Essential Genes

While new data has shown that the nucleus of a cell is more the cell’s gonads than brains, nuclei are still essential for many functions, if only because there’s an intricate interaction between the cell’s membrane and its many receptors (which can be characterized as the cell’s brain) in relation to the nucleus and its genome.

In this framework, essential genes are those genes of an organism that are thought to be critical for its survival. However, being essential is highly dependent on the circumstances in which an organism lives. For instance, a gene whose proteins digest starch is only essential if starch is the only source of energy. Recently, systematic attempts have been made to identify those genes that are absolutely required to maintain life, provided that all nutrients are available.[1] Such experiments have led to the conclusion that the required number of genes for bacteria is on the order of about 250–300. In humans, it’s a little more (See below). These essential genes encode proteins to maintain a central metabolism, replicate DNA, translate genes into proteins, maintain a basic cellular structure, and mediate transport processes into and out of the cell. Most genes are not essential but convey selective advantages and increased fitness.


Two main strategies have been employed to identify essential genes on a genome-wide basis: directed deletion of genes and random mutagenesis using transposons. In the first case, individual genes (or ORFs) are completely deleted from the genome in a systematic way. In transposon-mediated mutagenesis transposons are randomly inserted in as many positions in a genome as possible, aiming to inactivate the targeted genes (see figure below). Insertion mutants that are still able to survive or grow are not in essential genes. A summary of such screens is shown in the table.[1][2]

Organism Mutagenesis Method Readout ORFs Non-ess. Essential % Ess. Notes Ref.
Mycoplasma genitalium/pneumoniae Random Population Sequencing 482 130 265–350 55–73% [3]
Mycoplasma genitalium Random Clones Sequencing 482 100 382 79% b,c [4]
Staphylococcus aureus WCUH29 Random Clones Sequencing 2,600 n/a 168 n/a b,c [5]
Staphylococcus aureus RN4220 Random Clones Sequencing 2,892 n/a 658 23% [6]
Haemophilus influenzae Rd Random Population Footprint-PCR 1,657 602 670 40% [7]
Streptococcus pneumoniae Rx-1 Targeted Clones Colony formation 2,043 234 113 n/a c [8]
Streptococcus pneumoniae D39 Targeted Clones Colony formation 2,043 560 133 n/a c [9]
Streptococcus pyogenes 5448 Random Transposon Tn-seq 1,865 ? 227 12% [10]
Streptococcus pyogenes NZ131 Random Transposon Tn-seq 1,700 ? 241 14% [10]
Streptococcus sanguinis SK36 Targeted Clones Colony formation 2,270 2,052 218 10% a [11][12]
Mycobacterium tuberculosis H37Rv Random Population Microarray 3,989 2,567 614 15% [13]
Mycobacterium tuberculosis Random Transposon ? 3,989 ? 401 10% [14]
Mycobacterium tuberculosis H37Rv Random Transposon NG-Sequencing 3,989 ? 774 19% [15][16]
Mycobacterium tuberculosis H37Rv Random Transposon NG-Sequencing 3,989 3,364 625 16% h,i [17]
Mycobacterium tuberculosis Computational Computational 3,989 ? 283 7% [18]
Bacillus subtilis 168 Targeted Clones Colony formation 4,105 3,830 261 7% a,d,g [19][20]
Escherichia coli K-12 MG1655 Random Population Footprint-PCR 4,308 3,126 620 14% [21]
Escherichia coli K-12 MG1655 Targeted Clones Colony formation 4,308 2,001 n/a n/a a,e [22]
Escherichia coli K-12 BW25113 Targeted Clones Colony formation 4,390 3,985 303 7% a [23]
Pseudomonas aeruginosa PAO1 Random Clones Sequencing 5,570 4,783 678 12% a [24]
Porphyromonas gingivalis Random Transposon Sequencing 1,990 1,527 463 23% [25]
Pseudomonas aeruginosa PA14 Random Clones Sequencing 5,688 4,469 335 6% a,f [26]
Salmonella typhimurium Random Clones Sequencing 4,425 n/a 257 ~11% b,c [27]
Helicobacter pylori G27 Random Population Microarray 1,576 1,178 344 22% [28]
Campylobacter jejuni Random Population Microarray 1,654 ? 195 12% [29][30]
Corynebacterium glutamicum Random Population ? 3,002 2,352 650 22% [31]
Francisella novicida Random Transposon ? 1,719 1,327 392 23% [32]
Mycoplasma pulmonis UAB CTIP Random Transposon ? 782 472 310 40% [33]
Vibrio cholerae N16961 Random Transposon ? 3,890 ? 779 20% [34]
Salmonella Typhi Random Transposon ? 4,646 ? 353 8% [35]
Staphylococcus aureus Random Transposon ? ~2,600 ? 351 14% [36]
Caulobacter crescentus Random Transposon Tn-Seq 3,876 3,240 480 12.2% [37]
Neisseria meningitidis Random Transposon ? 2,158 ? 585 27% [38]
Desulfovibrio alaskensis Random Transposon Sequencing 3,258 2,871 387 12% [39]

Table 1. Essential genes in bacteria. Mutagenesis: targeted mutants are gene deletions; random mutants are transposon insertions. Methods: Clones indicate single gene deletions, population indicates whole population mutagenesis, e.g. using transposons. Essential genes from population screens include genes essential for fitness (see text). ORFs: number of all open reading frames in that genome. Notes: (a) mutant collection available; (b) direct essentiality screening method (e.g. via antisense RNA) that does not provide information about nonessential genes. (c) Only partial dataset available. (d) Includes predicted gene essentiality and data compilation from published single-gene essentiality studies. (e) Project in progress. (f) Deduced by comparison of the two gene essentiality datasets obtained independently in the P. aeruginosa strains PA14 and PAO1. (g) The original result of 271 essential genes has been corrected to 261, with 31 genes that were thought to be essential being in fact non-essential whereas 20 novel essential genes have been described since then.[20] (h) Counting genes with essential domains and those that lead to growth-defects when disrupted as essential, and those who lead to growth-advantage when disrupted as non-essential. (i) Involved a fully saturated mutant library of 14 replicates, with 84.3% of possible insertion sites with at least one transposon insertion.

Essential genes in Mycobacterium tuberculosis H37Rv as found by using transposons which insert in random positions in the genome. If no transposons are found in a gene, the gene is most likely essential as it cannot tolerate any insertion. In this example, essential heme biosynthetic genes hemA, hemB, hemC, hemD are devoid of insertions. The number of sequence reads (‘‘reads/TA’’) is shown for the indicated region of the H37Rv chromosome. Potential TA dinucleotide insertions sites are indicated. Image from Griffin et al. 2011.[15]

In Saccharomyces cerevisiae (budding yeast) 15-20% of all genes are essential. In Schizosaccharomyces pombe (fission yeast) 4,836 heterozygous deletions covering 98.4% of the 4,914 protein coding open reading frames have been constructed. 1,260 of these deletions turned out to be essential.[40]

Similar screens are more difficult to carry out in other multicellular organisms, including mammals (as a model for humans), due to technical reasons, and their results are less clear. However, various methods have been developed for the nematode worm C. elegans,[41] the fruit fly,[42] and zebrafish[43] (see table). A recent study of 900 mouse genes concluded that 42% of them were essential although the selected genes were not representative.[44]

Gene knockout experiments are not possible or at least not ethical in humans. However, natural mutations have led to the identification of mutations that lead to early embryonic or later death.[45] Note that many genes in humans are not absolutely essential for survival but can cause severe disease when mutated. Such mutations are catalogued in the Online Mendelian Inheritance in Man (OMIM) database. In a computational analysis of genetic variation and mutations in 2,472 human orthologs of known essential genes in the mouse, Georgi et al. found strong, purifying selection and comparatively reduced levels of sequence variation, indicating that these human genes are essential too.[46]

