BOL: Related items

JPred4: A Protein Secondary Structure Prediction Server

Poonam Mahapatra — Fri, 29 Dec 2017 16:14:28 -0600

JPred4 (http://www.compbio.dundee.ac.uk/jpred4) is the latest version of the popular JPred protein secondary structure prediction server which provides predictions by the JNet algorithm, one of the most accurate methods for secondary structure prediction.

Address of the bookmark: http://www.compbio.dundee.ac.uk/jpred4/

Ligand Docking Tools and Software !

Poonam Mahapatra — Wed, 25 Apr 2018 05:05:17 -0500

Ligand docking referred to cases where small molecule (“ligand”) is being docked into much larger macromolecule ("target"). The following is partial list of docking software, focusing on free (at least for academic institutes) and/or popular docking tools.

AutoDock

Stochastic (GA)

Flexible ligand and partially flexible target

ArgusLab

Systematic

Flexible ligandX-Score based

DOCK

Systematic (IC)

Flexible ligandDOCK 3.5 (force field)

eHITS

Systematic (RBD of fragments followed by reconstruction)Flexible ligand and partially flexible targetHiTS_Score (empirical)

FlexX

Systematic (IC)Flexible ligandFlexX SF (empirical)Commercial

FLIPDock

Stochastic (GA)Flexible ligand and flexible targetAUTODOCK (empirical)

FRED

Systematic (RBD)Flexible ligandChemScore, PLP, ScreenScore, ChemGauss (empirical/consensus)

GOLD

Stochastic (GA)

Flexible ligand and partially flexible targetGoldScore, ChemScore (empirical), ASP (knowledge based)

ICM

Stochastic (MC)

Flexible ligand and partially flexible targetICM SF (empirical)

ParDOCK

Stochastic (MC)

RigidBAPPL (empirical)

PLANTS

Stochastic (ACO)Flexible ligand and partially flexible target

CHEMPLP, PLP (empirical)

Surflex

Systematic (IC/MA)Flexible ligandHammerhead based (empirical)

Point to note:

Several studies have shown that the performance of most docking tools is highly dependent on the particular characteristics of both the binding site and the ligand to be investigated, and the determination which method would be more suitable in a specific context is difficult. We encouraged you to check several docking methods to determine which one(s) work best for your system.

Finding the Hidden Switches: The Story of KiNext and Protein Kinases

LEGE — Thu, 24 Sep 2026 02:51:28 -0500

Every newly sequenced genome contains thousands of proteins, but identifying what each protein does is a much harder task. Among these proteins are protein kinases, important molecular regulators that control processes such as cell growth, development, metabolism, stress responses, and signaling. Finding these kinases and determining which families they belong to can reveal important clues about how an organism functions and has evolved.

This is where KiNext comes into the picture. Introduced in a 2024 study published in BMC Bioinformatics, KiNext is a computational workflow designed to identify and classify protein kinases from predicted protein sequences. Instead of relying on a single search method, it brings together several approaches, including Hidden Markov Models, sequence alignment, phylogenetic analysis, and structural comparison.

The search begins with a simple question: does a protein contain the characteristics of a kinase? Protein kinases can change considerably during evolution, but important regions of their sequences often retain recognizable patterns. KiNext uses Hidden Markov Models, or HMMs, to detect these patterns. An HMM does not require a protein to be an exact match to a known kinase. Instead, it looks for a statistical sequence signature associated with kinase proteins, making it possible to detect more distant candidates.

Once potential kinases are identified, KiNext takes the analysis further. It distinguishes conventional eukaryotic protein kinases from atypical protein kinases and then attempts to classify them into different kinase groups and families. This distinction is important because simply identifying a protein as a kinase does not tell the complete story. Different kinase families can have very different evolutionary histories and biological functions.

The next stage brings evolution into the picture. KiNext can align kinase sequences and construct phylogenetic trees, allowing researchers to examine how newly identified proteins are related to previously characterized kinases. When sequence evidence alone is difficult to interpret, structural information can provide another clue. The workflow can incorporate AlphaFold-predicted structures and Foldseek-based structural comparisons to investigate whether an unusual protein resembles known kinase structures.

