BOL: Related items

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

J. Aires de Sousa Research Group

Wed, 12 Mar 2014 09:57:25 -0500

We are involved in the development of methods and software in chemoinformatics. Current main projects are:

1.automatic learning of chemical reactivity and metabolism,
2.simulation of NMR spectra,
3.modelling of properties of ionic liquids, and
4.representation of molecular chirality.

More at http://joao.airesdesousa.com/

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/

Linux SSH Client Commands for Bioinformatics

Rahul Nayak — Thu, 13 Mar 2014 17:16:32 -0500

Here come on let play with the following basic command line usage of the ssh client.

1. Check your SSH Client Version:

Checking for your SSH client is very sare, but sometimes it may be necessary to identify the SSH client that you are currently running and it’s corresponding version number. The SSh client can be identified as follows

$ ssh -V
OpenSSH_3.9p1, OpenSSL 0.9.7a Feb 19 2013

$ ssh -V
ssh: SSH Secure Shell 3.2.9.1 (non-commercial version) on i686-pc-linux-gnu

2. Connect and login to remote host:

The First time when you login to the remotehost from a localhost, it will display the host key not found message and you can give “yes” to continue. The host key of the remote host will be added under .ssh2/hostkeys directory of your home directory, as shown below.

localhost$ ssh -l jit remotehost.example.com

jit@remotehost.example.com password:

remotehost.example.com$

The Second time when you login to the remote host from the localhost, it will prompt only for the password as the remote host key is already added to the known hosts list of the ssh client.

localhost$ ssh -l jit remotehost.example.com
jit@remotehost.example.com password:
remotehost.example.com$

For some reason, if the host key of the remote host is changed after you logged in for the first time, you may get a warning message as shown below. This could be because of various reasons such as 1) Sysadmin upgraded/reinstalled the SSH server on the remote host 2) someone is doing malicious activity etc., The best possible action to take before saying “yes” to the message below, is to call your sysadmin and identify why you got the host key changed message and verify whether it is the correct host key or not.

localhost$ ssh -l jit remotehost.example.com

jit @remotehost.example.com's password:
remotehost$

4. Debug SSH Client:

Sometimes it is necessary to view debug messages to troubleshoot any SSH connection issues. For this purpose, pass -v (lowercase v) option to the ssh as shown below.

Example without debug message:

        localhost$ ssh -l jit remotehost.example.com
        warning: Connecting to remotehost.example.com failed: No address associated to the name
        localhost$

Example with debug message:

        locaclhost$ ssh -v -l jit remotehost.example.com
        debug: SshConfig/sshconfig.c:2838/ssh2_parse_config_ext: Metaconfig parsing stopped at line 3.
        debug: SshConfig/sshconfig.c:637/ssh_config_set_param_verbose: Setting variable 'VerboseMode' to 'FALSE'.
        debug: SshConfig/sshconfig.c:3130/ssh_config_read_file_ext: Read 17 params from config file.
        debug: Ssh2/ssh2.c:1707/main: User config file not found, using defaults. (Looked for '/home/jit/.ssh2/ssh2_config')
        debug: Connecting to remotehost.example.com, port 22... (SOCKS not used)
        warning: Connecting to remotehost.example.com failed: No address associated to

5. Escape Character: (Toggle SSH session, SSH session statistics etc.)

Escape character ~ get’s SSH clients attention and the character following the ~ determines the escape command.
Toggle SSH Session: When you’ve logged on to the remotehost using ssh from the localhost, you may want to come back to the localhost to perform some activity and go back to remote host again. In this case, you don’t need to disconnect the ssh session to the remote host. Instead follow the steps below.

