As the title says, why we have the motif(s) we have in polyadenylation? The first idea is biochemistry, something we see here
Let’s keep some helpful links of bioinformatics blogs and such.
>Bioinformatics Adventures in Unix:
Almost in any bioinformatics process you are going to use an alignment tool. The most known is BLAST. Although, for the work I have BLAT is better, it gives me the chromosome as an output and i don’t need to write extra code to search the sequence ID on GenBank.
But what are the differences between those two alignment tools, except the S in their name.
First of all, the algorithms are structured differently. On DNA, BLAT works by keeping an index of an entire genome in memory. Thus, the target database of BLAT is not a set of GenBank sequences, but instead an index derived from the assempbly of the entire genome. By default, the index consists of all non-overlapping 11-mets except for those heavily involved in repeats, and it uses less that a gigabyte of RAM. This smaller size means that BLAT is far more easily mirrored than BLAST. BLAT of DNA is designed to quickly find sequences of 95% and greater similarity of length 40 bases or more. It may miss more divergent or shorter sequence alignments.
On proteins, BLAT uses 4-mers rather than 11-mers, finding protein sequences of 80% and greater similarity to the query of length 20+ amino acids. The protein index requires slightly more than 2 GB RAM. in practice due to sequence divergence rates over evolutionary time DNA BLAT works well within humans and primates, while protein BLAT continues to find good matches within terrestrial vertebrates and even earlier organisms for conserved proteins. Within humans, protein BLAT gives a much better picture of gene families (paralogs) than DNA BLAT. However, BLAST and psi-BLAST at NCBI can find much more remote matches.
From a practical standpoint, BLAT has several advantages over BLAST:
- speed (no queues, response in seconds) at the price of lesser homology depth
- the ability to submit a long list of simultaneous queries in FASTA format.
- five convenient output sort options
- a direct link into the UCSC browser
- alignment block details in natural genomic order
- an option to launch the alignment later as part of custom track
An easy programming language that is widely used in Bioinformatics.
Let’s start with some links about it before we start learning.
- Official Python site
- Introduction to Programming using Python
- Beginning Python for Bioinformatics
- An interactive Python tutorial
- Think Python: How to think like a computer scientist
One of the things that I hate is having a long list of favorites on my browser, browser history and cookies, the browser ones, not the ones that I dip on my coffee. That’s why I will add some links here to keep them in mind.
In this article I will store some Programming Links.
There are tons of Bioinformatics «problems» that can be solved with just one line of code/commands, hence the title. Here is the link that I found:
Useful bash one-liners for bioinformatics
For the bioinformatics work I had to do in the research center I had to work with FASTA files, but the lab had Illumina machine that as output gives BAM files. So the question was clear, «how do I convert a BAM file to FASTA». If you search that on the internet you will realize that it is one of the most common problems/questions that people who work in bioinformatics have.
So I started looking around. I thought to use R/Bioconductor to do it, that doesn’t seem as a good idea, because R/Bioconductor is developed for statistical research on genomic data, which means, you have the file, you open it and the R/Bioconductor helps you to view them, and do statistics and graphs. Though I kinda found a way, didn’t manage to do something but here it is.
As in most blogs the first article is the reason that the blog was created. Well I won’t reinvent the wheel, so the reason of this blog is to keep a track of the things I learn about some stuff, mostly science, as this is the field of my work.
Let’s start from the domain of the blog. The general idea is that the knowledge is chaotic, you can never learn too much and most importantly you can’t keep the knowledge you gain in your head nice and tidy. That’s why I picked the name «khaognosis» (from chaos [chaos was already in use :P] and gnosis). So I will try to keep the knowledge I try to gain in here, at least a portion of it, and also to learn about blogging, as this is my first attempt to make a blog.
In this blog, the articles will be mostly in English and/or in Greek. The topic will be about bioinformatics, biology, physics, programming and probably anything that has to do with knowledge. Also, I am making this blog as a personal workblog. I don’t expect people finding it great or at least reading it, but I am creating it to help myself and anyone else that will find it useful or charming to learn or maintain knowledge.