While it may be difficult to prove that a gene is essential in humans, it can be demonstrated that a gene is not essential or not even causing disease. For instance, sequencing the genomes of 2,636 Icelandic citizens and the genotyping of 101,584 additional subjects found 8,041 individuals who had 1 gene completely knocked out (i.e. these people were homozygous for a non-functional gene).[47] Of the 8,041 individuals with complete knock-outs, 6,885 were estimated to be homozygotes, 1,249 were estimated to be compound heterozygotes (i.e. they had both alleles of a gene knocked out but the two alleles had different mutations). In these individuals, a total of 1,171 of the 19,135 human (RefSeq) genes (6.1%) were completely knocked out. It was concluded that these 1,171 genes are non-essential in humans — at least no associated diseases were reported.[47] Similarly, the exome sequences of 3222 British Pakistani-heritage adults with high parental relatedness revealed 1111 rare-variant homozygous genotypes with predicted loss of gene function (LOF = knockouts) in 781 genes.[48] This study found an average of 140 predicted LOF genotypes (per subject), including 16 rare (minor allele frequency <1%) heterozygotes, 0.34 rare homozygotes, 83.2 common heterozygotes and 40.6 common homozygotes. Nearly all rare homozygous LOF genotypes were found within autozygous segments (94.9%).[48] Even though most of these individuals had no obvious health issue arising from their defective genes, it is possible that minor health issues may be found upon more detailed examination.

A summary of essentiality screens is shown in the table below (mostly based on the Database of Essential Genes.[49]

Organism Method Essential genes Ref.
Arabidopsis thaliana T-DNA insertion 777 [50]
Caenorhabditis elegans (worm) RNA interference 294 [41]
Danio rerio (zebrafish) Insertion mutagenesis 288 [43]
Drosophila melanogaster (fruit fly) P-element insertion mutagenesis 339 [42]
Homo sapiens (human) Literature search 118 [45]
Homo sapiens (human) CRISPR/Cas9-based screen 1,878 [51]
Homo sapiens (human) Haploid gene-trap screen ~2,000 [52]
Homo sapiens (human) mouse orthologs 2,472 [53]
Mus musculus (mouse) Literature search 2114 [54]
Saccharomyces cerevisiae (yeast) Single-gene deletions 878 [55]
Saccharomyces cerevisiae (yeast) Single-gene deletions 1,105 [56]
Schizosaccharomyces pombe (yeast) Single-gene deletions 1,260 [40]

Screens for essential genes have been carried out in a few viruses. For instance, human cytomegalovirus (CMV) was found to have 41 essential, 88 nonessential, and 27 augmenting ORFs (150 total ORFs). Most essential and augmenting genes are located in the central region, and nonessential genes generally cluster near the ends of the viral genome.[57]

Tscharke and Dobson (2015) compiled a comprehensive survey of essential genes in Vaccinia Virus and assigned roles to each of the 223 ORFs of the Western Reserve (WR) strain and 207 ORFs of the Copenhagen strain, assessing their role in replication in cell culture. According to their definition, a gene is considered essential (i.e. has a role in cell culture) if its deletion results in a decrease in virus titre of greater than 10-fold in either a single or multiple step growth curve. All genes involved in wrapped virion production, actin tail formation, and extracellular virion release were also considered as essential. Genes that influence plaque size, but not replication were defined as non-essential. By this definition 93 genes are required for Vaccinia Virus replication in cell culture, while 108 and 94 ORFs, from WR and Copenhagen respectively, are non-essential.[58] Vaccinia viruses with deletions at either end of the genome behaved as expected, exhibiting only mild or host range defects. In contrast, combining deletions at both ends of the genome for VACV strain WR caused a devastating growth defect on all cell lines tested. This demonstrates that single gene deletions are not sufficient to assess the essentiality of genes and that more genes are essential in Vaccinia virus than originally thought.[58]

One of the bacteriophages screened for essential genes includes mycobacteriophage Giles. At least 35 of the 78 predicted Giles genes (45%) are non-essential for lytic growth. 20 genes were found to be essential.[59] A major problem with phage genes is that a majority of their genes remain functionally unknown, hence their role is difficult to assess. A screen of Salmonella enterica phage SPN3US revealed 13 essential genes although it remains a bit obscure how many genes were really tested.[60]

Most genes are neither absolutely essential nor absolutely non-essential. Ideally their contribution to cell or organismal growth needs to be measured quantitatively, e.g. by determining how much growth rate is reduced in a mutant compared to “wild-type” (which may have been chosen arbitrarily from a population). For instance, a particular gene deletion may reduce growth rate (or fertility rate or other characters) to 90% of the wild-type.

 Synthetic lethality

Two genes are synthetic lethal if neither one is essential but when both are mutated the double-mutant is lethal. Some studies have estimated that the number of synthetic lethal genes may be on the order of 45% of all genes.[61][62]

A schematic view of essential genes (or proteins) in lysine biosynthesis of different bacteria. The same protein may be essential in one species but not another.

Many genes are essential only under certain circumstances. For instance, if the amino acid lysine is supplied to a cell any gene that is required to make lysine is non-essential. However, when there is no lysine supplied, genes encoding enzymes for lysine biosynthesis become essential, as no protein synthesis is possible without lysine.[2]

Streptococcus pneumoniae appears to require 147 genes for growth and survival in saliva,[63] more than the 113-133 that have been found in previous studies.

The deletion of a gene may result in death or in a block of cell division. While the latter case may implicate “survival” for some time, without cell division the cell may still die eventually. Similarly, instead of blocked cell division a cell may have reduced growth or metabolism ranging from nearly undetectable to almost normal. Thus, there is gradient from “essential” to completely non-essential, again depending on the condition. Some authors have thus distinguished between genes “essential for survival” and “essential for fitness”.[2]

The role of genetic background. Similar to environmental conditions, the genetic background can determine the essentiality of a gene: a gene may be essential in one individual but not another, given his or her genetic background. Gene duplications are one possible explanation (see below).

Metabolic dependency. Genes involved in certain biosynthetic pathways, such as amino acid synthesis, can become non-essential if one or more amino acids are supplied by another organism.[64] This is the main reason why many parasitic or endosymbiontic bacteria lost many genes (e.g. Chlamydia). Such genes may be essential but only present in the host organism. For instance, Chlamydia trachomatis cannot synthesize purine and pyrimidine nucleotides de novo, so these bacteria are dependent on the nucleotide biosynthetic genes of the host.[65]

Many genes are duplicated within a genome. Such duplications (paralogs) often render essential genes non-essential because the duplicate can replace the original copy. For instance, the gene encoding the enzyme aspartokinase is essential in E. coli. By contrast, the Bacillus subtilis genome contains three copies of this gene, none of which is essential on its own. However, a triple-deletion of all three genes is lethal. In such cases, the essentiality of a gene or a group of paralogs can often be predicted based on the essentiality of an essential single gene in a different species. In yeast, few of the essential genes are duplicated within the genome: 8.5% of the non-essential genes, but only 1% of the essential genes have a homologue in the yeast genome.[56]

In the worm C. elegans, non-essential genes are highly over-represented among duplicates, possibly because duplication of essential genes causes overexpression of these genes. Woods et al. found that non-essential genes are more often successfully duplicated (fixed) and lost compared to essential genes. By contrast, essential genes are less often duplicated but upon successful duplication are maintained over longer periods.[66]

Conservation of essential genes in bacteria, adapted from [67]

