BOL: Related items

Google Genomics

Tenzin Paul — Thu, 18 Dec 2014 11:05:42 -0600

Explore genetic variation interactively. Compare entire cohorts in seconds with SQL-like queries. Compute transition/transversion ratios, genome-wide association, allelic frequency and more.
Process big genomic data easily. Run batch analyses like principal component analysis and Hardy-Weinberg equilibrium on as many samples as you like, in minutes or hours, with just a little code.
Use Google's infrastructure and big data expertise. Store one genome or a million using Google Genomics and take advantage of the same infrastructure that powers Search, Maps, YouTube, Gmail and Drive.
Support emerging global standards. Google Genomics is implementing the API defined by the Global Alliance for Genomics and Health for visualization, analysis and more. Compliant software can access Google Genomics, local servers, or any other implementation.

Address of the bookmark: https://cloud.google.com/genomics/

Comparative Genomics in Ensembl

Wed, 21 Jan 2015 08:31:11 -0600

The Ensembl browser provides viewable whole-genome alignments, homologues and phylogenetic gene trees, protein families, and ancestral sequences. Learn how to view and export these data in this video.

Ryan E. Mills Lab

Tue, 26 May 2015 09:29:24 -0500

Our research group is primarily focused on the analysis of whole genome sequence data to identify genetic variation (primarily structural variation) and examine their potential functional impact in disease phenotypes. We are particularly interested in analyzing complex regions of the genome that are not easily resolved through modern sequencing approaches and which may exhibit interesting mechanistic origins.

We are also interested in the large-scale integration of genomic, expression, methylation and proteomic data sets, as well as the application of whole genome sequence analysis in clinical diagnostics.

More at http://millslab.ccmb.med.umich.edu/index.html

Raphael Lab

Sat, 04 Jul 2015 19:05:29 -0500

Raphael Lab research is focused on Bioinformatics and Computational Biology.

Current research interests include next-generation DNA sequencing, structural variation, genome rearrangements in cancer and evolution, and network analysis of somatic mutations in cancer. Earlier research included topics in comparative genomics, multiple sequence alignment, and motif finding.

More athttp://compbio.cs.brown.edu/

BioRG

Tue, 04 Aug 2015 20:52:52 -0500

This research group works on problems from the fields of Bioinformatics, Biotechnology, Data Mining, and Information Retrieval. The group's research projects includes Comparative Genomics of Bacterial genomes, Metagenomics, Genomic databases, Pattern Discovery in sequences and structures, micro-array data analysis, prediction of regulatory elements, primer design, probe design, phylogenetic analysis, medical image processing, image analysis, data integration, data mining, information retrieval, knowledge discovery in electronic medical records, and more.

More at http://biorg.cis.fiu.edu/

liftover

Jitendra Narayan — Mon, 08 Feb 2016 15:45:03 -0600

Convenient conversions between genome assemblie. The liftover package makes it easy to remap genomic coordinates to a different genome assembly.

More at https://github.com/aaronwolen/liftover

https://www.bioconductor.org/help/workflows/liftOver/

Address of the bookmark: https://github.com/aaronwolen/liftover

Prime Minister’s 100k Genome Project

Jitendra Narayan — Thu, 08 Aug 2013 09:40:39 -0500

Genomics Ebgland is destined to sequence 100,000 patients over the next five year in England. A landmark project by british government.

Genomics England will play a key role in building on the UK’s long track record as leader in medical science advances to push the boundaries by unlocking the power of DNA data. The UK will become the first ever country to introduce this technology in its mainstream health system – leading the global race for better tests, better drugs and above all better, more personalised care.

http://www.genomicsengland.co.uk/100k-genome-project/

Pandey Lab

Fri, 20 Sep 2013 13:19:18 -0500

The Pandey Lab at Johns Hopkins University is a Systems Biology lab that combines molecular biology, analytical chemistry and computational biology with various "Omics" technologies including genomics and proteomics to understand signaling pathways and to identify therapeutic targets and biomarkers in a number of cancers.

More at http://pandeylab.igm.jhmi.edu/

http://scholar.google.com/citations?user=OhuG0FcAAAAJ&hl=en

Bergman Lab

Thu, 03 Oct 2013 17:20:09 -0500

Broad area of research:

Genome Annotation and Functional Genomics

Bergman Lab is actively engaged in the development and application of computational methods to improve the annotation of functional biological features in genome sequences. Bergman Lab work focuses on improving annotation of non-protein-coding regions of the genome including conserved noncoding sequences (CNSs), cis-regulatory modules (CRMs), transcription factor binding sites (TFBSs), transposable elements (TEs) and noncoding RNA (ncRNA) genes. Current projects include improving the (i) annotation of TEs in the fly and yeast genomes, (ii) annotation of CRMs and TFBSs in the fly genome, and (iii) analysis of transposon knockout collections in flies. Research in this area is supported by the EC FP7 programme.

Genome and Molecular Evolution
Text and Data Mining

More @ http://bergmanlab.smith.man.ac.uk/

Troyanskaya Lab

Fri, 18 Oct 2013 10:57:40 -0500

In our research, we combine computational methods with an experimental component in a unified effort to develop comprehensive descriptions of genetic systems of cellular controls, including those whose malfunctioning becomes the basis of genetic disorders, such as cancer, and others whose failure might produce developmental defects in model systems.

Research Interest
Genomic Data Integration

Microarray Analysis

Gene and Protein Function Prediction

Detection and Analysis of Chromosomal Abnormalities and Functional Evolution

Integration of Computation and Experiments

Identification of Biological Networks and Pathways

Evaluation and Validation of Computational Predictions

Scalable Visualization-Based Data Analysis

More @ http://reducio.princeton.edu/cm/
PI page @ http://reducio.princeton.edu/cm/ogt