BOL: Related items

National Training on Bioinformatics Computational Tools for Microbial Research Nov 19 to 30, 2013

Fri, 27 Sep 2013 10:49:34 -0500

Agricultural research in modern scientific arena is being represented by proper integration among various research fields of biological, chemical and physical sciences, because this field encompasses many more complexities of biology in nature. In the era of fast accumulating biological data coming out from the research on many crop plants, live stocks and microbes and the impact of changing climate, habitat and other interrelations on these biological entities, bioinformatics has come forward across the globe to solve the problems of analysis, prediction, storage, management, pattern recognition, submission, retrieval and storage of the data to find out a fruitful outcome. This area is becoming increasingly important in the context of systems biology approach where a holistic approach is required to understand the biology and chemistry of the biological entities and their behavior during environmental interactions to resolve the harmful impact of biotic or abiotic causes on crop plants, animals, fishes, livestock sector, beneficial insects as well as microbes. The National Training program on ‘Computational Tools for Microbial Research” is an initiative for the capacity building of NARS scientists/researchers in this most emerging area and fast developing area of i.e. agricultural bioinformatics.

Contact The Director, National Bureau of Agriculturally Important Microorganisms, Kusmaur, Maunath Bhanjan-275101 (U.P.); Phone: 0547-2530080, Fax: 0547-2530358, e mail: nbaimicar@gmail.com; website: www.nbaim.org.in OR

Dr. Dhananjaya P. Singh, Senior Scientist & CCPI, NABG project, NBAIM, Maunath Bhanjan, 275101; Mob.- 09415291703; e mail - dpsfarm@rediffmail.com, nabg.nbaim@gmail.com

More at http://www.nbaim.org.in/Announc.aspx?cd=36

Public Databases for Bioinformatics !

Jit — Tue, 23 Mar 2021 05:32:15 -0500

https://www.nature.com/articles/s41467-020-17155-y

Server Infrastructure:

File Server:

dhara: Synology 3614 Storage Appliance
4 Core Xeon
108TB disk storage
10Gb ethernet to SCG3
Access atx: dhara:5000
Has btsync server (try it - its much better than dropbox)

Compute Servers:

nandi: Kundaje and Phi Server
24 intel cores
256GB RAM
500GB of SSD storage 
36TB RAID6 local storage
4 Intel Phi's (space for 4 more GPU's)


durga: Montgomery and sensitive data
24 intel cores
256GB RAM
500GB of SSD RAID0 storage 
60TB RAID6 local storage

mitra: Bassik and Web/DB Server
24 core
256GB RAM 
500GB of SSD RAID0 storage 
36TB RAID6 local storage

vayu: Kundaje GPU server
4 core
64GB RAM 
200GB of SSD storage 
8TB RAID10 local storage
4 Nvidia GTX 970 4GB GPUs

amold: Bickel and SGE server
32 AMD core
128GB RAM 
200GB of SSD storage 
12TB RAID5 local storage

wotan: Bickel and SGE server
64 AMD core
256GB RAM 
200GB of SSD storage 
12TB RAID5 local storage

Filesystem:

/users/$USER
default home directory
full backups nightly 
nfs mount to dhara
should store code, papers, and other highly processed data here

/mnt/data/
globally accessible data
should store common data here
e.g. genomes and indexes, annotations, ENCODE data  
if you dont want this to count towards your quote you must chown

/mnt/lab_data/$LAB/
lab accessible data
should store lab project data here 
e.g. ATAC-seq prediction data, enhancer prediction, motif calls

/srv/scratch/$USER
fast local storage
not backed up, but on raid and data will never be deleted
most analysis should be performed here

/srv/persistent/$USER
fast local storage
synced nightly, but not backed up
       ie if the hard drives fail or you delete something and notice 
       within 24 hours we can recover. Otherwise not. (vs home which is 
       properly backed up )  
intermediate analysis products that would be hard to recover should be stored here 
       e.g. stochastic analysis results that need to be kept so that paper 
       results can be reproduced

/srv/www/$LABNAME/
web accessible from mitra.stanford.edu
*NOT BACKED UP*

Some parallel programming patterns:

# gzip a bunch of files
parallel gzip -- *.FILESTOGZIP

# fork example in python:
(for more detailed examples look at 
 https://github.com/nboley/grit/ grit/lib/multiprocessing_utils.py)

import os
import time
import random

import multiprocessing

class ProcessSafeOPStream( object ):
    def __init__( self, writeable_obj ):
        self.writeable_obj = writeable_obj
        self.lock = multiprocessing.Lock()
        self.name = self.writeable_obj.name
        return
    
    def write( self, data ):
        self.lock.acquire()
        self.writeable_obj.write( data )
        self.writeable_obj.flush()
        self.lock.release()
        return
    
    def close( self ):
        self.writeable_obj.close()

def worker(queue, ofp):
    # Try without this
    random.seed()
    while True:
        i = queue.get()
        if i == 'FINISHED': return
        # simulate an expensive function
        x = random.random()
        time.sleep(x/10)
        print i, x
        ofp.write("%i\t%s\n" % (i, x))

