Information

What are the rules for plasmid names?

What are the rules for plasmid names?


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

What are best practices for naming newly created plasmids?

For example, a common format ispABC123. What is the exact specification? Must there be 3 letters?

What databases of plasmid names exist? How much care is taken to avoid name collisions?


So there are no rules per say (although I wish there were). But there are commonalities among plasmid names and they can help people identify them:

Include the empty backbone name in your plasmid name. This simple piece of information can often convey many important details. Once you know the backbone a plasmid is based on, you can usually derive: a) the bacterial antibiotic resistance, b) the promoter that drives the insert, and c) any other selection markers (for use in other cell types, e.g. eukaryotic cells).

Include information about the insert in your plasmid name. This is often a 3-6 letter representation of the gene (or DNA sequence).

Often researchers will add a lower case letter to the beginning of their insert abbreviation to specify what species it is. Example: 'h' is for Human (homo sapiens), 'm' is for mouse (mus musculus), 'r' is for rat (rattus rattus or rattus norvegicus), etc.

Add any tags or fusions that are on your insert. Typically you would list any tag or fusion protein in the order they appear in the plasmid and their relative position to the insert. Example, if you have a Flag tag on the N-terminal of your insert, you would list it first.

e.g. pBACKBONE-Flag-hGene

If there was also an EGFP fused to the C-terminal of your insert you would list it after the insert.

pBACKBONE-Flag-hGene-EGFP

If your insert contains a mutation or modifcation, this should be included in the plasmid name. Mutations are generally listed as the amino acid change and not a nucleotide change. The proper way to denote an amino acid mutation is to list the one letter abbreviation of the wild type amino acid immediately followed by its position (number) relative to the start Methionine (Met) followed by the one letter abbreviation of the mutated amino acid currently at that position.

Check for more information at Addgene.com they are the largest plasmid repository in the world.

By collisions I'm guessing you mean naming your plasmid something that is already "taken." This is a benefit of submitting to a repository, they will ensure this doesn't happen.if your planning on filing a patent, your parent attorney will do it.


One of the most common methods (described by AddGene) isp(for plasmid), followed by the name of the backbone, a dash, and inserts delimited by more dashes. From this we get things like:pBluescript-CMV-mACT1-GFP.

Another method which I prefer is to name all your plasmids in the format given in the question:pABC123. HereABCis a 3 letter designation that the researcher should consistently use for every plasmid they create (often their initials), and the numbers are sequential (padded with zeros for better sorting by programs).

AddGene is one of the largest plasmid repositories out there. Unfortunately, collisions are a common problem - you should always search the name of a plasmid before assuming things based on the name. While the above two strategies help reduce the odds of collision, there are very few 3 letter combinations that haven't been taken.


Synthetic biologists rely on databases of biological parts to design genetic devices and systems. The sequences and descriptions of genetic parts are often derived from features of previously described plasmids using ad hoc, error-prone and time-consuming curation processes because existing databases of plasmids and features are loosely organized. These databases often lack consistency in the way they identify and describe sequences. Furthermore, legacy bioinformatics file formats like GenBank do not provide enough information about the purpose of features. We have analyzed the annotations of a library of � widely used plasmids to build a non-redundant database of plasmid features. We looked at the variability of plasmid features, their usage statistics and their distributions by feature type. We segmented the plasmid features by expression hosts. We derived a library of biological parts from the database of plasmid features. The library was formatted using the Synthetic Biology Open Language, an emerging standard developed to better organize libraries of genetic parts to facilitate synthetic biology workflows. As proof, the library was converted into GenoCAD grammar files to allow users to import and customize the library based on the needs of their research projects.

The concept of standard biological parts is central to synthetic biology. Biological parts are annotated DNA sequences that can be combined to make larger genetic systems (1,2). Initially, parts standardization focused on specific assembly strategies such as the BioBrick (3) or BglBrick (4) standards. Synthetic biologists have also recognized the need to use standard representations to describe parts. The Registry of Standard Biological Parts (www.partsregistry.org) supporting the iGEM competition was the first attempt at standardizing parts data (5). This pioneering experiment led to the idea of reporting data describing parts’ functions as standardized datasheets (6).

The most essential piece of information associated with a biological part is its sequence. Getting quality sequence information has proved more problematic than one might expect. For instance, an early review entitled ‘Genetic parts to program bacteria’ included hardly any references to sequence data (7). A frequent occurrence in the literature is defining constructs by names of standard promoters and CDSs, allowing the sequences to be deduced, but providing no information about other key sequences, such as 5′ non-coding regions. An assessment of the Registry of Standard Biological Parts uncovered several problems, such as missing part sequences or discrepancies between the published and physical sequences of biological parts (8). More generally, many journals have sequence disclosure policies, but these policies do not apply to plasmid sequences (9). A number of other reasons, such as tight budgets or limited access to a sequencing facility, also explain why so many plasmids described in the literature lack complete sequences. Repositories such as Addgene (10,11) aim to address this issue by documenting plasmids in their collections with sequence and annotation data, and also by associating plasmids with phenotype data, but such efforts have covered only a small fraction of the plasmids in published papers.

To overcome these limitations, we sought alternatives to peer-reviewed publications for obtaining quality sequence data that could provide a solid foundation for the development of a comprehensive database of genetic parts. Many cloning and expression vectors have been used for decades by molecular biologists. Companies and research consortia distributing these vectors typically provide detailed sequence information.

Developers of bioinformatics software have used annotations of plasmid sequences to develop automatic mapping capabilities. Using a list of features commonly found in plasmid sequences, the PlasMapper software was able to automatically annotate and generate maps of raw DNA sequences (12). This approach is now used by other bioinformatics packages including SnapGene (www.snapgene.com). However, each software team is developing its own database of common plasmid features using proprietary curation methods. As a result, it has not been possible to use an existing database of common plasmid features to develop a library of genetic parts.

We analyzed the features found in 1901 annotated sequence files with the goal of developing a database of plasmid features that supports unambiguous annotation of plasmid sequences. These features can be used as biological parts for synthetic biology with the aid of a sophisticated system of categories (13) compatible with the Synthetic Biology Open Language (SBOL) (14).


