BOL: Related items

TRITEX sequence assembly pipeline for Triticeae genomes

Jit — Tue, 20 Aug 2019 09:47:14 -0500

The pipeline is open-source and hosted in a public Bitbucket repository.

TRITEX has been run on highly inbred genotypes of barley (Hordeum vulgare), tetraploid wheat (Triticum turgidum) and hexaploid wheat (T. aestivum) with reasonable results: super-scaffold N50 values in the range of dozens of Mb and pseudomolecules with better gene space representation than a BAC-by-BAC assembly. It has never been tested and is not expected to work on heterozygous or autopolyploid genomes.

A protocol for generating chromosome-conformation capture sequencing (Hi-C) data suitable for use with the pipeline is described in Himmelbach et al. 2018. Refer to the technical notes of 10X Genomics on how to generate Chromium data.

Address of the bookmark: https://tritexassembly.bitbucket.io/

TRITEX, a computational pipeline for chromosome-scale assembly of plant genomes

LEGE — Fri, 14 Feb 2025 10:53:48 -0600

This is the documentation of TRITEX, a computational pipeline for chromosome-scale assembly of plant genomes. It was developed in the research group Domestication Genomics at the Leibniz Institute of Plant Genetics and Crop Research (IPK) Gatersleben.

Address of the bookmark: https://tritexassembly.bitbucket.io/

CrossMap

Jitendra Narayan — Mon, 08 Feb 2016 15:47:00 -0600

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.

More at http://crossmap.sourceforge.net/

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

QUAST: quality assessment tool for genome assemblies

Jitendra Prajapati — Wed, 06 Apr 2016 18:23:33 -0500

QUAST evaluates genome assemblies. For metagenomes, please see MetaQUAST project.
It can works both with and without a given reference genome.
The tool accepts multiple assemblies, thus is suitable for comparison.

More at http://bioinf.spbau.ru/quast

http://bioinformatics.oxfordjournals.org/content/early/2013/03/09/bioinformatics.btt086.long

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2013/03/09/bioinformatics.btt086.long

GATB : Genome Analysis Toolbox with de-Bruijn graph

Jit — Thu, 28 Apr 2016 11:16:51 -0500

The Genome Analysis Toolbox with de-Bruijn graph (GATB) provides a set of highly efficient algorithms to analyse NGS data sets. These methods enable the analysis of data sets of any size on multi-core desktop computers, including very huge amount of reads data coming from any kind of organisms such as bacteria, plants, animals and even complex samples (e.g. metagenomes).

More at https://gatb.inria.fr/

Address of the bookmark: https://gatb.inria.fr/

SWALO

Jit — Wed, 30 Nov 2016 05:06:05 -0600

SWALO (scaffolding with assembly likelihood optimization) is a method for scaffolding based on likelihood of genome assemblies computed using generative models for sequencing.

Download

Git repository of SWALO is at https://github.com/atifrahman/SWALO.

Address of the bookmark: https://atifrahman.github.io/SWALO/

Assembly Course

Shruti Paniwala — Mon, 10 Jul 2017 09:38:05 -0500

https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/lecture-slides/MIT7_91JS14_Lecture6.pdf

Address of the bookmark: https://ocw.mit.edu/courses/biology/7-91j-foundations-of-computational-and-systems-biology-spring-2014/lecture-slides/MIT7_91JS14_Lecture6.pdf

BEAP: Blast Extension and Assembly Program

Shruti Paniwala — Mon, 11 Jun 2018 04:52:56 -0500

The Blast Extension and Assembly Program (BEAP) is a computer program that uses a short starting DNA fragment, often a EST or partial gene segment, as "primer", to recursively blast nucleotide databases in an attempt to obtain all sequences that overlaps, directly or indirectly, with the "primer" therefore help to "extend" the length of the original sequence for constructing a "full length" sequence for functional analysis, or at least to obtain neighboring regions of the segment for SNP discovery and linkage disequilibrium analysis. The confidence of assembling the resulting sequences is achieved by using a known genome, such as human genome, as a reference. https://www.animalgenome.org/tools/beap/

Address of the bookmark: https://www.animalgenome.org/tools/beap/

SPADes team announce new version SPADes v3.15

BioStar — Fri, 15 Jan 2021 10:24:27 -0600

New SPAdes 3.15.0.0. announced by the SPADes team This release includes such new features as:
- CoronaSPAdes pipeline for the assembly of transcriptomic and metatranscriptomic data of full-length coronaviridae genomes;
- Meta-Viral and RNA-Viral pipelines for metagenomic and metatranscriptomic data defining viral genomes;
-New trusted contiguous use algorithm;
-Switched to the memory allocator mimalloc;
- PlasmidSPAdes and bgcSPAdes are now provided as an input assembly graph;
- Important improvements and corrections to the metaplasmid pipeline;
- Multiple performance improvements in procedures for simplification and repeat resolving.
Please, consider updating.

Check out more at https://cab.spbu.ru/software/spades/

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/