With this entry we conclude the series of posts where we have delved into the peculiarities of the different types of ELISA.

After knowing the rationale and procedure on which each of them is based and seeing the main characteristics that differentiate them , today we will focus on analyzing the advantages and disadvantages of the different types of ELISA .


In order to make this entry as visual and practical as possible, we will summarize and group the main advantages and disadvantages of the different types of ELISA in the following table:

ELISA direct1.- The protocol is simple and fast.2.- There is no possibility of cross reactivity with the secondary antibody.3.- Less probability of error due to the use of fewer reagents and steps in the procedure.1.- It can give more background noise, since other proteins present in the sample (in addition to the antigen of interest) can adhere to the plate.2.- There is no signal amplification since secondary antibodies are not used, which reduces the sensitivity of the assay.3.- The primary antibody must be labeled, which reduces the flexibility of the assay, and can be used to alter its immunoreactivity.
Indirect ELISA1.- High sensitivity, since the use of secondary antibodies makes it possible to amplify the signal.2.- High flexibility due to the fact that the same secondary antibody can be used with different primary antibodies, which also translates into an economic benefit.3.- The primary antibody maintains its immunoreactivity intact by not being conjugated.1.- Protocol more complex than the direct ELISA, which includes additional incubation steps with the secondary antibody.2.- The use of secondary antibodies can lead to cross reactivity.
ELISA sandwich1.- High flexibility, since detection can be done both by direct and indirect procedure.2.- High sensitivity and specificity, due to the use of two antibodies against the same antigen.1.- The antigen must be large enough to allow two antibodies to bind simultaneously.2.- It is not always easy or possible to have pairs of antibodies that work well in this type of assay.
Competitive ELISA1.- High flexibility: it can be based on a direct, indirect or sandwich procedure.2.- High sensitivity, robustness and consistency.3.- It allows the detection of small size antigens and in low concentrations.4.- It does not require the previous processing of the samples to be analyzed.1.- The protocol is relatively complex.2.- Requires the use of inhibition antigen.

As a conclusion after analyzing the advantages and disadvantages of the different types of ELISA, we can determine the ideal use of each of them :

  • Direct ELISA : it is the technique of choice to analyze the immune response to a certain antigen, for example, in the production of antibodies or in diagnostic procedures.
  • Indirect ELISA : test of choice to determine the total concentration of antibodies in a certain sample.
  • Sandwich ELISA : technique of choice when it comes to analyzing complex samples, without the need to purify the antigen previously.
  • Competitive ELISA : ideal technique to detect antigens of small size or that are present in very low concentrations in the sample.


For all medicines and therapeutic agents , the WHO (World Health Organization) assigns a generic name known as INN ( International Non- proprietary Name ) in order to facilitate the identification of the active pharmaceutical ingredients that comprise it.

This INN is also applicable in the case of monoclonal antibodies , which are assigned a generic name based on a specific schematic structure. The International Nonproprietary Name (INN) for each monoclonal antibody is composed of a prefix, two intermediate particles that serve the type of target to which the monoclonal antibody is directed and the species of origin thereof, respectively, and a common suffix for all of them.

Want to know what the names of the antibodies mean? In this entry we analyze the scheme and the specific structure that is followed in the monoclonal antibody nomenclature.


As we said, the name or INN of each monoclonal antibody is assigned based on a scheme that includes 4 aspects:

  • Random prefix
  • Target type
  • Origin species
  • Common suffix

Let’s take a closer look at each of them:


This part of the monoclonal antibody name or INN does not meet any specific criteria. Its “function” is to distinguish one monoclonal antibody from another (since the name of two antibodies directed at the same target and originating from the same species will only be distinguished by the prefix), so it must be unique, and it is free choice by the manufacturer of the same.


After the random prefix, a particle representing the type of target to which the monoclonal antibody is directed is added. This particle generally consists of a consonant to which a vowel is added only in the case where the particle representing the origin of the antibody begins with a consonant.


Third, the particle corresponding to the origin of the monoclonal antibody is added, and it can be of animal, chimeric, humanized or totally human origin.


The names of all monoclonal antibodies will always end with the common suffix -mab , indicating that it is an immunoglobulin or a fragment of it, provided that it includes at least one variable domain.

