Adapter sequences:
Optimal enzymes for amplifying sequencing libraries.
Quail, M. a et al. Nat. Methods 9, 10-1 (2012).

GAGE:
GAGE: A critical evaluation of genome assemblies and assembly algorithms.
Salzberg, S. L. et al. Genome Res. 22, 557-67 (2012).

KMC:
Disk-based k-mer counting on a PC.
Deorowicz, S., Debudaj-Grabysz, A. & Grabowski, S. BMC Bioinformatics 14, 160 (2013).

Kraken:
Kraken: ultrafast metagenomic sequence classification using exact alignments.
Wood, D. E. & Salzberg, S. L. Genome Biol. 15, R46 (2014).

MUMmer:
Versatile and open software for comparing large genomes.
Kurtz, S. et al. Genome Biol. 5, R12 (2004).

R:
R: A language and environment for statistical computing.
R Core Team (2013). R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/.

RATT:
RATT: Rapid Annotation Transfer Tool.
Otto, T. D., Dillon, G. P., Degrave, W. S. & Berriman, M. Nucleic Acids Res. 39, e57 (2011).

SAMtools:
The Sequence Alignment/Map format and SAMtools.
Li, H. et al. Bioinformatics 25, 2078-9 (2009).

Trimmomatic:
Trimmomatic: A flexible trimmer for Illumina Sequence Data.
Bolger, A. M., Lohse, M. & Usadel, B. Bioinformatics 1-7 (2014).

To cite BioNumbers please refer to: Milo et al. Nucl. Acids Res. (2010) 38: D750-D753. When using a specific entry from the database it is highly recommended that you also specify the BioNumbers 6 digit ID, e.g. "BNID 100986, Milo et al 2010". 

Address of the bookmark: http://bionumbers.hms.harvard.edu/

Postdoc position (m/w/d)

in the field of computational evolutionary microbiology is available
for an initially limited period of 36 months at the earliest possible
date. The weekly working time corresponds to 100% of full employment
(If the legal requirements under collective bargaining law are met, the
tariff grouping is carried out up to pay scale 13 TV-L. The obligation
to teach amounts to 4 hours.

The Genomic Microbiology Group research interests are focused on
microbial genome evolution with an emphasis on the study of lateral gene
transfer. In our research we use both computational and experimental
approaches (see www.uni-kiel.de/genomik). The position offers the
opportunity to develop an independent research profile within the group
research focus. The successful applicant is expected to be involved
in teaching of bioinformatics and molecular evolution, including the
development of teaching materials (lectures/exercises/short videos).

Your profile:
· Doctoral or PhD degree in Molecular Evolution, Bioinformatics or
related fields.
· Knowledge and experience in programming (e.g., Python) and
biostatistical analysis (e.g., with R or MatLab).
· Any of the following expertise is an advantage: the analysis of
genomic or transcriptomic data, phylogenetic reconstruction,
comparative genomics.
· Good oral and written communication skills in English.
· Ability to teach in German is an advantage (alternatively, an
indication to do so from the 2nd year on).
· Skills and motivation to communicate and interact with other
scientists.

The Christian-Albrechts-University sees itself as a modern and
cosmopolitan employer. We welcome your application regardless of your age,
gender, cultural and social background, religion, ideology, disability
or sexual identity. We promote equality of the sexes.

The Christian-Albrechts-University is committed to the employment of
people with disabilities. Preference will be given to applications from
severely handicapped persons and persons of equal standing, provided
they are suitable.

We expressly welcome applications from people with a migration background.

For enquiries regarding the position, teaching obligations and research
topic please contact Prof. Tal Dagan: tdagan@ifam.uni-kiel.de.

Applications should be submitted by email to Mrs. Haacks
(dhaacks@ifam.uni-kiel.de) as a single PDF and include: (1) a letter of
motivation (max 1 page, Arial 11, line spacing 1.15), (2) CV, (3) PhD
certificate. Please use 'GMG postdoc application - [your name]'
as a subject.

Please, refrain from sending us application photos.

Application deadline: August 31 2021 or until the position is
filled. Interviews will take place during September/October 2021. The
planned starting date for the position is flexible (but in 2021).

Address of the bookmark: https://biokit.readthedocs.io/en/latest/index.html

More at http://changlab.stanford.edu/index.html

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.

The biggest and perhaps most fearsome of the world's big cats, the tiger, shares 95.6 percent of its DNA with humans' cute and furry companions, domestic cats.

The new research showed that big cats have genetic mutations that enabled them to be carnivores. The team also identified mutations that allow snow leopards to thrive at high altitudes.

Reference:

http://www.nbcnews.com/science/your-cat-ferocious-tigers-share-lot-95-6-percent-their-4B11182690

http://timesofindia.indiatimes.com/home/environment/flora-fauna/Gene-mapping-of-tiger-completed/articleshow/22671681.cms

Paper:

http://www.nature.com/ncomms/2013/130917/ncomms3433/full/ncomms3433.html

More at http://www.hiit.fi/postdoc-call-2013