BOL: Related items

4DGenome

Jitendra Narayan — Mon, 04 Jul 2016 00:44:55 -0500

Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies.

All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology.

Address of the bookmark: http://4dgenome.research.chop.edu/

Reactome Pathway Database

BioStar — Mon, 31 Dec 2018 02:41:33 -0600

REACTOME is an open-source, open access, manually curated and peer-reviewed pathway database. Our goal is to provide intuitive bioinformatics tools for the visualization, interpretation and analysis of pathway knowledge to support basic and clinical research, genome analysis, modeling, systems biology and education. Founded in 2003, the Reactome project is led by Lincoln Stein of OICR, Peter D’Eustachio of NYULMC, Henning Hermjakob of EMBL-EBI, and Guanming Wu of OHSU.

Address of the bookmark: https://reactome.org/

New BLAST Core Nucleotide Database (core_nt)

LEGE — Tue, 13 Aug 2024 07:12:53 -0500

The Core Nucleotide Database (core_nt) is now the default nucleotide BLAST database. Core_nt is also available on the command line. You get faster searches & more focused results.

Core_nt contains the same eukaryotic transcript and gene-related sequences as nt. The core_nt database is nt without most eukaryotic chromosome sequences. Most nucleotide BLAST searches with core_nt will be similar to the nt database. However, core_nt is better than nt for accomplishing your most common BLAST search goals, such as identifying gene-related sequences like transcript sequences and complete bacterial chromosomes. This is because, in recent years, nt has acquired more low-relevance, non-annotated, and non-gene content.

Learn more: https://ncbiinsights.ncbi.nlm.nih.gov/2024/07/18/new-blast-core-nucleotide-database/

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/

Updated ranking of institutes and countries based on developed biological databases

Rahul Nayak — Fri, 11 Jan 2019 09:35:26 -0600

Updated ranking of institutes and countries based on developed biological databases is available at https://lnkd.in/fiVAdM6 , India is maintaing 4th position and "Institute of Microbial Technology, Chandigarh" is on 3rd Position (after EBI and NCBI). This is a big achievement for any institute to reach on 3rd position in the world.

More at http://bigd.big.ac.cn/databasecommons/stat

BUSCO datasets !

Jit — Wed, 13 Jan 2021 19:44:33 -0600

The BUSCO software now directly downloads the necessary datasets, specified by the user or automatically selected.

To display all available datasets

busco --list-datasets

You can also download them manually and see a list with the number of marker genes.

