BOL: Related items

Live Webinar on RNA-Seq Data Analysis on 9 Nov 2016

Strand — Wed, 19 Oct 2016 05:25:27 -0500

Live Webinar on RNA-Seq Data Analysis

Abstract: Strand NGS supports an extensive workflow for the analysis and visualization of RNA-Seq data. The workflow includes Transcriptome / Genome alignment, Differential expression analysis with Statistical approach and Splicing events detection. Strand NGS also supports novel discovery like identification of novel genes, exons and Novel splice junctions, alongside it can also detect gene fusion events. Further downstream analysis such as GO and pathway analysis can be performed on the set of interesting genes. The product has an option to create pipelines for time consuming jobs which automates analysis and leaves more time for end data interpretation. This webinar will give an overview of the features in the RNA-Seq data analysis workflow in Strand NGS and also highlights on parameters within each feature that can be optimized depending on datasets and analysis needs.

Speaker: Mr. Sugandan Sivamani, Senior Application Scientist, Strand Life Sciences

Date: 9th Nov, Session 1 for SAPK/ APFO: 2:30 PM IST Date: 9th Nov, Session 2 for AFO/ EMEA: 9:00 AM PST

Krona

Jit — Wed, 22 Mar 2017 04:47:35 -0500

Krona allows hierarchical data to be explored with zooming, multi-layered pie charts. Krona charts can be created using an Excel template or KronaTools, which includes support for several bioinformatics tools and raw data formats. The interactive charts are self-contained and can be viewed with any modern web browser (see Browser support).

Address of the bookmark: https://github.com/marbl/Krona/wiki

GAM-NGS: genomic assemblies merger for next generation sequencing

Jit — Fri, 19 May 2017 07:44:14 -0500

GAM-NGS is a tool able to merge two or more assemblies in order to improve contiguity and correctness. It can be used on all NGS-based assembly projects and it shows its full potential with multi-library Illumina-based projects. With more than 20 available assemblers it is hard to select the best tool. In this context we propose a tool that improves assemblies (and, as a by-product, perhaps even assemblers) by merging them and selecting the generating that is most likely to be correct.

Address of the bookmark: https://github.com/vice87/gam-ngs

snakepipes: A toolkit based on snakemake and python for analysis of NGS data

Jit — Thu, 30 May 2019 04:06:13 -0500

snakePipes are flexible and powerful workflows built using snakemake that simplify the analysis of NGS data.

DNA-mapping*
ChIP-seq*
RNA-seq*
ATAC-seq*
scRNA-seq
Hi-C
Whole Genome Bisulfite Seq/WGBS

(*Also available in "allele-specific" mode)

snakePipes can be installed via conda :

'conda install -c mpi-ie -c bioconda -c conda-forge snakePipes'.

Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online.

Address of the bookmark: https://github.com/maxplanck-ie/snakepipes

Choosing the Right NGS Sequencing Instrument for Your Study

Neel — Wed, 15 Jun 2022 00:37:29 -0500

The right sequencing instrument for your study depends on your project goal. Setting aside turnaround time and price, it essentially comes down to the numbers of reads and read length you need for your experiment. Below, we've described and compared metrics for each of the instruments available. If you’re new to high-throughput sequencing and have questions about how you should design your sequencing run, fill out our free consultation form and we'll get in touch with you to help.

More at https://genohub.com/ngs-instrument-guide/

Address of the bookmark: https://genohub.com/ngs-instrument-guide/

1mb long DNA with Nanopore technology

Jit — Tue, 19 Dec 2017 18:49:28 -0600

The first continuous DNA read of more than a million bases (>1Mb) has been achieved, using Oxford Nanopore sequencing technology. Congratulations to Martin Smith and collaborators! Read more: http://bit.ly/2j5TNCO

NanoPack: visualizing and processing long-read sequencing data

Jit — Tue, 25 Dec 2018 21:20:50 -0600

The NanoPack tools are written in Python3 and released under the GNU GPL3.0 License. The source code can be found at https://github.com/wdecoster/nanopack, together with links to separate scripts and their documentation. The scripts are compatible with Linux, Mac OS and the MS Windows 10 subsystem for Linux and are available as a graphical user interface, a web service at http://nanoplot.bioinf.be and command line tools.

