BOL: Related items

CLARK: Fast, accurate and versatile sequence classification system

Jit — Sat, 15 Feb 2020 01:49:01 -0600

CLARK, a method based on a supervised sequence classification using discriminative k-mers. Considering two distinct specific classification problems (see the article for details), namely (1) the taxonomic classification of metagenomic reads to known bacterial genomes, and (2) the assignment of BAC clones and transcript to chromosome arms/centromeres (in the absence of a finished assembly for the reference genome), CLARK outperforms in classification speed and precision the best state-of-the-art methods.

http://clark.cs.ucr.edu/Spaced/

Address of the bookmark: http://clark.cs.ucr.edu/Spaced/

GView: A Java application for viewing and examining prokaryotic genomes in a circular or linear context

Abhimanyu Singh — Fri, 14 Jul 2017 07:47:03 -0500

GView is a Java application for viewing and examining prokaryotic genomes in a circular or linear context. It accepts standard sequence file formats and an optional style specification file to generate customizable, publication quality genome maps in bitmap and scalable vector graphics formats. GView features an interactive pan-and-zoom interface, a command-line interface for incorporation in genome analysis pipelines, and a public Application Programming Interface for incorporation in other Java applications.

Availability: GView is a freely available application licensed under the GNU Public License. The application, source code, documentation, file specifications, tutorials and image galleries are available at http://gview.ca

Contact: ac.cg.cpsa-cahp@raalesmod.nav.yrag

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2995121/

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/

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/

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

Mash: fast genome and metagenome distance estimation using MinHash

Jit — Tue, 12 Dec 2017 17:30:12 -0600

Mash is normally distributed as a dependency-free binary for Linux or OSX (see https://github.com/marbl/Mash/releases). This source distribution is intended for other operating systems or for development. Mash requires c++11 to build, which is available in and GCC >= 4.8 and OSX >= 10.7.

See http://mash.readthedocs.org for more information.

Address of the bookmark: https://github.com/marbl/Mash/releases

GIGGLE: a search engine for large-scale integrated genome analysis

Jit — Wed, 10 Jan 2018 03:10:45 -0600

GIGGLE is a genomics search engine that identifies and ranks the significance of genomic loci shared between query features and thousands of genome interval files. GIGGLE (https://github.com/ryanlayer/giggle) scales to billions of intervals and is over three orders of magnitude faster than existing methods. Its speed extends the accessibility and utility of resources such as ENCODE, Roadmap Epigenomics, and GTEx by facilitating data integration and hypothesis generation.

https://www.nature.com/articles/nmeth.4556

Address of the bookmark: https://github.com/ryanlayer/giggle

MUMmer4: A fast and versatile genome alignment system

Jit — Sat, 03 Feb 2018 04:59:17 -0600

MUMmer4, a substantially improved version of MUMmer that addresses genome size constraints by changing the 32-bit suffix tree data structure at the core of MUMmer to a 48-bit suffix array, and that offers improved speed through parallel processing of input query sequences. With a theoretical limit on the input size of 141Tbp, MUMmer4 can now work with input sequences of any biologically realistic length. We show that as a result of these enhancements, the nucmer program in MUMmer4 is easily able to handle alignments of large genomes;

Address of the bookmark: https://mummer4.github.io/

G-compass: a comparative genome browser

Jit — Thu, 12 Apr 2018 10:00:27 -0500

G-compass (http://www.h-invitational.jp/g-compass/) is a comparative genome browser. It visualizes evolutionarily conserved genomic regions between human and other 12 vertebrates based on original genome alignments pursuing higher coverage (1,2). Annotations of human genes/transcripts and their ortholog information were derived from H-InvDB and its subdatabase Evola, respectively. G-compass is available for free of charge. [ Sample ]

Address of the bookmark: http://www.h-invitational.jp/g-compass/

Circlator: automated circularization of genome assemblies using long sequencing reads

Poonam Mahapatra — Tue, 15 May 2018 09:42:32 -0500

A tool to circularize genome assemblies. The algorithm and benchmarks are described in the Genome Biology manuscript. Citation: "Circlator: automated circularization of genome assemblies using long sequencing reads", Hunt et al, Genome Biology 2015 Dec 29;16(1):294. doi: 10.1186/s13059-015-0849-0. PMID: 26714481.

Address of the bookmark: http://sanger-pathogens.github.io/circlator/