BOL: Related items

C-DAC launch supercomputing facility "Param Bio Blaze" !!!

Rahul Nayak — Tue, 18 Feb 2014 11:55:14 -0600

The bioinformatics centre at Centre for Development of Advanced Computing (C-DAC) completed 10 years, this month. Established in 2004, the centre has been widely used by numerous researchers across the globe and has an ultimate aim of making personalised drugs depending on the composition of a human body.

When biological data is processed using computer science, statistics, mathematics and engineering, it constitutes bioinformatics. The technological advancements are bringing new dimensions to the understanding of molecular basis of living organisms. There is immense data generated due to computing, but storage and analysis of this data is becoming a challenge, therefore there is an urgent need of supercomputers.

The C-DAC will launch Param Bio Blaze, a supercomputing facility, to address the challenges in bioinformatics on Tuesday at a three-day symposium, titled: 'Accelerating biology: Computing life'. The supercomputing facility will be inaugurated on Tuesday by Ramakrishna Ramaswamy, vice-chancellor, Central University of Hyderabad at the Yashada. The new C-DAC's facility will have a capacity of 10 teraflop and will be able to analyse human cells and its functions.

Param Bio Blaze will help have a larger storage space and better computing facility for the bioinformatics sector. The facility will help capture the movement of molecules and also interaction between two molecules and the effects.

Applications of Param BioBlaze

- Collaboration with National Centre for Cell Science for research on Malaria and understanding how the disease spreads

- Collaborative work with Tata Memorial hospital on breast cancer and find out the difference between normal tissues and tissues from breast cancer patients

- Designing anti-cancer molecules, a collaborative research with the University of Pune

Reference:

Times of India

Image:datacenterjournal.com

BASE: a practical de novo assembler for large genomes using long NGS reads

Rahul Nayak — Fri, 19 Oct 2018 07:25:21 -0500

new de novo assembler called BASE. It enhances the classic seed-extension approach by indexing the reads efficiently to generate adaptive seeds that have high probability to appear uniquely in the genome. Such seeds form the basis for BASE to build extension trees and then to use reverse validation to remove the branches based on read coverage and paired-end information, resulting in high-quality consensus sequences of reads sharing the seeds. Such consensus sequences are then extended to contigs.

Address of the bookmark: https://github.com/dhlbh/BASE

Atlas of ancient inter-ethnic group !!!

Jit — Fri, 14 Feb 2014 13:16:20 -0600

Now a dayz, almost 3% of the world's population lived outside their country of origin. These migration is increasingly being perceived as a force that can contribute to development, and an integral aspect of the global development process. While migrants make important contributions to the economic prosperity of their host countries, the flow of financial, technological, social and human capital back to their countries of origin also is having a significant impact on poverty reduction and economic development.

However, the ancient invasions and migrations to slavery and trade, history is embroidered with events that led to interactions between previously separate populations. Early humans migrated due to many factors such as changing climate and landscape and inadequate food supply. Historical migration of human populations begins with the movement of Homo erectus out of Africa across Eurasia about a million years ago. Homo sapiens appear to have occupied all of Africa about 150,000 years ago, moved out of Africa 70,000 years ago, and had spread across Australia, Asia and Europe by 40,000 years BC. Indo-Aryan migration from the Indus Valley to the plain of the River Ganges in Northern India is presumed to have taken place in the Middle to Late Bronze Age, contemporary to the Late Harappan phase in India (ca. 1700 to 1300 BC). From 180 BC, a series of invasions from Central Asia followed, including those led by the Indo-Greeks, Indo-Scythians, Indo-Parthians and Kushans in the northwestern Indian subcontinent.

Using the recent advance technologies researchers have created a historical atlas of instances of such mixing. They use a sophisticated statistical method for making inferences about human history and infer populations interbredings ( happen over the past 4,000 years) with an ease.

The study published the findings and presented with an interactive map. http://admixturemap.paintmychromosomes.com/

These sort of genomic study have some limilation. It is hard to precisely define sources of mixing when it occurred between genetically similar groups, and scenarios involving multiple waves of mixing over time or between multiple groups can be difficult to tease apart. But it is believed that larger sample sizes will improve resolution. These high resolution will provide a deeper understanding of human history.

Reference:

http://www.sciencemag.org/content/early/2014/01/28/science.1245938

http://www.ncbi.nlm.nih.gov/pubmed/21390129?dopt=Abstract&holding=npg

http://www.csulb.edu/~kmacd/paper-ethnicity.html

Image: Wikipedia

nQuire: A statistical framework for ploidy estimation using NGS short-read data

Jit — Thu, 31 Jan 2019 05:12:19 -0600

nQuire implements a set of commands to estimate ploidy level of individuals from species, where recent polyploidization occurred and intraspecific ploidy variation is observed. Specifically, nQuire uses next-generation sequencing data to distinguish between diploids, triploids and tetraploids, on the basis of frequency distributions at variant sites where only two bases are segregating.

For more background see also the publication at BMC Bioinformatics.

https://github.com/clwgg/nQuire

Address of the bookmark: https://github.com/clwgg/nQuire

Computational Biologist Scientist @ Strand Life Sciences

Fri, 14 Mar 2014 11:36:56 -0500

We are looking for a motivated application scientist to help evaluate, compare, and develop next generation sequencing (NGS) data analysis methods. The successful candidate should be able to quickly understand the state-of-art computational biology techniques, prototype them and perform benchmarking studies. The candidate must also be comfortable working with people from different disciplines and be able to present data analysis results in a clear and effective manner. The candidate is also expected to interact with customers as needed, write technical reports and publish new methods and/or data analysis findings in public forums.