Earlier versions: v3, v2, v1

../
acidobacteria_odb10.2020-03-06.tar.gz              16-Oct-2020 13:19     14M
aconoidasida_odb10.2020-08-05.tar.gz               16-Oct-2020 13:19     51M
actinobacteria_class_odb10.2020-03-06.tar.gz       16-Oct-2020 13:19      8M
actinobacteria_phylum_odb10.2020-03-06.tar.gz      16-Oct-2020 13:20      9M
actinopterygii_odb10.2020-08-05.tar.gz             16-Oct-2020 13:20    173M
agaricales_odb10.2020-08-05.tar.gz                 16-Oct-2020 13:20    186M
agaricomycetes_odb10.2020-08-05.tar.gz             16-Oct-2020 13:19    152M
alphaproteobacteria_odb10.2020-03-06.tar.gz        16-Oct-2020 13:20     11M
alteromonadales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:20     18M
alveolata_odb10.2020-09-10.tar.gz                  16-Oct-2020 13:20     13M
apicomplexa_odb10.2020-09-10.tar.gz                16-Oct-2020 13:19     27M
aquificae_odb10.2020-03-06.tar.gz                  16-Oct-2020 13:19     12M
arachnida_odb10.2020-08-05.tar.gz                  16-Oct-2020 13:19    136M
archaea_odb10.2020-03-06.tar.gz                    16-Oct-2020 13:20      4M
arthropoda_odb10.2020-09-10.tar.gz                 16-Oct-2020 13:20     95M
ascomycota_odb10.2020-09-10.tar.gz                 16-Oct-2020 13:20    295M
aves_odb10.2020-09-10.tar.gz                       16-Oct-2020 13:20    465M
bacillales_odb10.2020-03-06.tar.gz                 16-Oct-2020 13:20     10M
bacilli_odb10.2020-03-06.tar.gz                    16-Oct-2020 13:19      7M
bacteria_odb10.2020-03-06.tar.gz                   16-Oct-2020 13:20      6M
bacteroidales_odb10.2020-03-06.tar.gz              16-Oct-2020 13:20     13M
bacteroidetes-chlorobi_group_odb10.2020-03-06.t..> 16-Oct-2020 13:19      9M
bacteroidetes_odb10.2020-03-06.tar.gz              16-Oct-2020 13:20     10M
bacteroidia_odb10.2020-03-06.tar.gz                16-Oct-2020 13:20     13M
basidiomycota_odb10.2020-09-10.tar.gz              16-Oct-2020 13:19    165M
betaproteobacteria_odb10.2020-03-06.tar.gz         16-Oct-2020 13:19     13M
boletales_odb10.2020-08-05.tar.gz                  16-Oct-2020 13:19    197M
brassicales_odb10.2020-08-05.tar.gz                16-Oct-2020 13:20    158M
burkholderiales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     16M
campylobacterales_odb10.2020-03-06.tar.gz          16-Oct-2020 13:20     14M
capnodiales_odb10.2020-08-05.tar.gz                16-Oct-2020 13:19    178M
carnivora_odb10.2020-08-05.tar.gz                  16-Oct-2020 13:19    376M
cellvibrionales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     19M
cetartiodactyla_odb10.2020-08-05.tar.gz            16-Oct-2020 13:19    407M
chaetothyriales_odb10.2020-08-05.tar.gz            16-Oct-2020 13:20    288M
chlamydiae_odb10.2020-03-06.tar.gz                 16-Oct-2020 13:20     13M
chlorobi_odb10.2020-03-06.tar.gz                   16-Oct-2020 13:20     22M
chloroflexi_odb10.2020-03-06.tar.gz                16-Oct-2020 13:20      6M
chlorophyta_odb10.2020-08-05.tar.gz                16-Oct-2020 13:20     58M
chromatiales_odb10.2020-03-06.tar.gz               16-Oct-2020 13:19     14M
chroococcales_odb10.2020-03-06.tar.gz              16-Oct-2020 13:19     27M
clostridia_odb10.2020-03-06.tar.gz                 16-Oct-2020 13:19      6M
clostridiales_odb10.2020-03-06.tar.gz              16-Oct-2020 13:20      6M
coccidia_odb10.2020-08-05.tar.gz                   16-Oct-2020 13:20     24M
coriobacteriales_odb10.2020-03-06.tar.gz           16-Oct-2020 13:19     12M
coriobacteriia_odb10.2020-03-06.tar.gz             16-Oct-2020 13:20      9M
corynebacteriales_odb10.2020-03-06.tar.gz          16-Oct-2020 13:19     16M
cyanobacteria_odb10.2020-03-06.tar.gz              16-Oct-2020 13:19     17M
cyprinodontiformes_odb10.2020-08-05.tar.gz         16-Oct-2020 13:19    504M
cytophagales_odb10.2020-03-06.tar.gz               16-Oct-2020 13:19     18M
cytophagia_odb10.2020-03-06.tar.gz                 16-Oct-2020 13:20     18M
delta-epsilon-subdivisions_odb10.2020-03-06.tar.gz 16-Oct-2020 13:19      6M
deltaproteobacteria_odb10.2020-03-06.tar.gz        16-Oct-2020 13:19      6M
desulfobacterales_odb10.2020-03-06.tar.gz          16-Oct-2020 13:20     13M
desulfovibrionales_odb10.2020-03-06.tar.gz         16-Oct-2020 13:19     17M
