BOL: Related items

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/

ChIPBase: open database for studying the transcription factor binding sites and motifs

Abhi — Wed, 29 Dec 2021 05:36:03 -0600

ChIPBase v2.0 is an open database for studying the transcription factor binding sites and motifs, and decoding the transcriptional regulatory networks of lncRNAs, miRNAs, other ncRNAs and protein-coding genes from ChIP-seq data. Our database currently contains ~10,200 curated peak datasets derived from ChIP-seq methods in 10 species.

Address of the bookmark: https://rna.sysu.edu.cn/chipbase/

EuGI: a novel resource for studying genomic islands to facilitate horizontal gene transfer detection in eukaryotes

Surabhi Chaudhary — Sat, 12 May 2018 07:26:59 -0500

SWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes.

https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8

Address of the bookmark: https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-018-4724-8

FGENESH - Program for predicting multiple genes in genomic DNA sequences

BioStar — Thu, 20 Dec 2018 11:55:08 -0600

FGENESH is the fastest (50-100 times faster than GenScan) and most accurate gene finder available - see the figure and the table below. In recent rice genome sequencing projects, it was cited "the most successful (gene finding) program (Yu et al. (2002) Science 296:79) and was used to produce 87% of all high-evidence predicted genes (Goff et al. (2002) Science 296:79).

Address of the bookmark: http://www.softberry.com/berry.phtml?topic=fgenesh&group=help&subgroup=gfind

GEO2R: compare two or more groups of Samples

Rahul Nayak — Sun, 20 Dec 2020 11:49:41 -0600

GEO2R to compare two or more groups of Samples in order to identify genes that are differentially expressed across experimental conditions.

Address of the bookmark: https://www.ncbi.nlm.nih.gov/geo/geo2r/

doubletrouble: identify duplicated genes from whole-genome protein sequences and classify

LEGE — Tue, 05 Mar 2024 00:23:49 -0600

doubletrouble aims to identify duplicated genes from whole-genome protein sequences and classify them based on their modes of duplication. The duplication modes are i. segmental duplication (SD); ii. tandem duplication (TD); iii. proximal duplication (PD); iv. transposed duplication (TRD) and; v. dispersed duplication (DD). Transposon-derived duplicates (TRD) can be further subdivided into rTRD (retrotransposon-derived duplication) and dTRD (DNA transposon-derived duplication). If users want a simpler classification scheme, duplicates can also be classified into SD- and SSD-derived (small-scale duplication) gene pairs. Besides classifying gene pairs, users can also classify genes, so that each gene is assigned a unique mode of duplication. Users can also calculate substitution rates per substitution site (i.e., Ka and Ks) from duplicate pairs, find peaks in Ks distributions with Gaussian Mixture Models (GMMs), and classify gene pairs into age groups based on Ks peaks.

Address of the bookmark: https://bioconductor.org/packages/release/bioc/html/doubletrouble.html

Fourth Branch of Life

Jitendra Narayan — Mon, 09 Sep 2013 21:48:37 -0500

Scientist have found the biggest viruses known, pandoraviruses which opened up entirely /completely... new questions questions and raise objections to in science. It even suggesting a fourth domain of life.

The new visrus are about one micron—a thousandth of a millimeter—in length, the newfound genus Pandoravirus dwarfs other viruses, which range in size from about 50 nanometers up to 100 nanometers. A genus is a taxonomic ranking between species and family.

Find more at @ http://www.nature.com/scitable/blog/viruses101/newly_found_pandoraviruses_hint_at

http://news.nationalgeographic.co.uk/news/2013/07/130718-viruses-pandoraviruses-science-biology-evolution/