BOL: Related items

Studentship

Tue, 24 Jan 2017 04:26:14 -0600

Advt. No. ACBR/BTBI/BIF/Manpower/ADV/16-17
Research Associate/ Studentship/ Traineeship Jobs opportunity in University of Delhi - Dr. B.R. Ambedkar Center for Biomedical Research (ACBR) on temporary basis
Studentship
No. of Post : 01
Qualifications : Applicants who are pursuing Post Graduate Degree course/ Diploma in Bioinformatics/ Biomedical Sciences/ Biotechnology/Life Sciences/Chemistry/ Biochemistry with a programme in Bioinformatics to carry out the project.
Remuneration : Rs. 8,000/-
Traineeship
No. of Post : 01
Qualifications : Applicants who have completed their Post Graduate degree in Bioinformatics /Biomedical Sciences/Biotechnology/Life Sciences/Chemistry/Biochemistry or any other branch of Life Sciences with knowledge in Bioinformatics
Remuneration : Rs. 8,000/-
Research Associate
No. of Post : 01
Qualifications : Ph.D. in Bio-Medical Sciences/Bioinformatics/ Biotechnology/Life Sciences/ Chemistry/ Bio-Chemistry and related areas
Remuneration : Rs. 36,000/-

Applicant are required to bring applications on plain paper, stating the name, address, date of birth. educational qualifications and experiences and Institute, along with attested photocopies of mark sheets and certificates etc. at the time of Interview. Applications should also be sent by Entail to Dr. Madhu Chopra, Coordinator, BIF Facility. Email: acbrdu.blisnet@nic.in; mchopradu@gmail.com.
Interview Date : 13.02.2017
Time : 10:00 am-12:00 noon
Venue : ACBR

Ra assembler - a de novo DNA assembler for third generation sequencing data

biogeek — Wed, 27 Dec 2017 20:36:54 -0600

Integration of the Ra assembler - a de novo DNA assembler for third generation sequencing data developed on Faculty of Electrical Engineering and Computing (FER), Ruder Boskovic Institute (RBI) and Genome Institute of Singapore (GIS).

Ra is in development since 2014 in the form of several separate components that used to be run individually.
This project aims to ease the usage of Ra by integrating it into a complete de novo assembly tool.

Unlike other state-of-the-art assemblers, Ra does not have an error correction step. Instead, it relies on detecting overlaps using a very sensitive and specific overlapper ("graphmap -w owler", https://github.com/isovic/graphmap) and constructing and reducing an overlap graph (Ra layout, https://github.com/mariokostelac/ra).

Address of the bookmark: https://github.com/mariokostelac/ra-integrate/

Many-Core Engine (MCE) for Perl example

Jit — Tue, 31 Jan 2017 05:37:50 -0600

MCE spawns a pool of workers and therefore does not fork a new process per each element of data. Instead, MCE follows a bank queuing model. Imagine the line being the data and bank-tellers the parallel workers. MCE enhances that model by adding the ability to chunk the next n elements from the input stream to the next available worker.

CORE MODULES

Three modules make up the core engine for MCE.

MCE::Core: Provides the Core API for Many-Core Engine. The various MCE options are described here.
MCE::Signal: Temporary directory creation, cleanup, and signal handling.
MCE::Util: Utility functions for Many-Core Engine.

MCE EXTRAS

There are 4 add-on modules for use with MCE.

MCE::Candy: Provides a collection of sugar methods and output iterators for preserving output order.
MCE::Mutex: Provides a simple semaphore implementation supporting threads and processes.
MCE::Queue: Provides a hybrid queuing implementation for MCE supporting normal queues and priority queues from a single module. MCE::Queue exchanges data via the core engine to enable queuing to work for both children (spawned from fork) and threads.
MCE::Relay: Enables workers to receive and pass on information orderly with zero involvement by the manager process while running.

MCE MODELS

The models take Many-Core Engine to a new level for ease of use. Two options (chunk_size and max_workers) are configured automatically as well as spawning and shutdown.

MCE::Loop: Provides a parallel loop utilizing MCE for building creative loops.
MCE::Flow: A parallel flow model for building creative applications. This makes use of user_tasks in MCE. The author has full control when utilizing this model. MCE::Flow is similar to MCE::Loop, but allows for multiple code blocks to run in parallel with a slight change to syntax.
MCE::Grep: Provides a parallel grep implementation similar to the native grep function.
MCE::Map: Provides a parallel map model similar to the native map function.
MCE::Step: Provides a parallel step implementation utilizing MCE::Queue between user tasks. MCE::Step is a spin off from MCE::Flow with a touch of MCE::Stream. This model, introduced in 1.506, allows one to pass data from one sub-task into the next transparently.
MCE::Stream: Provides an efficient parallel implementation for chaining multiple maps and greps together through user_tasks and MCE::Queue. Like with MCE::Flow, MCE::Stream can run multiple code blocks in parallel with a slight change to syntax from MCE::Map and MCE::Grep.