In bacteria, essential genes appear to be more conserved than nonessential genes [68] but the correlation is not very strong. For instance, only 34% of the B. subtilis essential genes have reliable orthologs in all Firmicutes and 61% of the E. coli essential genes have reliable orthologs in all Gamma-proteobacteria.[67] Fang et al. (2005) defined persistent genes as the genes present in more than 85% of the genomes of the clade.[67] They found 475 and 611 of such genes for B. subtilisand E. coli, respectively. Furthermore, they classified genes into five classes according to persistence and essentiality: persistent genes, essential genes, persistent nonessential (PNE) genes (276 in B. subtilis, 409 in E. coli), essential nonpersistent (ENP) genes (73 in B. subtilis, 33 in E. coli), and nonpersistent nonessential (NPNE) genes (3,558 in B. subtilis, 3,525 in E. coli). Fang et al. found 257 persistent genes, which exist both in B. subtilis (for the Firmicutes) and E. coli (for the Gamma-proteobacteria). Among these, 144 (respectively 139) were previously identified as essential in B. subtilis (respectively E. coli) and 25 (respectively 18) of the 257 genes are not present in the 475 B. subtilis (respectively 611 E. coli) persistent genes. All the other members of the pool are PNE genes.[67]

In eukaryotes, 83% of the one-to-one orthologs between Schizosaccharomyces pombe and Saccharomyces cerevisiae have conserved essentiality, that is, they are nonessential in both species or essential in both species. The remaining 17% of genes are nonessential in one species and essential in the other.[69] This is quite remarkable, given that S. pombe is separated from S. cerevisiae by approximately 400 million years of evolution.[70]

In general, highly conserved and thus older genes (i.e. genes with earlier phylogenetic origin) are more likely to be essential than younger genes – even if they have been duplicated.[71]

The experimental study of essential genes is limited by the fact that, by definition, inactivation of an essential gene is lethal to the organism. Therefore they cannot be simply deleted or mutated to analyze the resulting phenotypes (a common technique in genetics).

There are, however, some circumstances in which essential genes can be manipulated. In diploid organisms, only a single functional copy of some essential genes may be needed (haplosufficiency), with the heterozygote displaying an instructive phenotype. Some essential genes can tolerate mutations that are deleterious, but not wholly lethal, since they do not completely abolish the gene’s function.

Computational analysis can reveal many properties of proteins without analyzing them experimentally, e.g. by looking at homologous proteins, function, structure etc. (see also below, Predicting essential genes). The products of essential genes can also be studied when expressed in other organisms, or when purified and studied in vitro.

Conditionally essential genes are easier to study. Temperature-sensitive variants of essential genes have been identified which encode products that lose function at high temperatures, and so only show a phenotype at increased temperature.[72]

If screens for essential genes are repeated in independent laboratories, they often result in different gene lists. For instance, screens in E. coli have yielded from ~300 to ~600 essential genes (see Table 1). Such differences are even more pronounced when different bacterial strains are used (see Figure 2). A common explanation is that the experimental conditions are different or that the nature of the mutation may be different (e.g. a complete gene deletion vs. a transposon mutant).[2] Transposon screens in particular are hard to reproduce, given that a transposon can insert at many positions within a gene. Insertions towards the 3′ end of an essential gene may not have a lethal phenotype (or no phenotype at all) and thus may not be recognized as such. This can lead to erroneous annotations (here: false negatives).[73]

Comparison of CRISPR/cas9 and RNAi screens. Screens to identify essential genes in the human chronic myelogenous leukemia cell line K562 with these two methods showed only limited overlap. At a 10% false positive rate there were ~4,500 genes identified in the Cas9 screen versus ~3,100 in the shRNA screen, with only ~1,200 genes identified in both.[74]

Different organisms have different essential genes. For instance, Bacillus subtilis has 271 essential genes.[19] About one-half (150) of the orthologous genes in E. coli are also essential. Another 67 genes that are essential in E. coli are not essential in B. subtilis, while 86 E. coli essential genes have no B. subtilis ortholog.[23]

In Mycoplasma genitalium at least 18 genes are essential that are not essential in M. bovis.[75]

Essential genes can be predicted computationally. However, most methods use experimental data (“training sets”) to some extent. Chen et al.[76] determined four criteria to select training sets for such predictions: (1) essential genes in the selected training set should be reliable; (2) the growth conditions in which essential genes are defined should be consistent in training and prediction sets; (3) species used as training set should be closely related to the target organism; and (4) organisms used as training and prediction sets should exhibit similar phenotypes or lifestyles. They also found that the size of the training set should be at least 10% of the total genes to yield accurate predictions. Some approaches for predicting essential genes are:

Comparative genomics. Shortly after the first genomes (of Haemophilus influenzae and Mycoplasma genitalium) became available, Mushegian et al.[77] tried to predict the number of essential genes based on common genes in these two species. It was surmised that only essential genes should be conserved over the long evolutionary distance that separated the two bacteria. This study identified approximately 250 candidate essential genes.[77] As more genomes became available the number of predicted essential genes kept shrinking because more genomes shared fewer and fewer genes. As a consequence, it was concluded that the universal conserved core consists of less than 40 genes.[78][79] However, this set of conserved genes is not identical to the set of essential genes as different species rely on different essential genes.

A similar approach has been used to infer essential genes from the pan-genome of Brucella species. 42 complete Brucella genomes and a total of 132,143 protein-coding genes were used to predict 1252 potential essential genes, derived from the core genome by comparison with a prokaryote database of essential genes.[80]

Hua et al. used Machine Learning to predict essential genes in 25 bacterial species.[81]

Hurst index. Liu et al. (2015)[82] used the Hurst exponent, a characteristic parameter to describe long-range correlation in DNA to predict essential genes. In 31 out of 33 bacterial genomes the significance levels of the Hurst exponents of the essential genes were significantly higher than for the corresponding full-gene-set, whereas the significance levels of the Hurst exponents of the nonessential genes remained unchanged or increased only slightly.

Minimal genomes. It was also thought that essential genes could be inferred from minimal genomes which supposedly contain only essential genes. The problem here is that the smallest genomes belong to parasitic (or symbiontic) species which can survive with a reduced gene set as they obtain many nutrients from their hosts. For instance, one of the smallest genomes is that of Hodgkinia cicadicola, a symbiont of cicadas, containing only 144 Kb of DNA encoding only 188 genes.[83] Like other symbionts, Hodgkinia receives many of its nutrients from its host, so its genes do not need to be essential.

Metabolic modelling. Essential genes may be also predicted in completely sequenced genomes by metabolic reconstruction, that is, by reconstructing the complete metabolism from the gene content and then identifying those genes and pathways that have been found to be essential in other species. However, this method can be compromised by proteins of unknown function. In addition, many organisms have backup or alternative pathways which have to be taken into account (see figure 1). Metabolic modeling was also used by Basler (2015) to develop a method to predict essential metabolic genes.[84] Flux balance analysis, a method of metabolic modeling, has recently been used to predict essential genes in clear cell renal cell carcinoma metabolism.[85]

Genes of unknown function. Surprisingly, a significant number of essential genes has no known function. For instance, among the 385 essential candidates in M. genitalium, no function could be ascribed to 95 genes[4] even though this number had been reduced to 75 by 2011.[79]

ZUPLS. Song et al. presented a novel method to predict essential genes that only uses the Z-curve and other sequence-based features.[86] Such features can be calculated readily from the DNA/amino acid sequences. However, the reliability of this method remains a bit obscure.

Essential gene prediction servers. Guo et al. (2015) have developed three online services to predict essential genes in bacterial genomes. These freely available tools are applicable for single gene sequences without annotated functions, single genes with definite names, and complete genomes of bacterial strains.[87]

Although most essential genes encode proteins, many essential proteins consist of a single domain. This fact has been used to identify essential protein domains. Goodacre et al. have identified hundreds of essential domains of unknown function (eDUFs).[88] Lu et al.[89] presented a similar approach and identified 3,450 domains that are essential in at least one microbial species.

