Use the Terminal or an Anaconda Prompt for the following steps.

1. To create an environment:

   conda create --name myenv
   

   NOTE: Replace myenv with the environment name.

2. When conda asks you to proceed, type y:

   proceed ([y]/n)?
   

This creates the myenv environment in /envs/. This environment uses the same version of Python that you are currently using, because you did not specify a version.

To create an environment with a specific version of Python:

conda create -n myenv python=3.4

To create an environment with a specific package:

conda create -n myenv scipy

OR:

conda create -n myenv python
conda install -n myenv scipy

To create an environment with a specific version of a package:

conda create -n myenv scipy=0.15.0

OR:

conda create -n myenv python
conda install -n myenv scipy=0.15.0

To create an environment with a specific version of Python and multiple packages:

conda create -n myenv python=3.4 scipy=0.15.0 astroid babel

TIP: Install all the programs that you want in this environment at the same time. Installing 1 program at a time can lead to dependency conflicts.

To automatically install pip or another program every time a new environment is created, add the default programs to the create_default_packages section of your .condarc configuration file. The default packages are installed every time you create a new environment. If you do not want the default packages installed in a particular environment, use the --no-default-packages flag:

conda create --no-default-packages -n myenv python

TIP: You can add much more to the conda create command. For details, run conda create --help.

➜ redundans git:(master) ✗ conda create --name py27 python=2.7
Solving environment: done

==> WARNING: A newer version of conda exists. <==
current version: 4.5.0
latest version: 4.5.2

Please update conda by running

$ conda update -n base conda

## Package Plan ##

environment location: /home/urbe/anaconda3/envs/py27

added / updated specs:
- python=2.7

The following packages will be downloaded:

package | build
---------------------------|-----------------
wheel-0.31.0 | py27_0 61 KB
python-2.7.15 | h1571d57_0 12.1 MB
certifi-2018.4.16 | py27_0 142 KB
sqlite-3.23.1 | he433501_0 1.5 MB
setuptools-39.1.0 | py27_0 582 KB
openssl-1.0.2o | h20670df_0 3.4 MB
pip-10.0.1 | py27_0 1.7 MB
ca-certificates-2018.03.07 | 0 124 KB
------------------------------------------------------------
Total: 19.6 MB

The following NEW packages will be INSTALLED:

ca-certificates: 2018.03.07-0
certifi: 2018.4.16-py27_0
libedit: 3.1-heed3624_0
libffi: 3.2.1-hd88cf55_4
libgcc-ng: 7.2.0-hdf63c60_3
libstdcxx-ng: 7.2.0-hdf63c60_3
ncurses: 6.0-h9df7e31_2
openssl: 1.0.2o-h20670df_0
pip: 10.0.1-py27_0
python: 2.7.15-h1571d57_0
readline: 7.0-ha6073c6_4
setuptools: 39.1.0-py27_0
sqlite: 3.23.1-he433501_0
tk: 8.6.7-hc745277_3
wheel: 0.31.0-py27_0
zlib: 1.2.11-ha838bed_2

Proceed ([y]/n)? y

Downloading and Extracting Packages
wheel 0.31.0: #################################################################################################################################################################################################### | 100%
python 2.7.15: ################################################################################################################################################################################################### | 100%
certifi 2018.4.16: ############################################################################################################################################################################################### | 100%
sqlite 3.23.1: ################################################################################################################################################################################################### | 100%
setuptools 39.1.0: ############################################################################################################################################################################################### | 100%
openssl 1.0.2o: ################################################################################################################################################################################################## | 100%
pip 10.0.1: ###################################################################################################################################################################################################### | 100%
ca-certificates 2018.03.07: ###################################################################################################################################################################################### | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use:
# > source activate py27
#
# To deactivate an active environment, use:
# > source deactivate
#

➜ redundans git:(master) ✗ source activate py27

https://www.forbes.com/sites/jasonevangelho/2018/07/23/5-reasons-you-should-switch-from-windows-to-linux-right-now/#70c74923777b

The following table shows SLURM commands on the SOE cluster.

