypic groups. This allows quick and affordable typing, using PCR instead of whole-genome sequencing, while retaining high discriminative power.

When using non-housekeeping marker genes for the MLST scheme, choosing the right ones is critical: non-representative or non-unique genes can lead to erroneous typing [8], or to an inefficient process due to requiring too many marker genes per group in order to verify the genomes' membership. Genetic markers are often selected ad hoc, using too few reference genomes and/or manual inspection of the results [9]. Therefore, an algorithm for finding optimal markers for specific groups of organisms is of great value to ecological and medical research [10,11]. Several software tools were developed for this purpose, but these are mostly limited to a single pathogenic organism [12], are computationally intensive [13], or can only identify marker genes for defined taxonomic groups [14,15]. Existing methods are not capable of creating novel typing schemes for any group of genomes (from one to thousands) by user choice, in a user-friendly manner while operating quickly and efficiently on any personal computer. We use an innovative approach for this problem, mapping it using the well-known Hitting Set (HS) mathematical problem. To increase the discriminative power of the approach, we use polypeptides instead of genes. Given a set of bacteria where each bacterium is represented by the set of the proteins it contains, a subset of bacteria to type will contain a minimum subset of specific proteins not found in the other bacteria. This minimum subset of proteins is thus present in every bacterium of the subset, marking the phenotype or taxon, while at least one protein is missing in the other bacteria, which do not have the same phenotype or do not belong to the taxon. The problem of typing a set of bacteria can thus be solved mathematically.

To solve this problem, we start from the set of all of the proteins that are present in all the bacteria in our database. These proteins will be marked as P1, P2 etc. in our case; a bacterium is defined as the set of proteins that it consists of.

For example, as seen in Fig 1A, bacterium no. 1 (denoted B1) is defined as the set of P1, P2 and P3, meaning that this bacterium contains only these three proteins, and bacterium B2 contains only proteins P1, P2 and P4. Given that B1 and B2 form a group of interest which we wish to identify, P1 or P2 can serve as marker proteins for this group of interest. Thus, the minimal set of proteins which identify this group can be either P1 or P2. If a third bacterium exists, which is not included in the group of interest and consists of P2 and P5, then P2 loses its identifying property for the group of interest and the minimal set becomes P1 only (Fig 1B). Not every group of interest can be exclusively identified by a set of proteins: in some cases, such as Fig 1C, where B4 is not a part of the group of interest and contains P1, P2 and P5, there is no set of proteins that can identify the B1B2 group. As the number of proteins and bacteria increases, an exact solution for finding a minimal set of proteins out of millions of known proteins in order to identify any group of interest out of thousands of organisms becomes impossible to solve in a reasonable timeframe. We will show that this problem cannot be solved efficiently since it is NP-hard [16,17], namely, no efficient algorithm for solving this problem is known. The definition of the hitting set problem is the following: given a ground set S and a collection C of subsets of S, find a hitting set with a minimum cardinality, i.e., a subset S′ ⊆ S such that S′ contains at least one element from each subset in C. We will elaborate on the exact connection between the hitting set problem and our problem of identifying a set of proteins in the Materials and Methods subsection named Problem Definitions and Notations. In S1 Algorithm, we show that even if we are willing to relax our problem to that of finding a hitting set of a limited size, an exact approach is impractical. Since this problem is of great importance, a lot of effort has been made to find efficient approximation algorithms to it. That is, efficient algorithms that return a solution to the hitting set problem, which is at most r times the size of a minimum set, where r is the approximation factor. Here, we apply an approximation algorithm which finds relatively small sets of proteins that identify the group of interest. Obviously, these sets are not necessarily minimal. For example, in Fig 1A, an approximate solution might be the set containing both P1 and P2, whereas an exact (i.e. minimal) solution will be either P1 or P2.

Fig 1.

Graphical representation of the proteins (denoted P1, P2, P3, P4, P5) which can serve as markers for the bacterial (denoted B1, B2, B3, B4) group of interest consisting of B1 and B2: (A) shows that P1, P2 can serve as a minimal set of markers for the group of interest; (B) P1 only can serve as a marker for the group of interest; and (C) there are no markers for the group of interest.