Resolvase Mediated Deletion

Evolutionary Advantages of Resolvase-Mediated Deletion

Bacterial plasmids and transposons contribute to the so-called mobile (horizontal) gene pool by generating new, potentially beneficial, combinations of genes. Cointegrate resolution during transposition has several advantages: it efficiently separates the donor and target DNA molecules which may have mismatched replication systems detrimental to the cell, and it leads to the target molecule acquiring a transposon, thereby expanding its gene repertoire and enabling further spread of the transposon via its new carrier. In addition, if a DNA molecule with a transposon acquires a second copy of the same, or similar, transposon (e.g., via transposition within the same DNA molecule or between two molecules, or by homologous recombination joining two DNA molecules), resolution between directly oriented res sites can generate single molecules that contain a hybrid transposon. Such hybrids occur in the Tn3 and Tn5053/Tn402 families and may have a selective advantage over the original forms in some bacteria. Resolution can also result in the deletion of target DNA sequences located between the two transposon copies. Such gene reorganization events are not detrimental unless key genes are lost. The resulting molecules are unusual in that the transposon they contain lacks the expected DR flanking sequence.

Plasmid-associated resolution systems contribute to plasmid stability and are particularly important in large plasmids that are present in a low number of copies in the bacterial host. Homologous recombination that may occur between plasmids to form multimers, or multimers that arise during plasmid replication involving the so-called rolling circle process, reduces the number of independent plasmid molecules, thereby compromising the distribution of plasmids to the daughter cells on cell division. Plasmid-associated resolution systems reduce multimers to the monomeric form by the action of the resolvase on directly oriented resolution sites. Plasmids RP4 and R46 encode resolution systems (ParA-res and Per46-per, respectively) that are functionally similar to the Tn3-type cointegrate resolvase system and may have been derived from them. Other evolutionary links between the plasmid and transposon systems are reflected in the ability of a transposon system to serve in multimer resolution (e.g., in pJHCMW1) and for res sites in RP4 and some Tn3-transposons to serve as targets for the ‘res-hunter’ transposons (the Tn5053/Tn402 family) whose efficient transposition involves the target resolvase.


ORI names - name of a plasmid ori (Nov/16/2010 )

I have a question about the different kinds of the "origins of replication".

I know that the sequence for a autonomous plasmid in yeast (S.pombe) is called "ARS".
But how is it called in other organisms?
For example S.cerevisiae?

I think that in E.coli this plasmid ori is called "oriT"?

I hope you can understand my question and will be able to help me!

In the bacterial plasmid world, "oriT" indicates the origin of transfer in a promiscuous plasmid. "OriV" is the plasmid's origin of vegetative growth (aka replication). All plasmids will have an oriV, as the all must replicate, but not all will have an oriT, as not all are transmissible.

Thank you for this expeditious answer.

I think it's a typing error and you mean "OriT" instead of "OriV" in the last part of your last sentence?

I hope you might forgive the unknowing student if he asked you: "Does all bacteria have the same DNA-sequence in the OriV?
Or does every bacterium have his own individual OriV-Sequence?"

Ikar on Wed Nov 17 09:31:28 2010 said:

Yes, you're right. I've fixed the typo.

Ikar on Wed Nov 17 09:31:28 2010 said:

Are we still talking about plasmids? If so, then the answer is no -- not all bacteria have a plasmid, so those without a plasmid would not have a plasmid-borne OriV. Moreover, the exact sequence of the oriV varies from plasmid to plasmid.

If you mean on the chromosome, each bacterial chromosome has a single origin of replication -- in E. coli, this is called the oriC. Archaea have several origins of replication along their circular chromosome, and Eukaryotes usually have multiple origins of replication on each of their linear chromosomes.

Excuse me for my imprecise explanations. I am from Germany and do not often use english language so I have my difficulties to illustrate something.
But that is also the reason why I try using an english forum: to improve my language skills while I get the answers to my questions

Maybe it is easier for me if I start anew:

A short summary on what I've understood:
- E.coli has on his chromosome a oriC for replication of its genome
- It has a oriV on a plasmid for its autonomous replication (the replication of this plasmid)
- each type of bacterium has it's own kind of oriC
- if the species has plasmids, the oriV sequence will be a bit different between the species

But if you transform a vector plasmid in any kind of bacterium, do you always use the same oriV which is located on this
plasmid for its replication?
Or do you use different kinds of "oriV"s on your vector plasmids for each species?

Some plasmids replicate in multiple species -- these are known as broad host range plasmids. Other plasmids only replicate in a single species or in a small handful of closely related species. There are engineered plasmids that contain two oriVs -- one that operates in one species and a second one that is functional in another species -- these are called shuttle vectors, and allow one to do things like cloning manipulations in E. coli and then move the completed construct into another genus, like Bacteroides, or even another kingdom, like fungi.

Thank you. I think I got it.

When I am going to use such a shuttle vector e.g. in E.Coli and S.cerevisiae, then this plasmid should contain
an ARS (replication in the yeast) and a oriV (replication in E.Coli)!?

Yes. In addition, the plasmid needs a selectable marker for each organism. In E. coli, this is usually an antibiotic resistance gene (ampicillin, tetracyline, kanamycin, etc.), and in yeast selection is usually made by using a shuttle vector that complements an auxotrophic mutation in the recipient yeast strain (ura3-52, his3-D1, leu2-D1, etc.). See here for examples.

Thank you. I think my question is perfectly answered!

While reading the marvelous homepage you gave me (http://dbb.urmc.rochester.edu/labs/sherman_f/yeast/Cont.html)
I faced another problem or rather question:

If I transform a plasmid (with an ORI) into a yeast cell the plasmid will replicate once per cell cycle.
How can I be sure that the daughter will get one of this two plasmids?
Therefore I need to integrate a CEN-DNA-Sequence in the plasmid, so the mitotic spindle
can be attached.
-> That is only a consederation of me. I would be happy if you could tell me whether I'm right or wrong!


NEW SIZES

The CGSC Database of E. coli genetic information includes genotypes and reference information for the strains in the CGSC collection, the names, synonyms, properties, and map position for genes, gene product information, and information on specific mutations and references to primary literature. The public version of the database includes this information and can be queried directly via this CGSC DB WebServer. For help, use the help links located above and on each query form, or contact us directly.


