BOL: Related items

SeQuiLa-cov: A fast and scalable library for depth of coverage calculations

Jit — Sun, 15 Dec 2019 10:19:35 -0600

The Docker image is available at https://hub.docker.com/r/biodatageeks/. Supplementary information on benchmarking procedure as well as test data are publicly accessible at the project documentation site http://biodatageeks.org/sequila/benchmarking/benchmarking.html#depth-of-coverage. An archival copy of the code and supporting data is also available via the GigaScience database GigaDB

• Project name: SeQuiLa-cov

• Project home page: http://biodatageeks.org/sequila/

• Source code repository: https://github.com/ZSI-Bio/bdg-sequila

• Operating system: Platform independent

• Programming language: Scala

• Other requirements: Docker

• License: Apache License 2.0

Address of the bookmark: https://academic.oup.com/gigascience/article/8/8/giz094/5543653

MMseqs2: ultra fast and sensitive sequence search and clustering suite

Abhi — Wed, 06 Oct 2021 07:01:14 -0500

MMseqs2 (Many-against-Many sequence searching) is a software suite to search and cluster huge protein and nucleotide sequence sets. MMseqs2 is open source GPL-licensed software implemented in C++ for Linux, MacOS, and (as beta version, via cygwin) Windows. The software is designed to run on multiple cores and servers and exhibits very good scalability. MMseqs2 can run 10000 times faster than BLAST. At 100 times its speed it achieves almost the same sensitivity. It can perform profile searches with the same sensitivity as PSI-BLAST at over 400 times its speed.

Address of the bookmark: https://github.com/soedinglab/MMseqs2

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/

Genomicus: genome browser that enables users to navigate in genomes in several dimensions

Jit — Sat, 18 Nov 2017 16:10:16 -0600

Genomicus is a genome browser that enables users to navigate in genomes in several dimensions: linearly along chromosome axes, transversaly across different species, and chronologicaly along evolutionary time.

Once a query gene has been entered, it is displayed in its genomic context in parallel to the genomic context of all its orthologous and paralogous copies in all the other sequenced metazoan genomes. Moreover, Genomicus stores and displays the predicted ancestral genome structure in all the ancestral species within the phylogenetic range of interest.

All the data on extant species displayed in this browser are from Ensembl.

Address of the bookmark: http://genomicus.biologie.ens.fr/genomicus-90.01/cgi-bin/search.pl

Scripts for the analysis of HGT in genome sequence data.

Jit — Wed, 29 Nov 2017 16:44:10 -0600

Scripts for the analysis of HGT in genome sequence data

Address of the bookmark: https://github.com/reubwn/hgt

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

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/

ARCS: scaffolding genome drafts with linked reads

Rahul Nayak — Tue, 06 Mar 2018 16:35:26 -0600

ARCS, an application that utilizes the barcoding information contained in linked reads to further organize draft genomes into highly contiguous assemblies. We show how the contiguity of an ABySS H.sapiensgenome assembly can be increased over six-fold, using moderate coverage (25-fold) Chromium data. We expect ARCS to have broad utility in harnessing the barcoding information contained in linked read data for connecting high-quality sequences in genome assembly drafts.

Address of the bookmark: https://github.com/bcgsc/ARCS/

CrossMap: a program for convenient conversion of genome coordinates

Jit — Thu, 31 May 2018 06:00:47 -0500

CrossMap is a program for convenient conversion of genome coordinates (or annotation files) between different assemblies (such as Human hg18 (NCBI36) <> hg19 (GRCh37), Mouse mm9 (MGSCv37) <> mm10 (GRCm38)). It supports most commonly used file formats including SAM/BAM, Wiggle/BigWig, BED, GFF/GTF, VCF. CrossMap is designed to liftover genome coordinates between assemblies. It’s not a program for aligning sequences to reference genome. We do not recommend using CrossMap to convert genome coordinates between species.

