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9

This question drops firmly into the lap of molecular evolution and the constraints that are placed upon genes by the forces of mutation, selection, drift and recombination. There are numerous situations, particularly gene duplication, that can result in a gene that is free from the selective constraints of it's parent, many of which will accumulate so many ...


9

You might be interested in the INK4A locus (chromosome 9p), encoding both p19 and p16 genes, very close to p15. You can read a description here. All three proteins are known experimentally to exist. Now, whether these are two different genes or the same gene with alternative splicing and start sites leading to different reading frames it's up to discussion....


5

Let's try and answer all three parts of your question. Sequencing The general method is the same. Sequencing is just sequencing. But as for every single sequencing, there are factors to consider and protocols to be selected. One important thing is, that you might want comparably long reads to cope with the repeats and the general large size of plant ...


4

It is because the two genes overlap: CLCN6 is on the plus strand and MTHFR is on the minus strand. The SNP is annotated to both because it falls in a region of the genome claimed by both genes.


4

Well here's my take. Too bad there is no open access to this article. I see that they only had statistics for the top 50 annotations, which implies a fairly manual process - you are going to have to read a bunch of articles or abstracts to decide directly whether the software is right. In this case, the authors are using a weighted latent semantic ...


3

EDIT Personal correspondence to representative at FlyBase.org In FlyBase we have been gathering information about genes and phenotypes for over 20 years including information from papers and resources older than that. We annotate gene models on the Drosophila melanogaster genome assembly and when possible associate those annotations with other ...


3

In general, the compactnes of genomes is a characteristic of prokaryotes, but there are several eykaryotes that have overlapping genes: many parasites and endosymbionts. The best studied of these are the fungal parasites of the phylum microsporidia and the nucleomorphs (remnant nuclei of algal endosymbionts in cryptophytes and chlorarachniophytes). cDNA ...


3

Agree with mbq - titan is the longest gene I know of and it has well over 100 exons. Titin and dystrophin are well characterized genetically and not predictions. titin is the champion exoner with 363 exons. Its only examples like this that can allow the gene predictors to run on as long as they do I think as the predictions are trimmed heuristically to ...


3

I doubt there is really any direct limitation; the best test would be to check if the size matches, i.e. that 1kbp gene with 100 exons would rather have to have way too short introns. Quick search over NCBI Genes show even a 317-exon gene, although all those edge cases seem to be some unclear siblings of titin which is just huge on its own.


2

This is the FlyBase page for the example gene: Dsim\GD10095. There, you have a section "orthologs", linking to OrthoDB. So my suggestion is: Find the list of synonyms for D. simulans on FlyBase (perhaps here?), download the Drosophila section of OrthoDB, and finally find the 1:1 orthologs.


2

You might find some success with using WebApollo or JBrowse WebApollo allows you to perform many manual annotation functions for your genes, such as editing exon boundaries, fixing coding sequences, and merging or splitting gene predictions, which looks like what might have been done in the UCA-At2g42245. It can also be used for adding various attributes, ...


2

The arrow direction denote transcription direction and its location denotes the transcription start site. The circle is probably the transcription terminator (I cannot access this article at the moment, but this is what it should mean). The purple boxes refer to the protospacers which are derived from foreign DNA. These elements help in recognizing and ...


1

I had the same problem This how i solved it, i found the solution somewhere here i dont remember where and it worked for me. macs.anno <- annotatePeakInBatch(gr_broadPeak, AnnotationData=TSS.human.GRCh38, output="both", maxgap=5000L) macs.annoL=addGeneIDs(macs.anno,"org.Hs.eg.db",c("symbol", "genename","entrez_id"))...


1

There is no such convention. People, mostly name their primers arbitrarily (or just FP and RP) and mention in the text about their binding sites. In general, in the context of specific genes, the positions are described with respect to the transcription start site (TSS).


1

GO is based on experimental evidences; biomart is quite good. GO:0006383 seems to be the right tag. If you want to use the ChIPseq data then you can do this: Map the ChIPseq reads to the genome. If you already have the tracks then you need not do the mapping. You would have the position of the reads in the genome. Now count the number of reads mapping to ...



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