RESULTS

Characterization of the plasmid library

In order to get a better understanding of plasmid characteristics by application domain, we manually assigned the plasmids into different functional categories and host organisms. For each host organism, we generated scatter plots of plasmid lengths versus number of features subdivided by functional category (Figure 1). As expected, the number of features per plasmid tends to increase with plasmid length. Most of the plasmids cluster in the 2–10 kb size range with 5–25 features per plasmid. Unsurprisingly, plasmids for higher order hosts (mammalian, insect) were larger than simpler hosts (bacteria, fungus). Similarly, multi-hosts plasmids and retroviral vectors tend to have more features than other categories of plasmids. In general, it appears that most plasmids have a large amount of extraneous and apparently non-functional sequence and could be made more compact by designing the plasmids with shorter functional features. Good examples of efficient design are the pGREEN plasmids for expression in plant cells, and the multi-host expression plasmids in the pTriEx and pQE-TriSystem series (Figure 1).

Correlation between plasmid length and number of features per plasmid. (A) All plasmids. Types of plasmid are indicated by color in the figure legends. Panels (B)–(F) are grouped by lab host with the specific type of plasmid indicated in color as in panel (A). Outliers with low or high feature densities are labeled. The outlined data points denote plasmids that had three or more additional features detected by SnapGene that were not annotated in the original downloaded files. The outlined circles show the original feature densities for these plasmids and the outlined triangles show the updated feature densities.

Correlation between plasmid length and number of features per plasmid. (A) All plasmids. Types of plasmid are indicated by color in the figure legends. Panels (B)–(F) are grouped by lab host with the specific type of plasmid indicated in color as in panel (A). Outliers with low or high feature densities are labeled. The outlined data points denote plasmids that had three or more additional features detected by SnapGene that were not annotated in the original downloaded files. The outlined circles show the original feature densities for these plasmids and the outlined triangles show the updated feature densities.

Resolving duplicate and inconsistent features

After the initial extraction of features from the files in the Non-Redundant File Library, there were 21 594 features in our dataset. Because many features are used across multiple plasmids, this first raw set of plasmid features included duplicates and inconsistent sequences that were not appropriate for our Standard Features Library. The steps we took to refine the data are described below:

First, we queried the database to find perfect duplicate features, or those with the same sequence, name and description. We included only one copy of such a feature in the Standard Features Library while keeping track of all the instances of this feature in the SnapGene Plasmid Library. This step reduced our initial dataset from 21 594 features to 2046.

Next, we removed all features that we flagged as ‘inconsistent’. This would include features with sequences that contained characters other than a, t, g and c (for example, n, h, d, w and y) because these features are too ambiguous to be included in a database of standard features. Similarly, we eliminated CDS features with joined locations corresponding to introns and exons because these features add a new level of complexity not well supported by automated mapping algorithms. This reduced the remaining feature set from 2046 to 2036.

We also considered the case of features with the same sequence but different names in the raw feature set. In this case, we included the feature with the most commonly used name in the Standard Features Library while including the name variants as synonyms in a separate field. This step reduced the feature set from 2036 to 1994 features. We also noted a couple of cases where the sequences were the same, but the names included some position information. HIV-1 5′ LTR and HIV-1 3′ LTR have the same sequence. We eliminated the duplicate and renamed this feature HIV-1 LTR. We did the same operation for the truncated version of this feature.

Then, we considered the case where the name and sequence were the same, but the description was different. As in the case of different names for the same sequence, we selected the most commonly used description for that feature. This step reduced the feature set from 1994 to 1943.

Finally, we looked for features having the same names but different sequences. This situation corresponds to sequence variants calling for disambiguation of the feature name. Hence, we indexed the different feature variants when including them in the Standard Features Library by adding a number after the name, as in MCS-001, MCS-002, MCS-003, etc. In addition, there were four features that did not have any name or description those were examined and manually named. These steps had no impact on the number of features, but 1518 features had adjustments to their names.

After all the duplicates had been eliminated, the Standard Features Library included 1943 features.

Statistical analysis of standard features library

Usage statistics

We examined the frequency with which each feature occurred in the plasmid library (Supplementary Figure S2, top). Surprisingly, 766 features (∼40%) appeared only once in the plasmid set, but most of them (448) are variants of more common features. A number of fluorescent proteins that were imported from single-feature files are also in this situation because they are not used in any of the plasmids. At the other end of the distribution, 13 features were used more than 200 times in the Non-Redundant File Library (Supplementary Table S2). This set includes features required for plasmid propagation in Escherichia coli (antibiotic resistance, origins of replication), sequencing primer sites, and prokaryotic and mammalian promoters.

Many important common features have multiple variants. For instance, AmpR promoter-009 occurs in 967 (62%) of the plasmids, but there are 12 AmpR promoter variants that occur in 1110 (71%) of the plasmids. In some cases, one of the variants is used much more frequently than any other, but in other cases different variants have substantial usage statistics (discussed below).

The variability of feature sequences may result from annotation errors, errors in the plasmid sequences or from mutations—deliberate or not (Table 2). For instance, codon optimization can be a source of sequence variability at the DNA level. To evaluate the possible contribution of annotation and sequence errors to the overall variability of feature sequences, we partitioned the feature database into variable features versus conserved features having no variants. We found 272 variable features and 432 conserved features (Supplemental File S1). Only six of the conserved features are present in more than 100 plasmids (T7 promoter, ATG, M13 rev, M13 fwd, lacI promoter and EM7 promoters). Apart from the lacI promoter, these features do not appear under a different name in the list of variable features. This observation shows that, at least in the case of well-defined features with short sequences, the process used to edit and annotate sequences is robust enough to prevent the introduction of spurious errors.

