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/

Is reference genome necessary for gene expression study in transcriptome sequencing or for variant discovery in genome sequencing?

Rahul Agarwal — Wed, 17 Jul 2013 15:25:09 -0500

Like in case of plant genomes where nature of genome is too complex and huge in size to accomplish complete de novo assembly by current sequencing technology. What would be alternate solution? Can we live in reference free world?

TAREAN: A computational tool for identification and characterization of satellite DNA from unassembled short reads

Surabhi Chaudhary — Tue, 15 May 2018 02:53:11 -0500

TAndem REpeat ANalyzer -TAREAN – is a computational pipeline for unsupervised identification of satellite repeats from unassembled sequence reads. The pipeline uses low-pass whole genome sequence reads and performs their graph-based clustering. Resulting clusters, representing all types of repeats, are then examined for the presence of circular structures and putative satellite repeats are reported.

How to use TAREAN:

Install a local instance of the pipeline using its source code available from bitbucket repository.
Use public Galaxy-based server at https://repeatexplorer-elixir.cerit-sc.cz/. The server is provided in frame of the Elixir CZ project and is maintained by CESNET and CERIT-SC. Simple registration is required to use this service.

Development of TAREAN was supported by ELIXIR CZ research infrastructure project (MEYS Grant No: LM2015047).

References

Novak, P., Avila Robledillo, L., Koblizkova, A., Vrbova, I., Neumann, P., Macas, J. (2017) – TAREAN: a computational tool for identification and characterization of satellite DNA from unassembled short reads. Nucleic Acids Res., doi:10.1093/nar/gkx257

Address of the bookmark: https://bitbucket.org/petrnovak/repex_tarean

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 :)

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/

mScaffolder: A comparative genome scaffolding tool

Jit — Fri, 15 Jun 2018 04:48:01 -0500

A comparative genome scaffolding tool based on MUMmer

mScaffolder scaffolds a genome using an existing high quality genome as the reference. It aligns the two genomes using nucmer utility from MUMmer and then orders and orients the contigs of the candidate genome guided by their alignments to the reference genome. Please send your questions and comments to mchakrab@uci.edu.

Citation https://www.nature.com/articles/s41588-017-0010-y

Address of the bookmark: https://github.com/mahulchak/mscaffolder

Genome U-Plot: a whole genome visualization

Rahul Nayak — Fri, 13 Jul 2018 19:50:41 -0500

Genome U-Plot for producing clear and intuitive graphs that allows researchers to generate novel insights and hypotheses by visualizing SVs such as deletions, amplifications, and chromoanagenesis events. The main features of the Genome U-Plot are its layered layout, its high spatial resolution and its improved aesthetic qualities.

https://github.com/gaitat/GenomeUPlot

Address of the bookmark: https://github.com/gaitat/GenomeUPlot

GRSR: a tool for deriving genome rearrangement scenarios from multiple unichromosomal genome sequences

Jit — Fri, 28 Sep 2018 09:35:10 -0500

GRSR is a Tool for Deriving Genome Rearrangement Scenarios for Multiple Uni-chromosomal Genomes. This tool will do the following steps:

Step 1. Run mugsy to get multiple sequence alignment results.
Step 2 & 3. Extraction of the Coordinates of Core Blocks, Construction of Synteny Blocks and Generating Signed Permutations.
Step 4. Generate pairwise genome rearrangement scenarios and find repeats at the breakpoints of each rearrangement events.

https://github.com/DanwangJessica/GRSR

Address of the bookmark: https://github.com/DanwangJessica/GRSR

ARCS: scaffolding genome drafts with linked reads

Jit — Mon, 17 Dec 2018 17:40:28 -0600

ARCS requires two input files:

Draft assembly fasta file
Interleaved linked reads file (Barcode sequence expected in the BX tag of the read header or in the form "@readname_barcode" ; Run Long Ranger basic on raw chromium reads to produce this interleaved file)

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

Cogent: a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences.

Jit — Tue, 18 Jun 2019 05:33:04 -0500

Cogent is a tool that identifies gene families and reconstructs the coding genome using high-quality transcriptome data without a reference genome, and can be used to check assemblies for the presence of these known coding sequences.

Cogent is a tool for reconstructing the coding genome using high-quality full-length transcriptome sequences. It is designed to be used on Iso-Seq data and in cases where there is no reference genome or the ref genome is highly incomplete.

See a recent presentation on Cogent being applied to the Cuttlefish Iso-Seq data.

Cogent preliminary draft paper (updated 2016Dec version), Supplementary

Please see wiki for details on usage.

Address of the bookmark: https://github.com/Magdoll/Cogent