Text under construction

  1. Zhang, R.; Lin, Y. (2009). “DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes”. Nucleic Acids Research. 37 (Database issue): D455–D458. doi:10.1093/nar/gkn858. PMC 2686491. PMID 18974178.
  2.  Gerdes, S.; Edwards, R.; Kubal, M.; Fonstein, M.; Stevens, R.; Osterman, A. (2006). “Essential genes on metabolic maps”. Current Opinion in Biotechnology. 17 (5): 448–456. doi:10.1016/j.copbio.2006.08.006. PMID 16978855.
  3. ^ Hutchison, C. A.; Peterson, S. N.; Gill, S. R.; Cline, R. T.; White, O.; Fraser, C. M.; Smith, H. O.; Venter, J. C. (1999). “Global transposon mutagenesis and a minimal Mycoplasma genome”. Science. 286 (5447): 2165–2169. doi:10.1126/science.286.5447.2165. PMID 10591650.
  4.  Glass, J. I.; Assad-Garcia, N.; Alperovich, N.; Yooseph, S.; Lewis, M. R.; Maruf, M.; Hutchison III, C. A.; Smith, H. O.; Venter, J. C. (2006). “Essential genes of a minimal bacterium”. Proceedings of the National Academy of Sciences. 103(2): 425–430. Bibcode:2006PNAS..103..425G. doi:10.1073/pnas.0510013103. PMC 1324956. PMID 16407165.
  5. ^ Ji, Y.; Zhang, B.; Van, S. F.; Warren, Patrick; Warren, P.; Woodnutt, G.; Burnham, M. K.; Rosenberg, M. (2001). “Identification of Critical Staphylococcal Genes Using Conditional Phenotypes Generated by Antisense RNA”. Science. 293 (5538): 2266–2269. Bibcode:2001Sci…293.2266J. doi:10.1126/science.1063566. PMID 11567142.
  6. ^ Forsyth, R. A.; Haselbeck, R. J.; Ohlsen, K. L.; Yamamoto, R. T.; Xu, H.; Trawick, J. D.; Wall, D.; Wang, L.; Brown-Driver, V.; Froelich, J. M.; c, K. G.; King, P.; McCarthy, M.; Malone, C.; Misiner, B.; Robbins, D.; Tan, Z.; Zhu Zy, Z. Y.; Carr, G.; Mosca, D. A.; Zamudio, C.; Foulkes, J. G.; Zyskind, J. W. (2002). “A genome-wide strategy for the identification of essential genes in Staphylococcus aureus”. Molecular Microbiology. 43 (6): 1387–1400. doi:10.1046/j.1365-2958.2002.02832.x. PMID 11952893.
  7. ^ Akerley, B. J.; Rubin, E. J.; Novick, V. L.; Amaya, K.; Judson, N.; Mekalanos, J. J. (2002). “A genome-scale analysis for identification of genes required for growth or survival of Haemophilusinfluenzae”. Proceedings of the National Academy of Sciences. 99 (2): 966–971. Bibcode:2002PNAS…99..966A. doi:10.1073/pnas.012602299. PMC 117414. PMID 11805338.
  8. ^ Thanassi, J. A.; Hartman-Neumann, S. L.; Dougherty, T. J.; Dougherty, B. A.; Pucci, M. J. (2002). “Identification of 113 conserved essential genes using a high-throughput gene disruption system in Streptococcus pneumoniae”. Nucleic Acids Research. 30 (14): 3152–3162. doi:10.1093/nar/gkf418. PMC 135739. PMID 12136097.
  9. ^ Song, J. H.; Ko, K. S.; Lee, J. Y.; Baek, J. Y.; Oh, W. S.; Yoon, H. S.; Jeong, J. Y.; Chun, J. (2005). “Identification of essential genes in Streptococcus pneumoniae by allelic replacement mutagenesis”. Molecules and Cells. 19 (3): 365–374. PMID 15995353.
  10. ^ Jump up to: a b Le Breton, Y; Belew, A. T.; Valdes, K. M.; Islam, E; Curry, P; Tettelin, H; Shirtliff, M. E.; El-Sayed, N. M.; McIver, K. S. (2015). “Essential Genes in the Core Genome of the Human Pathogen Streptococcus pyogenes”. Scientific Reports. 5: 9838. Bibcode:2015NatSR…5E9838L. doi:10.1038/srep09838. PMC 4440532. PMID 25996237.
  11. ^ Xu, P; Ge, X; Chen, L; Wang, X; Dou, Y; Xu, J. Z.; Patel, J. R.; Stone, V; Trinh, M; Evans, K; Kitten, T; Bonchev, D; Buck, G. A. (2011). “Genome-wide essential gene identification in Streptococcus sanguinis”. Scientific Reports. 1: 125. Bibcode:2011NatSR…1E.125X. doi:10.1038/srep00125. PMC 3216606. PMID 22355642.
  12. ^ Chen, L; Ge, X; Xu, P (2015). “Identifying Essential Streptococcus sanguinis Genes Using Genome-Wide Deletion Mutation”. Gene Essentiality. Methods in Molecular Biology. 1279. pp. 15–23. doi:10.1007/978-1-4939-2398-4_2. ISBN 978-1-4939-2397-7. PMC 4819415. PMID 25636610.
  13. ^ Sassetti, C. M.; Boyd, D. H.; Rubin, E. J. (2001). “Comprehensive identification of conditionally essential genes in mycobacteria”. Proceedings of the National Academy of Sciences. 98 (22): 12712–12717. Bibcode:2001PNAS…9812712S. doi:10.1073/pnas.231275498. PMC 60119. PMID 11606763.
  14. ^ Lamichhane, G.; Freundlich, J. S.; Ekins, S.; Wickramaratne, N.; Nolan, S. T.; Bishai, W. R. (2011). “Essential Metabolites of Mycobacterium tuberculosis and Their Mimics”. mBio. 2 (1): e00301–e00310. doi:10.1128/mBio.00301-10. PMC 3031304. PMID 21285434.
  15. ^ Jump up to: a b Griffin, J. E.; Gawronski, J. D.; Dejesus, M. A.; Ioerger, T. R.; Akerley, B. J.; Sassetti, C. M. (2011). “High-resolution phenotypic profiling defines genes essential for mycobacterial growth and cholesterol catabolism”. PLoS Pathogens. 7 (9): e1002251. doi:10.1371/journal.ppat.1002251. PMC 3182942. PMID 21980284.
  16. ^ Long, J. E.; Dejesus, M; Ward, D; Baker, R. E.; Ioerger, T; Sassetti, C. M. (2015). “Identifying Essential Genes in Mycobacterium tuberculosis by Global Phenotypic Profiling”. Gene Essentiality. Methods in Molecular Biology. 1279. pp. 79–95. doi:10.1007/978-1-4939-2398-4_6. ISBN 978-1-4939-2397-7. PMID 25636614.
  17. ^ DeJesus MA, Gerrick ER, Xu W, Park SW, Long JE, Boutte CC, Rubin EJ, Schnappinger D, Ehrt S, Fortune SM, Sassetti CM, Ioerger TR (2017). “Comprehensive Essentiality Analysis of the Mycobacterium tuberculosis Genome via Saturating Transposon Mutagenesis”. mBio. 8 (1): e02133–16. doi:10.1128/mBio.02133-16. PMC 5241402. PMID 28096490.
  18. ^ Ghosh, S; Baloni, P; Mukherjee, S; Anand, P; Chandra, N (2013). “A multi-level multi-scale approach to study essential genes in Mycobacterium tuberculosis”. BMC Systems Biology. 7: 132. doi:10.1186/1752-0509-7-132. PMC 4234997. PMID 24308365.