Statistics for non-coding and protein coding feature variants
Feature . No. of variants a . No. of occurrences . No. of bp changes b . Total length (bp) . Changes/ Variant . Changes/ 1000 bp . Length only variants c .
Non-coding features
AmpR promoter 12 1110 12 1154 1.0 10.4 3 (25.0%)
CMV enhancer 15 519 15 4954 1.0 3.0 5 (35.7%)
CMV promoter 10 511 29 2039 2.9 14.2 3 (30.0%)
SV40 promoter d 23 897 28 4613 1.2 6.1 7 (30.4%)
f1/M13 ori 22 651 85 9773 3.9 8.7 3 (13.6%)
ori 22 1490 48 12 689 2.2 3.8 2 (9.1%)
IRES 16 82 21 8767 1.3 2.4 4 (25.0%)
Total 120 5260 238 43 989 2.0 5.4 27 (22.5%)
Mean/Feature17751346284
Coding features
AmpR/bla(M) 23 1065 161 19 734 7.0 8.2 4 (17.4%)
CmR 16 211 25 10 605 1.6 2.4 1 (6.3%)
HygR 14 101 282 14 376 20.1 19.6 3 (21.4%)
KanR 19 119 131 15 474 6.9 8.5 0 (0.0%)
NeoR/KanR 23 354 66 18 312 2.9 3.6 2 (8.7%)
PuroR 11 75 131 6627 11.9 19.8 0 (0.0%)
lacZ-α 74 144 14 27 102 0.2 0.5 70 (95.0%)
MBP 10 36 40 11 022 4.0 3.6 1 (10.0%)
Total e 116 1961 836 96 150 7.2 8.7 11 (9.5%)
190 2105 850 123 152 4.5 6.9 81 (42.6%)
Mean/Feature1726311913 736
24 280 106 15 394
Feature . No. of variants a . No. of occurrences . No. of bp changes b . Total length (bp) . Changes/ Variant . Changes/ 1000 bp . Length only variants c .
Non-coding features
AmpR promoter 12 1110 12 1154 1.0 10.4 3 (25.0%)
CMV enhancer 15 519 15 4954 1.0 3.0 5 (35.7%)
CMV promoter 10 511 29 2039 2.9 14.2 3 (30.0%)
SV40 promoter d 23 897 28 4613 1.2 6.1 7 (30.4%)
f1/M13 ori 22 651 85 9773 3.9 8.7 3 (13.6%)
ori 22 1490 48 12 689 2.2 3.8 2 (9.1%)
IRES 16 82 21 8767 1.3 2.4 4 (25.0%)
Total 120 5260 238 43 989 2.0 5.4 27 (22.5%)
Mean/Feature17751346284
Coding features
AmpR/bla(M) 23 1065 161 19 734 7.0 8.2 4 (17.4%)
CmR 16 211 25 10 605 1.6 2.4 1 (6.3%)
HygR 14 101 282 14 376 20.1 19.6 3 (21.4%)
KanR 19 119 131 15 474 6.9 8.5 0 (0.0%)
NeoR/KanR 23 354 66 18 312 2.9 3.6 2 (8.7%)
PuroR 11 75 131 6627 11.9 19.8 0 (0.0%)
lacZ-α 74 144 14 27 102 0.2 0.5 70 (95.0%)
MBP 10 36 40 11 022 4.0 3.6 1 (10.0%)
Total e 116 1961 836 96 150 7.2 8.7 11 (9.5%)
190 2105 850 123 152 4.5 6.9 81 (42.6%)
Mean/Feature1726311913 736
24 280 106 15 394

a After consolidation of identical features upon correction of sequence or annotation errors.

b Base pair changes relative to the consensus sequence, including missense mutations and indels, but excluding differences in feature borders.

c Variants that differ from the consensus only by their borders. It does not include variants missing only START or STOP codons.

d Includes all variants of SV40 ori, SV40 enhancer and SV40 promoter.

e Values in bold exclude lacZ-α variants as the majority of these differ only in their in-frame multiple cloning sites.

Feature . No. of variants a . No. of occurrences . No. of bp changes b . Total length (bp) . Changes/ Variant . Changes/ 1000 bp . Length only variants c .
Non-coding features
AmpR promoter 12 1110 12 1154 1.0 10.4 3 (25.0%)
CMV enhancer 15 519 15 4954 1.0 3.0 5 (35.7%)
CMV promoter 10 511 29 2039 2.9 14.2 3 (30.0%)
SV40 promoter d 23 897 28 4613 1.2 6.1 7 (30.4%)
f1/M13 ori 22 651 85 9773 3.9 8.7 3 (13.6%)
ori 22 1490 48 12 689 2.2 3.8 2 (9.1%)
IRES 16 82 21 8767 1.3 2.4 4 (25.0%)
Total 120 5260 238 43 989 2.0 5.4 27 (22.5%)
Mean/Feature17751346284
Coding features
AmpR/bla(M) 23 1065 161 19 734 7.0 8.2 4 (17.4%)
CmR 16 211 25 10 605 1.6 2.4 1 (6.3%)
HygR 14 101 282 14 376 20.1 19.6 3 (21.4%)
KanR 19 119 131 15 474 6.9 8.5 0 (0.0%)
NeoR/KanR 23 354 66 18 312 2.9 3.6 2 (8.7%)
PuroR 11 75 131 6627 11.9 19.8 0 (0.0%)
lacZ-α 74 144 14 27 102 0.2 0.5 70 (95.0%)
MBP 10 36 40 11 022 4.0 3.6 1 (10.0%)
Total e 116 1961 836 96 150 7.2 8.7 11 (9.5%)
190 2105 850 123 152 4.5 6.9 81 (42.6%)
Mean/Feature1726311913 736
24 280 106 15 394
Feature . No. of variants a . No. of occurrences . No. of bp changes b . Total length (bp) . Changes/ Variant . Changes/ 1000 bp . Length only variants c .
Non-coding features
AmpR promoter 12 1110 12 1154 1.0 10.4 3 (25.0%)
CMV enhancer 15 519 15 4954 1.0 3.0 5 (35.7%)
CMV promoter 10 511 29 2039 2.9 14.2 3 (30.0%)
SV40 promoter d 23 897 28 4613 1.2 6.1 7 (30.4%)
f1/M13 ori 22 651 85 9773 3.9 8.7 3 (13.6%)
ori 22 1490 48 12 689 2.2 3.8 2 (9.1%)
IRES 16 82 21 8767 1.3 2.4 4 (25.0%)
Total 120 5260 238 43 989 2.0 5.4 27 (22.5%)
Mean/Feature17751346284
Coding features
AmpR/bla(M) 23 1065 161 19 734 7.0 8.2 4 (17.4%)
CmR 16 211 25 10 605 1.6 2.4 1 (6.3%)
HygR 14 101 282 14 376 20.1 19.6 3 (21.4%)
KanR 19 119 131 15 474 6.9 8.5 0 (0.0%)
NeoR/KanR 23 354 66 18 312 2.9 3.6 2 (8.7%)
PuroR 11 75 131 6627 11.9 19.8 0 (0.0%)
lacZ-α 74 144 14 27 102 0.2 0.5 70 (95.0%)
MBP 10 36 40 11 022 4.0 3.6 1 (10.0%)
Total e 116 1961 836 96 150 7.2 8.7 11 (9.5%)
190 2105 850 123 152 4.5 6.9 81 (42.6%)
Mean/Feature1726311913 736
24 280 106 15 394

a After consolidation of identical features upon correction of sequence or annotation errors.

b Base pair changes relative to the consensus sequence, including missense mutations and indels, but excluding differences in feature borders.

c Variants that differ from the consensus only by their borders. It does not include variants missing only START or STOP codons.

d Includes all variants of SV40 ori, SV40 enhancer and SV40 promoter.

e Values in bold exclude lacZ-α variants as the majority of these differ only in their in-frame multiple cloning sites.

