Pine Biotech, Inc., a US-based startup working with the Tauber Bioinformatics Research Center is offering a full curriculum online preparing students without any technical background for real-life challenges with large scale biomedical data. Workshops on processing, analysis and biomedical interpretation of Next Generation Sequencing data cover important up-to-date algorithms and machine learning approaches. The most important thing is that there are virtually no pre-requisites such as coding, biostatistics or advanced medical skills. If you know what gene is and how the genes are expressed, you are ready to take the courses or join our workshops. Learn more: https://edu.t-bio.info/workshops/

2013 Oct Advertisement from Indian Statistical Institute

Post: Network Analysis

No. of Positions: 01

Educational Qualifications:

Candidate should have passed BE/B.Tech Or Equivalent in Computer Science / Electrical Engineering / Electronics / Information Technology / Bioinformatics / Biotechnology with throughout first Class
Experience:

(details of experience required)
Pay Scale: INR Rs.16000-20000/-P.M.

Walk-In-Interview : 22 Oct 2013 at 10:30 AM

Download Official Notification:
http://www.isical.ac.in/JobApplicationFiles/MIU_0310201311433700.pdf

java -mx512m -jar BamView.jar

and extra command line options are given when '-h' is used:

java -jar BamView.jar -h

BAM files can be specified on the command line with the '-a' option:

java -mx512m -jar BamView.jar -a pathToFile/sorted.bam

If a BAM filename is not given on the command line BamView will prompt for a file to be entered. The BAM index file should have the same name as the BAM file but with a '.bai' suffix. Multiple BAM files can be loaded and overlaid in the viewer. To make this easier BamView will read in files that contain a list of filenames.

Address of the bookmark: http://bamview.sourceforge.net/

1. ElasticBLAST can handle much LARGER queries! 

ElasticBLAST can search query sets that have hundreds to millions of sequences and against BLAST databases of all sizes.

2. ElasticBLAST is FASTER

ElasticBLAST distributes your searches across multiple cloud instances to process them simultaneously. The ability to scale resources in this way allows you to process large numbers of queries in a shorter time than you could with BLAST+.

3. ElasticBLAST is EASY to run on the cloud

ElasticBLAST is easy to set up using our step-by-step instructions (Amazon Web Services (AWS), Google Cloud Platform (GCP)) and allows you to leverage the power of the cloud. Once configured, it manages the software and database installation, handles partitioning of the BLAST workload among the various instances, and deallocates cloud resources when the searches are done.

ElasticBLAST also selects the instance (i.e., machine) type for you based on database size. Of course, you can also choose the instance type manually if you prefer. 

Address of the bookmark: https://blast.ncbi.nlm.nih.gov/doc/elastic-blast/

More at https://nikbaya.github.io/brava_browser/#/

Address of the bookmark: https://nikbaya.github.io/brava_browser/#/

Please add ur links/bookmarks in comment section.

Address of the bookmark: http://physicsdatabase.com/free-math-books/

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/23/14/1713.full

Initial customers for the HiSeq X Ten System, which will ship in Q1 2014, include Macrogen, based in Seoul, South Korea and its CLIA laboratory in Rockville, Maryland, the Broad Institute in Cambridge, Massachusetts, and the Garvan Institute of Medical Research in Sydney, Australia.

“For the first time, it looks like it will be possible to deliver the $1,000 genome, which is tremendously exciting,” said Eric Lander, founding director of the Broad Institute and a professor of biology at MIT. “The HiSeq X Ten should give us the ability to analyze complete genomic information from huge sample populations. Over the next few years, we have an opportunity to learn as much about the genetics of human disease as we have learned in the history of medicine.”

“The HiSeq X Ten is an ideal platform for scientists and institutions focused on the discovery of genotypic variation to enable a deeper understanding of human biology and genetic disease,” Illumina stated. “It can sequence tens of thousands of samples annually with high-quality, high-coverage sequencing, delivering a comprehensive catalog of human variation within and outside coding regions.”

HiSeq X Ten utilizes a number of advanced design features to generate massive throughput. Patterned flow cells, which contain billions of nanowells at fixed locations, combined with a new clustering chemistry deliver a significant increase in data density (6 billion clusters per run). Using state-of-the art optics and faster chemistry, HiSeq X Ten can process sequencing flow cells more quickly than ever before — generating a 10x increase in daily throughput when compared to current HiSeq 2500 performance.

The HiSeq X Ten is sold as a set of 10 or more ultra-high throughput sequencing systems, each generating up to 1.8 terabases (Tb) of sequencing data in less than three days or up to 600 gigabases (Gb) per day, per system, providing the throughput to sequence tens of thousands of high-quality, high-coverage genomes per year. Illumina says the $1,000 includes typical instrument depreciation, DNA extraction, library preparation, and estimated labor.

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.