To find an approximation algorithm to the hitting set problem, we explore approximation algorithms to an equivalent problem, the “set cover” problem [16], which in addition to several known approximation algorithms has many heuristics for solving it efficiently [1827]. The definition of the set cover problem is the following: given a ground set S and a collection C of subsets of S, find a subset C′ ⊆ C such that every element in S is contained in some subset in C′. When considering the most suitable algorithm for our work, we looked for algorithms that will be both theoretically and practically efficient. It was proven that the set cover problem is approximable within O(log|S|), where |S| is the size of the ground set S, using a greedy algorithm [2831]. Therefore, a greedy algorithm can achieve the same corresponding approximation ratio as the hitting set problem. Moreover, a randomized algorithm based on linear programming achieves the same approximation ratio with a probability of at least 1-1/|S| [32]. It was shown that the set cover problem cannot be approximated in polynomial time to within a factor of (1 − o(1)) · ln|S| (unless P = NP) [33,34]. This means that the greedy approximation algorithm and the randomized algorithm theoretically achieve the best possible ratio. Thus, we implemented the greedy algorithm and a randomized algorithm based on randomized rounding of linear program constraints [3538]. Both algorithms were used to identify non-anammox nitrifying bacteria and predatory bacteria as phenotypic groups, Archaea and Escherichia/Shigella as taxonomic groups, and 13 different pathogenic sub-groups of E. coli as combined phenotypic/taxonomic groups.

Materials and methods

Biological Data. Genome and protein data were obtained as outlined in [6] from the 2016 version of the orthologous protein cluster table created and maintained by the microbial genome database (MBGD) [39] and freely available at This table is updated yearly and is arranged so that each row is an orthologous cluster (i.e. the same protein) and each column is a genome. Ortholog identification and grouping is performed by the DomClust [40] and DomRefine [39] procedures, with MergeTree [41] adding new genomes to the table. Ortholog classification is based on all-against-all clustering with local alignment of the protein domain sequences. The raw MBGD data were automatically cleaned, as outlined in [6]: unnecessary and redundant data were deleted, and all protein occurrences were transformed from protein names into binary data so that each datapoint in the table contains either a one or a zero only (the protein is present or absent in the genome, respectively). This reduces the file size by two orders of magnitude and enables efficient algorithm usage. This approach enables one to make use of any orthologous group classification, for example splitting or merging groups according to the biological system. In order to fully challenge our methods and algorithms, we implemented the algorithm using 17 different microbial groups which represent a wide variety of classification criteria: non-anammox nitrifying bacteria and predatory bacteria as phenotypic groups, Archaea and Escherichia/Shigella as taxonomic Groups, and 13 different subtypes of pathogenic E. coli as taxonomic/phenotypic groups. First, of the 4742 complete (i.e. non-draft) genomes of bacteria and Archaea available in the MBGD database, 12 belonged to non-anammox nitrifying bacteria from two groups: ammonia oxidizers (Nitrosomonas europaea ATCC 19718, Nitrosomonas eutropha C71, Nitrosomonas sp. AL212, Nitrosomonas sp. Is79A3, Nitrosospira multiformis ATCC 25196, Nitrosococcus halophilus Nc4, Nitrosococcus oceani ATCC 19707, Nitrosococcus watsoni C-113, Nitrobacter hamburgensis X14, Nitrosomonas communis Nm2) and nitrite oxidizers (Nitrobacter winogradskyi Nb-255, Nitrospira defluvii). Genomes of bacterial nitrifiers using anaerobic ammonium oxidation (anammox) were not available. Second, MBGD contained 16 genomes belonging to known predatory bacteria: Bdellovibrio bacteriovorus HD100, Bdellovibrio bacteriovorus 109J, Bdellovibrio bacteriovorus tiberius, Bdellovibrio bacteriovorus W, Bdellovibrio exovorus, Bacteriovorax marinus SJ, Cytophaga hutchinsonii ATCC 33406, Flavobacterium johnsoniae UW10, Herpetosiphon aurantiacus ATCC 23779, Micavibrio aeruginosavorus ARL-13, Micavibrio aeruginosavorus EPB, Myxococcus xanthus DK 1622, Sorangium cellulosum So ce 56, Sorangium cellulosum So0157-2, Saprospira grandis Lewin, Stigmatella aurantiaca DW4/3-1. Third, we obtained all 226 archaeal genomes available at MBGD, comprising all archaeal classes and families (Table A in S1 File); fourth, the 147 genomes belonging to the Escherichia/Shigella bacterial genus (Table B in S1 File); and fifth, the genomes comprising 13 E. coli pathotypes (Table B in S1 File). The taxonomic and phenotypic identifications were based on the latest published data[42]. E. coli strain pathogroup assignment was according to the EnteroBase database freely available at

Problem Definition and Notation. Given a set of bacteria, a subset B of , and a set of orthologous proteins P, we want to find a minimum subset of P that would identify the bacteria in B. Namely, all orthologous proteins in are in every bacterium in B, and for every bacterium in there is at least one ortholog protein in that is missing. We consider each bacterium as a subset of the orthologous proteins it contains. Thus, the formal definition of the problem is as follows: given a ground set of m elements P = {p1…,pm}, a collection of n subsets , such that BjP and |Bj| ≥ 1 for all 1 ≤ jn, and a subcollection of size k, B = {B1…,Bk}, we want to find a minimum size subset of P, , such that for each it holds that , and for each it holds that there exists some such that pBj. Algorithm Development. We use a greedy approximation algorithm and a randomized approximation that is based on Linear Programming for the hitting set problem. An advantage for the random algorithm is that different runs of the algorithm may produce different results in an efficient manner.