MISCELLANEOUS

Miscellaneous additions included with the distribution.

MCE::Examples: Describes various demonstrations for MCE including a Monte Carlo simulation.
MCE::Subs: Exports functions mapped directly to MCE methods; e.g. mce_wid. The module allows 3 options; :manager, :worker, and :getter.

REQUIREMENTS

Perl 5.8.0 or later. PDL::IO::Storable is required in scripts running PDL.

SOURCE AND FURTHER READING

The source, cookbook, and examples are hosted at GitHub.

Open Positions in Pasini’s lab

Sat, 04 Feb 2017 08:17:18 -0600

Computational Biologists
Open to PhD-student and Post-doc candidates
We are looking for wet and computational biologists to work on an ERC funded project in our
laboratory located at the Department of Experimental Oncology of the European Institute of
Oncology in Milan (Italy). The project will focus on different aspects of the function of Polycomb
Group proteins and other chromatin modifying activities in relation to their role in regulating cellular
identity in the development of adult tissues.
The candidates will be in charge of computational analysis and data management related to the
project. She/he will directly interact with the wet scientists working in our laboratory while working
embedded in the community of computational biologists present at our institution. The work will
involve the analysis of sequencing data produced with cutting edge technologies to study gene
expression and chromatin environment including data produced on rare cell populations and single
cells. The applicants must have a good knowledge of programming in python/perl/java along with
strong statistical background and performing analysis in R platform. A biological background is
also recommended however it’s not mandatory for application.
Each applicant should submit a full CV (with a detailed description of her/his background,
expertise, achievements and publication records) together with a letter of intent and at least two
contacts for recommendations (for a post-doc position). Competitive salary will be offered based
on the experience of the candidate. Non Italian as well as Italian applicants that have been working
outside Italy (>3yrs.) will have the opportunity to benefit of a full tax deduction for the first three
years of contract.
Applications should be submitted as single PDF to diego.pasini@ieo.it

Lab https://www.ieo.it/en/RESEARCH/People/Researchers/Pasini-Diego/

PERGA: A Paired-End Read Guided De Novo Assembler for Extending Contigs Using SVM and Look Ahead Approach

Rahul Nayak — Tue, 05 Jun 2018 09:57:11 -0500

PERGA - Paired End Reads Guided Assembler PERGA is a novel sequence reads guided de novo assembly approach which adopts greedy-like prediction strategy for assembling reads to contigs and scaffolds. Instead of using single-end reads to construct contig, PERGA uses paired-end reads and different read overlap sizes from O ≥ Omax to Omin to resolve the gaps and branches. Moreover, by constructing a decision model using machine learning approach based on branch features, PERGA can determine the correct extension in 99.7% of cases. PERGA will try to extend the contigs by all feasible nucleotides and determine if these multiple extensions due to sequencing errors or repeats by using looking ahead technology, and it also try to separate the different repeats of nearby genomic regions to make the assembly result more longer and accurate. The simulated E.coli paired-end reads data are generated using GemSim (KE McElroy, F Luciani, T Thomas. Gemsim: General, Error-Model Based Simulator of Next-Generation Sequencing Data. BMC Genomics 2012, 13:74), with coverage 50x, 60x, 100x, read lengths 100-bp, and can be downloaded from https://github.com/zhuxiao/data_PERGA.

Address of the bookmark: https://github.com/hitbio/PERGA

Ideoplot

Shruti Paniwala — Mon, 13 Feb 2017 09:47:32 -0600

Simple ideogram plotting and annotation in R.

Basic usage:

Rscript Ideoplot.R --heatmap hm.bed --annotate annotations.bed --out ideogram.pdf
-or-
Rscript Ideoplot.R --annotate annotations.bed

Options
  --ideobed, i      A bed file of reference contig lengths/chromosome names
  --heatmap, -h     Fill chromosomes with normalized heatmap
                   (described below)
  --annotate, -a    Add character annotations.
  --out, -o         PDF output name.
  --stripes, -s     Specify a file containing the layout of the
                    annotations (description below)
  --bars, -b        Add track annotations
  --reference, -f   Either hg19, or hg38
  --topdown, r      Flag, when set, flips the orientation (P arms
                    drawn on top).