Although the monoclonal antibody nomenclature may seem confusing or complicated at first glance, it is actually very precise and easy to understand based on these criteria that we have just discussed, and which we summarize in the following table:

PrefixTarget typeOrigin speciesSuffix
Random-b (a) – bacterial-am (i) – serum amyloid protein (SAP) / amyloidosis (pre-substem)-c (i) – cardiovascular-f (u) – fungal-gr (o) – skeletal muscle mass related growth factors and receptors (pre-substem)-k (i) – interleukin-l (i) – immunomodulating-n (e) – neural-s (o) – bone-tox (a) – toxin-t (u) – tumor-v (i) – virala- rat-axo- rat-mouse (pre-substem)-e- hamster-i- primate-o- mouse-u- human-vet- veterinary use (pre-substem)-xi- chimeric-xizu- chimeric-humanized-zu- humanized-mab

To finish, we leave you some examples to understand the information provided by the monoclonal antibody nomenclature:

    • Basi- (prefix)
    • -li- (immunomodulator)
    • -xi- (of chimeric origin)
    • -mab (monoclonal antibody)
    • Inf- (prefix)
    • -li- (immunomodulator)
    • -xi- (of chimeric origin)
    • -mab (monoclonal antibody)
    • Pali- (prefix)
    • -vi- (against a viral antigen)
    • -zu- (humanized)
    • -mab (monoclonal antibody)
    • Tras- (prefix)
    • -tu- (against tumor antigens)
    • -zu- (humanized)
    • -mab (monoclonal antibody)
Comparison among three variant callers and assessment of the accuracy of imputation from SNP array data to whole-genome sequence level in chicken.

Comparison among three variant callers and assessment of the accuracy of imputation from SNP array data to whole-genome sequence level in chicken.

BACKGROUNDThe technical progress in the final decade has made it attainable to sequence tens of millions of DNA reads in a comparatively quick time-frame.

Several variant callers primarily based on totally different algorithms have emerged and have made it attainable to extract single nucleotide polymorphisms (SNPs) out of the whole-genome sequence.

Often, only some people of a inhabitants are sequenced utterly and imputation is used to receive genotypes for all sequence-based SNP loci for different people, which have been genotyped for a subset of SNPs utilizing a genotyping array.

METHODSFirst, we in contrast the units of variants detected with totally different variant callers, specifically GATK, freebayes and SAMtools, and checked the high quality of genotypes of the known as variants in a set of 50 totally sequenced white and brown layers. Second, we assessed the imputation accuracy (measured as the correlation between imputed and true genotype per SNP and per particular person, and genotype battle between father-progeny pairs) when imputing from excessive density SNP array data to whole-genome sequence utilizing data from round 1000 people from six totally different generations.

Three totally different imputation applications (Minimac, FImpute and IMPUTE2) have been checked in totally different validation eventualities.RESULTSThere have been 1,741,573 SNPs detected by all three callers on the studied chromosomes 3, 6, and 28, which was 71.6 % (81.6 %, 88.0 %) of SNPs detected by GATK (SAMtools, freebayes) in whole.

Genotype concordance (GC) outlined as the proportion of people whose array-derived genotypes are the identical as the sequence-derived genotypes over all non-missing SNPs on the array have been 0.98 (GATK), 0.97 (freebayes) and 0.98 (SAMtools). Furthermore, the share of variants that had excessive values >>0.9) for an additional three measures (non-reference sensitivity, non-reference genotype concordance and precision) have been 90 (88, 75) for GATK (SAMtools, freebayes).

With all imputation applications, correlation between unique and imputed genotypes was>>0.95 on common with randomly masked 1000 SNPs from the SNP array and>>0.85 for a leave-one-out cross-validation inside sequenced people.CONCLUSIONSPerformance of all variant callers studied was superb in normal, significantly for GATK and SAMtools.

FImpute carried out barely worse than Minimac and IMPUTE2 in phrases of genotype correlation, particularly for SNPs with low minor allele frequency, whereas it had lowest numbers in Mendelian conflicts in accessible father-progeny pairs. Correlations of actual and imputed genotypes remained consistently excessive even when people to be imputed have been a number of generations away from the sequenced people.

 Comparison among three variant callers and assessment of the accuracy of imputation from SNP array data to whole-genome sequence level in chicken.
Comparison among three variant callers and assessment of the accuracy of imputation from SNP array data to whole-genome sequence level in hen.

Genome-wide evaluation reveals the extent of EAV-HP integration in home hen.

BACKGROUNDEAV-HP is an historic retrovirus pre-dating Gallus speciation, which continues to flow into in trendy hen populations, and led to the emergence of avian leukosis virus subgroup J inflicting vital financial losses to the poultry trade.

