BOL: Related items

May 17, 2013 - Differential Network Analysis

Wed, 04 Sep 2013 16:22:28 -0500

Steve Horvath presents Differential Network Analysis at the UCLA Human Genetics/Biostatistics Network Course

Fully funded position as PhD Research Fellow in genomics/bioinformatics

Wed, 03 Feb 2021 04:18:57 -0600

A fully funded position as PhD Research Fellow in genomics/bioinformatics is available at the Section for Genetics and Evolutionary Biology (EVOGENE) at the Department of Biosciences, University of Oslo.

The fellowship will be for a period of 3 years, or for a period of 4 years, with 25 % compulsory work (e.g. teaching responsibilities at the department) contingent on the qualifications of the candidate and the teaching needs of the department.

Starting date no later than October 1, 2021.

More at https://www.jobbnorge.no/en/available-jobs/job/199984/phd-research-fellow-in-genomics-and-bioinformatics

Project Fellow @ Alagappa University

Sat, 07 Sep 2013 14:24:13 -0500

Advertisement for the post of project Fellow in UGC project

Project Fellow – One Post

Duration: Three Years

Fellowship : Rs.14, 000/- per month + HRA

Walk-in-interview: 16th Sept, 2013 at 10.00 am

Venue : Department of Bioinformatics, Alagappa University

Eligible candidates can attend walk in interview for the post of Project Fellow in UGC supported project entitled “Shape and chemical feature based 3D- Pharmacophore Model generation, Virtual Screening and MESP studies to identify Potential Leads for Antifungal Azoles”

Interested candidates may apply to Dr. Sanjeev Kumar Singh, Principal Investigator, Department of Bioinformatics, Karaikudi with their Curriculum Vitae along with the copies of the certificates in proof of their educational qualification and experience at skysanjeev@gmail.com

Qualification:

PG Degree in Bioinformatics / Chemistry/ Molecular Biology/ Biotechnology / Biophysics / Biochemistry / Life Sciences/ Pharmacy with minimum of 55% marks in the PG examinations

Desirable:

Research experience in Molecular modeling and CADD will be given preference. UGC-NET/ CSIR-JRF qualified candidates will get preference.

The posts are purely on temporary basis. No TA/DA will be paid for attending the interview.

Advertisement: http://www.alagappauniversity.ac.in/files/news_files/new%20advt-UGC-16sept,13.doc

Interactive Bioinformatics Resources !

Jit — Thu, 12 Aug 2021 00:09:00 -0500

Learn how to use bioinformatics tools right from your browser.
Everything runs in a sandbox, so you can experiment all you want.

More at sandbox.bio

Address of the bookmark: http://sandbox.bio

Bioinformatics Training Material !

BioStar — Sat, 18 Mar 2023 11:26:18 -0500

Glittr is a curated list of bioinformatics training material.
All material is:

In a GitHub or GitLab repository
Free to use
Written in markdown or similar

NOTE: This list of courses is selected only based on the above criteria.
There are no checks on quality.

https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

Address of the bookmark: https://glittr.org/?per_page=25&sort_by=stargazers&sort_direction=desc

Mathivanan Lab

Fri, 20 Sep 2013 13:09:38 -0500

The major research interests are in exploring the role of extracellular matrix components (soluble secreted proteins and membrane vesicles) in cancer and intercellular communication. The lab integrates proteomic, genomic and bioinformatics methodologies to explore cancer cells.

More at http://www.mathivananlab.org/index.html

http://scholar.google.com/citations?user=U6PyEdYAAAAJ&hl=en

BioKit: a set of tools dedicated to bioinformatics, data visualisation

Neel — Tue, 18 Jun 2024 02:04:39 -0500

BioKit is a set of tools dedicated to bioinformatics, data visualisation (biokit.viz), access to online biological data (e.g. UniProt, NCBI thanks to bioservices). It also contains more advanced tools related to data analysis (e.g., biokit.stats). Since R is quite common in bioinformatics, we also provide a convenient module to run R inside your Python scripts or shell (:mod:biokit.rtools module).

Address of the bookmark: https://biokit.readthedocs.io/en/latest/index.html

Junior Research Fellow @ IIT

Sat, 21 Sep 2013 18:04:50 -0500

Applications are invited from the citizens of India for filling up the following temporary position for the sponsored project undertaken in the Department of Biosciences and Bioengineering of this Institute. The position is temporary initially for a period of 1 Year and tenable only for the duration of the project. The requisite qualification & experience etc. are given below:

Project Code, Project Title & Funding Agency
13DST016 : "Studies on the component of mimivirus DNA replication machinery" (Department of Science & Technology)

Position & Salary
Junior Research Fellow (1 Post )
Consolidated salary
Rs.16000/- p.m. + HRA
Qualification
MSc or MTech or BTech or BE in one of the following branches with first class-Biochemistry, Microbiology, genetic Engineering, Biotechnology, Medical Microbiology, Bioinformatics, life sciences etc.
Job Profile
Project involves virus culturing and purification, cloning, protein purification and measurement of helicase, primase, nuclease, translocase activities using various methods. Person should be highly motivated and some experience in cloning and protein purification is desirable. Experience in handling insect cell lines will be an added advantage.

More at http://www.ircc.iitb.ac.in/IRCC-Webpage/rnd/RecruitmentGenerateCircular.jsp?srno=2013086

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/

3 days intensive course on Understanding 'omics data in Basel, Switzerland, 19-21st November

Mon, 23 Sep 2013 10:46:57 -0500

Benefits for the participants

- Plan more efficient experiments
- Correctly interpret results
- Communicate results in publications more effectively

The course focus is on methodologies, not on particular software tools. After the course participants should be able to apply the methods in their respective environment. However, during the course, hands-on sessions will be performed using the Genedata Expressionist® software, which enables participants to quickly apply the discussed methods and visualize results. No previous knowledge on Expressionist® is required; access to the software is free of charge during the course.

More @ http://www.dixa-fp7.eu/dixa-training/dixa-training-agenda/genedata-academy#!