Frequently asked questions for GOMI

1. Can I compare my one breed sample with all recognised goat breed of India?

Ans. Yes, the GOMI server database contains 55000 allelic data which is from 22 recognized registered goat breeds of India viz. Blackbengal, Ganjam, Gohilwari, Jharkhandblack, Attapaddy, Changthangi, Kutchi, Mehsana, Sirohi, Malabari, Jamunapari, Jhakarana, Surti, Gaddi, Marwari, Barbari, Beetal, Kanniadu, Sangamnari, Osmanabadi, Zalawari and Cheghu. Each goat breed in the database is represented by 50 genetically unrelated goat animals having true to the breed type phenotype from native breeding tract of each goat breed. They work as standard reference data in computational analysis for breed prediction. 

2. If I have admixture, can I get quantified degree of admixture?

Ans. Yes, if your goat query is with an unknown or a crossbred or breed with potentially different degree of admixture, you will get degree of admixture with quantified value in a pie chart.

3. From where can I get my goat breeds genotype data (DNA data) for breed test using this server?

Ans. Your blood samples/carcass samples/biological samples can be sent to any lab with microsatellite genotyping facility or to any commercial vendors along with primer sequence given on the website or its link to get commercial service.  

4. What is the probability of getting wrongly predicted goat breed?

Ans. The DNA based prediction gives very high accuracy and in our method, it  has been up to more than 95 %, however if the sample belongs to a new breed which has not been used in the training data, that may not give as high accuracy as mentioned. 

5. If genotype value for any locus is missing, then what would be done?

Ans. If genotype value for any locus is missing, one should put the value of zero on server while uploading the data. Such missing value of one or two locus usually do not compromise with accuracy of breed prediction. 



Allele: Any of two or more genes/DNA sequences that have the same relative position on homologous chromosomes and are responsible for alternative characteristics
Bayesian Network: A Bayesian network, is a probabilistic graphical model (a type of statistical model) that represents a set of random variables and their conditional dependencies
Breed: A group of organisms having common ancestors and certain distinguishable characteristics
CGI PERL: CGI PERL is a stable, complete and mature solution for processing and preparing HTTP requests and responses. Major features including processing form submissions, file uploads, reading and writing cookies, query string generation and manipulation, and processing and preparing HTTP headers. Some HTML generation utilities are included as well.
Distribution: An order or pattern formed by the tendency of a sufficiently large number of observations to group themselves around a central value.
DNA marker: Alleles of DNA polymorphisms, used as experimental tags to keep track of an individual, a tissue, a cell, a nucleus, a chromosome, or a gene. Stated another way, any character that acts as a signpost or signal of the presence OR location of a gene OR heredity characteristic in an individual of a population
Genotype: The actual alleles present in an individual; the genetic makeup of an organism.
Locus: The position on a chromosome of a gene or a particular segment of DNA (marker).
Microsatellite: Tandem repeats of short simple DNA sequence, generally of 1-6 bases.
Naïve Bayes: A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions.
Parameters: Parameters are used to identify a characteristic, a feature, a measurable factor that can help in defining a particular system
PCR: A method of DNA analysis that exponentially amplifies a specific DNA sequence or region allowing rapid DNA analysis
Population: A population is all the organisms that both belong to the same group or species and live in the same geographical area.
Primer: A short oligonucleotide sequence used to amplify DNA sequences in a polymerase chain reaction
WEKA: Weka is a collection of machine learning algorithms for solving real-world data mining problems. It is written in Java and runs on almost any platform.