desulfurococcales_odb10.2020-03-06.tar.gz          16-Oct-2020 13:20      9M
desulfuromonadales_odb10.2020-03-06.tar.gz         16-Oct-2020 13:19     18M
diptera_odb10.2020-08-05.tar.gz                    16-Oct-2020 13:19    185M
dothideomycetes_odb10.2020-08-05.tar.gz            16-Oct-2020 13:19    194M
embryophyta_odb10.2020-09-10.tar.gz                16-Oct-2020 13:19    138M
endopterygota_odb10.2020-09-10.tar.gz              16-Oct-2020 13:19    190M
enterobacterales_odb10.2020-03-06.tar.gz           16-Oct-2020 13:19     11M
entomoplasmatales_odb10.2020-03-06.tar.gz          16-Oct-2020 13:19      7M
epsilonproteobacteria_odb10.2020-03-06.tar.gz      16-Oct-2020 13:20     13M
euarchontoglires_odb10.2020-09-10.tar.gz           16-Oct-2020 13:20    728M
eudicots_odb10.2020-09-10.tar.gz                   16-Oct-2020 13:19    168M
euglenozoa_odb10.2020-08-05.tar.gz                 16-Oct-2020 13:20     10M
eukaryota_odb10.2020-09-10.tar.gz                  16-Oct-2020 13:19    100M
eurotiales_odb10.2020-08-05.tar.gz                 16-Oct-2020 13:19    211M
eurotiomycetes_odb10.2020-08-05.tar.gz             16-Oct-2020 13:19    197M
euryarchaeota_odb10.2020-03-06.tar.gz              16-Oct-2020 13:20      5M
eutheria_odb10.2020-09-10.tar.gz                   16-Oct-2020 13:20    920M
fabales_odb10.2020-08-05.tar.gz                    16-Oct-2020 13:19    184M
firmicutes_odb10.2020-03-06.tar.gz                 16-Oct-2020 13:20      6M
flavobacteriales_odb10.2020-03-06.tar.gz           16-Oct-2020 13:19     18M
flavobacteriia_odb10.2020-03-06.tar.gz             16-Oct-2020 13:20     18M
fungi_odb10.2020-09-10.tar.gz                      16-Oct-2020 13:19    181M
fusobacteria_odb10.2020-03-06.tar.gz               16-Oct-2020 13:19     11M
fusobacteriales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     11M
gammaproteobacteria_odb10.2020-03-06.tar.gz        16-Oct-2020 13:19     10M
glires_odb10.2020-08-05.tar.gz                     16-Oct-2020 13:19    549M
glomerellales_odb10.2020-08-05.tar.gz              16-Oct-2020 13:19    259M
halobacteria_odb10.2020-03-06.tar.gz               16-Oct-2020 13:19     18M
halobacteriales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:20     18M
haloferacales_odb10.2020-03-06.tar.gz              16-Oct-2020 13:19     20M
helotiales_odb10.2020-08-05.tar.gz                 16-Oct-2020 13:19    224M
hemiptera_odb10.2020-08-05.tar.gz                  16-Oct-2020 13:19    115M
hymenoptera_odb10.2020-08-05.tar.gz                16-Oct-2020 13:19    321M
hypocreales_odb10.2020-08-05.tar.gz                16-Oct-2020 13:19    224M
insecta_odb10.2020-09-10.tar.gz                    16-Oct-2020 13:20    119M
lactobacillales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19      9M
laurasiatheria_odb10.2020-09-10.tar.gz             16-Oct-2020 13:20    688M
legionellales_odb10.2020-03-06.tar.gz              16-Oct-2020 13:19     16M
leotiomycetes_odb10.2020-08-05.tar.gz              16-Oct-2020 13:19    164M
lepidoptera_odb10.2020-08-05.tar.gz                16-Oct-2020 13:20    241M
liliopsida_odb10.2020-09-10.tar.gz                 16-Oct-2020 13:20    163M
mammalia_odb10.2020-09-10.tar.gz                   16-Oct-2020 13:19    677M
metazoa_odb10.2020-09-10.tar.gz                    16-Oct-2020 13:19    127M
methanobacteria_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     15M
methanococcales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:20     17M
methanomicrobia_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     12M
methanomicrobiales_odb10.2020-03-06.tar.gz         16-Oct-2020 13:20     16M
micrococcales_odb10.2020-03-06.tar.gz              16-Oct-2020 13:19     12M
microsporidia_odb10.2020-08-05.tar.gz              16-Oct-2020 13:20     26M
mollicutes_odb10.2020-03-06.tar.gz                 16-Oct-2020 13:20      3M
mollusca_odb10.2020-08-05.tar.gz                   16-Oct-2020 13:20    266M
mucorales_odb10.2020-08-05.tar.gz                  16-Oct-2020 13:20    105M
mucoromycota_odb10.2020-08-05.tar.gz               16-Oct-2020 13:19     75M
mycoplasmatales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19      4M
natrialbales_odb10.2020-03-06.tar.gz               16-Oct-2020 13:19     24M