Analysis of feature variants

We examined the common features that had 10 or more variants to identify the sources of this variability. We performed sequence alignments of the feature variant sequences and the translation products for coding regions. The variants were either pure length variants in which only the borders of the feature differed, or pure sequence variants that had the same borders as the consensus feature, but contained mismatches or indels or a mix of both. Usually, the most used variant matches the consensus sequence. Interestingly, many of the variants were specific to plasmids from a single source or supplier, even when there were dozens of instances of the variant (Supplemental File S1).

Supplementary Figure S3 shows the usage distributions for features that have 10 or more variants. Variant usage for non-coding features such as enhancers, promoters and origins of replication tends to be conservative, with one or two variants dominating the number of instances, and a large proportion of the non-coding feature variants differing only in their borders (Table 2). The exceptions were the IRES (internal ribosome entry site), which showed more even use of the variants (Supplementary Figure S3). However, most of the IRES variants are functionally distinct (Supplemental File S1).

In contrast to most non-coding features, variants of protein coding features were more broadly used, and few of the coding variants differed in length (Supplementary Figure S3). Instead, these features displayed a high level of sequence variation (Table 2). Nevertheless, the majority of sequence changes were synonymous codon changes and many of the variants encoded identical translation products (Table 3).

Types of variations in protein coding features
Feature . Synonymous codon changes a . Conservative residue changes a . Non-conservative residue changes a . Variants no aa changes b .
AmpR/bla(M) 73% 17% 10% 39%
CmR 84% 16% 0% 81%
HygR 87% 1% 12% 57%
KanR 54% 6% 40% 32%
NeoR/KanR 62% 14% 24% 65%
PuroR 94% 1% 5% 73%
lacZ-α c 93% 7% 0% 95%
MBP 27% 2% 71% 60%
Total no. 641 54 156 134
Total Mean 75% 7% 18% 71%
Feature . Synonymous codon changes a . Conservative residue changes a . Non-conservative residue changes a . Variants no aa changes b .
AmpR/bla(M) 73% 17% 10% 39%
CmR 84% 16% 0% 81%
HygR 87% 1% 12% 57%
KanR 54% 6% 40% 32%
NeoR/KanR 62% 14% 24% 65%
PuroR 94% 1% 5% 73%
lacZ-α c 93% 7% 0% 95%
MBP 27% 2% 71% 60%
Total no. 641 54 156 134
Total Mean 75% 7% 18% 71%

a Percentage of all bp changes including mismatches and indels but excluding border differences.

b Percentage of variants that produce no changes in the translated protein.

c Excluding the multiple cloning sites.

Feature . Synonymous codon changes a . Conservative residue changes a . Non-conservative residue changes a . Variants no aa changes b .
AmpR/bla(M) 73% 17% 10% 39%
CmR 84% 16% 0% 81%
HygR 87% 1% 12% 57%
KanR 54% 6% 40% 32%
NeoR/KanR 62% 14% 24% 65%
PuroR 94% 1% 5% 73%
lacZ-α c 93% 7% 0% 95%
MBP 27% 2% 71% 60%
Total no. 641 54 156 134
Total Mean 75% 7% 18% 71%
Feature . Synonymous codon changes a . Conservative residue changes a . Non-conservative residue changes a . Variants no aa changes b .
AmpR/bla(M) 73% 17% 10% 39%
CmR 84% 16% 0% 81%
HygR 87% 1% 12% 57%
KanR 54% 6% 40% 32%
NeoR/KanR 62% 14% 24% 65%
PuroR 94% 1% 5% 73%
lacZ-α c 93% 7% 0% 95%
MBP 27% 2% 71% 60%
Total no. 641 54 156 134
Total Mean 75% 7% 18% 71%

a Percentage of all bp changes including mismatches and indels but excluding border differences.

b Percentage of variants that produce no changes in the translated protein.

c Excluding the multiple cloning sites.

In contrast to marker genes, affinity tag variants such as MBP and GST differed mostly in whether they included START/STOP codons or in-frame extensions such as linkers or MCS sites (Supplemental File S1), but epitope tag variants such as HA and Myc were uniform in length and rife with synonymous codon changes, usually as a result of codon optimization ( 16, 17). Affinity tags are almost exclusively used for bacterial expression and protein purification, while the epitope tags are used in a variety of host cells for immunoprecipitation and immunofluorescence, and therefore require codon optimization for each host. More details on feature variants are provided in the Online Supplement.

Feature sequence length

The sequence length for the features was highly variable, with the shortest features (pUC sequence origin and splice donor mutation) coming in at 1 bp and the longest (adenoviral DNA) at 30 549 bp, with a median sequence length of 267 bp. The statistical distribution of feature sequence lengths is bimodal (Supplementary Figure S2, bottom). The majority of features have sequences shorter than 120 bp. Another peak centered around 700 bp consists mostly of coding sequences.

Number of features in each feature qualifier

Of the 63 GenBank feature keys currently available from the International Nucleotide Sequence Database Collaboration http://www.insdc.org/documents/feature-table, 25 were represented in the SnapGene Plasmid Library. Plotting the distribution of features according to feature keys shows that the vast majority (71%) falls in only two categories (Supplementary Figure S4). CDS was used the most often (867 times) followed by MISC_FEATURE (515 times). The over-representation of two categories is an indication that the GenBank feature keys do not have the resolution necessary to represent plasmid features. For instance, purification tags are annotated as CDS, but should be identified as tags. Similarly, one could argue that sequences coding for fluorescent proteins should be distinguished from other coding sequences, and multiple cloning sites or stop codons/signals are common enough to justify identifying them with new feature keys.

