Jarvis et al. (2014)

Reference https://link.springer.com/article/10.1007/s00412-018-0685-6

Address of the bookmark: http://tree.bio.ed.ac.uk/software/figtree/

More recently, we've been enthused about using k-mer based similarity measures and computing and searching k-mer-based sketch search databases for all the things.

But I haven't spent too much talking about using k-mers for taxonomy, although that has become an ahem area of interest recently, if you read into our papers a bit.

In this blog post I'm going to fix this by doing a little bit of a literature review and waxing enthusiastic about other people's work. Then in a future blog post I'll talk about how we're building off of this work in fun! and interesting? ways!

Address of the bookmark: http://ivory.idyll.org/blog/2017-something-about-kmers.html

1. Update new things continuously
As per my personal experience, it’s not always easy to work as a bioinformatician!  There are couple of reasons to say that; First computational part of biology make our life’s a little harder compared to other professional categories. The fact - for instance - that the technology cycle in the bioinformatics world is very short, the actual knowledge becomes outdated in a few months or years. Therefore, we need to learn continuously - new things get important. Second, to stay on top of things we really need the strong will to be good at our job. That's probably the most important characteristic to bioinformatician. They are usually an excellent knowledge worker with great technical abilities, and have the will to be that over decades!

2. Avoid the sentence "I did not know what to do!"
In our computational biology lab, we generally face lots of technical problems. But as you know, it's impossible to know everything to do the computational biology jobs ( Yup.. because you need diverse and multidisciplinary knowledge to understand biological problems and resolve their respective solutions), therefore it's absolutely necessary that a bioinformatician finds its way through a new topic. How I typically do that is I use google and I talk to other experts in our laboratory or online biostar community to find out what they think. "I did not know what to do!" should not be an argument for us.

3. To make oneself useful
Several time it does happen, you finished our task earlier than expected; in such cases if you have some time left then: Take a coffee and play chess; reversi, etc. In my case I take a rest. Afterwards I think about what I could do that helps the team to achieve its targets, 'cause some of my team mates probably didn't finish! (at least if I didn't met them at coffee bar !!)

4. Care for all
During my rigorous research duration; I attended several workshop organized by my University departments. I had a discussion with other research fellow, professors; I generally ask … what it really takes to make a team successful or to be a successful research leader. They always said: "Well, you need some caring people!" I think there is a lot truth in that statement. If we do not care about quality, timelines, good team culture, respectful communication (!!), clean code, if all this doesn’t matter to us, then I believe the probability is higher that we fail in research and analysis.

5. Be good with people
Because bioinformatician and computational biologist jobs typically involves to work in a (most wanted J cross-departmental!) team, therefore it's important that we're (more or less) good in dealing with other individuals. Everyone have their own strengths and weaknesses, just like us. It's important to treat all the research team mates with respect, regardless of their technical competence or contributions. Of course, sometimes people deserve a clear statement (!!!), but try to do these things one-on-one. Make sure nobody loses his face. Attend the meetings at the coffee bar; be good at table top soccer and go out once in a while to have a beer with your team. You know what I'm talking about.

At the end of a week I look back and I ask myself what I have produced. This could be paperwork, community days or (best!!) programming code. Always remember there is always a solution to a problem. Most of the times there are at least three solutions. So, don’t just blame, suggest a solution.

That's it. I am looking forward to your thoughts and comments!

There are several pre-computed trees available for display, including the main Tree Of Life, described in Ciccarelli, et al., 2006. In addition to the precomputed trees, users can upload and display personal trees and data, using the 'Data upload' page or through a personal user account.

Address of the bookmark: http://itol.embl.de/

Other tools

https://github.com/shenwei356/taxonkit

• ETE, version: 3.1.1
• BioPython, version: 1.73
• taxadb, version: 0.10.1
• TaxonKit, version: 0.5.0

Address of the bookmark: https://pypi.org/project/ete3/3.1.1/

Address of the bookmark: http://christophergandrud.github.io/d3Network/

General information.

Field terminator is "\t|\t"

Row terminator is "\t|\n"

nodes.dmp file consists of taxonomy nodes. The description for each node includes the following

fields:

tax_id -- node id in GenBank taxonomy database

  parent tax_id -- parent node id in GenBank taxonomy database

  rank -- rank of this node (superkingdom, kingdom, ...) 

  embl code -- locus-name prefix; not unique

  division id -- see division.dmp file

  inherited div flag  (1 or 0) -- 1 if node inherits division from parent

  genetic code id -- see gencode.dmp file

  inherited GC  flag  (1 or 0) -- 1 if node inherits genetic code from parent

  mitochondrial genetic code id -- see gencode.dmp file

  inherited MGC flag  (1 or 0) -- 1 if node inherits mitochondrial gencode from parent

  GenBank hidden flag (1 or 0)            -- 1 if name is suppressed in GenBank entry lineage

  hidden subtree root flag (1 or 0)       -- 1 if this subtree has no sequence data yet

  comments -- free-text comments and citations

Taxonomy names file (names.dmp):

tax_id -- the id of node associated with this name

name_txt -- name itself

unique name -- the unique variant of this name if name not unique

name class -- (synonym, common name, ...)

Divisions file (division.dmp):

division id -- taxonomy database division id

division cde -- GenBank division code (three characters)

division name -- e.g. BCT, PLN, VRT, MAM, PRI...

comments

Genetic codes file (gencode.dmp):

genetic code id -- GenBank genetic code id

abbreviation -- genetic code name abbreviation

name -- genetic code name

cde -- translation table for this genetic code

starts -- start codons for this genetic code

Deleted nodes file (delnodes.dmp):

tax_id -- deleted node id

Merged nodes file (merged.dmp):

old_tax_id                              -- id of nodes which has been merged

new_tax_id                              -- id of nodes which is result of merging

Citations file (citations.dmp):

cit_id -- the unique id of citation

cit_key -- citation key

pubmed_id -- unique id in PubMed database (0 if not in PubMed)

medline_id -- unique id in MedLine database (0 if not in MedLine)

url -- URL associated with citation

text -- any text (usually article name and authors).

-- The following characters are escaped in this text by a backslash:

-- newline (appear as "\n"),

-- tab character ("\t"),

-- double quotes ('\"'),

-- backslash character ("\\").

taxid_list -- list of node ids separated by a single space

Address of the bookmark: https://www.r-bloggers.com/2022/03/r-shiny-in-life-sciences-top-7-dashboard-examples/