For more information, run man on the commands above. See some examples below.

1. Info about the partitions and nodes
List all the partitions available to you and the nodes therein:

sinfo

Nodes in state idle can accept new jobs.

Show a partition configuratuin, for example, SOE_main

scontrol show partition=SOE_main

Show current info about a specific node:

scontrol show node=<nodename>

You can also specify a group of nodes in the command above. For example, if your MPI job is running across soenode05,06,35,36, you can execute the command below to get the info on the nodes you are interested in:

scontrol show node=soenode[05-06,35-36]

An informative parameter in the output to look at would be CPULoad. It allows you to see how your application utilizes the CPUs on the running nodes.

2. Submit scripts
The header in a submit script specifies job name, partition (queue), time limit, memory allocation, number of nodes, number of cores, and files to collect standard output and error at run time, for example

#!/bin/bash

#SBATCH --job-name=OMP_run     # job name, "OMP_run"
#SBATCH --partition=SOE_main   # partition (queue)
#SBATCH -t 0-2:00              # time limit: (D-HH:MM) 
#SBATCH --mem=32000            # memory per node in MB 
#SBATCH --nodes=1              # number of nodes
#SBATCH --ntasks-per-node=16   # number of cores
#SBATCH --output=slurm.out     # file to collect standard output
#SBATCH --error=slurm.err      # file to collect standard errors

If the time limit is not specified in the submit script, SLURM will assign the default run time, 3 days. This means the job will be terminated by SLURM in 72 hrs. The maximum allowed run time is two weeks, 14-0:00.
If the memory limit is not requested, SLURM will assign the default 16 GB. The maximum allowed memory per node is 128 GB. To see how much RAM per node your job is using, you can run commands sacct or sstat to query MaxRSS for the job on the node - see examples below.
Depending on a type of application you need to run, the submit script may contain commands to create a temporary space on a computational node - see the discussion about using the file systems on the cluster.
Then it sets the environment specific to the application and starts the application on one or multiple nodes - see sbatch sample scripts in directory /usr/local/Samples on soemaster1.hpc.rutgers.edu.
You can submit your job to the cluster with sbatch command:

sbatch myscript.sh

3. Query job information
List all currently submitted jobs in running and pending states for a user:

squeue -u <username>

Command squeue can be run with format options to expose specific information, for example, when pending job #706 is scheduled to start running:

squeue -j 706 --format="%S"

START_TIME
2015-04-30T09:54:32

More info can be shown by placing additional format options, for example:

squeue -j 706 --format="%i %P %j %u %T %l %C %S"

JOBID PARTITION   NAME    USER STATE   TIMELIMIT  CPUS START_TIME
706   SOE_main  Par_job_3 mike PENDING 3-00:00:00 64   2015-04-30T09:54:32

To see when all the jobs, pending in the queue, are scheduled to start:

squeue --start 

List all running and completed jobs for a user

sqlog -u <username>

or

sqlog -j <JobID>

The following appreviations are used for the job states:

       CA   CANCELLED      Job was cancelled.

       CD   COMPLETED      Job completed normally.

       CG   COMPLETING     Job is in the process of completing.

       F    FAILED         Job termined abnormally.

       NF   NODE_FAIL      Job terminated due to node failure.

       PD   PENDING        Job is pending allocation.

       R    RUNNING        Job currently has an allocation.

       S    SUSPENDED      Job is suspended.

       TO   TIMEOUT        Job terminated upon reaching its time limit.