Segmentation by expression host

Some features are host-specific. For example, promoters are often specific to an expression host. Other features, such as coding sequences and structural elements allowing the propagation of a plasmid in E. coli, can be used in shuttle plasmids for a number of different hosts. We looked at the expression host specified in the GenBank files of the plasmids. After some cleanup to address inconsistent spelling of the hosts, we found 12 different hosts represented in this dataset, 13 if including those where the host was unspecified this list of lab hosts includes E. coli, Mammalian Cells, Bacillus subtilis, Gram-negative bacteria, Drosophila melanogaster, Saccharomyces cerevisiae, Insect Cells, Plant Cells, Schizosaccharomyces pombe, Pichia pastoris, Aspergillus nidulans, Kluyveromyces lactis and Unspecified. We then associated each feature with their expression hosts by querying the features’ hosts from their related plasmids. Most of the features (1629) were associated with only one lab host (and, of those, 139 were associated only with Unspecified hosts). One hundred seventy-five features were associated with more than one lab host, and 21 of those were associated with five or more lab hosts.

Development of a library of biological parts

The SBOL is a community-driven standard for exchanging synthetic biology data between applications ( 14). In order to generate SBOL files of the Standard Features Library, we first developed a short Java program that could read the contents of a flat file that we could generate from the database. This program relied on the libSBOLj library (https://github.com/SynBioDex/libSBOLj) to reformat that information and output the features as collections of parts (DnaComponents). One challenge for this approach is that the features were categorized using the GenBank feature keys, but SBOL relies on the Sequence Ontology (SO) to categorize its parts ( 18, 19). BioPerl provides a script for translating GenBank Feature Keys to SO identifiers that we used for developing the mapping table reported in Supplementary Table S3. The flat file output included a display_id, the feature name, the description, the sequence and the SO identifier corresponding to the associated GenBank feature key.

We generated an SBOL file for each lab host, and one for the parts with the Unspecified host. Finally, we generated a file that includes the collections for all of the hosts (Supplemental File S4).

Development of a GenoCAD grammar

In order to facilitate the use of these standard features as genetic parts, we edited the features database and imported it into GenoCAD, a computer assisted design application for synthetic biology ( 20, 21). We used GenoCAD to edit the database of genetic parts by adding new parts, defining new categories of parts and rules ( 22) describing relations between part categories, and finally organizing the parts in different libraries as previously described ( 23). The grammar is available online as Supplemental File S5.

Removal of START and STOP codons

Many of the features annotated as CDS included coding sequences such epitope tags or fluorescent protein domains that could be used in fusion with other coding sequences. In order to facilitate the combination of coding sequences, we removed the start and stop codons found at the extremities of CDS features. After removal of these codons, 14 CDS feature variants were identical to other variants and were merged with them. We introduced START and STOP codons as separate parts in the database.

Part categorization

The GenBank qualifiers do not provide the resolution necessary to properly describe the function of genetic parts and organize a large library accordingly. As a result, we recategorized the parts library using a custom categorization system that relies as much as possible on existing SO terms. In some cases, we took advantage of commonly used terms that may not yet be part of the SO. The specification of each category includes a long category name and a short category code. The category description includes a reference to the corresponding SO terms along with the SO definition when applicable. The names of categories without a corresponding SO term start with a + in order to facilitate their identification. Each category is mapped to a GenBank feature key (Supplementary Table S3). Finally, each category is associated with an icon used to represent it graphically.

In addition, we defined syntactic rules for relationships between part categories. These rules are mostly derived from the SO parts definition. For instance, CDS (SO:0000316) is defined as ‘A contiguous sequence which begins with, and includes, a start codon and ends with, and includes, a stop codon’. Using the information in this definition, it is possible to define a rule stating that a CDS is composed of a START codon (SO:00003180), an ORF (so:0000236) and a STOP codon (SO:0000319). Other rules express that some categories of parts are subsets of a larger category. For instance, it is possible to express that a Bacterial terminator (SO:0000614) is a Terminator (SO:0000141).

Correction of annotation errors

A methodic review of the database content unveiled a number of sequence annotation issues, such as feature orientation errors and sequence errors resulting in nonsense mutations. We also merged parts that differed only in the START or STOP codons (see Online Supplement for details).

Addition of new parts

We noticed that many of the coding features in some plasmids had no annotated promoters, and this was still the case after we updated the annotations in the current version of SnapGene. To determine the functional promoters for these genes, we aligned their sequences upstream of the start codon with a set of all annotated promoters (one or two variants of each) from our library and performed BLAST searches on the sequences. Some of these promoter regions were new variants of the AmpR promoter, the CAT promoter and the Pc promoter (Supplemental File S3). The rest were promoters that had no counterparts in our features set. We have defined new native promoters for NeoR/KanR, KanR (aph(3)-Ia), KanR (aphA-3), P2 (SmR works in combination with Pc promoter) and ccdB. Plasmids from Oxford Genetics (pSF series) also have an apparent synthetic promoter used for both NeoR/KanR and AmpR. A total of seven new promoters were added to the parts database after this analysis. We also recommend 36 variants that match the consensus/natural (GenBank) sequences for highly variant features, and 17 new versions of features that match the consensus when none of the existing variants do, or comply with optimal sequences from structure-function studies ( 24–32). See Supplemental Files S1 and S2.

Parts libraries

We organized the parts in libraries. One library includes all of the parts in the database. We also have libraries for each of the 13 expression hosts and for the parts having an unspecified expression host. Finally, we have singled out the most popular parts as those having been used in 17 or more plasmids in the SnapGene Sequence library. We also created a library for the new parts described above.

Use of the GenoCAD grammar

The GenoCAD grammar can be customized for specific applications as previously described ( 23). Customization starts by adding new categories of parts specific to the application. By convention, the custom category names all start with ‘c-’ i.e. c-Lac Promoter or c-AmpR gene to help identify them quickly among all the existing categories. In the second step, rules are added to describe how parts of different categories can be combined to make a valid construct. Finally, a new parts library specific to a project is created and populated with a selection of parts found in other libraries. It is also possible to import new parts not already in the grammar.