  19. ^ Jump up to: a b Kobayashi, K.; Ehrlich, S. D.; Albertini, A.; Amati, G.; Andersen, K. K.; Arnaud, M.; Asai, K.; Ashikaga, S.; Aymerich, S.; Bessieres, P.; Boland, F.; Brignell, S. C.; Bron, S.; Bunai, K.; Chapuis, J.; Christiansen, L. C.; Danchin, A.; Debarbouille, M.; Dervyn, E.; Deuerling, E.; Devine, K.; Devine, S. K.; Dreesen, O.; Errington, J.; Fillinger, S.; Foster, S. J.; Fujita, Y.; Galizzi, A.; Gardan, R.; Eschevins, C. (2003). “Essential Bacillus subtilis genes”. Proceedings of the National Academy of Sciences. 100 (8): 4678–4683. Bibcode:2003PNAS..100.4678K. doi:10.1073/pnas.0730515100. PMC 153615. PMID 12682299.
  20. ^ Jump up to: a b Commichau, F. M.; Pietack, N; Stülke, J (2013). “Essential genes in Bacillus subtilis: A re-evaluation after ten years”. Molecular BioSystems. 9 (6): 1068–75. doi:10.1039/c3mb25595f. PMID 23420519.
  21. ^ Gerdes, S. Y.; Scholle, M. D.; Campbell, J. W.; Balázsi, G.; Ravasz, E.; Daugherty, M. D.; Somera, A. L.; Kyrpides, N. C.; Anderson, I.; Gelfand, M. S.; Bhattacharya, A.; Kapatral, V.; d’Souza, M.; Baev, M. V.; Grechkin, Y.; Mseeh, F.; Fonstein, M. Y.; Overbeek, R.; Barabási, A. -L.; Oltvai, Z. N.; Osterman, A. L. (2003). “Experimental determination and system level analysis of essential genes in Escherichia coli MG1655”. Journal of Bacteriology. 185 (19): 5673–5684. doi:10.1128/JB.185.19.5673-5684.2003. PMC 193955. PMID 13129938.
  22. ^ Kang, Y.; Durfee, T.; Glasner, J. D.; Qiu, Y.; Frisch, D.; Winterberg, K. M.; Blattner, F. R. (2004). “Systematic Mutagenesis of the Escherichia coli Genome”. Journal of Bacteriology. 186 (15): 4921–4930. doi:10.1128/JB.186.15.4921-4930.2004. PMC 451658. PMID 15262929.
  23. ^ Jump up to: a b Baba, T.; Ara, T.; Hasegawa, M.; Takai, Y.; Okumura, Y.; Baba, M.; Datsenko, K. A.; Tomita, M.; Wanner, B. L.; Mori, H. (2006). “Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: The Keio collection”. Molecular Systems Biology. 2: 2006.0008. doi:10.1038/msb4100050. PMC 1681482. PMID 16738554.
  24. ^ Jacobs, M. A.; Alwood, A.; Thaipisuttikul, I.; Spencer, D.; Haugen, E.; Ernst, S.; Will, O.; Kaul, R.; Raymond, C.; Levy, R.; Chun-Rong, L.; Guenthner, D.; Bovee, D.; Olson, M. V.; Manoil, C. (2003). “Comprehensive transposon mutant library of Pseudomonas aeruginosa”. Proceedings of the National Academy of Sciences. 100 (24): 14339–14344. Bibcode:2003PNAS..10014339J. doi:10.1073/pnas.2036282100. PMC 283593. PMID 14617778.
  25. ^ Hutcherson JA, Gogeneni H, Yoder-Himes D, Hendrickson EL, Hackett M, Whiteley M, Lamont RJ, Scott DA (2015). “Comparison of inherently essential genes of Porphyromonas gingivalis identified in two transposon sequencing libraries”. Mol Oral Microbiol. 31 (4): 354–64. doi:10.1111/omi.12135. PMC 4788587. PMID 26358096.
  26. ^ Liberati, N. T.; Urbach, J. M.; Miyata, S.; Lee, D. G.; Drenkard, E.; Wu, G.; Villanueva, J.; Wei, T.; Ausubel, F. M. (2006). “An ordered, nonredundant library of Pseudomonas aeruginosa strain PA14 transposon insertion mutants”. Proceedings of the National Academy of Sciences. 103 (8): 2833–2838. Bibcode:2006PNAS..103.2833L. doi:10.1073/pnas.0511100103. PMC 1413827. PMID 16477005.
  27. ^ Knuth, K.; Niesalla, H.; Hueck, C. J.; Fuchs, T. M. (2004). “Large-scale identification of essential Salmonella genes by trapping lethal insertions”. Molecular Microbiology. 51 (6): 1729–1744. doi:10.1046/j.1365-2958.2003.03944.x. PMID 15009898.
  28. ^ Salama, N. R.; Shepherd, B.; Falkow, S. (2004). “Global Transposon Mutagenesis and Essential Gene Analysis of Helicobacter pylori”. Journal of Bacteriology. 186 (23): 7926–7935. doi:10.1128/JB.186.23.7926-7935.2004. PMC 529078. PMID 15547264.
  29. ^ Stahl, M; Stintzi, A (2011). “Identification of essential genes in C. Jejuni genome highlights hyper-variable plasticity regions”. Functional & Integrative Genomics. 11(2): 241–57. doi:10.1007/s10142-011-0214-7. PMID 21344305.
  30. ^ Stahl, M; Stintzi, A (2015). “Microarray Transposon Tracking for the Mapping of Conditionally Essential Genes in Campylobacter jejuni”. Gene Essentiality. Methods in Molecular Biology. 1279. pp. 1–14. doi:10.1007/978-1-4939-2398-4_1. ISBN 978-1-4939-2397-7. PMID 25636609.
  31. ^ Suzuki, N.; Inui, M.; Yukawa, H. (2011). “High-Throughput Transposon Mutagenesis of Corynebacterium glutamicum”. Strain Engineering. Methods in Molecular Biology. 765. pp. 409–417. doi:10.1007/978-1-61779-197-0_24. ISBN 978-1-61779-196-3. PMID 21815106.
  32. ^ Gallagher, L. A.; Ramage, E.; Jacobs, M. A.; Kaul, R.; Brittnacher, M.; Manoil, C. (2007). “A comprehensive transposon mutant library of Francisella novicida, a bioweapon surrogate”. Proceedings of the National Academy of Sciences. 104(3): 1009–1014. Bibcode:2007PNAS..104.1009G. doi:10.1073/pnas.0606713104. PMC 1783355. PMID 17215359.
  33. ^ French, C. T.; Lao, P.; Loraine, A. E.; Matthews, B. T.; Yu, H.; Dybvig, K. (2008). “Large-scale transposon mutagenesis ofMycoplasma pulmonis”. Molecular Microbiology. 69 (1): 67–76. doi:10.1111/j.1365-2958.2008.06262.x. PMC 2453687. PMID 18452587.
  34. ^ Cameron, D. E.; Urbach, J. M.; Mekalanos, J. J. (2008). “A defined transposon mutant library and its use in identifying motility genes in Vibrio cholerae”. Proceedings of the National Academy of Sciences. 105 (25): 8736–8741. Bibcode:2008PNAS..105.8736C. doi:10.1073/pnas.0803281105. PMC 2438431. PMID 18574146.
  35. ^ Langridge, G. C.; Phan, M. -D.; Turner, D. J.; Perkins, T. T.; Parts, L.; Haase, J.; Charles, I.; Maskell, D. J.; Peters, S. E.; Dougan, G.; Wain, J.; Parkhill, J.; Turner, A. K. (2009). “Simultaneous assay of every Salmonella Typhi gene using one million transposon mutants”. Genome Research. 19 (12): 2308–2316. doi:10.1101/gr.097097.109. PMC 2792183. PMID 19826075.