Algorithm 1, based on hitting set

  1. for all do
  2. if p ∉ B2 ∩ B3 ∩…∩ Bk then
  3. for k + 1 ≤ j ≤ n let .
  4. let .
  5. if contains an empty set–return "no hitting set".
  6. run an algorithm of the hitting set problem on the input Namely, sub algorithm 1 or 2 on input , respectively.

We can conclude that there is no possible hitting set for an instance of a problem in step 5 even before running an explicit sub-algorithm for the hitting set problem. Let and be the result sets at the end of stage 4 in algorithm 1. If at least one of the sets in is empty, then obviously there is no hitting set. Notice that if all sets in are not empty, there is always a hitting set since we can take . By the construction of sets in must hit each set if it is not empty. As noted in the previous subsection, the hitting set problem is equivalent to the set cover problem. Moreover, we can use any algorithm to the set cover problem to solve the hitting set problem. Consider an instance S, C to the set cover problem. We define the following instance to the hitting set problem. The ground set is defined to be = C, namely, each element is a subset of the instance to the set cover problem. For each element in eS, we define a subset of which is the set of subsets of S that contain e. Therefore, a minimum cardinality cover of S, C is a minimum cardinality hitting set of . To solve Item 6 of Algorithm 1 we first use the following greedy algorithm for the hitting set problem:

Sub algorithm 1, greedy algorithm for hitting set (S, C)

Input: universe S = {s1,…,sm}, C = {C1,…,Cn}, s.t. Ci ⊆ S for all 1 ≤ i ≤ n.

Output: .

  1. .
  2. while do
    1. Select s ∈ S such that s hits the largest number of subsets in (i.e. select s s.t. is of maximum cardinality)
    2. Remove the hit subsets from , namely .
    3. .
  3. Return .

Lemma 1 given an instance (S,C), sub-algorithm 1 finds a hitting set of size of at most OPTO(log|C|) = OPTO(log n), where OPT is the size of an optimal HS, with time complexity O(min{m,n} ⋅ nm).

Proof The correctness and approximation ratio of the lemma follows from the correctness and approximation ratio of the greedy algorithm for the set cover problem [29], and the tight connection between the set cover problem and the hitting set problem [43]. To compute the time complexity of the algorithm, let be the set that needs to be hit at step j, and let be the set of elements that have not been selected yet at step j. The most expensive operation in the loop of Item 3 is Item 3a. In the worst case of Item 3a for every sS, the algorithm goes over all the subsets to check whether sCi. Using a reasonable data structure, we can assume that the access time for checking whether sCi is O(1). Therefore, the running time of Item 3a at step j is . Let be the maximum number of steps the loop in Item 3 is performed. Obviously . Thus, time complexity of Sub-Algorithm 1 is.

Notice that the theoretical upper bound on the time complexity can be reduced for specific instances of the problem. For example, if every element in S appears in many subsets in C then the number of steps , of performing the loop in Item 3 of Sub-Algorithm 1 is much smaller than n.

Lemma 1 implies the following theorem:

Theorem 1 Algorithm 1 finds a set of proteins such that , where POPT is an optimal set of proteins that identifies B, with time complexity of O(m2n).

Proof: We first note that the initial set is an intersection of all the subsets of proteins in the tested set {B1,…,Bk}. Therefore, any set returned by the algorithm to identify the tested set must be a subset of the initial Moreover, the returned set must not identify the control set {Bk+1,…,Bn}, Since Sub-Algorithm 1 returns a subset of that is a hitting set of , for each bacteria Bj of the control set {Bk+1,…,Bn} the returned must include at least one protein that does not exist in Bj. This implies the correctness of the algorithm.

By lemma 1, with S = B1 ∩…∩ Bk and , it holds that , where OPT is an optimal solution to the HS problem on the given S, C. Notice that by the definition of our problem, every optimal solution for identifying B needs to find a HS of as well. Thus, it holds that OPT = |POPT|, so , as claimed.

To prove the bound on the time complexity, it must be noted that the running time of Item 2 of Algorithm 1 is . The running time of Item 3 of Algorithm 1 is By Lemma 1 the time complexity of finding the hitting set in item 5 on is

Therefore, the total time complexity of the algorithm is

This completes the theorem.

Notice that again, in practice, the actual time complexity is smaller than our upper bound, which makes our algorithm very fast in reality, as demonstrated in the next section. We now show another version of Algorithm 1 in which we replace Sub-Algorithm 1 by a Linear Programming randomized algorithm for the hitting set problem. For 1 ≤ im set xi = 1 if and xi = 0 otherwise. Thus, finding a hitting set can be formulated as an integer linear program. We relax the integer linear program to a fractional one, and then use randomized rounding to get an integer solution.