We illustrate this feature by modifying the grammar to make it suitable to design cassettes for tagging S. cerevisiae genes with a fluorescent protein (Figure 2). The plasmid has an ampicillin resistance marker and an origin of replication. It also includes a cloning module allowing for blue/white screening. The LacZ-alpha gene is placed under the control of a Lac promoter. A cloning site and two sequencing primers are placed between the start codon and the LacZ-alpha ORF. Randomly generated sequences are inserted between the primers and the cloning site in order to ensure that the borders of the insert can be sequenced.

Structure of a plasmid to tag S. cerevisiae genes with a fluorescent protein. (A) Map of the empty vector and insert derived from the GenBank files exported from GenoCAD. (B) Structure of the same plasmid represented using SBOLv icons.

Structure of a plasmid to tag S. cerevisiae genes with a fluorescent protein. (A) Map of the empty vector and insert derived from the GenBank files exported from GenoCAD. (B) Structure of the same plasmid represented using SBOLv icons.

The cassette itself is composed of a fluorescent protein tag and an auxotrophic marker separated by a short random sequence. The entire cassette is flanked by two polymerase chain reaction (PCR) primer binding sites F2 and R1 used to amplify the cassette to generate PCR fragments for homologous recombination.


Northern Illinois University Department of Biological Sciences College of Liberal Arts and Sciences

The health and safety of our students, faculty and staff is our priority. Please visit the NIU coronavirus (COVID-19) website for current updates and information. See details about available services and hours and frequently asked questions.

Biology is a diverse and rapidly expanding field of study that addresses issues relevant to health, agriculture, industry and the environment. Biologists are responsible for new discoveries in medicine and molecular biology, increasing crop yields and pest resistance, defining the ecological relationships that maintain our planet, and examining the origins and evolution of species, to name just a few.

You will learn and conduct research alongside our faculty, who are highly-regarded and internationally known for their discoveries. Beyond the classroom, we encourage students to seek out faculty mentors and to conduct research very early in their college careers. Not only does this provide you the opportunity to apply knowledge learned in the classroom, but it also establishes you in the field and paves the way for future success.

Our program is highly regarded by both employers and educational institutions, allowing our graduates to pursue careers in government, education, and industry. Many students go onto graduate or professional schools, such as medical, dental, podiatric medicine, optometry, veterinary medicine and pharmacy.

Diversity Statement

The Department of Biological Sciences at NIU stands against oppression in all its forms. We stand for social and racial justice and are working to improve diversity, equity and inclusion (DEI) in our department. We recognize that biological sciences has a long history of colonialism, racism and white supremacy and has participated in oppressive endeavors, including biological racism, eugenics and inhumane treatment of and experimentation on Black, Indigenous and People of Color (BIPOC). That history and that of our society mean institutionalized, systemic racism is still a part of biology today.

We have recently formed a DEI committee that has helped to remove the GRE from consideration in our graduate application process, instituted DEI discussions as part of regular faculty meetings, and edited our bylaws to ensure search committees have student representation and that tenure/promotion criteria are clear and equitable. We commit to further amending our policies, practices and curricula, to continue to make our department a better, more welcoming place for all faculty, students and staff.


Identification of the cut and uncut plasmid on gel - (Jun/27/2005 )

would u plz tell me how we will differentiate between the gel electrophoresis of cut and uncut plasmid( in reference to pBluescript).

if possible can u show me the photo.


M1 : Lambda Hind III marker,
lane 1: pGEM-T(uncut, insert 1kb),
lane 2 : pGEM-T(EcoRⅠ),
lane 3 : pGEM-T(SphⅠ),
lane 4 : pUWL201(6.40 kb) ,
lane 5 : pUWL201(EcoRⅠ),
lane 6 : pUWL201(XbaⅠ)
M2 : 1 kb ladder marker.

would u plz tell me how we will differentiate between the gel electrophoresis of cut and uncut plasmid( in reference to pBluescript).

if possible can u show me the photo.

As a rule, the uncut plasmid is supercoiled, so it will run faster that the linearized (cut) plasmid.

Just run your digested sample next to the uncut plasmid you can identify it.

really feeling very nice to see your warm response.

actually veteran i have one more problem, that my vector is 2.9kb. whenever i isolate my plsmid by mini prep(DH5, E.Coli strain) , i get only one band of around 2.3kbp.

why dont around 2.9 kb. yet the transformtion is very normal by CaCl2 method.

i used the alkline lysis method for plasmid isolation.

if u have any information regarding this, then plz guide me , i will be be very thank ful to u.

I would guess that supercoiled pBluescript would run at about 2.3kB. Linearize it with NotI and run it next to undigested. Compare the migration pattern you see to your marker bands.


Principle:-

DNA ligation is the act of joining together DNA strands with covalent bonds with the aim of making new viable DNA or plasmids. There are currently three methods for joining DNA fragments in vitro. The first of these is DNA ligase that covalently joins the annealed cohesive ends produced by certain restriction enzymes. The second depends upon the ability of DNA ligase from phage T4-infected E. coli to catalyse the formation of phosphodiester bonds between sticky or blunt-ended fragments. The third utilizes the enzyme terminal deoxynucleotidyl transferase to synthesize homopolymeric 3′ single-stranded tails at the ends of fragments. The most commonly used is the T4 DNA ligase method.

E.coli and phage T4 encode an enzyme, DNA ligase, which seals single-stranded nicks between adjacent nucleotides in a duplex DNA chain. Although the reactions catalyzed by the enzymes of E. coli and T4-infected E. coli are very similar, they differ in their cofactor requirements. The T4 enzyme requires ATP, while the E. coli enzyme requires NAD+. In each case the cofactor is split and forms an enzyme–AMP complex. The complex binds to the nick, which must expose a 5′ phosphate and 3′ OH group, and makes a covalent bond in the phosphodiester chain.

DNA fragments with either sticky ends or blunt ends can be inserted into vector DNA with the aid of DNA ligases. During normal DNA replication, DNA ligase catalyzes the end-to-end joining (ligation) of short fragments of DNA, called Okazaki fragments. For purposes of DNA cloning, purified DNA ligase is given to covalently join the ends of a restriction fragment and vector DNA that have complementary ends. The vector DNA and restriction fragment are covalently ligated together through the 3’ → 5’ phosphodiester bonds of DNA. When termini created by a restriction endonuclease that creates cohesive ends associate, the nicks in the joints has few base pairs apart in opposite strands. DNA ligase can then repair these nicks to form an intact duplex.