neisseriales_odb10.2020-03-06.tar.gz               16-Oct-2020 13:19     16M
nematoda_odb10.2020-08-05.tar.gz                   16-Oct-2020 13:20    138M
nitrosomonadales_odb10.2020-03-06.tar.gz           16-Oct-2020 13:19     15M
nostocales_odb10.2020-03-06.tar.gz                 16-Oct-2020 13:19     35M
oceanospirillales_odb10.2020-03-06.tar.gz          16-Oct-2020 13:19     13M
onygenales_odb10.2020-08-05.tar.gz                 16-Oct-2020 13:19    233M
oscillatoriales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     29M
passeriformes_odb10.2020-08-05.tar.gz              16-Oct-2020 13:20    298M
pasteurellales_odb10.2020-03-06.tar.gz             16-Oct-2020 13:19     19M
planctomycetes_odb10.2020-03-06.tar.gz             16-Oct-2020 13:19     13M
plasmodium_odb10.2020-08-05.tar.gz                 16-Oct-2020 13:20    199M
pleosporales_odb10.2020-08-05.tar.gz               16-Oct-2020 13:19    288M
poales_odb10.2020-08-05.tar.gz                     16-Oct-2020 13:19    186M
polyporales_odb10.2020-08-05.tar.gz                16-Oct-2020 13:20    206M
primates_odb10.2020-08-05.tar.gz                   16-Oct-2020 13:19    451M
propionibacteriales_odb10.2020-03-06.tar.gz        16-Oct-2020 13:19     14M
proteobacteria_odb10.2020-03-06.tar.gz             16-Oct-2020 13:19      8M
pseudomonadales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     17M
rhizobiales_odb10.2020-03-06.tar.gz                16-Oct-2020 13:19     15M
rhizobium-agrobacterium_group_odb10.2020-03-06...> 16-Oct-2020 13:19     34M
rhodobacterales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     19M
rhodospirillales_odb10.2020-03-06.tar.gz           16-Oct-2020 13:19     14M
rickettsiales_odb10.2020-03-06.tar.gz              16-Oct-2020 13:19      8M
saccharomycetes_odb10.2020-08-05.tar.gz            16-Oct-2020 13:20    109M
sauropsida_odb10.2020-09-10.tar.gz                 16-Oct-2020 13:19    499M
selenomonadales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     16M
solanales_odb10.2020-08-05.tar.gz                  16-Oct-2020 13:19    196M
sordariomycetes_odb10.2020-08-05.tar.gz            16-Oct-2020 13:19    206M
sphingobacteriia_odb10.2020-03-06.tar.gz           16-Oct-2020 13:20     21M
sphingomonadales_odb10.2020-03-06.tar.gz           16-Oct-2020 13:19     22M
spirochaetales_odb10.2020-03-06.tar.gz             16-Oct-2020 13:20      8M
spirochaetes_odb10.2020-03-06.tar.gz               16-Oct-2020 13:20      6M
spirochaetia_odb10.2020-03-06.tar.gz               16-Oct-2020 13:19      6M
stramenopiles_odb10.2020-08-05.tar.gz              16-Oct-2020 13:19      5M
streptomycetales_odb10.2020-03-06.tar.gz           16-Oct-2020 13:20     31M
streptosporangiales_odb10.2020-03-06.tar.gz        16-Oct-2020 13:19     22M
sulfolobales_odb10.2020-03-06.tar.gz               16-Oct-2020 13:19     22M
synechococcales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     16M
synergistetes_odb10.2020-03-06.tar.gz              16-Oct-2020 13:19     14M
tenericutes_odb10.2020-03-06.tar.gz                16-Oct-2020 13:20      4M
tetrapoda_odb10.2020-09-10.tar.gz                  16-Oct-2020 13:19    561M
thaumarchaeota_odb10.2020-03-06.tar.gz             16-Oct-2020 13:20     13M
thermoanaerobacterales_odb10.2020-03-06.tar.gz     16-Oct-2020 13:19     10M
thermoplasmata_odb10.2020-03-06.tar.gz             16-Oct-2020 13:20      7M
thermoproteales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19      8M
thermoprotei_odb10.2020-03-06.tar.gz               16-Oct-2020 13:20      6M
thermotogae_odb10.2020-03-06.tar.gz                16-Oct-2020 13:19     16M
thiotrichales_odb10.2020-03-06.tar.gz              16-Oct-2020 13:20     11M
tissierellales_odb10.2020-03-06.tar.gz             16-Oct-2020 13:19      9M
tissierellia_odb10.2020-03-06.tar.gz               16-Oct-2020 13:19      8M
tremellomycetes_odb10.2020-08-05.tar.gz            16-Oct-2020 13:19    210M
verrucomicrobia_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     11M
vertebrata_odb10.2020-09-10.tar.gz                 16-Oct-2020 13:19    409M
vibrionales_odb10.2020-03-06.tar.gz                16-Oct-2020 13:20     26M
viridiplantae_odb10.2020-09-10.tar.gz              16-Oct-2020 13:20     36M
xanthomonadales_odb10.2020-03-06.tar.gz            16-Oct-2020 13:19     24M