Sub Algorithm 2, linear programming randomized algorithm for hitting set (S, C)

Input: universe S = {s1,…,sm}, C = {C1,…,Cn}, s.t. Ci ⊆ S for all 1 ≤ i ≤ n.

Output: .

  1. Solve the following fractional linear programming problem:
  2. For all 1 ≤ i ≤ m let be the value assigned to xi, in an optimal fractional solution of the previous linear program.
  3. Let Xi, 1 ≤ i ≤ m be independent random variables such that
  4. Return .

According to [3538] Sub-Algorithm 2 returns a hitting set of size c ⋅ log n times the size of an optimal solution, with a probability of at least . Therefore Algorithm 1, when using Sub-Algorithm 2, achieves, with high probability, the same theoretical approximation ratio of Algorithm 1 when using Sub-Algorithm 1. As the constant c presented in item 3 of the algorithm grows, the probability that the result is indeed a valid HS grows, but the expected size of the result also increases. Based on our experience, choosing the constant c = 1 gives us a relatively small error probability, while not increasing the received HS size. In each case the random algorithm returned a HS, we verified that it is indeed a HS. The time complexity of Sub-Algorithm 2 is derived mainly from the time complexity of solving the Linear Program of the problem in step 1. We have used Dantzig's Simplex method [44] to solve linear programs. This method has an exponential time complexity in the worst case but is highly efficient in practice. It is possible to rerun this sub algorithm efficiently as we note that once we have solved the linear program, we are left with assigning a binary value for each variable and then deciding whether the result is an actual hitting set, so the time complexity of a single rerun for an instance of this algorithm is nm. For our purposes, we have found that 10,000 reruns of this algorithm per instance produces reasonably low runtime and at the same time produces a large variety of interesting outcomes. In some occasions, the result of the randomized algorithm may be optimal and deterministic. If the solution for the linear program is binary, that is, each variable receives either 0 or 1, then we have found a hitting set without the need for linear relaxation. That is the optimum value for the integer program of the given instance for exact hitting set program. In that case, it is redundant to rerun the algorithm as it will generate the same optimal result. This randomized approach presents several different hitting sets.

An overview representation of the software structure is presented in Fig 2. This section details the description of the algorithm. The source code can be downloaded directly from

The algorithm may produce three types of solutions: (i) Optimal minimal solutions. It is sometimes possible to guarantee for a result set to be minimal (rather than an approximation) as a result of a possible optimal solution for the linear program for the HS formulation with sub-algorithm 2. (ii) Collection of approximated solutions. One or more hitting sets that are no larger than OPTO(log n) where OPT is the minimal solution and n is the number of sets to hit (microbial groups in our case). (iii) No solution. In the process of algorithm 1, it is possible to say whether there is no set that exclusively hits all of the tested groups of an instance of the problem.

Results and discussion

Our algorithms were employed to find the hitting set of 17 different microbial groups representing a wide variety of classification criteria (Table 1): 1) The non-anammox nitrifying bacteria, a group distinct from all other bacteria by its metabolic phenotype; 2) The second phenotypic group was the predatory bacteria, a group distinct by its trophic phenotype; 3) The third tested group was a taxonomic one–Archaea, a group distinct from Bacteria by domain-level taxonomy; 4) The fourth tested group was a taxonomic one: the Escherichia/Shigella groups, distinct by genus-level taxonomy, and; 5) The other 13 tested groups were of pathogenic E. coli. each distinguished from all other bacteria by both taxonomy and phenotype. In all cases where hitting sets were found, one of the hitting sets found by the random algorithm was identical to the hitting sets found by the greedy algorithm. The non-anammox nitrifiers used in this study are a group of 12 bacteria with a known genome, which perform the nitrification of ammonia or ammonium to nitrite. Five minimal HS were discovered, each containing five proteins (Table C in S1 File). Among these proteins were nitrite reductase (NirK)[45], an enzyme that catalyzes the reduction of nitrite to nitric oxide; siroheme synthase (CysG), an enzyme involved in the synthesis of siroheme, a heme-like prosthetic group used by the nitrite reductase [46]; Formate/nitrite transporter (NirC), a transmembrane channel that transports nitrite in and out of the cell [44]; and a nitric oxide reductase activation protein (NorD)[45], involved in reducing nitric oxide to nitrous oxide. Although it may appear that these proteins are only relevant to the first group of non-anammox nitrifiers (i.e. nitrite oxidizers), it was actually discovered that the second group (ammonia oxidizers) also depend on the nitrite reductase enzyme for efficient growth by its oxidation of ammonia to nitrite via hydroxylamine [47]. Thus, many of the HS proteins that our algorithm discovered are highly relevant to the biological phenotype that is unique to all non-anammox nitrifying bacteria, serving as a kind of "positive control" for our algorithm. The second phenotypic group, predatory bacteria, was not found to have any hitting sets proteins when compared to other (i.e. non-predatory) bacteria. This confirms previous studies (e.g. Pasternak et al. [48]), which also concluded that bacterial predation is not facilitated by unique proteins, and serves as a kind of "negative control" for our algorithm. In many examples bacteria performing similar metabolic functions (e.g. nitrification) all use similar enzymes to carry out the process. However, predation is functionally a more diverse process, and there are no molecular signatures specific to bacterial predation. Indeed, several features were shown to be highly enriched in predators, including adhesins, proteases and particular metabolic proteins, used for binding to, processing and consuming prey, respectively; in addition, most predators use the mevalonate pathway of isoprenoid biosynthesis, whereas almost all other bacteria use the DOXP pathway [48]. However, all of these proteins are also found in the genomes of non-predatory bacteria, and the differences might not lie in their presence or absence but rather in transcriptional and/or post-transcriptional regulation.

