GI (GenBank) - A GI or "GenInfo Identifier" is a sequence identifier that can be assigned to a nucleotide sequence or protein translation. Each GI is a numeric value of one or more digits. The protein translation and the nucleotide sequence contained in the same record will each be assigned different GI numbers. Every time the sequence data for a particular record is changed, its version number increases and it receives a new GI. However, while each new version number is based upon the previous version number, a new GI for an altered sequence may be completely different from the previous GI. For example, in the GenBank record M12345, the original GI might be 7654321, but after a change in the sequence is submitted, the new GI for the changed sequence could be 10529376. Individuals can search for nucleotide sequences and protein translations by GI using the UID search field in the NCBI sequence databases. Use NCBI's Sequence Revision History page to view the different gi numbers, version numbers, or update dates associated with a particular GenBank record.

GI number (sometimes written in lower case, "gi") is simply a series of digits that are assigned consecutively to each sequence record processed by NCBI. The GI number bears no resemblance to the Accession number of the sequence record

• Nucleotide sequence GI number is shown in the VERSION field of the database record
• Protein sequence GI number is shown in the CDS/db_xref field of a nucleotide database record, and the VERSION field of a protein database record

For more informatin on this topic, please visit here.

Its suggested to enter the gi numbers is the following fashion (one GI in a line)

The genetic code is the set of rules by which information encoded in genetic material (DNA or mRNA sequences) is translated into proteins (amino acid sequences) by living cells. The code defines a mapping between tri-nucleotide sequences, called codons, and amino acids. With some exceptions,a triplet codon in a nucleic acid sequence usually specifies a single amino acid. Because the vast majority of genes are encoded with exactly the same code, this particular code is often referred to as the canonical or standard genetic code, or simply the genetic code, though in fact there are many variant codes. Thus the canonical genetic code is not universal. In humans, for example, protein synthesis in mitochondria relies on a genetic code that varies from the standard genetic code.

The following genetic systems have been incorporated in the study:

• The Standard Code
• The Vertebrate Mitochondrial Code
• The Yeast Mitochondrial Code
• The Mold, Protozoan, and Coelenterate Mitochondrial Code and the Mycoplasma/Spiroplasma Code
• The Invertebrate Mitochondrial Code
• The Ciliate, Dasycladacean and Hexamita Nuclear Code
• The Echinoderm and Flatworm Mitochondrial Code
• The Euplotid Nuclear Code
• The Bacterial, Archaeal and Plant Plastid Code
• The Alternative Yeast Nuclear Code
• The Ascidian Mitochondrial Code
• The Alternative Flatworm Mitochondrial Code
• Blepharisma Nuclear Code
• Chlorophycean Mitochondrial Code
• Trematode Mitochondrial Code
• Scenedesmus Obliquus Mitochondrial Code
• Thraustochytrium Mitochondrial Code

Ambush hypothesis implies, if the reading frame of the ribosome is not zero; the earlier a stop terminates translation, the earlier mRNA and ribosomes are available for interacting correctly and it will also help in the reduction of off-frame noise. Variation in gene expression levels or noise strength shows a strong positive correlation with translational efficiency (Gheysan et al, 1982; Ozbudak et al, 2002). There are several experimental evidences of independent regulation of transcriptional units and all experiments are indicative of manipulations towards 5’ end of coding sequences.

We have implemented half gene analysis to observe such effects towards 5’ end of the coding sequences i.e. to identify the favorable positions or locations of hidden stops in coding sequences. If ambush hypothesis is correct then more hidden stops should be in 5’ portion of the gene, as costs of off-frame translation are presumably higher when frame-shifts occur near 5’ end of coding genes. We tested this aspect on primates and vertebrates mt coding sequences and found positive evidences supporting this putative event.

According to ambush hypothesis, early termination of off frame transcription should increase the efficiency of expression of a gene, because less time and resources are invested in unproductive off frame contexts. If the reading frame of the ribosome is not zero, earlier a stop terminates translation; the earlier mRNA and ribosome are available for interaction. If the ambush hypothesis is correct, hidden stops should be more frequent for large and frequently expressed genes, since costs of off-frame translation are likely to increase with gene size and expression levels.

Ambush hypothesis implies that the need for hidden stops increases with the probability of frameshifts. It seems plausible that less stable ribosomes are more likely to frameshift and vice versa. In order to resolve this assumption we performed analysis to predict any correlation between the ribosomal stability, counted as a function of predicted stability or ?G of secondary structure (Lück et al 1996, Zuker et al. 1999). Analysis with various kinds of RNAs can help to elucidate novel trends in ribosomal sequences and their correlations with process of transcription and translation can be identified along with functional relatedness.

The GC contents of the first, second, and third positions of codons, of tRNAs, of rRNAs and of spacer elements are highly correlated. In fact, the GC content accounts for 98% of the variance in coding sequences and 84% of the variance in GC content in rRNA genes (Muto and Osawa 1987). Therefore we can evaluate the effect of GC content on codon disappearance and codon reassignment (Knight et al 2001). SHIFT calculates correlation between HSC and GC % in all coding sequences to evaluate this parameter and its evolutionary implications.