We mapped EAV-HP integration websites in Ethiopian village chickens, a Silkie, Taiwan Country hen, purple junglefowl Gallus gallus and a number of inbred experimental strains utilizing whole-genome sequence data.RESULTSAn common of 75.22 ± 9.52 integration websites per fowl have been recognized, which collectively group into 279 intervals of which 5 % are frequent to 90 % of the genomes analysed and are suggestive of pre-domestication integration occasions.

More than a 3rd of intervals are particular to particular person genomes, supporting energetic circulation of EAV-HP in trendy chickens. Interval density is correlated with chromosome size (P < 2.31(-6)), and 27 % of intervals are situated inside 5 kb of a transcript.

Functional annotation clustering of genes reveals enrichment for immune-related capabilities (P < 0.05).CONCLUSIONSOur outcomes illustrate a non-random distribution of EAV-HP in the genome, emphasising the significance it might have performed in the adaptation of the species, and present a platform from which to lengthen investigations on the co-evolutionary significance of endogenous retroviral genera with their hosts.

Prevalence of Salmonella Isolates and Their Distribution Based on Whole-Genome Sequence in a Chicken Slaughterhouse in Jiangsu, China.

Prevalence of Salmonella Isolates and Their Distribution Based on Whole-Genome Sequence in a Chicken Slaughterhouse in Jiangsu, China.

Salmonella has been referred to as a very powerful foodborne pathogen, which might infect people through consuming contaminated meals. Chicken meat has been referred to as an essential car to transmit Salmonella by the meals provide chain.

This examine decided the prevalence, antimicrobial resistance, and genetic traits of Salmonella at totally different rooster slaughtering levels in East China. In whole, 114 out of 200 (57%) samples had been Salmonella constructive, whereas Salmonella contamination was steadily rising from the scalding and unhairing stage (17.5%) to the subdividing stage (70%) all through the slaughtering.

Whole-genome sequencing (WGS) was then carried out to investigate the serotype, antimicrobial resistance gene profiles, and genetic relationship of all Salmonella isolates.

The most typical serotypes had been S. Kentucky (51/114, 44.7%) and S. Enteritidis (37/114, 32.5%), which had been distributed all through the 4 slaughtering levels, and had been additionally recognized in the corresponding environments.

The multilocus sequence typing (MLST) evaluation revealed that seven sequence varieties (STs) had been occupied by six totally different serotypes, respectively. Only S. Kentucky had two STs, ST314 was the predominant ST shared by 50 isolates, whereas the ST198 has 1 isolate. The antimicrobial resistance gene evaluation demonstrated that almost all of the strains belonging to S. Kentucky (39/51, 76.5%) and S. Indiana (15, 100%) contained over 5 teams of antimicrobial resistance genes. Based on the core genome evaluation, 50 S. Kentucky isolates had been genetically similar, indicating that one S. Kentucky pressure with the identical genetic background was prevalent in the rooster slaughtering line. Although 37 S.

Enteritidis isolates solely had three totally different antimicrobial resistance gene profiles, the core genome sequence evaluation subtyped these S. Enteritidis isolates into 5 totally different clusters, which revealed the varied genetic background of S. Enteritidis in the slaughterhouse.

The antimicrobial resistance phenotypes had been in step with the presence of the corresponding resistance genes of S. Kentucky and S. Enteritidis, together with tetA, floR, blaTEM-1B, strA/B, sul1/sul2, and gyrA (D87Y). Our examine noticed a excessive prevalence of Salmonella in the rooster slaughter line and recognized the slaughtering surroundings as a important supply of inflicting Salmonella cross-contamination throughout rooster slaughtering. Further research shall be wanted to restrict the transmission of Salmonella in the slaughterhouse.

Prevalence of Salmonella Isolates and Their Distribution Based on Whole-Genome Sequence in a Chicken Slaughterhouse in Jiangsu, China.
Prevalence of Salmonella Isolates and Their Distribution Based on Whole-Genome Sequence in a Chicken Slaughterhouse in Jiangsu, China.

Genome-Wide Detection of Key Genes and Epigenetic Markers for Chicken Fatty Liver.

Chickens are one of a very powerful sources of meat worldwide, and the incidence of fatty liver syndrome (FLS) is intently associated to manufacturing effectivity. However, the potential mechanism of FLS stays poorly understood.