Address of the bookmark: http://crossmap.sourceforge.net

Download blasr 1.3 version

Jit — Fri, 15 Jun 2018 03:01:20 -0500

DOWNLOAD LINK: https://github.com/BioInf-Wuerzburg/proovread/raw/master/util/blasr-1.3.1/blasr

I'm running "OPERA-LG_v2.0.5/bin/preprocess_reads.pl" and have the following error:

fail to open file './temporarySam'

[bwa_aln_core] write to the disk... 0.09 sec
[bwa_aln_core] 70778880 sequences have been processed.
[bwa_aln_core] calculate SA coordinate... 161.35 sec
[bwa_aln_core] write to the disk... 0.06 sec
[bwa_aln_core] 70989574 sequences have been processed.
[main] Version: 0.7.15-r1140
[main] CMD: bwa aln -t 30 all_p_ctg.fa -
[main] Real time: 2402.523 sec; CPU: 53429.488 sec
[E::hts_open_format] Failed to open file temporarySam
samtools sort: can't open "temporarySam": No such file or directory
[bwa_aln_core] convert to sequence coordinate... 1.00 sec
[bwa_aln_core] refine gapped alignments... 6.07 sec
[bwa_aln_core] print alignments... PREPROCESS:
Fastq format is recognized
[Thu Jun 14 18:16:47 2018] Building bwa index...
bwa index -p all_p_ctg.fa /home/urbe/Tools/OPERA-LG_v2.0.6/all_p_ctg.fa
[Thu Jun 14 18:18:35 2018] Finding the SA coordinates of the reads using BWA aln...
[Thu Jun 14 18:58:37 2018] Generate alignments of reads using bwa sampe...
bwa samse -n 1 all_p_ctg.fa read.sai - | grep '\(^@\|XT:A:U\)' | /usr/local/bin/samtools view -S -h -b -F 0x4 - | /usr/local/bin/samtools sort -@ 20 -no - temporarySam > FALCON-Unzip-Scaff.bam
Mapping long-reads using blasr...
/home/urbe/Tools/SSpace/SSPACE-LongRead_v1-1/blasr -nproc 40 -m 1 -minMatch 5 -bestn 10 -noSplitSubreads -advanceExactMatches 1 -nCandidates 1 -maxAnchorsPerPosition 1 -sdpTupleSize 7 /media/urbe/MyDDrive/ONTdata/allONT/allONT.fasta /home/urbe/Tools/OPERA-LG_v2.0.6/all_p_ctg.fa | cut -d ' ' -f1-5,7-12 | sed 's/ /\t/g' > FALCON-Unzip-Scaff.map
sh: 1: /home/urbe/Tools/SSpace/SSPACE-LongRead_v1-1/blasr: Permission denied
Sorting mapping results...
sort -k1,1 -k9,9g FALCON-Unzip-Scaff.map > FALCON-Unzip-Scaff.map.sort
Analyzing sorted results...
Extracting linking information...
i3 2000 5000
i2 1000 2000
i4 5000 15000
i0 -200 300
i5 15000 40000
i1 300 1000
Repeat detection...
/home/urbe/Tools/OPERA-LG_v2.0.6/bin//filter_conflicting_edge.pl pairedEdges_i0 contig_length.dat 100 2
Illegal division by zero at /home/urbe/Tools/OPERA-LG_v2.0.6/bin//filter_conflicting_edge.pl line 93.
readline() on closed filehandle FILE at bin/OPERA-long-read.pl line 250.
rm anchor_contig_info.dat contig_length.dat filtered_edges.dat filtered_edges_cov.dat *.sai
rm: cannot remove 'anchor_contig_info.dat': No such file or directory
mv FALCON-Unzip-Scaff.bam FALCON-Unzip-Scaff-with-repeat.bam
/home/urbe/Tools/OPERA-LG_v2.0.6/bin//filter_repeat.pl FALCON-Unzip-Scaff-with-repeat.bam repeat.dat | /usr/local/bin/samtools view - -h -S -b > FALCON-Unzip-Scaff.bam
rm FALCON-Unzip-Scaff-with-repeat.bam
/home/urbe/Tools/OPERA-LG_v2.0.6/bin/OPERA-LG config > log
Analyzing 1 library: FALCON-Unzip-Scaff.bam
min library mean : 0
minimum contig length is 500
Current library: 1 out of 7
Analyzing file: pairedEdges_no_repeat_i0
Analyzing file: pairedEdges_no_repeat_i1
Analyzing file: pairedEdges_no_repeat_i2
Analyzing file: pairedEdges_no_repeat_i3
Analyzing file: pairedEdges_no_repeat_i4
Analyzing file: pairedEdges_no_repeat_i5
ln -s results/scaffoldSeq.fasta scaffoldSeq.fasta

To resolve this, try downloading blasr version 1.3 above and re-run :)