BOL: Related items

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/

Postodoc in Computational/Systems Biology and Machine Learning

Wed, 09 Apr 2014 20:47:57 -0500

One profile of Computational/Systems Biology and Machine Learning at Postdoc level is needed at the Laboratory of Immunobiology of Neurological Disorders led by Cinthia Farina, Institute of Experimental Neurology, Ospedale San Raffaele, Milano. The projects of interest for this application involve research on translational bioinformatics in complex human disorders.

You have a PhD in Computational Science, Bioinformatics, or equivalent.

Especially relevant skills for the profile are:
1. In-depth understanding and implementation of methods and development of
algorithms for statistical and machine learning classification, clustering, predictive
modelling.
2. Experience on transcriptomics and clinical data analysis, in particular gene regulatory networks, protein interactomes, development of diagnostic tools.

3. Solid experience with data mining, bioinformatics programming and statistics for bioinformatics.
4. Flexibility and willing to work across multiple projects and technology in a rapidly evolving scientific context.

Candidates with programming/scripting experience are also welcome. In particular, proficiency in one or more mainstream programming languages (C, C++, Java, Python, Perl, etc.), together with the understanding of relational database design and SQL/DBMS systems (e.g. MySQL, PHP, Oracle).
Experience with the analysis of next-generation sequencing data is a plus.
Clear demonstration of experience in analysis and modelling of omics and clinical data must be provided.

Interested candidates should send to farina.cinthia@hsr.it:

1. CV (please show evidences of relevant titles, projects, courses, references, etc.)
2. One page with a list of research topics (i.e. ongoing projects)
3. earliest availability
4. 2-3 contact names

Research Associate (RA) at INSTITUTE OF ADVANCED STUDY IN SCIENCE AND TECHNOLOGY

Mon, 05 May 2014 08:44:24 -0500

INSTITUTE OF ADVANCED STUDY IN SCIENCE AND TECHNOLOGY
(An Autonomous Institute under Department of Science and Technology, Govt. of India)
Paschim Boragaon, Garchuk, Guwahati-781035

Appointment Adv.No.2

Applications in plain paper are invited from Indian citizens for one/two position each of Research Associate, Traineeship and Studentship for BIF facility, Division of Life Sciences, IASST.

Applications with complete Bio-data containing contact address, e-mail and phone number, two recent passport size photographs and attested copies of mark sheets, certificates etc., should be sent to the Registrar, IASST, Paschim Boragaon, Garchuk, Guwahati – 781035, Assam, so as to reach on or before 5/05/2014.

A. Research Associate:

Number of vacancies: 1 (One)

Qualifications:

PhD in Bioinformatics or allied disciplines with knowledge of Bioinformatics. The candidates who have submitted PhD thesis may also apply.

In case, candidates having PhD are not found, candidates having MSc in Bioinformatics or allied disciplines with sound knowledge of Bioinformatics will be preferred.

Remuneration: Candidate having PhD will get a consolidated remuneration of Rs. 22,000/- +HRA per month. MSc having NET/GATE/SLET qualified candidate will get a remuneration of Rs. 16,000/= and HRA and candidate with only MSc will get a remuneration of Rs.14,000/- and HRA.

Tenure:

The post is initially for one year and may be extended depending on the performance till the tenure of the project.

B. Traineeship:

Number of vacancies: 2 (Two)

Qualifications:

Candidate with a postgraduate degree in Bioinformatics/Biotechnology/Life sciences from a recognised University

Remuneration: Rs. 5000/month for 6 months

C. Studentship:

Number of vacancies: 2 (Two)

Qualifications:

Candidate pursuing M.Sc in bioinformatics in a recognised University.

Remuneration: Rs. 5000/month for 6 months

http://iasst.gov.in/pdf/recruitment/advt%20no_2_24042014.pdf

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/

“On” and “Off” the neuron !!!

Rahul Nayak — Fri, 25 Apr 2014 19:31:13 -0500

Optogenetics is a recent innovation in neuroscience that gives researchers the ability to control the activity of neurons with light. With this powerful tool, researchers are teasing apart the biological basis of memory, behavior, and disease (see “Scientists Make Mice ‘Remember’ Things That Didn’t Happen” and “An On-Off Switch for Anxiety,”). But for the first several years of this technology’s existence, the proteins that scientists added to neurons to make them react to light were only good at activating neurons. That limited researchers’ ability to understand neuronal circuits, sets of interconnected neurons that are thought to control behavior and, when misfiring, to underlie many brain conditions. Problems can arise from any imbalance in circuit activity, whether too much or too little.