i. Login to remotehost from localhost: localhost$ssh -l jit remotehost
ii. Now you are connected to the remotehost: remotehost$
iii. To come back to the localhost temporarily, type the escape character ~ and Control-Z. When you type ~ you will not see that immediately on the screen until you press and press enter. So, on the remotehost in a new line enter the following key strokes for the below to work: ~

    remotehost$ ~^Z
    [1]+ Stopped                 ssh -l jit remotehost
    localhost$

iv. Now you are back to the localhost and the ssh remotehost client session runs as a typical unix background job, which you can check as shown below:

    localhost$ jobs
    [1]+ Stopped                 ssh -l jit remotehost

v. You can go back to the remote host ssh without entering the password again by bringing the background ssh remotehost session job to foreground on the localhost

    localhost$ fg %1
    ssh -l jit remotehost
    remotehost$

Basic Notions in Molecular Biology and Genetics

Antony Campos — Sun, 16 Mar 2014 18:15:29 -0500

This is a presentation about some fundamental concepts applied in molecular biology and genetics, also it contains a little bit of the experience that one of our members has gained in his years of undergraduate state related to molecular cloning. Our research group, called "BIOPHARM" (Acronymus of Laboratory of Bioinformatics and Pharmacogenetics), was stablished on 2007, took it a bit of years to make it real this initative, although, nowadays, we're working on some projects involved in those fields. This research group belongs to the Department of Biochemistry, Faculty of Pharmacy and Biochemistry, Universidad Nacional Mayor de San Marcos, Lima, Perú. We try to encourage research initiatives, helping them and also we use to participate in differents courses, congress and symposiums.

Bandage: interactive visualization of de novo genome assemblies

Shruti Paniwala — Mon, 04 Dec 2017 10:09:37 -0600

Bandage (a Bioinformatics Application for Navigating De novo Assembly Graphs Easily) is a tool for visualizing assembly graphs with connections. Users can zoom in to specific areas of the graph and interact with it by moving nodes, adding labels, changing colors and extracting sequences. BLAST searches can be performed within the Bandage graphical user interface and the hits are displayed as highlights in the graph. By displaying connections between contigs, Bandage presents new possibilities for analyzing de novo assemblies that are not possible through investigation of contigs alone.

Availability and implementation: Source code and binaries are freely available at https://github.com/rrwick/Bandage. Bandage is implemented in C++ and supported on Linux, OS X and Windows. A full feature list and screenshots are available at http://rrwick.github.io/Bandage.

Address of the bookmark: http://rrwick.github.io/Bandage/

Gerstein Lab

Wed, 19 Mar 2014 12:48:20 -0500

The focus of the Gerstein Lab is interpreting personal genomes, particularly in relation to disorders, such as cancer. This endeavor has a number of related aspects described below. Moreover, the approaches we take have broad connections to a variety of data-intensive fields, within the emerging discipline of data science.

Personal Genome Variation: SVs
Human Genome Annotation: Processing Next-Gen Sequencing Data
Comparative Genomics: Pseudogenes as Molecular Fossils
Protein Structure and Function: Macromolecular Motions
Analysis of Diverse Networks
Genomics at the Forefront of Data Science

Lab page: http://www.gersteinlab.org/

Tools for bacterial whole genome annotation

Radha Agarkar — Sat, 16 Dec 2017 17:37:47 -0600

RAST – Web tool (upload contigs), uses the subsystems in the SEED database and provides detailed annotation and pathway analysis. Takes several hours per genome but I think this is the best way to get a high quality annotation (if you have only a few genomes to annotate).

Prokka – Standalone command line tool, takes just a few minutes per genome. This is the best way to get good quality annotation in a flash, which is particularly useful if you have loads of genomes or need to annotate a pangenome or metagenome. Note however that the quality of functional information is not as good as RAST, and you will need several extra steps if you want to do functional profiling and pathway analysis of your genome(s)… which is in-built in RAST.

NCBI Prokaryotic Genome Annotation Pipeline is designed to annotate bacterial and archaeal genomes (chromosomes and plasmids).

Genome annotation is a multi-level process that includes prediction of protein-coding genes, as well as other functional genome units such as structural RNAs, tRNAs, small RNAs, pseudogenes, control regions, direct and inverted repeats, insertion sequences, transposons and other mobile elements.

PGAP: NCBI has developed an automatic prokaryotic genome annotation pipeline that combines ab initio gene prediction algorithms with homology based methods. The first version of NCBI Prokaryotic Genome Automatic Annotation Pipeline (PGAAP; see Pubmed Article) developed in 2005 has been replaced with an upgraded version that is capable of processing a larger data volume. NCBI's annotation pipeline depends on several internal databases and is not currently available for download or use outside of the NCBI environment.

BEACON (automated tool for Bacterial GEnome Annotation ComparisON), a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/.