Table 1. Hitting sets (marker proteins) of 17 microorganism groups.

HS, hitting set. Min., minimal. Greedy and random refer to the algorithm type. Phen., phenotypic. Tax., taxonomic. AIEC, adherent-invasive E. coli. EPEC, enteropathogenic E. coli. UPEC, uropathogenic E. coli. STEC, Shiga toxin-producing E. coli. NMEC, neonatal meningitis-associated E. coli. ExPEC, extra-intestinal pathogenic E. coli. ETEC, enterotoxigenic E. coli. EIEC, enteroinvasive E. coli. EHEC, enterohemorrhagic E. coli. EAEC, enteroaggregative E. coli. APEC, avian pathogenic E. coli. EAHEC, enteroaggregative hemorrhagic E. coli.

The next group, Archaea, was compared to all the bacterial genomes in the database and an optimal single protein (NusG) HS solution was found to sufficiently distinguish the groups. NusG is a transcription elongation factor that binds to RNA polymerases and assists in RNA synthesis from a DNA template[49]. RNA polymerases are under strong evolutionary pressure to maintain their structure; therefore, the structure and sequence of NusG should also be conserved. In fact, this protein is considered to be the only transcription elongation factor whose sequence is universally conserved in all three domains of life: Bacteria, Archaea and Eukarya [50]. Surprisingly, our analysis found the amino-acid sequence to be very distinct between Archaea and the other two domains, and a consequent phylogenetic analysis of 500 NusG protein sequences (Fig 3) clearly confirmed this difference. Since this solution is an optimal solution the algorithms are deterministic—meaning there might be other genes specifically conserved only in Archaea in the current database which the algorithm ignores.

Fig 3. Maximum-likelihood phylogenetic tree of the NusG protein.

All archaeal NusG sequences were taken from the GenBank database, along with their most similar bacterial and eukaryotic homologs for a total of 500 protein sequences. The bootstrap consensus tree inferred from 100 replicates was taken to represent the evolutionary history of the taxa analyzed. Branches were merged at the domain level.

The fourth tested group is a taxonomic one; the bacterial genus Escherichia/Shigella was characterized by ten hitting sets, all with two proteins, where one of them is the YhiF protein which is a transcription regulator (Table D in S1 File). YhiF takes part in modulating levels of expression of LEE (locus of enterocyte effacement) proteins—a group of proteins responsible for attaching and effacing to the intestine of the host. Through the mechanisms of control of LEE expression, YhiF appears to play a central role in Escherichia/Shigella colonizing the host intestinal epithelium [51]. However, as it is present in all Escherichia/Shigella strains, including commensal ones, it is a taxonomic marker rather than a pathogenicity marker.

Finally, we searched for hitting sets which define 13 different subgroups—or pathotypes—of pathogenic E. coli bacteria. These pathotypes are defined according to their clinical symptoms (e.g. enterohemorrhagic) and therefore most do not have perfect marker proteins, i.e. ones that exist in all the strains of a specific pathotype and only in those strains [49]. Indeed, our analysis found that 10 of these 13 pathogroups showed no hitting sets, confirming that genomes within each group do not necessarily have taxonomic or phenotypic affiliation with each other and therefore non-housekeeping marker proteins are impossible to find in most groups. Even so, three important pathotypes could be efficiently defined: adherent-invasive E. coli (AIEC) with minimal hitting sets containing three proteins (Table E in S1 File), enteropathogenic E. coli (EPEC) with five proteins (Table F in S1 File) and enteroaggregative hemorrhagic E. coli (EAHEC) with three proteins (Table G in S1 File). EPEC is an important cause of diarrhea and premature death in children, especially in developing countries [51]; currently, the major diagnostic marker for EPEC is the eae gene (coding for intimin, an outer membrane adhesive protein), yet genomes from other pathotypes also possess this gene [52], making it a non-optimal marker. AIEC is not associated with diarrhea but is thought to contribute to the development of Crohn’s disease, a chronic inflammatory bowel syndrome [53]. It currently has no known diagnostic markers [52]. EAHEC is associated with food poisoning in the developed world [54]. Many of the hitting sets proteins that we found are either uncharacterized (i.e. 'hypothetical') or are from a viral (bacteriophage) source with DNA cut-and-paste functionality, e.g. transposases and integrases. One of our EAHEC HS members, FliC, has been used before as a marker for this group [54]. E. coli subtyping schemes are invaluable in identifying outbreaks and treating infection patients, but the current subtyping technology is imprecise and potentially misleading because E. coli genomes constantly change and evolve.