Now, two research groups have engineered new optogenetic proteins that can be used to efficiently silence neurons. One of the two new proteins comes from the lab of Karl Deisseroth, a psychiatrist and neuroscientist at Stanford University who helped develop optogenetics as a research tool. His group’s new “off” switch for neurons was created by changing 10 of the 333 amino acids in an existing optogenetic protein, which itself had been engineered by combining natural proteins from green algae. That advance “creates a powerful tool that allows neuroscientists to apply a brake in any specific circuit with millisecond precision,” said Thomas Insel, director of the National Institute of Mental Health, in a released statement. The other new silencing protein, developed by scientists at the Humboldt University of Berlin and collaborators, was created by changing amino acids in the same existing optogenetic protein.

Some researchers are also looking to optogenetics as a potential treatment for patients with a variety of conditions (see “For Mice, and Maybe Men, Pain Is Gone in a Flash,” and “Flipping on the Lights to Halt Seizures”) but there are huge challenges to overcome. The method requires genetic modification of cells to make them light-sensitive. It also requires implanted light sources for all but the shallowest of nerve endings.

jobTree based python wrapper to run the genome simulation tool suite Evolver

Jit — Fri, 08 Dec 2017 16:26:32 -0600

evolverSimControl (eSC) can be used to simulate multi-chromosome genome evolution on an arbitrary phylogeny (Newick format). In addition to simply running evolver, eSC also automatically creates statistical summaries of the simulation as it runs including text and image files. Also included are convenience scripts to: check on a running simulation and see detailed status and logging information; extract fasta sequence files from the leaf nodes of a completed simulation; extract pairwise multiple alignment files (.maf) from leaf and branch nodes from a completed simulation and with the help of mafJoin, join them together into a single maf covering the entire simulation.

Address of the bookmark: https://github.com/dentearl/evolverSimControl

Check Linux server configuration !!

Rahul Nayak — Tue, 06 May 2014 01:10:57 -0500

Bioinformatician uses servers for computational analysis. Sometime we need to check the server details before running our programs or tools. Here I am showing some basic commands using them you can gather the system/server information.

To check what version of Operating System is installed on the server you can use the following commands:-
=================================================================
1.cat /etc/issue
[root@localhost ~]# cat /etc/issue
Red Hat Enterprise Linux Server release 5.5 (Tikanga)
Kernel \r on an \m

2.cat /etc/redhat-release
[root@localhost ~]# cat /etc/redhat-release
Red Hat Enterprise Linux Server release 5.5 (Tikanga)

3.lsb_release -a
[root@localhost ~]# lsb_release -a
LSB Version:    :core-3.1-ia32:core-3.1-noarch:graphics-3.1-ia32:graphics-3.1-noarch
Distributor ID: RedHatEnterpriseServer
Description:    Red Hat Enterprise Linux Server release 5.5 (Tikanga)
Release:        5.5
Codename:       Tikanga

To check whether the operating system is 32 or 64bit:-
================================
# uname -i
[root@localhost ~]# uname -i
i386
(i386 represents that server is having 32bit operating system)

[root@localhost ~]# uname -i
x86_64
(x86_64 represents that server is having 64bit operating system)

To see the processor/CPU information:-
=============================
# cat /proc/cpuinfo
[root@localhost ~] cat /proc/cpuinfo
processor       : 0
vendor_id       : GenuineIntel
cpu family      : 6
model           : 15
model name      : Intel(R) Xeon(R) CPU            5130 @ 2.00GHz
stepping        : 6
cpu MHz         : 1995.087
cache size      : 4096 KB
physical id     : 0
siblings        : 2
core id         : 0
cpu cores       : 2
apicid          : 0
fdiv_bug        : no
hlt_bug         : no
f00f_bug        : no
coma_bug        : no
fpu             : yes
fpu_exception   : yes
cpuid level     : 10
wp              : yes
flags           : fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe nx lm constant_tsc pni monitor ds_cpl vmx tm2 ssse3 cx16 xtpr lahf_lm
bogomips        : 3990.17
(Here processor number 0 indicates that the system is having one process(processor number starts with zero))