Address of the bookmark: https://github.com/wdecoster/nanopack

poRe: an R package for the visualization and analysis of nanopore sequencing data

Jit — Thu, 23 Nov 2017 09:55:57 -0600

Motivation: The Oxford Nanopore MinION device represents a unique sequencing technology. As a mobile sequencing device powered by the USB port of a laptop, the MinION has huge potential applications. To enable these applications, the bioinformatics community will need to design and build a suite of tools specifically for MinION data.

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

BFC: a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data

Jit — Thu, 31 May 2018 09:35:23 -0500

BFC is a standalone high-performance tool for correcting sequencing errors from Illumina sequencing data. It is specifically designed for high-coverage whole-genome human data, though also performs well for small genomes. The BFC algorithm is a variant of the classical spectrum alignment algorithm introduced by Pevzner et al (2001). It uses an exhaustive search to find a k-mer path through a read that minimizes a heuristic objective function jointly considering penalties on correction, quality and k-mer support. This algorithm was first implemented in my fermi assembler and then refined a few times in fermi, fermi2 and now in BFC. In the k-mer counting phase, BFC uses a blocked bloom filter to filter out most singleton k-mers and keeps the rest in a hash table (Melsted and Pritchard, 2011). The use of bloom filter is how BFC is named, though other correctors such as Lighter and Bless actually rely more on bloom filter than BFC. https://github.com/lh3/bfc

Address of the bookmark: https://github.com/lh3/bfc

Software engineer/Computational Biologist - Equinome Ltd., Dublin, Ireland

Thu, 04 Sep 2014 19:21:26 -0500

Equinome (www.equinome.com) is the world leader in the research and
development of state-of-the-art novel genomic tools to inform the breeding,
selection and training of Thoroughbred racehorses. Since its launch in 2010,
Equinome has successfully commercialised three performance-related genetic
tests, with a pipeline of further genetic tests in development. We work with
many of the world's leading racehorse trainers and breeders in Europe,
Australasia, USA and South Africa. The company has been featured on CNN,
Bloomberg, RTE, BBC, The Guardian, Discovery Channel and Channel 4, among
others.

The Role

We are looking for a Software Engineer - Computational Biologist with 3+
years' experience in a similar role to design and implement a backend system
to support an online individualised genomics interface. This position is a
great opportunity for an ambitious, self-motivated individual to work in a
demanding, challenging and interesting role.

Position Description:
. Participate in planning, design, and implementation of Equinome back
end systems and technologies.
. Implement interfaces and management tools for back end services.
. Manage, analyse, interpret and visualise large genomics data sets.
. Work closely with scientific team to develop new features and
application enhancements
. Design, develop and manage a genomics research database.

Qualification/Experience:
. Minimum MSc in Computer Science, Genetics, Bioinformatics or in a
related field (A Ph.D qualification would be an advantage).
. Proven 3+ years of experience in similar role.
. Highly proficient in Python, SQL, MySQL.
. Excellent knowledge of mammalian genomics, bioinformatics and
statistical/population genetics.
. Hands-on experience working with large data sets.
. Experience with front-end technologies (HTML/CSS/Javascript) an
advantage.
. Experience in rapid web application development: e.g. Django.
. Knowledge or experience of Unix Scripting and R statistical
programming language would be an advantage.
. Ability to work with minimum supervision to deliver high-quality
code on time.
. Fluency in English and good written and communication skills.
. Meticulous attention to detail.

Applications should be submitted before Friday, 26 September 2014 using the
following link:
http://bit.ly/WgbhxS

Note: Full information and application procedure is available at this link:
http://bit.ly/WgbhxS