Our algorithm enables the accurate identification of marker genes for any microbial group, depending on the completeness of the database. Once marker genes are established and confirmed, new and unknown genomes can quickly be assigned to their group via MLST, without the need for whole-genome sequencing. specifically, our study found potential marker genes which in the future may enable reliable diagnosis of the EAHEC, EPEC and AIEC strains of pathogenic E. coli, thus improving the treatment of E. coli-related diseases. In addition to microbial identification, such analysis may help uncover novel genes pertinent to the grouping, such as virulence-associated or habitat-specific genes. As these genes are group-specific, they are prime candidates for further research which aims to understand the genetic basis of the group's phenotype as well as possible targets for antibiotic treatment. Finally, our algorithm may also help discover novel functions which are uniquely shared among a group of microbes. For example, several uncharacterized ("hypothetical") genes were found in this study to be pathogroup-specific; further investigation of these genes and proteins may reveal their possible function in connection to their specific group, leading to improved understanding and specific antibacterial treatments.

Supporting information

S1 File. Microbial groups and their genomes, and hitting sets (HS) found in this study.

Table A in S1 Fie) Archaea genomes; Table B in S1 File) Escherichia/Shigella genomes; Table C in S1 File) nitrifiers HS; Table D in S1 File) Escherichia/Shigella HS; Table E in S1 File) AIEC (adherent-invasive E. coli) HS; Table F in S1 File) EPEC (enteropathogenic E. coli) HS. Table G in S1 File) EAHEC (enteroaggregative hemorrhagic E. coli) HS.


S1 Algorithm. A mathematical explanation for the necessity of approximation solution.