Address of the bookmark: https://github.com/mchaisso/Ideoplot

ASplice: a scalable and memory-efficient algorithm for de novo transcriptome assembly

Rahul Nayak — Tue, 03 Jul 2018 04:09:46 -0500

With increased availability of de novo assembly algorithms, it is feasible to study entire transcriptomes of non-model organisms. While algorithms are available that are specifically designed for performing transcriptome assembly from high-throughput sequencing data, they are very memory-intensive, limiting their applications to small data sets with few libraries. Texas A&M University researchers develop a transcriptome assembly algorithm that recovers alternatively spliced isoforms and expression levels while utilizing as many RNA-Seq libraries as possible that contain hundreds of gigabases of data. New techniques are developed so that computations can be performed on a computing cluster with moderate amount of physical memory. Availability – A software program that implements the algorithm is available at: http://faculty.cse.tamu.edu/shsze/asplice. Sze SH, Pimsler ML, Tomberlin JK, Jones CD, Tarone AM. (2017) A scalable and memory-efficient algorithm for de novo transcriptome assembly of non-model organisms. BMC Genomics 18(Suppl 4):387.

Address of the bookmark: http://faculty.cse.tamu.edu/shsze/asplice/

FinisherSC:a repeat-aware tool for upgrading de novo assembly using long reads

Jit — Mon, 20 Aug 2018 04:08:50 -0500

Here is the command to run the tool:

python finisherSC.py destinedFolder mummerPath

If you are running on server computer and would like to use multiple threads, then the following commands can generate 20 threads to run FinisherSC.

python finisherSC.py -par 20 destinedFolder mummerPath

Sometimes, if the names of raw reads and contigs consists of special characters/formats, FinisherSC/MUMmer may not parse them correctly. In that case, you want to have a quick renaming of the names of contigs/reads in contigs.fasta or raw_reads.fasta using the following command.

    perl -pe 's/>[^\$]*$/">Seg" . ++$n ."\n"/ge' raw_reads.fasta > newRaw_reads.fasta
    cp newRaw_reads.fasta raw_reads.fasta
    perl -pe 's/>[^\$]*$/">Seg" . ++$n ."\n"/ge' contigs.fasta > newContigs.fasta
    cp newContigs.fasta contigs.fasta

Address of the bookmark: https://github.com/kakitone/finishingTool

Laj: viewing and manipulating the output from pairwise alignment programs

Abhimanyu Singh — Wed, 01 Mar 2017 08:35:40 -0600

Laj is a tool for viewing and manipulating the output from pairwise alignment programs such as blastz. It can display interactive dotplot, pip, and text representations of the alignments, a diagram showing the locations of exons and repeats, and annotation links to other web sites containing additional information about particular regions.

The program is written in Java in order to provide a graphical user interface that is portable across a variety of computer platforms; indeed its name stands for "Local Alignments with Java". Currently it exists in two forms, a stand-alone application and a web-based applet, with slightly different capabilities.

Address of the bookmark: http://www.bx.psu.edu/~ratan/

Bioinformatics opening at ICGEB NEW DELHI

Thu, 02 Mar 2017 04:16:36 -0600

ICGEB NEW DELHI

Applications are invited for:

Junior Research Fellow, in a DBT funded project, is available in Translational Health Group, ICGEB, New Delhi

Qualifications:

Education: M.Sc. (preferably in Biotechnology, Life Sciences or Zoology, Chemistry, Bioinformatics). Candidates with hands on experience on GC-MS data acquisition and analysis will be given preference. Bioinformatics expertise required.

Fellowship: As per DBT guidelines.

Tenure: The position is purely on temporary basis with an initial tenure of six months and based on satisfactory performance may continue until the completion of the project.

Closing date for applications: 04/03/2017

Please send a "TWO PAGE" CV by email to: th.icgeb@gmail.com on or before the last date.

Research Associate, in a DBT funded project, is available in Translational Health Group, ICGEB, New Delhi

Qualifications:

Education: Ph.D. (in Biology, Biotechnology, Chemistry, Bioinformatics). Candidates with hands on experience on GC-MS data acquisition and analysis will be given preference.

Fellowship: As per DBT guidelines.

Tenure: The position is purely on temporary basis with an initial tenure of six months and based on satisfactory performance may continue until the completion of the project.

Closing date for applications: 04/03/2017

Please send a "TWO PAGE" CV by email to: th.icgeb@gmail.com on or before the last date.