To check memory information:-
===========================
# free -m
[root@localhost ~]# free -m
             total       used       free     shared    buffers     cached
Mem:          5066       3513       1552          0        612       2319
-/+ buffers/cache:        582       4484
Swap:         1983          0       1983

# cat /proc/meminfo
[root@localhost ~]# cat /proc/meminfo
MemTotal:      5187752 kB
MemFree:       1639300 kB
Buffers:        627024 kB
Cached:        2374944 kB
SwapCached:          0 kB
Active:        2458788 kB
Inactive:       920964 kB
HighTotal:     4325164 kB
HighFree:      1561936 kB
LowTotal:       862588 kB
LowFree:         77364 kB
SwapTotal:     2031608 kB
SwapFree:      2031608 kB
Dirty:             704 kB
Writeback:           0 kB
AnonPages:      377892 kB
Mapped:          35328 kB
Slab:           153036 kB
PageTables:       6316 kB
NFS_Unstable:        0 kB
Bounce:              0 kB
CommitLimit:   4625484 kB
Committed_AS:   977132 kB
VmallocTotal:   116728 kB
VmallocUsed:      4492 kB
VmallocChunk:   112124 kB
HugePages_Total:     0
HugePages_Free:      0
HugePages_Rsvd:      0
Hugepagesize:     2048 kB

To check the model and serial name of the server:-
=======================================
[root@localhost ~]# dmidecode | egrep -i "product name|Serial number"
Product Name: PowerEdge R710
Serial Number: AB8CDE1


To check the host name:-
=====================
[root@localhost ~]# uname -n
localhost

[root@localhost ~]# hostname
localhost

To check the kernel version:-
========================
[root@localhost ~]# uname -r
2.6.18-238.9.1.el5PAE

Delta: a new Web-based 3D genome visualization and analysis platform

Jit — Wed, 20 Dec 2017 08:49:55 -0600

Delta is an integrative visualization and analysis platform to facilitate visually annotating and exploring the 3D physical architecture of genomes. Delta takes Hi-C or ChIA-PET contact matrix as input and predicts the topologically associating domains and chromatin loops in the genome. It then generates a physical 3D model which represents the plausible consensus 3D structure of the genome. Deltafeatures a highly interactive visualization tool which enhances the integration of genome topology/physical structure with extensive genome annotation by juxtaposing the 3D model with diverse genomic assay outputs.

https://github.com/zhangzhwlab/delta

Address of the bookmark: https://github.com/zhangzhwlab/delta

Assistant Professor at Jawaharlal Nehru University in Delhi

Wed, 07 May 2014 00:29:22 -0500

Advt. No. RC/48/2014

SCHOOL OF COMPUTATIONAL AND INTEGRATIVE SCIENCES (SC&IS)

ESSENTIAL QUALIFICATION : - M.Sc./M.Tech. in Physics/ Chemistry/ Biology/ Mathematics/ Statistics/ Bioinformatics/ Computational Biology. Ph.D. in the broad areas of Bioinformatics/ Computational Biology. Candidates must have demonstrated capabilities in terms of high impact research publications in either of the above mentioned areas.

Scale of Pay : - 15600-39100/- (PB-III) AGP Rs. 6000/-

For more details on Centre/School, Specializations etc. please visit JNU website www.jnu.ac.in or contact Section Officer, Room Nos. 131-132, Recruitment Cell, Administrative Block, JNU, New Delhi – 110067, Email: recruitmentjnu2013@gmail.com The last date for the receipt of application is 15 May, 2014.

http://www.jnu.ac.in/Career/

http://www.jnu.ac.in/Career/ADVTNo_RC_48_2014.pdf
Last Apply Date:

15 May 2014

MGcV: the microbial genomic context viewer for comparative genome analysis

Jit — Mon, 29 Jan 2018 04:55:46 -0600

MGcV is an interactive web-based visalization tool tailored to facilitate small scale genome analysis. To start using MGcV:

Supply your genes/genomic segments/phylogenetic tree of interest in the input-box by
- selecting the type of identifier and pasting identifiers (one per line)
- or by using the gene ID search tool
- or with the BLAST search tool
Click "Visualize context".

Consult the documentation to learn more about MGcV.

Address of the bookmark: http://mgcv.cmbi.ru.nl/