The researchers tested KiNext using two very different organisms: the Pacific oyster, Crassostrea gigas, and the green alga Ostreococcus tauri. In C. gigas, KiNext recovered previously reported kinases while identifying additional candidates. Structural analysis provided further evidence for many of the newly detected proteins. In O. tauri, the workflow similarly recovered most previously reported kinases and identified additional candidates while refining some of their classifications.

What makes KiNext particularly interesting is not just its ability to find kinases, but how the entire analysis is organized. The workflow uses Nextflow, allowing the different computational steps to be connected into a reproducible pipeline. Containers can also help manage software dependencies, making it easier to run the workflow across different computing environments.

This reproducibility becomes increasingly important as the number of available genomes continues to grow. A researcher studying one organism may be able to perform an analysis manually, but repeating the same process across hundreds or thousands of genomes quickly becomes impractical. A standardized workflow provides a way to perform the analysis consistently while keeping track of how the results were generated.

At its core, KiNext demonstrates a broader change taking place in modern genomics. Sequencing a genome provides an enormous amount of information, but the real scientific challenge begins afterward: understanding what all those sequences mean. Protein kinases are only one part of this larger puzzle, yet they are particularly important because they act as molecular switches throughout the cell.

By combining sequence profiles, evolutionary analysis, and structural evidence within a reproducible computational framework, KiNext provides researchers with a systematic way to uncover these molecular switches. Its real value lies not only in finding more kinases, but in making the process scalable, repeatable, and easier to apply to new genomes.

As genome sequencing continues to expand across the tree of life, tools such as KiNext can help turn enormous collections of protein sequences into meaningful biological stories—one kinase at a time.

Look up a biological numbers

Jitendra Narayan — Fri, 23 Aug 2013 03:27:45 -0500

Did you ever need to look up a number like the volume of a cell or the cellular concentration of ATP, only to find yourself spending much more time than you wanted on the Internet or flipping through textbooks - all without much success?

Well, it didn’t happen only to you. It is often surprising how difficult it can be to find concrete biological numbers, even for properties that have been measured numerous times. To help solve this for one and all, BioNumbers (the database of key numbers in molecular biology) was created. Along with the numbers, you'll find the relevant references to the original literature, useful comments, and related numbers.

To cite BioNumbers please refer to: Milo et al. Nucl. Acids Res. (2010) 38: D750-D753. When using a specific entry from the database it is highly recommended that you also specify the BioNumbers 6 digit ID, e.g. "BNID 100986, Milo et al 2010".

Address of the bookmark: http://bionumbers.hms.harvard.edu/

HistoneDB 2.0 – with variants

Anjana — Fri, 03 Jun 2016 05:06:20 -0500

This histone database can be used to explore the diversity of histone proteins and their sequence variants in many organisms. The resource was established to better understand how sequence variation may affect functional and structural features of nucleosomes. To get started, select a histone type to explore its variants.

More at http://www.ncbi.nlm.nih.gov/projects/HistoneDB2.0/index.fcgi/browse/

Address of the bookmark: http://www.ncbi.nlm.nih.gov/projects/HistoneDB2.0/index.fcgi/browse/

GOLD:Genomes Online Database

Jit — Wed, 26 Jul 2017 07:49:29 -0500

GOLD:Genomes Online Database, is a World Wide Web resource for comprehensive access to information regarding genome and metagenome sequencing projects, and their associated metadata, around the world.

https://gold.jgi.doe.gov/

Address of the bookmark: https://gold.jgi.doe.gov/

HIV genome database !

Rahul Nayak — Fri, 21 Jan 2022 05:40:15 -0600

HIV resources

https://www.hiv.lanl.gov/components/sequence/HIV/search/search.html

Address of the bookmark: https://www.hiv.lanl.gov/components/sequence/HIV/search/search.html

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/