An built-in evaluation of information from whole-genome bisulfite sequencing and lengthy noncoding RNA (lncRNA) sequencing was performed. A complete of 1177 differentially expressed genes (DEGs) and 1442 differentially methylated genes (DMGs) had been discovered. There had been 72% of 83 lipid- and glucose-related genes upregulated; 81% of 150 immune-related genes had been downregulated in fatty livers.

Part of these genes was inside differentially methylated areas (DMRs). Besides, sixty-seven lncRNAs had been recognized differentially expressed and divided into 13 clusters primarily based on their expression sample.

Some lipid- and glucose-related lncRNAs (e.g., LNC_006756, LNC_012355, and LNC_005024) and immune-related lncRNAs (e.g., LNC_010111, LNC_010862, and LNC_001272) had been discovered by a co-expression community and practical annotation.

From the expression and epigenetic profiles, 23 goal genes (e.g., HAO1ABCD3, and BLMH) had been discovered to be hub genes that had been regulated by each methylation and lncRNAs.

We have supplied complete epigenetic and transcriptomic profiles on FLS in rooster, and the identification of key genes and epigenetic markers will increase our understanding of the molecular mechanism of rooster FLS.

New Insights From Imputed Whole-Genome Sequence-Based Genome-Wide Association Analysis and Transcriptome Analysis: The Genetic Mechanisms Underlying Residual Feed Intake in Chickens.

New Insights From Imputed Whole-Genome Sequence-Based Genome-Wide Association Analysis and Transcriptome Analysis: The Genetic Mechanisms Underlying Residual Feed Intake in Chickens.

Poultry feed constitutes the most important price in poultry manufacturing, estimated to be as much as 70% of the whole price. Moreover, there’s strain on the poultry business to extend manufacturing to fulfill the protein demand of people and concurrently scale back emissions to guard the surroundings.

Therefore, enhancing feed effectivity performs an essential function to enhance income and the environmental footprint in broiler manufacturing.

In this research, utilizing imputed whole-genome sequencing knowledge, genome-wide affiliation evaluation (GWAS) was carried out to establish single-nucleotide polymorphisms (SNPs) and genes related to residual feed consumption (RFI) and its element traits. Furthermore, a transcriptomic evaluation between the high-RFI and the low-RFI teams was carried out to validate the candidate genes from GWAS.

The outcomes confirmed that the heritability estimates of common every day achieve (ADG), common every day feed consumption (ADFI), and RFI have been 0.29 (0.004), 0.37 (0.005), and 0.38 (0.004), respectively. Using imputed sequence-based GWAS, we recognized seven important SNPs and 5 candidate genes [MTSS I-BAR domain containing 1, folliculin, COP9 signalosome subunit 3, 5′,3′-nucleotidase (mitochondrial), and gametocyte-specific factor 1] related to RFI, 20 important SNPs and one candidate gene (inositol polyphosphate multikinase) related to ADG, and one important SNP and one candidate gene (coatomer protein complicated subunit alpha) related to ADFI. After performing a transcriptomic evaluation between the high-RFI and the low-RFI teams, each 38 up-regulated and 26 down-regulated genes have been recognized in the high-RFI group.

Furthermore, integrating regional conditional GWAS and transcriptome evaluation, ras-related dexamethasone induced 1 was the one overlapped gene related to RFI, which additionally recommended that the area (GGA14: 4767015-4882318) is a brand new quantitative trait locus related to RFI.

In conclusion, utilizing imputed sequence-based GWAS is an environment friendly technique to establish important SNPs and candidate genes in rooster. Our outcomes present precious insights into the genetic mechanisms of RFI and its element traits, which might additional enhance the genetic achieve of feed effectivity quickly and cost-effectively in the context of marker-assisted breeding choice.

New Insights From Imputed Whole-Genome Sequence-Based Genome-Wide Association Analysis and Transcriptome Analysis: The Genetic Mechanisms Underlying Residual Feed Intake in Chickens.
New Insights From Imputed Whole-Genome Sequence-Based Genome-Wide Association Analysis and Transcriptome Analysis: The Genetic Mechanisms Underlying Residual Feed Intake in Chickens.

Genome-wide genetic construction and choice signatures for coloration in 10 conventional Chinese yellow-feathered rooster breeds.

Yellow-feathered chickens (YFCs) have an extended historical past in China. They are well-known for the dietary and business significance attributable to their yellow coloration phenotype. Currently, there’s a big paucity in data of the genetic determinants liable for phenotypic and biochemical properties of those iconic chickens.

