BOL: Related items

Bioinformatics Head (Bioinformatics Manager III), Cancer Genomics Research Laboratory at Frederick National Laboratory

Wed, 18 Aug 2021 00:19:48 -0500

Frederick National Laboratory seeking an enthusiastic, creative, and seasoned bioinformatics professional to join our leadership team and direct the exceptional Bioinformatics Group at the Cancer Genomics Research Laboratory (CGR). CGR has a diverse team of bioinformatics and computational scientists that support all areas of bioinformatics and data analysis (infrastructure, data QC, pipeline development and maintenance, data curation and sharing, methodology development, statistical analyses, machine learning approaches, and scientific interpretation).

More at https://leidosbiomed.csod.com/ats/careersite/jobdetails.aspx?site=4&c=leidosbiomed&id=2040

GABi

Fri, 06 Dec 2013 16:43:01 -0600

GABi Research
The major researching fields defined as the GABi scope are described next:
Sequence Analysis
Protein Structure Prediction
Comparative Genomics
Functional Analysis of Residues on Protein Families
Gene/Protein Networks
Genome structure & base composition
Highthroughput data analysis from NGS

Lab Page http://gabi.cidbio.org/index/

pipesnake: bioinformatics best-practice analysis pipeline for phylogenomic reconstruction

LEGE — Wed, 21 Feb 2024 06:19:41 -0600

ausarg/pipesnake is a bioinformatics best-practice analysis pipeline for phylogenomic reconstruction starting from short-read 'second-generation' sequencing data.

The pipeline is built using Nextflow, a workflow tool to run tasks across multiple compute infrastructures in a very portable manner. It uses Docker/Singularity containers making installation trivial and results highly reproducible. The Nextflow DSL2 implementation of this pipeline uses one container per process which makes it much easier to maintain and update software dependencies.

Address of the bookmark: https://github.com/AusARG/pipesnake

Next generation sequencing(NGS) books

Abhimanyu Singh — Fri, 30 May 2014 04:48:04 -0500

Employing different technologies, the purpose of NGS platform is to decode the identity or modification on the nucleotides. NGS platforms evolve quickly and capture the main stream.

This bookmark is created to provide NGS online books links.

Address of the bookmark: http://en.wikibooks.org/wiki/Next_Generation_Sequencing_%28NGS%29/Print_version

How to sequence the human genome - Mark J. Kiel

Fri, 30 May 2014 13:24:11 -0500

View full lesson: http://ed.ted.com/lessons/how-to-sequence-the-human-genome-mark-j-kiel Your genome, every human's genome, consists of a unique DNA sequence of A's, T's, C's and G's that tell your cells how to operate. Thanks to technological advances, scientists are now able to know the sequence of letters that makes up an individual genome relatively quickly and inexpensively. Mark J. Kiel takes an in-depth look at the science behind the sequence. Lesson by Mark J. Kiel, animation by Marc Christoforidis.

Orange-Bioinformatics 2.5.34

Robert M Willioms — Mon, 06 Oct 2014 12:51:37 -0500

Orange Bioinformatics extends Orange, a data mining software package, with common functionality for bioinformatics. The provided functionality can be accessed as a Python library or through a visual programming interface (Orange Canvas). The latter is also suitable for non-programmers.

Orange Bioinformatics provides access to publicly available data, like GEO data sets, Biomart, GO, KEGG, Atlas, ArrayExpress, and PIPAx database. As for the analytics, there is gene selection, quality control, scoring distances between experiments with multiple factors. All features can be combined with powerful visualization, network exploration and data mining techniques from the Orange data mining framework.

Address of the bookmark: https://pypi.python.org/pypi/Orange-Bioinformatics/2.5.34

Biinformamatics Lead at Google Life Sciences

Fri, 17 Oct 2014 02:24:55 -0500

Google Life Sciences is recruiting a technical lead with experience in bioinformatics and clinical bioinformatics, including for biomarker discovery projects such as the Baseline study.

Responsibilities

Lead teams of scientists in structuring, prototyping, and executing large-scale bioinformatic and other analysis.
Develop novel bioinformatics, statistical, data processing, pathway, data mining and other algorithms to identify biological signals and their clinical correlates in broad kinds of individual and population data.
Develop novel platform-level analytical tools for sequence-based assays (assembly, annotation, variant calling and interpretation, phasing, genome structure, etc.), expression assays (RNAseq and microarray), proteomics, and metabolomics.
Develop statistical models that robustly correlate complex laboratory-derived information with phenotypic and clinical information.
Create scientifically rigorous visualizations, communications, and presentations of results.

Reference @ https://www.google.com/about/careers/search#!t=jo&jid=62095001

dcGOR

Martin Jones — Sat, 08 Nov 2014 14:54:28 -0600

An R package for analysing ontologies and protein domain annotations has been published in PLoS Computational Biology (http://dx.doi.org/10.1371/journal.pcbi.1003929). The package is distributed as part of CRAN (http://cran.r-project.org/package=dcGOR), and also at GitHub for version control.

The dedicated website is available in http://supfam.org/dcGOR, from which several demos are also provided:

1. Analysing SCOP domains: http://supfam.org/dcGOR/demo-Fang.html

2. Analysing Pfam domains: http://supfam.org/dcGOR/demo-Basu.html

3. Analysing InterPro domains: http://supfam.org/dcGOR/demo-Customisation.html

MIT Computational Biology Group

Thu, 18 Dec 2014 14:47:01 -0600

My research group consists primarily of computer science graduate students and postdocs with expertise in algorithms, statistical inferences and machine learning, and sharing a passion for understanding fundamental biological problems.

We work in a highly interdisciplinary environment at the interface of Computer Science and Biology. Since its inception, our lab has eagerly engaged in collaborative research partnerships with biological and experimental collaborators, facilitated by our affiliation with the Broad Institute and the Computational and Systems Biology initiative (CSBi) at MIT, our participation in the Epigenome Roadmap, ENCODE, and modENCODE consortia, and by several other ongoing collaborations at MIT, Harvard, and the Harvard Medical School affiliated hospitals.

http://compbio.mit.edu/

Nicolas Corradi Lab

Tue, 26 May 2015 16:19:02 -0500

The goal of our research is to better understand the biology of microbial organisms of significant ecological, veterinary and medical importance.
To achieve this goal, our team combines the power of next generation DNA sequencing and bioinformatics with molecular biology and experimental procedures.

Main research topics:
- Comparative and Population Genomics of Plant Symbionts
- Parasite Genome Evolution
- Experimental Evolution of Microbial Symbionts and Parasites
- Phylogenomics of Early Branching Fungi

More at http://corradilab.weebly.com/