What are the rules for plasmid names? - Biology

Baby Steps Through the
PUNNETT SQUARE


(Get it? "Square" = nerd. Ha ha ha ha ha . )

  • genotype = the genes of an organism for one specific trait we use two letters to represent the genotype. A capital letter represents the dominant form of a gene (allele), and a lowercase letter is the abbreviation for the recessive form of the gene (allele).
  • phenotype = the physical appearance of a trait in an organism

      For example, let's say that for the red-thoated booby bird ( I am making this up ), red throat is the dominant trait and white throat is recessive.
      Since the "red-throat code" and the" white-throat code" are alleles (two forms of the same gene), we abbreviate them with two forms of the same letter. So we use "R" for the dominant allele/trait (red throat) and "r" for the recessive allele/trait (white throat).

    Our possible genotypes & phenotypes would be like so:

    Symbol Genotype Name Phenotype
    RR homozygous (pure)
    dominant
    red thoat
    Rr heterozygous (hybrid) red throat
    rr homozygous (pure)
    recessive
    white throat

    Note: Remember, we don't use "R" for red & "W" for white because that would make it two different genes which would code for two different traits, and throat color is one trait. What the genotype contains are two codes for the same trait, so we use two forms of the same letter (capital & lowercase).

    Here are the basic steps to using a Punnett Square when solving a genetics question. After you get good at this you should never miss a genetic question involving the cross of two organisms.
    BABY STEPS:
    1. determine the genotypes of the parent organisms
    2. write down your "cross" (mating)
    3. draw a p-square
    4. "split" the letters of the genotype for each parent & put them "outside" the p-square
    5. determine the possible genotypes of the offspring by filling in the p-square
    6. summarize results (genotypes & phenotypes of offspring)
    7. bask in the glow of your accomplishment !
    • Sometimes this already done in the question for you. If the question says "Cross two organims with the following genotype: Tt & tt", it's all right there in the question already.
    • More likely is a question like this: "Cross a short pea plant with one that is heterozygous for tallness". Here, you have to use your understanding of the vocab to figure out what letters to use in the genotypes of the parents. Heterozygous always means one of each letter, so we'd use "Tt" (where "T" = tall, & "t" = short). The only way for a pea plant to be short is when it has 2 lowercase "t's", so that short parent is "tt". So the cross ends-up the same as in my first example: Tt x tt.
    • Now, we (us mean teachers) can make things just a little more tricky. Let's use hamsters in this example. Brown is dominant (B), and white is recessive (b). What if a question read like this: "Predict the offspring from the cross of a white hamster and a brown hamster if the brown hamster's mother was white". Oooooh, is this a toughy? First things first: the only way for the white hamster to be white (the recessive trait) is if it's genotype is homozygous recessive (2 little letters), so the white hamster is "bb". Now, the brown hamster's genotype could be either "BB" or "Bb". If its mommy was white (bb), then this brown hamster MUST have inherited a little "b" from its mommy. So the brown one in our cross is "Bb" (not "BB"), and our hamster cross is: Bb x bb.

    Step #3: Draw a p-square.

    • For an example cross we'll use these parental genotypes: Tt x tt.
    • Take the genotype letters of one parent, split them and put them on the left, outside the rows of the p-square.

    What we've done is taken the hetrozygous tall plant (Tt) and put its big "T" out in front of the top row, and the little "t" out in front of the bottom row. When we fill-in the p-square, we will copy these "tees" into each of the empty boxes to their right. So the big "T" will be in each of the boxes of the top row, and the lowercase "t" will be in the two boxes of the bottom row.
    Isn't this exciting?

    • I kinda gave this away already, but to "determine the genotypes of the offspring" all we gotta do is fill-in the the boxes of the p-square. Again we do this be taking a letter from the left & matching it with a letter from the top. Like so:
    • Simply report what you came up with. You should always have two letters in each of the four boxes.
    • In this example, where our parent pea plants were Tt (tall) x tt (short), we get 2 of our 4 boxes with "Tt", and 2 of our 4 with "tt". The offspring that are "Tt" would end up with tall stems (the dominant trait) and the "tt" pea plants would have short stems (the recessive trait).
    • So our summary would be something like this:
      Parent Pea Plants
      ("P" Generation)
      Offspring
      ("F1" Generation)
      Genotypes:
      Tt x tt
      Phenotypes:
      tall x short
      Genotypes:
      50% (2/4) Tt
      50% (2/4) tt
      Phenotypes:
      50% tall
      50% short
    • We are so good I can't stand it.
    • We are genetics MONSTERS !

    A little scientific side-note:

    You know how, in Step #4, when we "split" the letters of the genotype & put them outside the p-square? What that step illustrates is the process of gametogenesis (the production of sex cells, egg & sperm). Gametogenesis is a cell division thing (also called meiosis) that divides an organism's chromosome number in half. For example, in humans, body cells have 46 chromosomes a piece. However, when sperm or eggs are produced (by gametogenesis/meiosis) they get only 23 chromosomes each. This makes sense (believe it or not), because now, when the sperm & egg fuse at fertilization, the new cell formed (called a zygote) will have 23 + 23 = 46 chromosomes. Cool, huh?

    So, when the chromosome number is split in half, all of the two letter genotypes for every trait of that person (or organism) get separated. Which is why we do what we do in Step #4.

    TAKE WHAT YOU'VE LEARNED & DAZZLE SOME PEOPLE.


    <Back to that Mendel Guy & his Laws
    On to a Punnett Square Practice Page>

    Back to Biology Topics Outline

    IF YOU HAVE COMMENTS (GOOD OR BAD) ABOUT THIS OR ANY OF MY BIOLOGY PAGES,
    OR ANYTHING ELSE IN GENERAL , DROP ME A NOTE


    Watch the video: how to make my oc new oc same name (July 2022).


Comments:

  1. Omeet

    I will definitely take a look ...

  2. Everhart

    It is visible, not fate.

  3. Granville

    I believe that you are wrong. I'm sure.

  4. Kadin

    In short, it's the night. After the fast I was worn out ... I went to bed.

  5. Zulukazahn

    It is an excellent variant

  6. Sale

    Delightful ..

  7. Neron

    This situation is familiar to me. It is possible to discuss.

  8. Baird

    I completely agree. Bullshit. But opinions, I see, are divided.



Write a message