Why Remote Session Management is a Must-Have

In bioinformatics, tasks like genome assembly, RNA-seq analyses, and phylogenetic computations often take hours or days. A dropped SSH connection can result in:

• Lost Progress: Restarting a job from scratch wastes valuable time.
• Workflow Interruptions: Disruptions can derail your focus and productivity.
• Corrupted Data: Interrupted processes may lead to incomplete or corrupted outputs.

By integrating screen, tmux, or mosh into your workflow, you can avoid these setbacks and ensure a seamless experience.

Screen: The Classic Workhorse

Screen is a terminal multiplexer that comes pre-installed on most Linux systems. It allows you to manage multiple terminal sessions and reconnect to them even after being disconnected.

Getting Started with Screen:

1. Start a Session:
   screen
2. Detach from a Session:
   Press Ctrl+A, then D.
3. Reattach to a Session:
   screen -r

Pro Tip: Enhance your screen experience with a customized .screenrc configuration file. Download one here: Get .screenrc.

Tmux: A Modern Alternative

Tmux takes everything great about screen and adds modern features, including better key bindings and intuitive session management. It\u2019s perfect for bioinformaticians who want more control over their workflow.

Getting Started with Tmux:

1. Start a Session:
   tmux
2. Detach from a Session:
   Press Ctrl+B, then D.
3. Reattach to a Session:
   tmux attach

Customize Your Tmux Experience:
Use a .tmux.conf file to personalize your setup. Grab one here: Download .tmux.conf.

Mosh: The Mobile Shell for Unreliable Connections

SSH works well for stable networks, but it struggles in areas with spotty connectivity. Enter Mosh, the Mobile Shell. Designed for intermittent networks, Mosh keeps your session alive even when the connection drops temporarily.

Why Mosh is a Game-Changer:

• No lag over high-latency networks.
• Automatically reconnects when the network is restored.
• Ideal for working on the go, from cafes to trains.

Getting Started with Mosh:

1. Install Mosh:
   sudo apt install mosh # For Debian/Ubuntu
2. Connect to a Server:
   mosh username@server

Learn more at mosh.org.

Why This Matters for Bioinformatics

Every bioinformatician knows the value of time and data integrity. Tools like screen, tmux, and mosh provide a lifeline when running long analyses, enabling you to:

• Safeguard your work against disconnections.
• Easily manage multiple workflows in parallel.
• Stay productive, even in challenging environments.

Quickstart Cheat Sheet

• Screen:

  screen # Start a session Ctrl+A, D # Detach screen -r # Reattach

• Tmux:

  tmux # Start a session Ctrl+B, D # Detach tmux attach # Reattach

• Mosh:

  mosh username@server

Final Thoughts

As a bioinformatician, your time is too valuable to spend restarting analyses due to technical hiccups. With screen, tmux, and mosh in your toolkit, you can work smarter, protect your progress, and stay productive no matter where you are. Start using these tools today and transform the way you work with remote systems.

Let me know how these tools work for you, and don\u2019t forget to follow for more bioinformatics tips!

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>

BioCircos.js provides SNP, CNV, HEATMAP, LINK, LINE, SCATTER, ARC, TEXT, and HISTGRAMmodules to display genome-wide genetic variations (SNPs, CNVs and chromosome rearrangement), gene expression and biomolecule interactions. BioCircos.js also provides BACKGROUND module to display background and axis circles. Tooltips showing detailed information of SVG elements are also provided.

Demo

Address of the bookmark: http://bioinfo.ibp.ac.cn/biocircos/document/index.html

The programs and the package are free software for academic users. Permission to use, copy, and modify any part of this software for educational, research and non-profit purposes is hereby granted. In this package or Docker image, number of other supported software has been integrated which may be under other licenses, along with any direct or indirect dependencies of the primary software being contained. As for any pre-built image usage, it is the image user's responsibility to ensure that any use of this image complies with any relevant licenses for all software contained within.

All software packages are distributed in the hope that they will be useful but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. If you have any query, please contact at raghava@iiitd.ac.in.

Address of the bookmark: https://hub.docker.com/r/raghavagps/gpsrdocker/

GSP4PDB is part of the services provided by the Bioinformatic Group of the University of Talca

http://gdblab.com/gsp4pdb/gsp4pdb2/

https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3352-x

Address of the bookmark: http://gdblab.com/gsp4pdb/gsp4pdb2/

Address of the bookmark: http://genomearchitect.github.io/

Address of the bookmark: https://saliweb.readthedocs.io/en/latest/frontend.html

More at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2745767/

Address of the bookmark: http://synorth.genereg.net/

Background/Motivation:

The dearth of structural information on alpha helical membrane protein (MPs) has hindered thus far the development of reliable knowledge –based potentials that can be used for automatic prediction of trans-membrane (TM) protein structure. While algorithm for identification of TM segments is available, modelling of the domains of alpha helical MPs involves assembling the segments into a bundle. This requires the correct assignment of the buried and lipid-exposed faces of the TM domains. 

Results: In a cross validated test on single sequences, our trans-membrane MM, correctly predicts the entire topology for 77% of the sequences in a standard dataset of 86 proteins with supervised topology. These results compare favorably with existing methods. 

Source Code: Matlab

Conclusion/Implementation: Here discriminant data mining approach was used to predict the location and orientation of alpha helices in membrane-spanning proteins. It is based on a first order Markov model (MM) with an architecture that corresponds closely to the biological systems. The model is enriched with three types of states for the loop on the cytoplasmic side (outer loop), loop for the non-cytoplasmic side (inner side), and trans-membrane part. The closed association between the biological and Markov states allows us to infer which part of the model architecture are important to capture the information which encodes the membrane topology, and gain a better understanding of the mechanism and constraints involved. Predictor Model was established by various  Markov decoder , and assignment of the membrane helix boundaries was apparent.

https://biosciences.hs-mittweida.de/equant/

Address of the bookmark: https://biosciences.hs-mittweida.de/equant/