  36. ^ Chaudhuri, R. R.; Allen, A. G.; Owen, P. J.; Shalom, G.; Stone, K.; Harrison, M.; Burgis, T. A.; Lockyer, M.; Garcia-Lara, J.; Foster, S. J.; Pleasance, S. J.; Peters, S. E.; Maskell, D. J.; Charles, I. G. (2009). “Comprehensive identification of essential Staphylococcus aureus genes using Transposon-Mediated Differential Hybridisation (TMDH)”. BMC Genomics. 10: 291. doi:10.1186/1471-2164-10-291. PMC 2721850. PMID 19570206.
  37. ^ Christen, B.; Abeliuk, E.; Collier, J. M.; Kalogeraki, V. S.; Passarelli, B.; Coller, J. A.; Fero, M. J.; McAdams, H. H.; Shapiro, L. (2011). “The essential genome of a bacterium”. Molecular Systems Biology. 7: 528. doi:10.1038/msb.2011.58. PMC 3202797. PMID 21878915.
  38. ^ Mendum, T. A.; Newcombe, J.; Mannan, A. A.; Kierzek, A. M.; McFadden, J. (2011). “Interrogation of global mutagenesis data with a genome scale model of Neisseria meningitidis to assess gene fitness in vitro and in sera”. Genome Biology. 12 (12): R127. doi:10.1186/gb-2011-12-12-r127. PMC 3334622. PMID 22208880.
  39. ^ Kuehl, J. V.; Price, M. N.; Ray, J; Wetmore, K. M.; Esquivel, Z; Kazakov, A. E.; Nguyen, M; Kuehn, R; Davis, R. W.; Hazen, T. C.; Arkin, A. P.; Deutschbauer, A (2014). “Functional genomics with a comprehensive library of transposon mutants for the sulfate-reducing bacterium Desulfovibrio alaskensis G20”. mBio. 5 (3): e01041–14. doi:10.1128/mBio.01041-14. PMC 4045070. PMID 24865553.
  40. ^ Jump up to: a b Kim, D. U.; Hayles, J; Kim, D; Wood, V; Park, H. O.; Won, M; Yoo, H. S.; Duhig, T; Nam, M; Palmer, G; Han, S; Jeffery, L; Baek, S. T.; Lee, H; Shim, Y. S.; Lee, M; Kim, L; Heo, K. S.; Noh, E. J.; Lee, A. R.; Jang, Y. J.; Chung, K. S.; Choi, S. J.; Park, J. Y.; Park, Y; Kim, H. M.; Park, S. K.; Park, H. J.; Kang, E. J.; et al. (2010). “Analysis of a genome-wide set of gene deletions in the fission yeast Schizosaccharomyces pombe”. Nature Biotechnology. 28 (6): 617–23. doi:10.1038/nbt.1628. PMC 3962850. PMID 20473289.
  41. ^ Jump up to: a b Kamath, R.; Fraser, A.; Dong, Y.; Poulin, G.; Durbin, R.; Gotta, M.; Kanapin, A.; Le Bot, N.; Moreno, S.; Sohrmann, M.; Welchman, D. P.; Zipperlen, P.; Ahringer, J. (2003). “Systematic functional analysis of the Caenorhabditis elegans genome using RNAi”. Nature. 421 (6920): 231–237. Bibcode:2003Natur.421..231K. doi:10.1038/nature01278. hdl:10261/63159. PMID 12529635.
  42. ^ Jump up to: a b Spradling, A.; Stern, D.; Beaton, A.; Rhem, E.; Laverty, T.; Mozden, N.; Misra, S.; Rubin, G. (1999). “The Berkeley Drosophila Genome Project gene disruption project: Single P-element insertions mutating 25% of vital Drosophila genes”. Genetics. 153 (1): 135–177. PMC 1460730. PMID 10471706.
  43. ^ Jump up to: a b Amsterdam, A.; Nissen, R. M.; Sun, Z.; Swindell, E. C.; Farrington, S.; Hopkins, N. (2004). “INAUGURAL ARTICLE: Identification of 315 genes essential for early zebrafish development”. Proceedings of the National Academy of Sciences. 101 (35): 12792–12797. Bibcode:2004PNAS..10112792A. doi:10.1073/pnas.0403929101. PMC 516474. PMID 15256591.
  44. ^ White, J. K.; Gerdin, A. K.; Karp, N. A.; Ryder, E.; Buljan, M.; Bussell, J. N.; Salisbury, J.; Clare, S.; Ingham, N. J.; Podrini, C.; Houghton, R.; Estabel, J.; Bottomley, J. R.; Melvin, D. G.; Sunter, D.; Adams, N. C.; Sanger Institute Mouse Genetics Project; Tannahill, D.; Tannahill, D. W.; Logan, D. G.; MacArthur, J.; Flint, V. B.; Mahajan, S. H.; Tsang, I.; Smyth, F. M.; Watt, W. C.; Skarnes, G.; Dougan, D. J.; Adams, R.; Ramirez-Solis, A.; Bradley, K. P. (2013). “Genome-wide Generation and Systematic Phenotyping of Knockout Mice Reveals New Roles for Many Genes”. Cell. 154 (2): 452–464. doi:10.1016/j.cell.2013.06.022. PMC 3717207. PMID 23870131.
  45. ^ Jump up to: a b Liao, B. -Y.; Zhang, J. (2008). “Null mutations in human and mouse orthologs frequently result in different phenotypes”. Proceedings of the National Academy of Sciences. 105 (19): 6987–6992. Bibcode:2008PNAS..105.6987L. doi:10.1073/pnas.0800387105. PMC 2383943. PMID 18458337.
  46. ^ Georgi, B.; Voight, B. F.; Bućan, M. (2013). Flint, Jonathan, ed. “From Mouse to Human: Evolutionary Genomics Analysis of Human Orthologs of Essential Genes”. PLoS Genetics. 9 (5): e1003484. doi:10.1371/journal.pgen.1003484. PMC 3649967. PMID 23675308.
  47. ^ Jump up to: a b Sulem, P; Helgason, H; Oddson, A; Stefansson, H; Gudjonsson, S. A.; Zink, F; Hjartarson, E; Sigurdsson, G. T.; Jonasdottir, A; Jonasdottir, A; Sigurdsson, A; Magnusson, O. T.; Kong, A; Helgason, A; Holm, H; Thorsteinsdottir, U; Masson, G; Gudbjartsson, D. F.; Stefansson, K (2015). “Identification of a large set of rare complete human knockouts”. Nature Genetics. 47 (5): 448–52. doi:10.1038/ng.3243. PMID 25807282.
  48. ^ Jump up to: a b Narasimhan VM, Hunt KA, Mason D, Baker CL, Karczewski KJ, Barnes MR, Barnett AH, Bates C, Bellary S, Bockett NA, Giorda K, Griffiths CJ, Hemingway H, Jia Z, Kelly MA, Khawaja HA, Lek M, McCarthy S, McEachan R, O’Donnell-Luria A, Paigen K, Parisinos CA, Sheridan E, Southgate L, Tee L, Thomas M, Xue Y, Schnall-Levin M, Petkov PM, Tyler-Smith C, Maher ER, Trembath RC, MacArthur DG, Wright J, Durbin R, van Heel DA (2016). “Health and population effects of rare gene knockouts in adult humans with related parents”. Science. 352 (6284): 474–7. Bibcode:2016Sci…352..474N. doi:10.1126/science.aac8624. PMC 4985238. PMID 26940866.
  49. ^ Luo H, Lin Y, Gao F, Zhang CT, Zhang R (2014). “DEG 10, an update of the database of essential genes that includes both protein-coding genes and noncoding genomic elements”. Nucleic Acids Research. 42 (Database issue): D574–80. doi:10.1093/nar/gkt1131. PMC 3965060. PMID 24243843.
