BOL: Related items

BOKU Lab

Wed, 08 Jan 2014 19:33:12 -0600

We are interested in the study of complex systems in living organisms. Novel views augmenting the classical gene by gene approaches are required to overcome the engineered redundancies and combinatorial effects prevalent in higher eukaryotes. We therefore combine work to establish improved quantitative experimental assays, such as microarrays or differential in-gel electrophoresis, and development of modern computational methods, such as hierarchical probabilistic models or integration of heterogeneous data sources, focussed by biological studies in our laboratory and collaborations.

Highlights of our research include:

Optimization of microarray design, probe signal interpretation
Advanced models and tools for expression profiling
State-of-the-art applications and integrated analyses

Lab page @ http://bioinf.boku.ac.at/

Metabuli 분리 improves metagenomic read classification

Abhi — Sat, 03 Jun 2023 20:15:04 -0500

Metabuli 분리 improves metagenomic read classification through metamers, DNA-AA k-mers, to be sensitive and specific, recovering 99% and 98% of DNA or AA classifiers.

Metabuli is metagenomic classifier that jointly analyze both DNA and amino acid (AA) sequences. DNA-based classifiers can make specific classifications, exploiting point mutations to distinguish close taxa. AA-based classifiers have higher sensitivity in detecting homology between query and reference sequences, leverageing higher conservation of AA sequences. Metabuli combines the information of both sequence types using a novel k-mer structure, metamer, to enable both specific and sensitive characterization of metagenomic samples. In addition, it can classify reads against a database of any size as long as it fits in the hard disk.

Address of the bookmark: https://github.com/steineggerlab/Metabuli

Postdoctoral Position (Bioinformatics/Computational Biology)

Thu, 12 Dec 2013 17:58:00 -0600

University College Cork
LAPTI
Cork-Co Cork-Ireland

Postdoctoral position is available for three years to work on development of Bioinformatics resources for the analysis and visualization of ribosome profiling data. Ribosome profiling (ribo-seq) is a technology that allows mapping positions of the ribosomes on the whole transcriptome level with a nucleotide precision. The technology allows obtaining high resolution digital snapshots of gene expression in cells. The position is available starting on the 1st of October, 2013.

Candidate is expected to have Ph.D. in Bioinformatics or Computational Biology. Candidates with the degree in non-Biological disciplines such as Computer Science, Statistics, Applied Mathematics, Physics or Electrical Engineering will also be considered.

The position is available at LAPTI (http://lapti.ucc.ie) that is located in the Western Gate Building (http://www.stwarchitects.com/project-information.php?c=1&p=09993) at University College Cork. Western Gate Building Research Complex hosts several UCC departments and provides ideal environment for interdisciplinary research. Cork (sometimes referenced as “Venice of Ireland”) is the second most populous city in the Republic. It has friendly cosmopolitan atmosphere and vibrant culture. A number of American industrial giants such as Apple , EMC and Pfizer have chosen Cork as a home for their European headquarters.

The details of the application process are given at http://lapti.ucc.ie/jobs.html. To ensure prompt processing of your application use the subject line: ‘Postdoc computational’. All applications received prior to August the 1st are guaranteed equal consideration. However, applications at the later dates will also be considered until the position is filled.

For more info visit http://lapti.ucc.ie

SVbyEye: R Package to visualize alignments between two or multiple DNA sequences

LEGE — Tue, 17 Sep 2024 02:34:57 -0500

R Package to visualize alignments between two or multiple DNA sequences including
a number of functionalities to facilitate processing of alignments in PAF format.

SVbyEye, an open-source R package to visualize and annotate sequence-to-sequence alignments along with various functionalities to process alignments in PAF format. The tool facilitates the characterization of complex SVs in the context of sequence homology helping resolve the mechanisms underlying their formation. Availability and implementation SVbyEye is available at https://github.com/daewoooo/SVbyEye.

Author: David Porubsky

Address of the bookmark: https://github.com/daewoooo/SVbyEye

Critical to discoveries in bioinformatics

Mon, 16 Dec 2013 17:13:24 -0600

EMBL-EBI distributes datasets worldwide using the Janet network. This biological data enables the discovery of new drugs, new diagnostics and increasingly new agro-chemicals. Their work, which includes the 1000-genome project, has generated petabytes of data and this growth is showing no signs of abating. On-demand bandwidth over Janet will therefore be critical to their ongoing work.

Research Associate @ INDIAN INSTITUTE OF SPICES RESEARCH

Wed, 25 Dec 2013 12:34:43 -0600

INDIAN INSTITUTE OF SPICES RESEARCH
(Indian Council of Agricultural Research)
Marikunnu P.O., Kozhikode – 673 012, Kerala

WALK -IN- TEST CUM INTERVIEW

Walk- in- Test cum Interview (based on test) for the selection of Research Associate (Bioinformatics) & Bioinformatic Trainees under the scheme ‘Distributed Information Sub Centre- DISC’ will be held at this Institute as per details indicated below.

Research Associate

Date of Interview : 21 -01-2014 at 10.00 A.M

Qualifications : a) Essential: Doctorate degree in Bioinformatics or Biotechnology/Life Sciences/Biochemistry with expertise in Bioinformatics as evidenced by publications.

Three years research experience after MVSc/MPharm/ME/MTech with Bioinformatics Specialization.

b Desirable: Experience in handling NGS data Programming skills in Python/Bioperl

Emoluments : Rs:22000/- per month + HRA (higher pay upto Rs.24000/- can be paid depending on the qualifications and experience.

Upper age limit : 40 years for Men & 45 years for Women as on date of Interview (Upper Age limits are relaxable for SC, ST and OBC candidates as per Govt. of India norms (at present 5 years for SC/ST and 3 years for OBC)

Duration of Project : Till the closure of the project.

General Terms and conditions

1. The above positions are purely on temporary basis and is co-terminus with the closure of the project. There is no provision of re-employment after termination of project. The selected candidate will not have any right for claiming pay scale or absorption against any regular post being vacant on a later date at this Institute.
2 . No TA/DA will be paid for attending the Interview.
3. Canvassing in any form will lead to cancellation of candidate.
4. The decision of Director, IISR would be final and binding in all aspects.
5. Candidates will not be permitted to enter the Examination Hall after 10.00 A.M.
6. Candidates who secure the minimum marks prescribed by the Institute in written test only will be eligible for calling for the interview. The number of candidates to be called for the interview will be decided by the Director of the Institute.
7 Those who do not possess original Degree/PG certificate or Provisional certificate will not be allowed to attend the Test/Interview.

Note: All relevant certificates (in original) and bio data
No objection certificate in case he/she is employed elsewhere and experience certificate in original (if any) need to be produced at the time of interview.
Location of IISR Kozhikode Main Campus - Pallithazham bus stop between Moozhikkal East and Chelavoor on the NH 212 ”Kozhikode - Kollegal” Road.

Advertisement: www.spices.res.in/pdf/DISC-Website.pdf

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/