ftp://ftp.ncbi.nih.gov/genomes/

https://hgdownload.soe.ucsc.edu/downloads.html

Address of the bookmark: ftp://ftp.ncbi.nih.gov/genomes/

Updates to the Conserved Domain Database include:

• Position-specific score matrices (PSSMs) have been recomputed for many models in CDD, and frequency tables have been added to the PSSMs;

• The search databases distributed as part of this release can now be used with the more recent versions of RPS-BLAST (BLAST release 2.2.28 and up) using composition-based scoring. This abolishes the need to mask out compositionally biased regions in query sequences;

• Domain annotation displays in CD-Search, BATCH CD-Search, and other services now all use a uniform display style. A new display option in CD-Search and BATCH CD-Search provides “standard” results, in addition to “concise” and “full” results. “Standard” results will provide, for each region on the query sequence, the best0-scoring domain model (if any) from each of CDD’s database providers (Pfam, SMART, COG, TIGRFAMs, Protein Clusters, and the NCBI in-house curation project), but will suppress redundancy from within a single provider's results list.

You can access CDD at the Conserved Domains homepage and find updated content on the CDD FTP site.

Reference:

NCBI Website

More at http://www.ncbi.nlm.nih.gov/projects/HistoneDB2.0/index.fcgi/browse/

Address of the bookmark: http://www.ncbi.nlm.nih.gov/projects/HistoneDB2.0/index.fcgi/browse/

For example, you can run a search in Assembly and use check boxes (see left side of screenshot below) to refine the set of genome assemblies of interest. Then, just open the “Download assemblies” menu, choose the source database (GenBank or RefSeq), choose the file type, and start the download. An archive file will be saved to your computer that can be expanded into a folder containing your selected genome data files.

More at https://ncbiinsights.ncbi.nlm.nih.gov/2017/05/08/genome-data-download-made-easy/

NCBI Hackathon specifically looking for folks who have experience in computational virus hunting or adjacent fields to identify known, taxonomically-definable and novel viruses from a few hundred thousand metagenomic datasets that we’ll put on cloud infrastructure. This event is for researchers, including students and postdocs, who are already engaged in the use of bioinformatics data or in the development of pipelines for virological analyses from high-throughput experiments. If this describes you, please apply! The event is open to anyone selected for the hackathon and willing to travel to SDSU (see below).

https://ncbiinsights.ncbi.nlm.nih.gov/2018/11/09/ncbi-sdsu-virus-hunting-data-science-hackathon-january-2019/

As mentioned before, working with BLAST+ in Docker and the cloud has several advantages:

• Docker manages installation and maintenance of the BLAST programs and databases.
• Docker makes it is easier to integrate BLAST with other tools in your pipelines.
• NCBI BLAST databases are pre-loaded now on the both the Google Cloud and Amazon Cloud, providing fast access.

You can also use the BLAST+ Docker image on any Docker-enabled platform, such as another cloud platform or on your local computer.

See the  BLAST+ in the Cloud and  database information documentation to get started.

If you have any questions, please email us at blast-help@ncbi.nlm.nih.gov

Source:Dave Arndt

Background/Motivation:

The dearth of structural information on alpha helical membrane protein (MPs) has hindered thus far the development of reliable knowledge –based potentials that can be used for automatic prediction of trans-membrane (TM) protein structure. While algorithm for identification of TM segments is available, modelling of the domains of alpha helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains. 

Results: In a cross validated test on single sequences, our trans-membrane MM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 86 proteins with supervised topology. These results compare favorably with existing methods. 

Source Code: Matlab

Conclusion/Implementation: Here discriminant data mining approach was used to predict the location and orientation of alpha helices in membrane-spanning proteins. It is based on a first order Markov model (MM) with an architecture that corresponds closely to the biological systems. The model is enriched with three types of states for the loop on the cytoplasmic side (outer loop), loop for the non-cytoplasmic side (inner side), and trans-membrane part. The closed association between the biological and Markov states allows us to infer which part of the model architecture are important to capture the information which encodes the membrane topology, and gain a better understanding of the mechanism and constraints involved. Predictor Model was established by various  Markov decoder , and assignment of the membrane helix boundaries was apparent.

https://biosciences.hs-mittweida.de/equant/

Address of the bookmark: https://biosciences.hs-mittweida.de/equant/

Address of the bookmark: https://zhanglab.ccmb.med.umich.edu/STRUM/