Candidate Requirements: A PhD in computer science, computational biology, Bioinformatics, or a related field, along with sufficient programming skills for prototyping. Experience with next generation sequencing data analysis is required. Candidates with MS degree but with relevant work experience can also be considered. The successful candidate must be motivated and capable of working independently as well as in team environment.

Eligible and interested candidates can email your resumes to rohit at strandls dot com

About Strand Life Sciences: Strand was founded in 2000 by computer science and mathematics professors who recognized the need to automate and integrate life science data analysis through an algorithmic and computational approach. Strand’s solutions for life sciences research are robust and easy to use by the most novice user while powerful and configurable for the bioinformatician. Using its award-winning application development platform, AVADIS®, Strand builds innovative products that enable fast and cutting-edge analysis for basic and clinical research, drug discovery and development.

http://www.avadis-ngs.com/careers

ClinCNV: Detection of copy number changes in Germline/Trio/Somatic contexts in NGS data

Neel — Thu, 16 Jan 2020 23:16:02 -0600

ClinCNV detects CNVs in germline and somatic context in NGS data (targeted and whole-genome). We work in cohorts, so it makes sense to try ClinCNV if you have more than 10 samples (recommended amount - 40 since we estimate variances from the data). By "cohort" we mean samples sequenced with the same enrichment kit with approximately the same depth (ie 1x WGS and 30x WGS better be analysed in separate runs of ClinCNV). Of course it is better if your samples were sequenced within the same sequencing facility.

Address of the bookmark: https://github.com/imgag/ClinCNV

Roth Lab

Tue, 11 Mar 2014 17:43:45 -0500

The Roth Lab seeks insight into biological systems through genome- and proteome-scale experimentation and analysis.

Current computational interests:

Systematic analysis of genetic epistasis to identify redundant or compensatory systems and to reveal order of action in genetic pathways.
Using knockout, knockdown, or overexpression, or other perturbation experiments in combinations of genes in S. cerevisiae, C. elegans or mouse.
Using genome-scale genotyping of natural polymorphisms in S. cerevisiae and human populations.
Alternative splicing and its relationship to protein interaction networks.
Integrating large-scale studies including phenotype, genetic epistasis, protein-protein and transcription-regulatory interactions and sequence patterns to quantitatively assign function to genes and guide experimentation.

More at http://llama.mshri.on.ca/index.html

LoFreq*: A sequence-quality aware, ultra-sensitive variant caller for NGS data

BioStar — Tue, 18 Feb 2020 03:24:22 -0600

LoFreq* (i.e. LoFreq version 2) is a fast and sensitive variant-caller for inferring SNVs and indels from next-generation sequencing data. It makes full use of base-call qualities and other sources of errors inherent in sequencing (e.g. mapping or base/indel alignment uncertainty), which are usually ignored by other methods or only used for filtering.

https://github.com/CSB5/lofreq

http://csb5.github.io/lofreq/installation/

https://github.com/CSB5/lofreq/tree/master/dist

Address of the bookmark: http://csb5.github.io/lofreq/

Postdoc Position in Computational Biology

Fri, 14 Mar 2014 01:38:49 -0500

The Computational Biology Group of Interdisciplinary Center for
Clinical Research (IZKF) Aachen, RWTH Aachen University Hospital,
Aachen, invites applicants for PhD candidate or postdoctoral position
in computational biology in one of the following topics:

1) Statistical machine learning methods for the analysis of medical
epigenomics data.

2) Sequence analysis algorithms for detection of RNA-DNA interactions.

Applicants should hold a M.Sc . or PhD in Computer Science or related
areas. Experience in the analysis of biological sequences, gene
expression and gene regulation is desirable. The candidate should have
solid programming skills (C, Python and/or R) and acquaintance with
Linux. Experience with high performance computing is a plus. The
working language of the group is English.

The position is based on the German TV-L 13 salary scale, including
all German social benefits (health insurance and pension scheme). The
expected starting date is September 2014. Interested candidates should
send a CV, statement of research interests and the names of three
references to jobs@costalab.org.

More at http://costalab.org/wp/phd-and-postdoc-position-in-computational-biology/

FiNGS: Filters for Next Generation Sequencing

Neel — Sat, 27 Feb 2021 01:18:35 -0600

Key features

Filters SNVs from any variant caller to remove false positives
Calculates metrics based on BAM files and provides filtering not possible with other tools
Fully user-configurable filtering (including which filters to use and their thresholds)
Option to use filters identical to ICGC recommendations

FiNGS provides researchers with a tool to reproducibly filter somatic variants that is simple to both deploy and use, with filters and thresholds that are fully configurable by the user. It ingests and emits standard variant call format (VCF) files and will slot into existing sequencing pipelines. It allows users to develop and implement their own filtering strategies and simple sharing of these with others.

FiNGS reliably improves upon the precision of default variant caller outputs and performs better than other tools designed for the same task.

Address of the bookmark: https://github.com/cpwardell/FiNGS