  1. 1. Fleischmann RD, Adams MD, White O, Clayton RA, Kirkness EF, Kerlavage AR, et al. Whole-Genome Random Sequencing and Assembly of Haemophilus Influenzae Rd. Science. 1995;269: 496–512. pmid:7542800
  2. 2. Hall N. Advanced sequencing technologies and their wider impact in microbiology. J Exp Biol. 2007;210: 1518–1525. pmid:17449817
  3. 3. Kersey PJ, Allen JE, Christensen M, Davis P, Falin LJ, Grabmueller C, et al. Ensembl Genomes 2013: scaling up access to genome-wide data. Nucleic Acids Res. 2014;42: D546–D552. pmid:24163254
  4. 4. Kersey PJ, Allen JE, Armean I, Boddu S, Bolt BJ, Carvalho-Silva D, et al. Ensembl Genomes 2016: more genomes, more complexity. Nucleic Acids Res. 2016;44: D574–D580. pmid:26578574
  5. 5. Land M, Hauser L, Jun S- R, Nookaew I, Leuze MR, Ahn T-H, et al. Insights from 20 years of bacterial genome sequencing. Funct Integr Genomics. 2015;15: 141–161. pmid:25722247
  6. 6. Pasternak Z, Sasson TB, Cohen Y, Segev E, Jurkevitch E. A New Comparative-Genomics Approach for Defining Phenotype-Specific Indicators Reveals Specific Genetic Markers in Predatory Bacteria. PLOS ONE. 2015;10: e0142933. pmid:26569499
  7. 7. Pérez-Losada M, Cabezas P, Castro-Nallar E, Crandall KA. Pathogen typing in the genomics era: MLST and the future of molecular epidemiology. Infect Genet Evol J Mol Epidemiol Evol Genet Infect Dis. 2013;16: 38–53. pmid:23357583
  8. 8. Leopold SR, Sawyer SA, Whittam TS, Tarr PI. Obscured phylogeny and possible recombinational dormancy in Escherichia coli. BMC Evol Biol. 2011;11: 183. pmid:21708031
  9. 9. Dutilh BE, Snel B, Ettema TJG, Huynen MA. Signature Genes as a Phylogenomic Tool. Mol Biol Evol. 2008;25: 1659–1667. pmid:18492663
  10. 10. Wu D, Jospin G, Eisen JA. Systematic Identification of Gene Families for Use as “Markers” for Phylogenetic and Phylogeny-Driven Ecological Studies of Bacteria and Archaea and Their Major Subgroups. PLOS ONE. 2013;8: e77033. pmid:24146954
  11. 11. Kristensen DM, Waller AS, Yamada T, Bork P, Mushegian AR, Koonin EV. Orthologous Gene Clusters and Taxon Signature Genes for Viruses of Prokaryotes. J Bacteriol. 2013;195: 941–950. pmid:23222723
  12. 12. Yoshida CE, Kruczkiewicz P, Laing CR, Lingohr EJ, Gannon VPJ, Nash JHE, et al. The Salmonella In Silico Typing Resource (SISTR): An Open Web-Accessible Tool for Rapidly Typing and Subtyping Draft Salmonella Genome Assemblies. PLOS ONE. 2016;11: e0147101. pmid:26800248
  13. 13. Brinkac LM, Beck E, Inman J, Venepally P, Fouts DE, Sutton G. LOCUST: a custom sequence locus typer for classifying microbial isolates. Bioinforma Oxf Engl. 2017;33: 1725–1726. pmid:28130240
  14. 14. Huang B, Zhao D, Fang N-X, Hall A, Eglezos S, Blair B. An optimized binary typing panel improves the typing capability for Campylobacter jejuni. Diagn Microbiol Infect Dis. 2013;77: 312–315. pmid:24139878
  15. 15. Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25: 1043–1055. pmid:25977477
  16. 16. Karp RM. Reducibility among Combinatorial Problems. Complexity of Computer Computations. Springer, Boston, MA; 1972. pp. 85–103.
  17. 17. Leeuwen J van. Algorithms and complexity. Amsterdam [u.a.: Elsevier [u.a.; 1998.
  18. 18. Parallel Randomized Heuristics For The Set Covering Problem—Semantic Scholar [Internet]. [cited 17 Sep 2017]. Available: /paper/Parallel-Randomized-Heuristics-For-The-Set-Coverin-STELLA-CATALANO/daf62fd516301432a6b649a4a464e7ccec795b27
  19. 19. Chu PC, Beasley JE. A Genetic Algorithm for the Multidimensional Knapsack Problem. J Heuristics. 1998;4: 63–86.
  20. 20. An effective and simple heuristic for the set covering problem—Semantic Scholar [Internet]. [cited 17 Sep 2017]. Available: /paper/An-effective-and-simple-heuristic-for-the-set-cove-Lan-DePuy/2eea0face6ad1e1f47d5954c023e99a2a68fa33c
  21. 21. Caprara A, Fischetti M, Toth P. A Heuristic Method for the Set Covering Problem. Oper Res. 1999;47: 730–743.
  22. 22. Cormode G, Karloff H, Wirth A. Set Cover Algorithms for Very Large Datasets. Proceedings of the 19th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM; 2010. pp. 479–488. 10.1145/1871437.1871501
  23. 23. Kordalewski D. New Greedy Heuristics For Set Cover and Set Packing. ArXiv13053584 Cs. 2013; Available:
  24. 24. Umetani S, Yagiura M. RELAXATION HEURISTICS FOR THE SET COVERING PROBLEM. J Oper Res Soc Jpn. 2007;50: 350–375.
  25. 25. Spasovski S, Madevska-Bogdanova A. Optimization of the Polynomial Greedy Solution for the Set Covering Problem. Proceedings of the Tenth Conference on Informatics and Information Technology. Skopje, Macedonia: Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University in Skopje, Macedonia; 2013. pp. 175–177. Available:
  26. 26. Goldschmidt O, Hochbaum DS, Yu G. A Modified Greedy Heuristic for the Set Covering Problem with Improved Worst Case Bound. Inf Process Lett. 1993;48: 305–310.
  27. 27. Hassin R, Levin A. A Better-Than-Greedy Approximation Algorithm for the Minimum Set Cover Problem. SIAM J Comput. 2005;35: 189–200.
  28. 28. Chvatal V. A Greedy Heuristic for the Set-Covering Problem. Math Oper Res. 1979;4: 233–235.
  29. 29. Lovász L. On the ratio of optimal integral and fractional covers. Discrete Math. 1975;13: 383–390.
  30. 30. Johnson DS. Approximation algorithms for combinatorial problems. J Comput Syst Sci. 1974;9: 256–278.
  31. 31. Stein SK. Two combinatorial covering theorems. J Comb Theory Ser A. 1974;16: 391–397.
  32. 32. Approximation Algorithms | Vijay V. Vazirani | Springer [Internet]. Available:
  33. 33. Dinur I, Steurer D. Analytical Approach to Parallel Repetition. Proceedings of the Forty-sixth Annual ACM Symposium on Theory of Computing. New York, NY, USA: ACM; 2014. pp. 624–633. 10.1145/2591796.2591884
  34. 34. Feige U. A Threshold of Ln N for Approximating Set Cover. J ACM. 1998;45: 634–652.
  35. 35. Khachiyan LG. Polynomial algorithms in linear programming. USSR Comput Math Math Phys. 1980;20: 53–72.
  36. 36. Peleg D, Schechtman G, Wool A. Randomized approximation of bounded multicovering problems. Algorithmica. 1997;18: 44–66.
  37. 37. Raghavan P. Probabilistic construction of deterministic algorithms: Approximating packing integer programs. 27th Annual Symposium on Foundations of Computer Science (sfcs 1986). 1986. pp. 10–18. 10.1109/SFCS.1986.45
  38. 38. Raghavan P, Tompson CD. Randomized rounding: A technique for provably good algorithms and algorithmic proofs. Combinatorica. 1987;7: 365–374.
  39. 39. Uchiyama I, Mihara M, Nishide H, Chiba H. MBGD update 2015: microbial genome database for flexible ortholog analysis utilizing a diverse set of genomic data. Nucleic Acids Res. 2015;43: D270–276. pmid:25398900
  40. 40. Uchiyama I. Hierarchical clustering algorithm for comprehensive orthologous-domain classification in multiple genomes. Nucleic Acids Res. 2006;34: 647–658. pmid:16436801
  41. 41. Uchiyama I, Mihara M, Nishide H, Chiba H. MBGD update 2013: the microbial genome database for exploring the diversity of microbial world. Nucleic Acids Res. 2013;41: D631–635. pmid:23118485
  42. 42. Rosenberg E, DeLong EF, Stackebrandt E, Lory S, Thompson F, editors. The Prokaryotes: Prokaryotic Biology and Symbiotic Associations [Internet]. 4th ed. Berlin Heidelberg: Springer-Verlag; 2013. Available: //
  43. 43. Ausiello G, D’Atri A, Protasi M. Structure preserving reductions among convex optimization problems. J Comput Syst Sci. 1980;21: 136–153.
  44. 44. HOW GOOD IS THE SIMPLEX ALGORITHM. Defense Technical Information Center; 1970.
  45. 45. Zumft WG. Cell biology and molecular basis of denitrification. Microbiol Mol Biol Rev. 1997;61: 533–616. pmid:9409151
  46. 46. Murphy MJ, Siegel LM, Tove SR, Kamin H. Siroheme: A New Prosthetic Group Participating in Six-Electron Reduction Reactions Catalyzed by Both Sulfite and Nitrite Reductases. Proc Natl Acad Sci U S A. 1974;71: 612–616. pmid:4595566
  47. 47. Cantera JJL, Stein LY. Role of nitrite reductase in the ammonia-oxidizing pathway of Nitrosomonas europaea. Arch Microbiol. 2007;188: 349–354. pmid:17541778
  48. 48. Pasternak Z, Pietrokovski S, Rotem O, Gophna U, Lurie-Weinberger MN, Jurkevitch E. By their genes ye shall know them: genomic signatures of predatory bacteria. ISME J. 2013;7: 756–769. pmid:23190728
  49. 49. Torres M, Balada J-M, Zellars M, Squires C, Squires CL. In vivo effect of NusB and NusG on rRNA transcription antitermination. J Bacteriol. 2004;186: 1304–1310. pmid:14973028
  50. 50. Yakhnin AV, Babitzke P. NusG/Spt5: are there common functions of this ubiquitous transcription elongation factor? Curr Opin Microbiol. 2014;18: 68–71. pmid:24632072
  51. 51. Robins-Browne RM. Traditional Enteropathogenic Escherichia coli of Infantile Diarrhea. Rev Infect Dis. 1987;9: 28–53. pmid:3547577
  52. 52. Robins-Browne RM, Holt KE, Ingle DJ, Hocking DM, Yang J, Tauschek M. Are Escherichia coli Pathotypes Still Relevant in the Era of Whole-Genome Sequencing? Front Cell Infect Microbiol. 2016;6. pmid:27917373
  53. 53. Alhagamhmad MH, Day AS, Lemberg DA, Leach ST. An overview of the bacterial contribution to Crohn disease pathogenesis. J Med Microbiol. 2016;65: 1049–1059. pmid:27501828
  54. 54. Brzuszkiewicz E, Thürmer A, Schuldes J, Leimbach A, Liesegang H, Meyer F-D, et al. Genome sequence analyses of two isolates from the recent Escherichia coli outbreak in Germany reveal the emergence of a new pathotype: Entero-Aggregative-Haemorrhagic Escherichia coli (EAHEC). Arch Microbiol. 2011;193: 883–891. pmid:21713444