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

https://dengue-lineages.org/

Address of the bookmark: https://dengue-lineages.org/

Before you go down that path, consider this critical biological question:
Are you sequencing human cells—or bacterial contamination?

The Silent Saboteur: Mycoplasma in Cell Cultures

Mycoplasma contamination remains one of the most widespread and underdiagnosed issues in tissue culture work. Studies suggest that 15–35% of cell lines in use may be contaminated, often without visible signs. Unlike other microbial infections, Mycoplasma does not produce cloudiness, odor, or a change in pH. Many researchers won’t detect it unless they specifically test for it.

The consequences, however, are profound. Mycoplasma can significantly alter:

• Host gene expression patterns

• Cell proliferation rates

• Epigenetic profiles and chromatin accessibility

• Cytokine signaling and immune responses

In short, it can skew your results, compromise your biological conclusions, and invalidate weeks or months of research.

A Simple Diagnostic Step: Map Against Mycoplasma Genomes

If you encounter poor alignment rates to the human genome, consider mapping your reads to a Mycoplasma reference genome—or better yet, use a combined human + Mycoplasma reference. There have been cases where over half of all reads, initially assumed to be from human cells, were in fact bacterial in origin. This check is fast, easy, and could save your project.

How Contamination Happens—and Persists

Mycoplasma is small (0.1–0.3 μm), lacks a cell wall, and can pass through standard filters undetected. Common sources include:

• Contaminated reagents (e.g., FBS)

• Infected cell lines obtained from other labs

• Poor aseptic technique or shared equipment

Once present, it spreads quickly between cultures and can persist for months, silently affecting results.

Why Treatment Is Difficult

While antibiotics such as Plasmocin or BM-Cyclin are sometimes used, they often offer only partial resolution and may themselves alter cell behavior. In many cases, the best course of action is to discard the contaminated culture and start with a fresh, verified stock.

Practical Recommendations for Researchers

• Routinely test for Mycoplasma using PCR, qPCR, or fluorescence-based assays

• Incorporate contamination screens into your sequencing QC pipeline

• Use combined reference genomes when mapping ambiguous reads

• Practice strict aseptic technique and monitor all incoming cell lines

• Don’t ignore unexplained data anomalies—they might point to contamination

Closing Thought: Contamination Is a Biological Variable

It’s easy to view poor mapping as a technical issue, but sometimes the problem lies deeper—in the biology itself. Mycoplasma contamination doesn’t just interfere with sequencing; it interferes with science. As a research community, we must treat contamination not as an afterthought, but as a key variable to control.

So next time your reads won’t align, don’t just tune the aligner. Ask if your cells are telling the truth—or if they're hiding something.

(A) Senior Research Fellow (One Post): Emoluments as per DST/DBT norms.

Candidates having M.Sc. degree (with minimum of 55% marks) or equivalent in Life Sciences/Biotechnology/Bioinformatics/ Molecular Biology or any other related field with minimum of two years of post M.Sc. research experience are eligible to apply. The candidate having computer skill (Linux, Perl, Java, MySQL) and/or experience in advanced molecular biology, next generation sequencing data analysis and molecular markers analysis will be preferred.

The position is completely on temporary basis and co-terminus with the project. The initial appointment will be for one year, which can be curtailed/extended on the basis of assessment of the candidate’s performance and discretion of the Competent Authority. NIPGR reserves the right to select the candidate against the above posts depending upon the qualifications and experience of the candidates. Reservation of posts shall be as per Govt. of India norms.

Eligible candidates may apply by sending hard copy of completed application in the given format with a cover letter showing interest and attested copies of the certificates and proof of research experience. The applications should reach at the address given below within 15 days from the date of the advertisement. The subject line on envelope must be superscribed by “Application for the Post of SRF in DST - AISRF project”.

Note: ONLY hard copy of the application in the given format will be accepted.

Last date April 03, 2014

Dr. Mukesh Jain
Staff Scientist
National Institute of Plant Genome Research
Aruna Asaf Ali Marg, P.O. Box NO. 10531,
New Delhi - 110067

Advertisement: http://www.nipgr.res.in/careers/vacancies_latest.php#

GEvol is unique as it will use, for the first time, a large taxonomic group to focus on one goal: to characterise the dynamics and mechanisms of genomic innovations underlying novel traits using comparative evolutionary genomics (and related data).
Thus, projects participating in GEvol we will ask fundamental evolutionary questions such as:
1. At what level is evolution repeatable?
2. How does genomic plasticity interfere with phenotypic plasticity during evolution?
3. How do inter- and intra-specific interactions influence genomic architectures?
4. How predictable is phenotypic variation given some knowledge about the dynamics and mechanisms of underlying genome evolution?

Address of the bookmark: https://g-evol.uni-muenster.de/open-positions/

Address of the bookmark: https://github.com/cauyrd/SVfinder

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8

Address of the bookmark: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8

http://www.putonti-lab.com/uploads/4/5/3/0/45307835/s-plot2_tutorial.pdf

http://journals.sagepub.com/doi/pdf/10.1177/1176934318797354

Address of the bookmark: https://bitbucket.org/lkalesinskas/splot

The only input requierement for UPhO is a tree (or trees) in Newick format in which the leaves are named with a species idenfifier, a field separator, and sequence identifier. By default, the field separator is the character "|" but custom delimiters can be defined. Examples of trees to test UPhO are provided in the TestData folder.

Address of the bookmark: https://github.com/ballesterus/UPhO

SyRI, a pairwise whole-genome comparison tool for chromosome-level assemblies. SyRI starts by finding rearranged regions and then searches for differences in the sequences, which are distinguished for residing in syntenic or rearranged regions. This distinction is important as rearranged regions are inherited differently compared to syntenic regions.

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1911-0

Address of the bookmark: https://github.com/schneebergerlab/syri