Deadline: 30.09.2019

Requirement:
Microsoft SQL Server: Administration, Transact-SQL
Machine Learning
Know Deutsch und Englisch langauge

The changes in nr preserve the taxonomic diversity of the entries in the database while reducing the number of titles for identical sequences. GenPept accessions are still accessible via www.ncbi.nlm.nih.gov/protein/$GENBANK_ACCESSION or the IPG website https://www.ncbi.nlm.nih.gov/ipg/.

The "Identical Proteins" link in the alignments section of the webBLAST results takes you to a full list of all accessions associated with a sequence.

For BLAST+ users downloading nr_v5: the database is now approximately 50% smaller, resulting in faster downloads and BLAST searches, and smaller disk space requirements. The database is downloadable at:  ftp://ftp.ncbi.nlm.nih.gov/blast/db/v5/

For BLAST+ there is a cleanup script to help you manage the transition to this smaller database. The script removes unused database volumes: ftp://ftp.ncbi.nlm.nih.gov/blast/temp/cleanup-blastdb-volumes.py

Here are the new rules on how we keep titles in nr_v5:

1.    We keep all refseq, swissprot, pir and PDB titles.

2.    We keep any GenPept titles with a TAXID that has not already been seen in the record.

3.    We keep at least five GenPept titles regardless of whether the TAXIDS have been seen before or not in this record.

DEG is freely available at the website http://tubic.tju.edu.cn/deg or http://www.essentialgene.org.

Address of the bookmark: http://www.essentialgene.org/

Address of the bookmark: https://rna.sysu.edu.cn/chipbase/

If you have a license key for Modeller 8 or 9, there is no need to reregister for Modeller 9.13 - the same license key will work. (It won't do any harm to reregister if you want to, though!)

9.13 is primarily a bugfix release relative to the last public release(9.12). Major user-visible changes include:

# Modeller now includes a variety of SOAP (statistically optimized atomic potential) scores for assessing proteins, loops, and interfaces.

# The Lennard-Jones interaction energy is now artificially truncated at very short distance; this makes simulations with poor starting conditions much less likely to 'blow up'.

# model.get_insertions(), model.get_deletions() and model.loops() now have an include_termini option; if False, residue ranges that include chain termini are excluded from the output.

See the Modeller manual for a full change log: http://salilab.org/modeller/9.13/manual/node39.html

If you encounter bugs in Modeller 9.13, please see http://salilab.org/modeller/9.13/manual/node10.html for information on how to report them.

Reference:

http://salilab.org/modeller/

Please cite:

Kim, D., Paggi, J.M., Park, C. et al. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat Biotechnol 37, 907–915 (2019). https://doi.org/10.1038/s41587-019-0201-4

Address of the bookmark: http://daehwankimlab.github.io/hisat2/download/#h-sapiens

Scientists who study antimicrobial resistance (AMR) face a similar challenge.

When scientists find an AMR gene, it shows that resistance exists. However, it does not always reveal where the gene is or what is around it. This genetic context can give important clues about how resistance genes work, change over time, and possibly move between bacteria.

A recent study in NAR Genomics and Bioinformatics introduces Sarand (https://github.com/beiko-lab/sarand), a computer-based method created to explore these genetic neighborhoods. Today, metagenomic sequencing lets scientists study DNA from whole groups of microbes, like those in wastewater. But these samples have DNA from thousands of organisms, creating millions of pieces. Putting the original genomes back together is a bit like trying to rebuild several books from a pile of shredded pages.esSarand uses a different method. It works with something called a metagenomic assembly graph, which is like a map showing how DNA fragments might connect. Once it finds an AMR gene, Sarand traces the connected DNA sequences before and after the gene to find its surrounding genetic neighborhood.od.

The method also uses sequencing coverage to help avoid false connections and mixed-up sequences.

The researchers tested Sarand with both simulated data and real samples from city sewage. Their results showed that Sarand could find AMR gene neighborhoods that other methods might miss. The authors reported that Sarand was on average 47% more sensitive than assembled contigs and 134% more sensitive than MetaCherchant in their tests.

This study marks an important change in AMR research.

Instead of just asking, “Which resistance genes are present?”, scientists can now start to ask:

“Where are these genes, what are they connected to, and what can their neighborhood tell us?”

When it comes to antimicrobial resistance, the gene might be the clue, but its neighborhood tells the whole story.

Read more about it @ https://academic.oup.com/nargab/article/8/3/lqag066/8733748

Results: Here we present poRe, a package for R that enables users to manipulate, organize, summarize and visualize MinION nanopore sequencing data. As a package for R, poRe has been tested on Windows, Linux and MacOSX. Crucially, the Windows version allows users to analyse MinION data on the Windows laptop attached to the device.

Availability and implementation: poRe is released as a package for R at http://sourceforge.net/projects/rpore/ . A tutorial and further information are available at https://sourceforge.net/p/rpore/wiki/Home/

Contact:mick.watson@roslin.ed.ac.uk

Address of the bookmark: https://academic.oup.com/bioinformatics/article/31/1/114/2365693

Address of the bookmark: https://github.com/SCAU-AnimalGenetics/GPOPSIM