NSIMS = 10000
NPROC = 25

# populate queue
todo = multiprocessing.Queue()
for i in xrange(NSIMS): todo.put(i)
for i in xrange(NPROC): todo.put('FINISHED')

ofp = ProcessSafeOPStream( open("output.txt", "w") )

pids = []
for i in xrange(NPROC):
    pid = os.fork()
    if pid == 0:
       worker(todo, ofp)
       os._exit(0)
    else:
       pids.append(pid)  

for pid in pids:
    os.waitpid(pid, 0)

ofp.close()

print "FINISHED"

For use case 1 we obtained the following ENCODE and ROADMAP datasets https://www.encodeproject.org/files/ENCFF446WOD/@@download/ENCFF446WOD.bed.gz, https://www.encodeproject.org/files/ENCFF546PJU/@@download/ENCFF546PJU.bam, https://www.encodeproject.org/files/ENCFF059BEU/@@download/ENCFF059BEU.bam. Blacklisted regions were obtained from http://mitra.stanford.edu/kundaje/akundaje/release/blacklists/hg38-human/hg38.blacklist.bed.gz. The human genome version hg38 was obtained from http://hgdownload.cse.ucsc.edu/goldenPath/hg38/bigZips/hg38.fa.gz.

For use case 2 we used the set of narrowPeak files summarized in https://github.com/wkopp/janggu_usecases/tree/master/extra/urls.txt (archived version v1.0.1). The human genome version hg19 was obtained from http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.fa.gz

For use case 3 we used the ENCODE datasets https://www.encodeproject.org/files/ENCFF591XCX/@@download/ENCFF591XCX.bam, https://www.encodeproject.org/files/ENCFF736LHE/@@download/ENCFF736LHE.bigWig, https://www.encodeproject.org/files/ENCFF177HHM/@@download/ENCFF177HHM.bam as we as the GENCODE annotation v29 from ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_29/gencode.v29.annotation.gtf.gz.

Address of the bookmark: http://mitra.stanford.edu/

EMBL Postdoc position in Bacterial Gene Gain Loss

Thu, 20 Aug 2015 14:09:21 -0500

A post-doctoral fellowship is available in the research groups of Nick Goldman (EBI) and John Welch (Genetics Department, Cambridge University) under the EMBL-EBI / Cambridge Computational Biomedical Postdoctoral Fellowship scheme.

The project is on bacterial gene gain and loss and emerging pathogenicity, and is described in full here: https://www.ebi.ac.uk/research/postdocs/ebpods/projects/goldman-welch-2015 . The EMBL-EBI / Cambridge Computational Biomedical Postdoctoral (“EBPOD”)

The closing date for applications is 3 September 2015. Nick Goldman EMBL-European Bioinformatics Institute Nick Goldman

More at https://www.ebi.ac.uk/research/postdocs/ebpods/projects/goldman-welch-2015

Mike Ritchie Lab

Wed, 02 Oct 2013 15:25:45 -0500

Mike Ritchie Lab primary research focus is the detection of susceptibility genes for common diseases such as cancer, diabetes, hypertension, and cardiovascular disease, among others. The approaches will involve the development and application of new statistical methods with a focus on the detection of gene-gene interactions associated with human disease.

Gene expression and protein expression patterns between normal and non-normal tissues is a growing area of research that may lead to the identification of candidate genes for understanding the etiology of common, complex diseases.

Lab homepage @ http://ritchielab.psu.edu/ritchielab/

Ribbon: Visualizing complex genome alignments and structural variation:

Jit — Wed, 29 Nov 2017 07:40:22 -0600

Ribbon can be used for long reads, short reads, paired-end reads, and assembly/genome alignments. Instructions for each data format are available by clicking on "instructions" in each tab on the right.

Local installation:

You can install Ribbon locally from Github by following the instructions here: https://github.com/MariaNattestad/Ribbon

Address of the bookmark: http://genomeribbon.com/

Research Associate @ ICGEB, New Delhi.

Wed, 09 Oct 2013 13:49:20 -0500

Applications are invited for Research Associate position in the DBT Sponsored Bioinformatics Infrastructure Facility at ICGEB, New Delhi.

Essential requirements: Experience of using bioinformatics tools.

Experience of working in Linux. Basic knowledge of computer network administration.

Desirable: Knowledge of Linux installation/administration and proficiency in either of the following:

Shell/PERL/Java/Python/VB/Oracle/MySQL/C/CUDA.

Qualification: PhD. or First class M.Sc degree in Bioinformatics or Biotechnology/life science with specialization in Bioinformatics.

Fellowships: Rs 22,000/- with HRA for PhD qualified, Rs 16000/- with HRA for NET/BET/BINC/GATE qualified and 12000/- with HRA for non NET qualified applicants.

Interested candidates may send their complete biodata along with a write-up of their experience and suitability for the position to Dr. Dinesh Gupta by email only to dinesh@icgeb.res.in within 15 days of publication of this advertisement. Kindly mark the email with subject “Application for BIF-RA-2013”

Closing date for applications: 18 October 2013

Only short listed candidates will be invited for an interview at ICGEB.

No TA/DA will be paid for attending the interview.

