BOL: Related items

CRCRI Bioinfomatics Walk In on 08.10.2013

Fri, 27 Sep 2013 10:59:53 -0500

Walk-in-Interview for recruitment of one Project Fellow for a period of 10 months purely on temporary basis is proposed to be held at Central Tuber Crops Research Institute, Sreekariyam, Thiruvananthapuram for a KSCSTE funded project entitled “PARTICIPATORY DEVELOPMENT OF A WEB BASED USER FRIENDLY CASSAVA EXPERT SYSTEM”

Salary: Rs. 10,000/- per month.

Age limit: 35 for men and 40 for women & SC/ST.

Qualification: First class in M. Sc (Agriculture)/MCA/M.Sc (IT)/ M. Sc (Computer Application)/M.Sc (Bioinformatics)/M.Sc (Geoinformatics).

Desirable: Two years experience in web design and web programming.

Date & time of interview: 08.10.2013, 10 am

Interested candidates may appear for an interview at this institute along with their application in plain paper containing the following particulars viz. (1) Name (2) Father/Husband/Guardian’s Name (3) date of birth & age as on 01.10.2013 (4) Permanent address (5) Address for communication (6) Email address and Telephone No. with code (7) Qualification (8) National fellowship like ICAR/CSIR/UGC etc. if any (9) Whether SC/ST/OBC (10) Details of experience (Attested copies of degree certificate, proof of age, mark sheets). Original certificates should be produced for verification.

No TA/DA will be admissible to the candidates attending the test. The selected candidate will have to join immediately.

Advertisement: http://www.ctcri.org/careers/mithra_SRF.doc

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/

Bioinformatics Algorithms (Part 1) with Pavel Pevzner, Phillip E. C. Compeau,

Mon, 30 Sep 2013 11:34:09 -0500

The course Bioinformatics Algorithms (Part 1) by Pavel Pevzner, Phillip E. C. Compeau, and Nikolay Vyahhi from University of California, San Diego will be offered free of charge to everyone on the Coursera platform. Sign up at http://www.coursera.org/course/bioinformatics.

Walk-in-Interview @ Indian Agricultural Statistics Research Institute

Wed, 02 Oct 2013 15:40:17 -0500

Indian Agricultural Statistics Research Institute
Library Avenue, Pusa, New Delhi – 110012

Walk-in-Interview

Walk-in-interview will be held on October 5, 2013 at 10:00 A.M. at IASRI, New Delhi for a project “A New Distributed Computing Framework for Data Mining” funded by Department of Electronics and Information Technology, Government of India for the following posts. The appointment will be on contractual basis upto 14th October, 2015 or till the termination of the project whichever is earlier and the incumbent shall not have any claim for regular appointment under ICAR.

Research Associate

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or

Post-Graduation in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 1st Division and at least two years of research experience

 Knowledge of Statistical Analysis /Bioinformatics tools for computational genomics.

 Knowledge of R/Perl programming language

Research Associate

Ph.D. in Computer Science/ Computer Application / Bioinformatics/ Agricultural
Statistics/ Statistics or equivalent or

Post-Graduation in Computer Science/ Computer Application /Bioinformatics/ Agricultural Statistics/ Statistics or equivalent with 1st Division and at least two years of research experience

 Expertise in Java programming.
 Knowledge of system administration and networking under Linux environment.
 Knowledge of parallel programming and cluster computing.

Emoluments for Research Associate: Consolidated Rs:24000/- per month + HRA (for Ph.D. Degree holders) and Rs:23000/- per month + HRA (for Master’s Degree holders)

Age Limit: Age should be not more than 40 years (5 years relaxation for SC/ST/women candidates and 3 years for OBC candidates as on date of interview).

Interested candidates are requested to appear for Walk-in-Interview on the date and time as specified above in Room No. 106, Training Cum Administrative Block of the Institute along with their application giving bio-data with attested copies of certificates, degrees, testimonials, etc. and one passport size photograph.

Original certificates/ Degrees are needed to be produced at the time of interview.

No T.A. /D.A. will be paid for appearing in the interview.

Advertisement: http://www.iasri.res.in/employment/employment.htm

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/

Shendure Lab

Wed, 09 Oct 2013 14:21:58 -0500

The Shendure Lab is part of the Department of Genome Sciences at the University of Washington (Seattle, WA). The mission of the lab is to develop and apply new technologies in genomics and molecular biology. Most projects in the lab exploit new DNA sequencing technologies (Shendure et al., Nature Reviews Genetics 2004; Shendure & Ji, Nature Biotechnology 2008; Shendure & Lieberman Aiden, Nature Biotechnology 2012), and generally fall into one of six areas: 1) next-generation human genetics; 2) genome contiguity & completeness; 3) massively parallel functional analysis; 4) molecular tagging; 5) synthetic biology; 6) translational genomics. Our interests in each of these areas are outlined briefly below, and a full list of publications is available via PubMed. http://www.ncbi.nlm.nih.gov/pubmed?cmd=search&term=shendure
More http://krishna.gs.washington.edu/research.html

Lab page @ http://krishna.gs.washington.edu/index.html

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/

Shankar Lab

Wed, 16 Oct 2013 07:02:22 -0500

Research Interest:

(A) Regulatory System Analysis with respect to microRNAs

(B) Computational Epigenomics & Regulomics:

Department of Biotechnology,
Institute of Himalyan Bioresources Technology
CSIR, Palampur(Himachal Pradesh), India.
Email: ravishihbt.res.in; ravish9gmail.com

More @ http://scbb.ihbt.res.in/SCBB_dept/Lab_Member.php

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/

DbBrowser: Attwood Lab

Fri, 18 Oct 2013 10:48:19 -0500

DbBrowser: Attwood Lab research concerns protein sequence analysis, primarily using the method of protein 'fingerprinting'. DbBrowser: Attwood Lab maintain a diagnostic fingerprint database (PRINTS), one of the founding partner of InterPro. We also design software to display sequence and structural data in visually-striking ways (e.g., Ambrosia, CINEMA); DbBrowser: Attwood Lab are building re-usable software components to create semantically integrated bioinformatics applications through UTOPIA, including a 'smart' PDF reader that links bioinformatics databases and tools directly with scientific articles (Utopia Documents); and have developed a number of tools for automatic annotation and text mining (e.g., MINOTAUR, PRECIS, METIS).

More @ http://www.bioinf.manchester.ac.uk/dbbrowser/index.php