This research aimed to uncover the genetic construction and the molecular underpinnings of the YFCs trademark coloration.The whole-genomes of 100 YFCs from 10 main conventional breeds and 10 Huaibei partridge chickens from China have been re-sequenced. Comparative inhabitants genomics based mostly on autosomal single nucleotide polymorphisms (SNPs) revealed three geographically based mostly clusters among the many YFCs.

Compared to different Chinese indigenous rooster genomes integrated from earlier research, a more in-depth genetic proximity inside YFC breeds than between YFC breeds and different rooster populations is clear. Through genome-wide scans for selective sweeps, we recognized RALY heterogeneous nuclear ribonucleoprotein (RALY), leucine wealthy repeat containing G protein-coupled receptor 4 (LGR4), solute provider household 23 member 2 (SLC23A2), and solute provider household 2 member 14 (SLC2A14), apart from the classical beta-carotene dioxygenase 2 (BCDO2), as main candidates pigment figuring out genes in the YFCs.

We present the primary complete genomic knowledge of the YFCs. Our analyses present phylogeographical patterns among the many YFCs and potential candidate genes giving rise to the yellow coloration trait of the YFCs. This research lays the muse for additional analysis on the genome-phenotype cross-talks that outline essential poultry traits and for formulating genetic breeding and conservation methods for the YFCs.

Notes on Nomenclature

At the Gene Mapping Workshop held during the 22nd Conference of the International Society for Animal Genetics (ISAG) held in East Lansing, Michigan, USA it was resolved unanimously that animal gene nomenclature should “follow the rules for human gene nomenclature, including the use of identical symbols for homologous genes and the reservation of human symbols for yet unidentified animals genes”. [Animal Genetics (1991) 22, 97-99] The report from the workshop cited the human gene nomenclature rules and lists of mapped genes as published in Cytogenetics and Cell Genetics proceedings of the Human Gene Mapping Workshops. Approved human gene symbols and nomenclature rules can now be accessed through the Web site maintained by the HUGO Gene Nomenclature Committee

The HUGO Gene Nomenclature Committee is responsible for approving and implementing unique human gene symbols and names, and works closely with the Mouse Genome Database and other organism databases. Considerable efforts are made to approve symbols acceptable to workers in the field, but sometimes it is not possible to use exactly what has previously appeared in the literature. In such cases the previously used symbols are listed as aliases for the approved nomenclature in the Human Gene Nomenclature Database (Genew) and LocusLink, to allow retrieval of all the information available for each gene.


Using ARKdb

ARKdb can be queried in a number of ways. The main ways of querying are by locus/marker, by published reference or by map. You can also query by clone or library, although these tend to be less well-used areas of the system. Each species database is accessed in the same way. Brief details of the options available are given below. A more detailed guide to the text-based interfaces (locus and reference queries) can be found using the help links in the relevant query pages..

You can query for a locus based on its symbol, its description (name), chromosome position or marker type. You can also limit the search to markers mapped on a specific mapping population and further refine this search by limiting the search to a particular mapping cross sex. However, you should be aware that it is possible to generate queries that return no markers simply by choosing the incorrect sex in this form, so the option should only be used if you really know what you are doing. You can also partially customise the report format by choosing to display primer details (in which case the query will return symbol, primer sequences and EMBL accession numbers) and sort criteria.
You can look for specific references by full or partial title, author, journal name or publication year. Alternatively you can use the internal database identifier unique to each reference (accession number).
You can query for maps using either the “advanced” or the “simpler” interface. Maps are selected by species, chromosome and published map name – you can alternatively use your own data to generate maps.
You can query the database for details of clones used in mapping experiments. The searchable fields are Clone Name, Vector and Cloning Sites.
You can query the database for details of libraries from which clones used in mapping experiments were derived. The searchable fields are Library Name, Vector and Host.

History of ARKdb

ARKdb arose initially from the needs of a project co-ordinated at Roslin Institute to map the pig genome. The original intention was to utilise the Jackson Laboratory’s then current mouseGBASE database design, and this was actually implemented for pigs, chickens and sheep. However, it soon became clear that the nature of genome mapping in farmed animal species differed significantly from the methods and population types employed in mouse genetics. These differences meant that the schema we had didn’t fit our data requirements and the decision was taken to develop our own database system.

Because we needed a system that would work for all of the species that we work with at Roslin, we tried to make the design as generic as possible. We hope that we have managed to strike a balance between generalisation and utility. In any case, the number of species that have adopted the ARKdb database system for their genome mapping informatics needs extends considerably beyond those initially in mind when we established the schema.

ARKdb species databases at this site