Advertisement: http://www.icgeb.org/tl_files/Vacancies/BIF-RA-Advt.pdf

Mugsy: multiple whole genome alignment tool

Jit — Fri, 08 Dec 2017 17:41:14 -0600

Mugsy is a multiple whole genome aligner. Mugsy uses Nucmer for pairwise alignment, a custom graph based segmentation procedure for identifying collinear regions, and the segment-based progressive multiple alignment strategy from Seqan::TCoffee. Mugsy accepts draft genomes in the form of multi-FASTA files and does not require a reference genome.

To cite Mugsy, use:

Angiuoli SV and Salzberg SL. Mugsy: Fast multiple alignment of closely related whole genomes.Bioinformatics 2011 27(3):334-4

Address of the bookmark: http://mugsy.sourceforge.net/

SRF/JRF/RA @ UNIVERSITY OF HYDERABAD

Mon, 14 Oct 2013 07:49:11 -0500

SCHOOL OF CHEMISTRY, UNIVERSITY OF HYDERABAD

Applications on plain paper along with details of CV (relevant photocopies of their
qualifications/experience and reprints of published work to be attached) are invited from qualified candidates for Research Fellowship in CSIR- sponsored research project.

JRF/SRF/RA (one vacancy)

CSIR sponsored “In silico design, identification and in vitro validation of lead molecule inhibitors to Bcr-Abl kinase”

JRF: M.Sc in Chemistry/ Bioinformatics/ Biotechnology with I division and NET or GATE qualified

SRF: M.Sc in chemistry/ Bioinformatics/ Biotechnology with at least two years of post- M.Sc research experience as evidenced from published papers in standard refereed journals in relevant area

RA: PhD in chemistry/ Bioinformatics/ Biotechnology with research experience in
relevant area.

As per CSIR guidelines

Notes:
1) You may visit the University of Hyderabad website www.uohyd.ernet.in to learn more about the University of Hyderabad.
2) Applicants should note that the appointment to be made is purely temporary and there is no right for claiming for any regular appointment in the University.
3) No TA/DA will be paid for attending the interview or at the time of joining the post, if selected.
4) The application should be submitted by post/courier/in-person to the address given below on or before November 1st 2013.

Prof. Lalitha Guruprasad
W-103, Gurbakhsh Singh Building
School of Chemistry
University of Hyderabad
Hyderabad- 500 046
5) Short-listed candidates will be called for interview at a short notice.

Advertisement: http://www.uohyd.ac.in/images/recruitment/chemisry_advt_101013.pdf

Magic-BLAST: a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome.

Jit — Tue, 26 Dec 2017 22:23:39 -0600

Magic-BLAST is a tool for mapping large next-generation RNA or DNA sequencing runs against a whole genome or transcriptome. Each alignment optimizes a composite score, taking into account simultaneously the two reads of a pair, and in case of RNA-seq, locating the candidate introns and adding up the score of all exons. This is very different from other versions of BLAST, where each exon is scored as a separate hit and read-pairing is ignored.

Magic-BLAST incorporates within the NCBI BLAST code framework ideas developed in the NCBI Magic pipeline, in particular hit extensions by local walk and jump (http://www.ncbi.nlm.nih.gov/pubmed/26109056), and recursive clipping of mismatches near the edges of the reads, which avoids accumulating artefactual mismatches near splice sites and is needed to distinguish short indels from substitutions near the edges.

Address of the bookmark: https://ncbi.github.io/magicblast/

Research Fellow in Bioinformatics @ Queen's University Belfast -Institute for Global Food Security, School of Biological Sciences

Thu, 17 Oct 2013 04:33:02 -0500

Ref: 13/102900

Available immediately until 30th November 2015, to work on the development of bioinformatics approaches to aid analysis of data derived from the metabolomic profiling of biological matrices. The successful applicant will lead research activities on an FP7 funded EU-wide collaborative project aimed at establishing biomarker-based strategies for high throughput diagnostic screening. Key tasks will involve multivariate analysis of large datasets, bioinformatic-based selection and validation of identified markers, construction of metabolomic spectral profile databases and development of machine learning/database searching approaches amenable to analytical screening techniques. This position will offer the opportunity to travel and undertake work with project collaborators based in the Republic of Ireland and Europe.

Informal enquiries may be directed to Dr Terry McGrath, email: terry.mcgrath@qub.ac.uk.

Anticipated interview date: Thursday 31st October 2013
Salary scale: £30,424 – £39,649 per annum (including contribution points)
Closing date: Monday 21st October 2013

Telephone (028) 90973044 FAX: (028) 90971040 or e-mail on personnel@qub.ac.uk

The University is committed to equality of opportunity and to selection on merit. It therefore welcomes applications from all sections of society and particularly welcomes applications from people with a disability.

Fixed term contract posts are available for the stated period in the first instance but in particular circumstances may be renewed or made permanent subject to availability of funding.

More @ https://hrwebapp.qub.ac.uk/tlive_webrecruitment/wrd/run/ETREC107GF.open?VACANCY_ID=5616943npO&WVID=6273090Lgx&LANG=USA