BlastKOLA: Assigns K numbers to the user's sequence data by BLAST searches, respectively, against a nonredundant set of KEGG GENES. KOALA (KEGG Orthology And Links Annotation) is KEGG's internal annotation tool for K number assignment of KEGG GENES using SSEARCH computation. Annotate Sequence in KEGG Mapper and Pathogen Checker in KEGG Pathogen are special interfaces to this server and can be executed in an interactive mode. BlastKOALA is suitable for annotating fully sequenced genomes.

PAGIT: Provides a toolkit for improving the quality of genome assemblies created via an assembly software. PAGIT compiled four tools: (i) ABACAS which classifies and orientates contigs and estimates the sizes of gaps between them; (ii) IMAGE uses paired-end reads to extend contigs and close gaps within the scaffolds; (iii) ICORN for identifying and correcting small errors in consensus sequences and; (iv) RATT for help annotation. The software was mainly created to analyze parasite genomes of up to about 300 Mb.

MAKER: A portable and easily configurable genome annotation pipeline. MAKER allows smaller eukaryotic and prokaryotic genome projects to independently annotate their genomes and to create genome databases. It identifies repeats, aligns ESTs and proteins to a genome, produces ab-initio gene predictions and automatically synthesizes these data into gene annotations having evidence-based quality values. MAKER's inputs are minimal and its ouputs can be directly loaded into a Generic Model Organism Database (GMOD). They can also be viewed in the Apollo genome browser; this feature of MAKER provides an easy means to annotate, view and edit individual contigs and BACs without the overhead of a database. MAKER is available for download and can be tested online via the MAKER Web Annotation Service (MWAS).

MyPro is a software pipeline for high-quality prokaryotic genome assembly and annotation. It was validated on 18 oral streptococcal strains to produce submission-ready, annotated draft genomes. MyPro installed as a virtual machine and supported by updated databases will enable biologists to perform quality prokaryotic genome assembly and annotation with ease.

Bioinformatics PhD at University of Calcutta

Mon, 31 Mar 2014 08:41:04 -0500

University of Calcutta
Department of Biophysics, Molecular Biology & Bioinformatics

Applications are invited for admission to the Ph.D. programme in the Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta for the year 2014 from eligible candidates who would be placed under the departmental teachers or affiliated research supervisors for the pursuance of their Ph.D. programme.

Candidates are requested to download the Ph.D. admission test application form from the University website and apply in the prescribed proforma by paying Rs. 100/- through a challan available through different University Cash counters. The challan is to be duly forwarded through the Head, Department of Biophysics, Molecular Biology & Bioinformatics, University of Calcutta.

The completed application form with a copy of the paid challan is to be submitted to the office of the Department by April 16, 2014.

Syllabus for the Test: The questions for the admission test and interview will be based on topics in the following areas:

Mathematical methods, Molecular and Cellular Biophysics, Molecular and Cell Biology, Biochemistry, Genetics, Plant Biology, Developmental biology, Neurobiology, Biotechnology and Bioinformatics.

However, the interview will be primarily based on the research emphasis of the candidate. Candidates must clearly indicate the program in which they want to apply.

Date of Admission test : April 22, 2014 (Tuesday)

Date of publication of selection list for the interview : April 22, 2014(Tuesday)

Date of Interview : April 23, 2014 (Wednesday)

Number of vacancies for the Ph.D. programme : 12

Reservation policy will be followed as per rules.

Candidates with valid NET/GATE/M.Phil. or equivalent qualifications are not required to appear at the admission test but would need to qualify in the interview.

http://www.caluniv.ac.in/admission%20notice/PHD_BIO_PHYSICS.pdf

AliTV—interactive visualization of whole genome comparisons

Jit — Wed, 10 Jan 2018 07:08:17 -0600

AliTV, which provides interactive visualization of whole genome alignments. AliTV reads multiple whole genome alignments or automatically generates alignments from the provided data. Optional feature annotations and phylo- genetic information are supported. The user-friendly, web-browser based and highly customizable interface allows rapid exploration and manipulation of the visualized data as well as the export of publication-ready high-quality figures. AliTV is freely available at https://github.com/AliTVTeam/AliTV

https://alitvteam.github.io/AliTV/

Address of the bookmark: https://github.com/AliTVTeam/AliTV