Hot answers tagged bioinformatics
As 'c.' stands for 'coding' sequence (instead of 'g.' for 'genomic'), according the HGVS guidelines it denotes the A to T substitution at nucleotide -35 of an intron (in the coding DNA positioned between nucleotides 1842 and 1843).
If what you are after are the structural co-ordinates of particular amino acids crystalized individually (i.e. considered as small molecules independently of any role in proteins), then you should find them along with other small molecules in the Cambridge Structural Database.
I am also a little confused about what exactly do you mean by "data type". As far as I know in programming "data type" is something you use to obstruct 0s and 1s e.g string, int and array is the way you'd reference your own data and something that language interpreter/compiler can understand and allocate right memory for. Whereas type of data you can get in ...
Gene-1 Gene-2 Gene-3 Gene-1 100.00 16.18 20.35 Gene-2 16.18 100.00 29.66 Gene-3 20.35 29.66 100.00 Gene-1 and Gene-1 have 100% similarity (and all the other diagonal elements). Gene-1 and Gene-2 have 16.15% similarity, and so on. Therefore the matrix is symmetric M(i,j) = M(j,i). You can also show just the upper ...
Normalization of expression data is a big topic with new methods being published regularly. When approaching something like this you generally want look at people who have done similar things to what you've done, and then once you understand why they did what they did, you can ask what you need to do to answer your questions. Always keep your biological ...
TLDR; Answer: You could consider this particular residue to belong to both structural elements, but it's a tricky call and depends on the method of secondary structure assignment. Ambiguous secondary structure allocation comes up fairly often. Whilst obviously, not many people will be able to use this protein specifically, the below approach could be ...
DP is the total number of read bases spanning a particular position. If you add up the different AD, you should get a number close to DP, the difference being merely in how the reads are filtered in either set of numbers.
You can use BLAST or NCBI resources to do an analysis of something like the duplication of a gene in the HIV virus, for example. If you're looking for a place to start, I would recommend just learning the NCBI database resources.
For mouse gene names and other details you should refer to the mouse genome informatics database (it is a standard organism-specific database like flybase [Drosophila], SGD [yeast], etc). However, it is much better to work with RefSeq IDs. Moreover, miRNAs are seldom referred to, using their gene names. Always stick to the miRNA naming convention that is ...
Generally speaking for RNA-seq data, you don't want to correct for GC content or other gene level effects (e.g. length) because you compare expression values between conditions WITHIN a gene. For this reason, it is recommended to use raw counts and not normalized values such as FPKM. See Section 2.7 of the edgeR user manual. This recent benchmark comparing ...
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