Address of the bookmark: https://busco-data.ezlab.org/v4/data/lineages/

ncbi-datasets-cli -- Quickstart: command line tools !

Jit — Tue, 07 Dec 2021 02:51:26 -0600

Install and use the NCBI Datasets command line tools

The NCBI Datasets datasets command line tools are datasets and dataformat .

Use datasets to download biological sequence data across all domains of life from NCBI.

Use dataformat to convert metadata from JSON Lines format to other formats.

Conda download:

https://anaconda.org/conda-forge/ncbi-datasets-cli

Buld Download

https://www.ncbi.nlm.nih.gov/datasets/builder/?tax_id=29979

Address of the bookmark: https://www.ncbi.nlm.nih.gov/datasets/docs/v1/quickstarts/command-line-tools/

dbCAN: a web server and DataBase for automated Carbohydrate-active enzyme ANnotation

Jit — Mon, 29 May 2017 05:39:29 -0500

dbCAN is a web server and DataBase for automated Carbohydrate-active enzyme ANnotation, funded by the BioEnergy Science Center of the DOE. Similar resources on the web include CAZy database and CAT. All data in dbCAN are generated based on the family classification from CAZy database while it has the following unique features compared with CAZy database and CAT:

dbCAN provides the capability of automated and comprehensive CAZyme annotation of a given genome submitted by the user;
dbCAN provides an explicitly defined signature domain for each and every CAZyme family along with its location in all the relevant full-length CAZyme proteins in all sequenced genomes;
dbCAN provides the most complete set of metagenomic CAZyme genes published so far and represents the first step towards discovering novel CAZyme catalysts in metagenomes;
dbCAN provides a subfamily classification of the existing CAZyme families based on sequence similarities;
dbCAN make all pre-computed data freely available to the public, including sequence alignments, hidden markov models (HMMs) and phylogenies of the signature domain regions in each and every CAZyme family and subfamily.

dbCAN is updated regularly when CAZy database created new families based on latest literature.

Address of the bookmark: http://csbl.bmb.uga.edu/dbCAN/index.php

Cancer research database

Neel — Tue, 01 Sep 2015 17:36:31 -0500

Researchers in Andhra Pradesh have developed a database to identify genes that are common in tumours to provide their colleagues with easy access to insights into the genetic alterations in cancer.

The database, hosted at the Sri Venkateswara University (SVU) in Tirupati, will integrate information on cancer genes and markers with experimental data.

The Cancer Gene Markers Database (CGMD) is meant to help scientists better understand tumour genes and markers at a molecular level by combining data with literature on treatment regimen and recent advances in cancer therapy.

The database is free to access, and already includes 309 genes and 206 markers that correspond to 40 different human cancers. Accompanying literature comes from databases such as the United States’ National Center for Biotechnology Information and the Kyoto Encyclopedia of Genes and Genomes. It also includes experimental data from PubMed.

In a paper published last month in Nature Scientific Reports, the researchers from SVU’s department of animal biotechnology, describes the need for a database for different genes and markers along with their molecular characteristics and pathway associations.

Gene Synteny Database

Jit — Fri, 16 Dec 2016 11:09:39 -0600

Comparative genomics remains a pivotal strategy to study the evolution of gene organization, and this primacy is reinforced by the growing number of full genome sequences available in public repositories. Despite this growth, bioinformatic tools available to visualize and compare genomes and to infer evolutionary events remain restricted to two or three genomes at a time, thus limiting the breadth and the nature of the question that can be investigated. Here we present Genomicus, a new synteny browser that can represent and compare unlimited numbers of genomes in a broad phylogenetic view. In addition, Genomicus includes reconstructed ancestral gene organization, thus greatly facilitating the interpretation of the data.

Availability: Genomicus is freely available for online use at http://www.dyogen.ens.fr/genomicus while data can be downloaded at ftp://ftp.biologie.ens.fr/pub/dyogen/genomicus

Contact: rf.sne.eigoloib@crh

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2853686/