  50. ^ Tzafrir, I.; Pena-Muralla, R.; Dickerman, A.; Berg, M.; Rogers, R.; Hutchens, S.; Sweeney, T. C.; McElver, J.; Aux, G.; Patton, D.; Meinke, D. (2004). “Identification of Genes Required for Embryo Development in Arabidopsis”. Plant Physiology. 135 (3): 1206–1220. doi:10.1104/pp.104.045179. PMC 519041. PMID 15266054.
  51. ^ Wang T, Birsoy K, Hughes NW, Krupczak KM, Post Y, Wei JJ, Lander ES, Sabatini DM (2015). “Identification and characterization of essential genes in the human genome”. Science. 350 (6264): 1096–101. Bibcode:2015Sci…350.1096W. doi:10.1126/science.aac7041. PMC 4662922. PMID 26472758.
  52. ^ Blomen VA, Májek P, Jae LT, Bigenzahn JW, Nieuwenhuis J, Staring J, Sacco R, van Diemen FR, Olk N, Stukalov A, Marceau C, Janssen H, Carette JE, Bennett KL, Colinge J, Superti-Furga G, Brummelkamp TR (2015). “Gene essentiality and synthetic lethality in haploid human cells”. Science. 350 (6264): 1092–6. Bibcode:2015Sci…350.1092B. doi:10.1126/science.aac7557. PMID 26472760.
  53. ^ Georgi, B; Voight, BF; Bućan, M (May 2013). “From mouse to human: evolutionary genomics analysis of human orthologs of essential genes”. PLOS Genetics. 9 (5): e1003484. doi:10.1371/journal.pgen.1003484. PMC 3649967. PMID 23675308.
  54. ^ Liao, B. Y.; Zhang, J. (2007). “Mouse duplicate genes are as essential as singletons”. Trends in Genetics. 23 (8): 378–381. doi:10.1016/j.tig.2007.05.006. PMID 17559966.
  55. ^ Mewes, H. W.; Frishman, D.; Güldener, U.; Mannhaupt, G.; Mayer, K.; Mokrejs, M.; Morgenstern, B.; Münsterkötter, M.; Rudd, S.; Weil, B. (2002). “MIPS: A database for genomes and protein sequences”. Nucleic Acids Research. 30 (1): 31–34. doi:10.1093/nar/30.1.31. PMC 99165. PMID 11752246.
  56. ^ Jump up to: a b Giaever, G.; Chu, A. M.; Ni, L.; Connelly, C.; Riles, L.; Véronneau, S.; Dow, S.; Lucau-Danila, A.; Anderson, K.; André, B.; Arkin, A. P.; Astromoff, A.; El-Bakkoury, M.; Bangham, R.; Benito, R.; Brachat, S.; Campanaro, S.; Curtiss, M.; Davis, K.; Deutschbauer, A.; Entian, K. D.; Flaherty, P.; Foury, F.; Garfinkel, D. J.; Gerstein, M.; Gotte, D.; Güldener, U.; Hegemann, J. H.; Hempel, S.; Herman, Z. (2002). “Functional profiling of the Saccharomyces cerevisiae genome”. Nature. 418(6896): 387–391. Bibcode:2002Natur.418..387G. doi:10.1038/nature00935. PMID 12140549.
  57. ^ Yu, D.; Silva, M. C.; Shenk, T. (2003). “Functional map of human cytomegalovirus AD169 defined by global mutational analysis”. Proceedings of the National Academy of Sciences. 100 (21): 12396–12401. Bibcode:2003PNAS..10012396Y. doi:10.1073/pnas.1635160100. PMC 218769. PMID 14519856.
  58. ^ Jump up to: a b Tscharke DC, Dobson BM (2015). “Redundancy complicates the definition of essential genes for vaccinia virus”. J. Gen. Virol. 96 (11): 3326–3337. doi:10.1099/jgv.0.000266. PMC 5972330. PMID 26290187.
  59. ^ Dedrick, R. M.; Marinelli, L. J.; Newton, G. L.; Pogliano, K; Pogliano, J; Hatfull, G. F. (2013). “Functional requirements for bacteriophage growth: Gene essentiality and expression in mycobacteriophage Giles”. Molecular Microbiology. 88 (3): 577–89. doi:10.1111/mmi.12210. PMC 3641587. PMID 23560716.
  60. ^ Thomas, Julie A.; Benítez Quintana, Andrea Denisse; Bosch, Martine A.; Coll De Peña, Adriana; Aguilera, Elizabeth; Coulibaly, Assitan; Wu, Weimin; Osier, Michael V.; Hudson, André O. (2016-09-07). “Identification of essential genes in the Salmonella phage SPN3US reveals novel insights into giant phage head structure and assembly”. Journal of Virology. 90 (22): 10284–10298. doi:10.1128/JVI.01492-16. ISSN 1098-5514. PMC 5105663. PMID 27605673.
  61. ^ Pál, C.; Papp, B. Z.; Lercher, M. J.; Csermely, P. T.; Oliver, S. G.; Hurst, L. D. (2006). “Chance and necessity in the evolution of minimal metabolic networks”. Nature. 440 (7084): 667–670. Bibcode:2006Natur.440..667P. doi:10.1038/nature04568. PMID 16572170.
  62. ^ Mori, H; Baba, T; Yokoyama, K; Takeuchi, R; Nomura, W; Makishi, K; Otsuka, Y; Dose, H; Wanner, B. L. (2015). “Identification of Essential Genes and Synthetic Lethal Gene Combinations in Escherichia coli K-12”. Gene Essentiality. Methods in Molecular Biology. 1279. pp. 45–65. doi:10.1007/978-1-4939-2398-4_4. ISBN 978-1-4939-2397-7. PMID 25636612.
  63. ^ Verhagen, L. M.; De Jonge, M. I.; Burghout, P; Schraa, K; Spagnuolo, L; Mennens, S; Eleveld, M. J.; van der Gaast-de Jongh CE; Zomer, A; Hermans, P. W.; Bootsma, H. J. (2014). “Genome-Wide Identification of Genes Essential for the Survival of Streptococcus pneumoniae in Human Saliva”. PLoS ONE. 9 (2): e89541. Bibcode:2014PLoSO…989541V. doi:10.1371/journal.pone.0089541. PMC 3934895. PMID 24586856.
  64. ^ D’Souza, Glen; Kost, Christian (2016-11-04). “Experimental Evolution of Metabolic Dependency in Bacteria”. PLOS Genetics. 12 (11): e1006364. doi:10.1371/journal.pgen.1006364. ISSN 1553-7404. PMC 5096674. PMID 27814362.
  65. ^ Tipples, Graham; McClarty, Grant (1993-06-01). “The obligate intracellular bacterium Chlamydia trachomatis is auxotrophic for three of the four ribonucleoside triphosphates”. Molecular Microbiology. 8 (6): 1105–1114. doi:10.1111/j.1365-2958.1993.tb01655.x. ISSN 1365-2958.
  66. ^ Woods, S.; Coghlan, A.; Rivers, D.; Warnecke, T.; Jeffries, S. J.; Kwon, T.; Rogers, A.; Hurst, L. D.; Ahringer, J. (2013). Sternberg, Paul W, ed. “Duplication and Retention Biases of Essential and Non-Essential Genes Revealed by Systematic Knockdown Analyses”. PLoS Genetics. 9 (5): e1003330. doi:10.1371/journal.pgen.1003330. PMC 3649981. PMID 23675306.
  67. ^ Jump up to: a b c d Fang, G.; Rocha, E.; Danchin, A. (2005). “How Essential Are Nonessential Genes?”. Molecular Biology and Evolution. 22 (11): 2147–2156. doi:10.1093/molbev/msi211. PMID 16014871.
