BOL: Related items

PLAR: Pipeline for lncRNA annotation from RNA-seq data

Abhi — Fri, 07 Jan 2022 06:18:01 -0600

Due to several requests, we are releasing an assingment of orthologs, determined using the same methods used in Hezroni et al. (BLAST, Whole Genome Alignment (WGA), and synteny). One is comparing human GENCODE genes (from GENCODE v30) to lncRNAs from other species identified by PLAR. Available here.

Species	Assembly	Code	Transcriptome	lncRNAs	Protein-coding
Human	hg19	hg19	Download	Download	Download
Rhesus	rheMac3	rm3	Download	Download	Download
Marmoset	calJac3	cj3	Download	Download	Download
Mouse	mm9	mm9	Download	Download	Download
Rabbit	oryCun2	oc2	Download	Download	Download
Dog	canFam3	cf3	Download	Download	Download
Ferret	musFur1	oa3	Download	Download	Download
Opossum	monDom5	md5	Download	Download	Download
Chicken	galGal4	gg4	Download	Download	Download
Lizard	anoCar2	ac2	Download	Download	Download
Coelacanth	latCha1	lc1	Download	Download	Download
Zebrafish	danRer7	dr7	Download	Download	Download
Stickleback	gasAcu1	ga1	Download	Download	Download
Nile tilapia	oreNil2	ot2	Download	Download	Download
Spotted gar	lepOcu1	lo1	Download	Download	Download
Elephant shark	calMil1	cm1	Download	Download	Download
Sea urchin	strPur4	sp4	Download	Download	Download

Address of the bookmark: http://www.weizmann.ac.il/Biological_Regulation/IgorUlitsky/PLAR

odgi: optimized dynamic genome/graph implementation

Abhimanyu Singh — Tue, 01 Feb 2022 23:42:21 -0600

odgi provides an efficient and succinct dynamic DNA sequence graph model, as well as a host of algorithms that allow the use of such graphs in bioinformatic analyses.

Careful encoding of graph entities allows odgi to efficiently compute and transform pangenomes with minimal overheads. odgi implements a dynamic data structure that leveraged multi-core CPUs and can be updated on the fly.

The edges and path steps are recorded as deltas between the current node id and the target node id, where the node id corresponds to the rank in the global array of nodes. Graphs built from biological data sets tend to have local partial order and, when sorted, the deltas be small. This allows them to be compressed with a variable length integer representation, resulting in a small in-memory footprint at the cost of packing and unpacking.

The RAM and computational savings are substantial. In partially ordered regions of the graph, most deltas will require only a single byte.

Address of the bookmark: https://github.com/pangenome/odgi

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

Jit — Mon, 28 Feb 2022 23:27:37 -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.

Summary statistics of Genomicus version 105.01: (view species tree in pdf or newick)


Number of extant species	200
Number of extant genes	4303993
Number of ancestral species	196
Number of ancestral genes	4624213
Number of ancestral synteny blocks	83342

Address of the bookmark: https://www.genomicus.bio.ens.psl.eu/genomicus-105.01/cgi-bin/search.pl

Entire Human Genome Sequencing !

LEGE — Tue, 02 Apr 2024 01:19:29 -0500

Cost-effective whole human genome sequencing has revolutionized the landscape of genetic research and personalized medicine by making comprehensive genetic analysis accessible to a wider population. Through advancements in sequencing technologies, such as next-generation sequencing (NGS), costs have significantly decreased, enabling researchers and healthcare providers to analyze an individual's complete genetic makeup with greater efficiency and affordability. This has profound implications for disease diagnosis, prognosis, and treatment, as it allows for the identification of genetic predispositions and the customization of healthcare interventions based on an individual's unique genetic profile. Moreover, as the cost continues to decline, the potential for population-scale genomic studies and large-scale screening programs becomes increasingly feasible, promising to further enhance our understanding of human genetics and improve healthcare outcomes on a global scale.

Here are few companies:

https://mynucleus.com/

https://myome.com/

https://nebula.org/whole-genome-sequencing-dna-test/

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/

DFAST: a flexible prokaryotic genome annotation pipeline for faster genome publication

Jit — Tue, 14 Nov 2017 10:26:16 -0600

We developed a prokaryotic genome annotation pipeline, DFAST, that also supports genome submission to public sequence databases. DFAST was originally started as an on-line annotation server, and to date, over 7,000 jobs have been processed since its first launch in 2016. Here, we present a newly implemented background annotation engine for DFAST, which is also available as a standalone command-line program. The new engine can annotate a typical-sized bacterial genome within 10 minutes, with rich information such as pseudogenes, translation exceptions, and orthologous gene assignment between given reference genomes. In addition, the modular framework of DFAST allows users to customize the annotation workflow easily and will also facilitate extensions for new functions and incorporation of new tools in the future.

Availability and Implementation

The software is implemented in Python 3 and runs in both Python 2.7 and 3.4– on Macintosh and Linux systems. It is freely available at https://github.com/nigyta/dfast_core/ under the GPLv3 license with external binaries bundled in the software distribution. An on-line version is also available at https://dfast.nig.ac.jp/.

Address of the bookmark: https://dfast.nig.ac.jp/

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

VCF Compare !

Rahul Nayak — Wed, 19 Jan 2022 10:30:14 -0600

compare two BWA mapping methods with the online hg18-mapped data

We first operate a rapid inspection of the different BAM files using samtools flagstat. Illumina provided chr21 read mapping obtained with their GA IIx deep sequencing platform <ftp://webdata:webdata@ussd-ftp.illumina.com/Data/SequencingRuns/NA18507_GAIIx_100_chr21.bam>, aligned to the b36/hg18 reference genome)

Address of the bookmark: https://wiki.bits.vib.be/index.php/NGS_Exercise.6#compare_aln_.26_mem_results_with_vcf-compare

LUMPY

Shruti Paniwala — Thu, 25 Aug 2016 08:05:02 -0500

A probabilistic framework for structural variant discovery.

Ryan M Layer, Colby Chiang, Aaron R Quinlan, and Ira M Hall. 2014. "LUMPY: a Probabilistic Framework for Structural Variant Discovery." Genome Biology 15 (6): R84. doi:10.1186/gb-2014-15-6-r84.

More at https://github.com/arq5x/lumpy-sv

Address of the bookmark: https://github.com/arq5x/lumpy-sv

Maq: Mapping and Assembly with Quality

Jit — Tue, 22 Nov 2016 04:51:39 -0600

Maq stands for Mapping and Assembly with Quality It builds assembly by mapping short reads to reference sequences. Maq is a project hosted by SourceForge.net. The project page is available athttp://sourceforge.net/projects/maq/. Maq is previously known as mapass2.

Run Maq Now

Follow these steps to try Maq. All you need is a reference sequence file in the FASTA format.

Prepare a reference sequence (ref.fasta). Better a bacterial genome.
Download maq, maq-data and maqview at the download page.
Copy maq, maq.pl and maq_eval.pl to the $PATH or to the same directory.
Simulate diploid reference and read sequences, map reads, call variants and evaluate the results in one go:
```
maq.pl demo ref.fasta calib-30.dat
```
where calib-30.dat is contained in maq-data.

View the alignment:

cd maqdemo/easyrun;
maqindex -i -c consensus.cns all.map;
maqview -c consensus.cns all.map

Even for advanced maq users, running `maq.pl demo' is recommended. You may find something helpful.

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