Address of the bookmark: http://pacb.com/blog/the-hifi-difference-true-long-reads-vs-synthetic-long-reads/

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.

You can find the detail of reconstructed data at http://bioinfo.konkuk.ac.kr/DESCHRAMBLER/

Address of the bookmark: https://github.com/jkimlab/DESCHRAMBLER

Address of the bookmark: https://github.com/YoannAnselmetti/DeCoSTAR_pipeline

Like other indel callers, Scalpel works by performing de novo assembly of regions of interest, so that misalignment to the reference genome cannot obscure the presence of an insertion or deletion. Scalpel's innovation is to repeatedly check its assembly before comparing to the reference genome, to account for simple sequence repeats that are a regular source of error in indel calling. When Scalpel assembles an exon, it collects reads that map to that exon (including partial matches), splits them into k-mers, and creates a de Bruijn graph to span the exon; however, if it detects repeats in the map, it iteratively increases the size of the k-mers by one base until the repeats are eliminated. This ensures that the final assembly of the exon is highly accurate while minimizing compute time.

The Cold Spring Harbor team's validation of Scalpel, published over the weekend in Nature Methods, compares Scalpel's performance on a live whole exome against HaplotypeCaller and SOAPindel. The donor is an individual with serious neurological disorders, which may be linked to a high incidence of indels. One thousand indels from this individual's exome, called by one or more of the informatics pipelines, were selected for focused resequencing. This resequencing revealed a 77% true positive rate for Scalpel calls, dramatically better than the rates for either of the competing tools; Scalpel performed especially well with indels longer than five base pairs, a traditional weak point for indel callers.

Finally, the authors demonstrate Scalpel's use on a large set of genetic data from nearly 600 families who donated samples to the Simons Simplex Collection, a project of the Simons Foundation Autism Research Initiative. Scalpel found a very high enrichment for indels in children affected by autism, compared with their unaffected siblings, a pattern that persisted even after excluding common variants.

Note that the information on this page is targeted at end-users. For developers, the source code, building instructions and implementation/development resources are available on GitHub.

The Picard toolkit is open-source under the MIT license and free for all uses.

Enjoy!

Address of the bookmark: http://broadinstitute.github.io/picard/

This readme covers all necessary instructions for the impatient to get andi up and running. For extensive instructions please consult the manual.

More at https://github.com/evolbioinf/andi/

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2015/01/13/bioinformatics.btu815.full

• January 20th, 2016 - New! Bpipe 0.9.9 released!
• Download latest, all
• Documentation
• Mailing List (Google Group)

Bpipe has been published in Bioinformatics! If you use Bpipe, please cite:

Sadedin S, Pope B & Oshlack A, Bpipe: A Tool for Running and Managing Bioinformatics Pipelines, Bioinformatics

Address of the bookmark: http://docs.bpipe.org/

• Speed: Prodigal is an extremely fast gene recognition tool (written in very vanilla C). It can analyze an entire microbial genome in 30 seconds or less.
• Accuracy: Prodigal is a highly accurate gene finder. It correctly locates the 3' end of every gene in the experimentally verified Ecogene data set (except those containing introns). It possesses a very sophisticated ribosomal binding site scoring system that enables it to locate the translation initiation site with great accuracy (96% of the 5' ends in the Ecogene data set are located correctly).
• Specificity: Prodigal's false positive rate compares favorably with other gene identification programs, and usually falls under 5%.
• GC-Content Indifferent: Prodigal performs well even in high GC genomes, with over a 90% perfect match (5'+3') to the Pseudomonas aeruginosa curated annotations.
• Metagenomic Version: Prodigal can run in metagenomic mode and analyze sequences even when the organism is unknown.
• Ease of Use: Prodigal can be run in one step on a single genomic sequence or on a draft genome containing many sequences. It does not need to be supplied with any knowledge of the organism, as it learns all the properties it needs to on its own.
• Open Source: Prodigal source code is freely available under the General Public License.

Address of the bookmark: http://prodigal.ornl.gov/