  68. ^ Jordan, I. K.; Rogozin, I. B.; Wolf, Y. I.; Koonin, E. V. (2002). “Essential Genes Are More Evolutionarily Conserved Than Are Nonessential Genes in Bacteria”. Genome Research. 12 (6): 962–968. doi:10.1101/gr.87702. PMC 1383730. PMID 12045149.
  69. ^ Ryan, C. J.; Krogan, N. J.; Cunningham, P; Cagney, G (2013). “All or nothing: Protein complexes flip essentiality between distantly related eukaryotes”. Genome Biology and Evolution. 5 (6): 1049–59. doi:10.1093/gbe/evt074. PMC 3698920. PMID 23661563.
  70. ^ Sipiczki, M (2000). “Where does fission yeast sit on the tree of life?”. Genome Biology. 1 (2): REVIEWS1011. doi:10.1186/gb-2000-1-2-reviews1011. PMC 138848. PMID 11178233.
  71. ^ Chen WH, Trachana K, Lercher MJ, Bork P (2012). “Younger genes are less likely to be essential than older genes, and duplicates are less likely to be essential than singletons of the same age”. Mol. Biol. Evol. 29 (7): 1703–6. doi:10.1093/molbev/mss014. PMC 3375470. PMID 22319151.
  72. ^ Kofoed M, Milbury KL, Chiang JH, Sinha S, Ben-Aroya S, Giaever G, Nislow C, Hieter P, Stirling PC (2015). “An Updated Collection of Sequence Barcoded Temperature-Sensitive Alleles of Yeast Essential Genes”. G3: Genes, Genomes, Genetics. 5 (9): 1879–87. doi:10.1534/g3.115.019174. PMC 4555224. PMID 26175450.
  73. ^ Deng, J.; Su, S.; Lin, X.; Hassett, D. J.; Lu, L. J. (2013). Kim, Philip M, ed. “A Statistical Framework for Improving Genomic Annotations of Prokaryotic Essential Genes”. PLoS ONE. 8 (3): e58178. Bibcode:2013PLoSO…858178D. doi:10.1371/journal.pone.0058178. PMC 3592911. PMID 23520492.
  74. ^ Morgens, David W.; Deans, Richard M.; Li, Amy; Bassik, Michael C. (2016-05-09). “Systematic comparison of CRISPR/Cas9 and RNAi screens for essential genes”. Nature Biotechnology. 34 (6): 634–6. doi:10.1038/nbt.3567. ISSN 1546-1696. PMC 4900911. PMID 27159373.
  75. ^ Sharma, S; Markham, P. F.; Browning, G. F. (2014). “Genes Found Essential in Other Mycoplasmas Are Dispensable in Mycoplasma bovis”. PLoS ONE. 9 (6): e97100. Bibcode:2014PLoSO…997100S. doi:10.1371/journal.pone.0097100. PMC 4045577. PMID 24897538.
  76. ^ Cheng, J; Xu, Z; Wu, W; Zhao, L; Li, X; Liu, Y; Tao, S (2014). “Training set selection for the prediction of essential genes”. PLoS ONE. 9 (1): e86805. Bibcode:2014PLoSO…986805C. doi:10.1371/journal.pone.0086805. PMC 3899339. PMID 24466248.
  77. ^ Jump up to: a b Mushegian, A. R.; Koonin, E. V. (1996). “A minimal gene set for cellular life derived by comparison of complete bacterial genomes”. Proceedings of the National Academy of Sciences of the United States of America. 93 (19): 10268–10273. Bibcode:1996PNAS…9310268M. doi:10.1073/pnas.93.19.10268. PMC 38373. PMID 8816789.
  78. ^ Charlebois, R. L.; Doolittle, W. F. (2004). “Computing prokaryotic gene ubiquity: Rescuing the core from extinction”. Genome Research. 14 (12): 2469–2477. doi:10.1101/gr.3024704. PMC 534671. PMID 15574825.
  79. ^ Jump up to: a b Juhas, M.; Eberl, L.; Glass, J. I. (2011). “Essence of life: Essential genes of minimal genomes”. Trends in Cell Biology. 21 (10): 562–568. doi:10.1016/j.tcb.2011.07.005. PMID 21889892.
  80. ^ Yang X, Li Y, Zang J, Li Y, Bie P, Lu Y, Wu Q (2016). “Analysis of pan-genome to identify the core genes and essential genes of Brucella spp”. Mol. Genet. Genomics. 291 (2): 905–12. doi:10.1007/s00438-015-1154-z. PMID 26724943.
  81. ^ Hua, Hong-Li; Zhang, Fa-Zhan; Labena, Abraham Alemayehu; Dong, Chuan; Jin, Yan-Ting; Guo, Feng-Biao (2016-01-01). “An Approach for Predicting Essential Genes Using Multiple Homology Mapping and Machine Learning Algorithms”. BioMed Research International. 2016: 7639397. doi:10.1155/2016/7639397. ISSN 2314-6141. PMC 5021884. PMID 27660763.
  82. ^ Liu, X; Wang, B; Xu, L (2015). “Statistical Analysis of Hurst Exponents of Essential/Nonessential Genes in 33 Bacterial Genomes”. PLoS ONE. 10 (6): e0129716. Bibcode:2015PLoSO..1029716L. doi:10.1371/journal.pone.0129716. PMC 4466317. PMID 26067107.
  83. ^ McCutcheon, J. P.; McDonald, B. R.; Moran, N. A. (2009). Matic, Ivan, ed. “Origin of an Alternative Genetic Code in the Extremely Small and GC–Rich Genome of a Bacterial Symbiont”. PLoS Genetics. 5 (7): e1000565. doi:10.1371/journal.pgen.1000565. PMC 2704378. PMID 19609354.
  84. ^ Basler, G (2015). “Computational Prediction of Essential Metabolic Genes Using Constraint-Based Approaches”. Gene Essentiality. Methods in Molecular Biology. 1279. pp. 183–204. doi:10.1007/978-1-4939-2398-4_12. ISBN 978-1-4939-2397-7. PMID 25636620.
  85. ^ Gatto, F; Miess, H; Schulze, A; Nielsen, J (2015). “Flux balance analysis predicts essential genes in clear cell renal cell carcinoma metabolism”. Scientific Reports. 5: 10738. Bibcode:2015NatSR…5E0738G. doi:10.1038/srep10738. PMC 4603759. PMID 26040780.
  86. ^ Song, K; Tong, T; Wu, F (2014). “Predicting essential genes in prokaryotic genomes using a linear method: ZUPLS”. Integrative Biology. 6 (4): 460–9. doi:10.1039/c3ib40241j. PMID 24603751.
  87. ^ Guo, F. B.; Ye, Y. N.; Ning, L. W.; Wei, W (2015). “Three Computational Tools for Predicting Bacterial Essential Genes”. Gene Essentiality. Methods in Molecular Biology. 1279. pp. 205–17. doi:10.1007/978-1-4939-2398-4_13. ISBN 978-1-4939-2397-7. PMID 25636621.
  88. ^ Goodacre, N. F.; Gerloff, D. L.; Uetz, P. (2013). “Protein Domains of Unknown Function Are Essential in Bacteria”. mBio. 5 (1): e00744–e00713. doi:10.1128/mBio.00744-13. PMC 3884060. PMID 24381303.
  89. ^ Lu, Y; Lu, Y; Deng, J; Lu, H; Lu, L. J. (2015). “Discovering Essential Domains in Essential Genes”. Gene Essentiality. Methods in Molecular Biology. 1279. pp. 235–45. doi:10.1007/978-1-4939-2398-4_15. ISBN 978-1-4939-2397-7. PMID 25636623.


Translate »
error: Content is protected !!