You can specify the fields you would like to see in the output of sqlog:

sqlog --format=list

The command below, for example, provides Job ID, user name, exit state, start date-time, and end date-time for job #2831:

sqlog -j 2831 --format=jid,user,state,start,end

List status info for a currently running job:

sstat -j <jobid>

A formatted output can be used to gain only a specific info, for example, the maximum resident RAM usage on a node:

sstat --format="JobID,MaxRSS" -j <jobid>

To get statistics on completed jobs by jobID:

sacct --format="JobID,JobName,MaxRSS,Elapsed" -j <jobid>

To view the same information for all jobs of a user:

sacct --format="JobID,JobName,MaxRSS,Elapsed" -u <username>

To print a list of fields that can be specified with the --format option:

sacct --helpformat

For example, to get Job ID, Job name, Exit state, start date-time, and end date-time for job #2831:

sacct -j 2831 --format="JobID,JobName,State,Start,End"

Another useful command to gain information about a running job is scontrol:

scontrol show job=<jobid>

4. Cancel a job
To cancel one job:

scancel <jobid>

To cancel one job and delete the TMP directory created by the submit script on a node:

sdel <jobid>

To cancel all the jobs for a user:

scancel -u <username>

To cancel one or more jobs by name:

scancel --name <myJobName>

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/23/14/1713.full

Spines

http://www.broadinstitute.org/science/programs/genome-biology/spines

Address of the bookmark: http://www.broadinstitute.org/science/programs/genome-biology/spines

https://github.com/pditommaso/awesome-pipeline

Address of the bookmark: https://github.com/pditommaso/awesome-pipeline

LPI scores are inversely proportional to the phylogenetic distance between database match sequences and the query genome. These scores are useful not only for large-scalede novo predictions of horizontally transferred proteins, but can also serve as an independent quality control test for potential horizontal transfer candidates identified by alternative methods, especially those based on nucleic acid signatures. Candidates having high LPI scores are unlikely to have been horizontally transferred, since they are highly conserved among closely related organisms.

One unique and powerful feature of the DarkHorse HGT Candidate database is the opportunity to explore the phylogenetic background of potential HGT donors as well as recipients. The breadth of the database allows not only query sequences, but also their database match partners to be evaluated for sequence similarity or novelty compared to taxonomically related organisms.

DarkHorse is configurable for varying degrees of phylogenetic granularity and protein sequence conservation. Users should consult the references cited below for a complete explanation of parameter selection and result interpretation. A brief tutorial page is also available on-line.

Address of the bookmark: http://darkhorse.ucsd.edu/download.html

The MEME Suite allows the biologist to discover novel motifs in collections of unaligned nucleotide or protein sequences, and to perform a wide variety of other motif-based analyses.

The MEME Suite supports motif-based analysis of DNA, RNA and protein sequences. It provides motif discovery algorithms using both probabilistic (MEME) and discrete models (MEME), which have complementary strengths. It also allows discovery of motifs with arbitrary insertions and deletions (GLAM2). In addition to motif discovery, the MEME Suite provides tools for scanning sequences for matches to motifs (FIMO, MAST and GLAM2Scan), scanning for clusters of motifs (MCAST), comparing motifs to known motifs (Tomtom), finding preferred spacings between motifs (SpaMo), predicting the biological roles of motifs (GOMo), measuring the positional enrichment of sequences for known motifs (CentriMo), and analyzing ChIP-seq and other large datasets (MEME-ChIP).

The MEME Suite is comprised of a collection of tools that work together, as shown below. Not all the tools are available as webservices, so to get the full power of the MEME Suite you will need to download and install a local copy of the software. To see what has changed recently you can peruse the release notes.

http://meme-suite.org/

Address of the bookmark: http://meme-suite.org/

Following are the popular tool to detect HGT in genomes:

T-REX / 3.22

HGT detection / download & compile

20525630

RANGER-DTL / 2.0

HGT detection / download binary

22689773

PhyloNet / 3.6.1

HGT detection / download binary

18662388

Jane / 4.01

HGT detection / download binary (!license!)

20181081

TREE-PUZZLE / 5.3.rc16

HGT detection / download & compile

11934758

CONSEL / 0.20

HGT detection / download

11751242

DarkHorse / 1.5 rev170

HGT detection / download & install

17274820

HGTector / 0.2.1

HGT detection / git clone

25159222

EGID / 1.0

HGT detection / download

22355228

GeneMarkS / 4.30

HGT detection / download binary (!license!)

9461475