Required Qualifications: Masters in physics/chemistry/mathematics (or) MTech in bioengineering, chemical (or) Masters in any traditional field of science with outstanding performance throughout the program. Candidate should have cleared GATE/UGC-CSIR examinations. Applicant should have done basic mathematics courses like calculus, differential equations, numerical analysis etc in their degree program and have obtained good grades in those courses. Knowledge of MATLAB and C or at least one traditional programming language is absolutely necessary. Strong inclination to understand biological concepts is a must for this research work as this project is about modeling biological systems.

Salary: A fixed salary of Rs 18000 PM including HRA will be paid.

Last date for application: This advertisement is open until suitable candidate is found for the project.

Preferred Qualifications: - Expertise in dynamical systems theory, bifurcation theory, numerical simulations, parameter estimation.

Independence and high motivation for carrying out interdisciplinary research. - Excellent communication skills and ability to work independently. - Good working habits.

Interested candidates should submit both curriculum vitae and statement of interest in PDF format to sriramk@iiitd.ac.in and should clearly mention in the subject "Application for SRF".

• DNA-mapping*
• ChIP-seq*
• RNA-seq*
• ATAC-seq*
• scRNA-seq
• Hi-C
• Whole Genome Bisulfite Seq/WGBS

(*Also available in "allele-specific" mode)

snakePipes can be installed via conda :

'conda install -c mpi-ie -c bioconda -c conda-forge snakePipes'.

Source code (https://github.com/maxplanck-ie/snakepipes) and documentation (https://snakepipes.readthedocs.io/en/latest/) are available online.

Address of the bookmark: https://github.com/maxplanck-ie/snakepipes

Our research is at the crossroad between cell biology, ecological genomics, systems biology, molecular evolution and population genetics. We study the architecture and evolution of protein and signalling networks.

More at http://landrylab.ibis.ulaval.ca/

gapFinisher can fill gaps in draft genomes quickly and reliably.

Address of the bookmark: https://github.com/kammoji/gapFinisher

1.automatic learning of chemical reactivity and metabolism,
2.simulation of NMR spectra,
3.modelling of properties of ionic liquids, and
4.representation of molecular chirality.

More at http://joao.airesdesousa.com/

Parliament2 runs a combination of tools to generate structural variant calls on whole-genome sequencing data. It can run the following callers: Breakdancer, Breakseq2, CNVnator, Delly2, Manta, and Lumpy. Because of synergies in how the programs use computational resources, these are all run in parallel. Parliament2 will produce the outputs of each of the tools for subsequent investigation.

Address of the bookmark: https://github.com/dnanexus/parliament2

The First time when you login to the remotehost from a localhost, it will display the host key not found message and you can give “yes” to continue. The host key of the remote host will be added under .ssh2/hostkeys directory of your home directory, as shown below.

localhost$ ssh -l jit remotehost.example.com

jit@remotehost.example.com password:

remotehost.example.com$

The Second time when you login to the remote host from the localhost, it will prompt only for the password as the remote host key is already added to the known hosts list of the ssh client.

localhost$ ssh -l jit remotehost.example.com
jit@remotehost.example.com password:
remotehost.example.com$

For some reason, if the host key of the remote host is changed after you logged in for the first time, you may get a warning message as shown below. This could be because of various reasons such as 1) Sysadmin upgraded/reinstalled the SSH server on the remote host 2) someone is doing malicious activity etc., The best possible action to take before saying “yes” to the message below, is to call your sysadmin and identify why you got the host key changed message and verify whether it is the correct host key or not.

localhost$ ssh -l jit remotehost.example.com

jit @remotehost.example.com's password:
remotehost$

4. Debug SSH Client:

Sometimes it is necessary to view debug messages to troubleshoot any SSH connection issues. For this purpose, pass -v (lowercase v) option to the ssh as shown below.

Example without debug message:

        localhost$ ssh -l jit remotehost.example.com
        warning: Connecting to remotehost.example.com failed: No address associated to the name
        localhost$

Example with debug message:

        locaclhost$ ssh -v -l jit remotehost.example.com
        debug: SshConfig/sshconfig.c:2838/ssh2_parse_config_ext: Metaconfig parsing stopped at line 3.
        debug: SshConfig/sshconfig.c:637/ssh_config_set_param_verbose: Setting variable 'VerboseMode' to 'FALSE'.
        debug: SshConfig/sshconfig.c:3130/ssh_config_read_file_ext: Read 17 params from config file.
        debug: Ssh2/ssh2.c:1707/main: User config file not found, using defaults. (Looked for '/home/jit/.ssh2/ssh2_config')
        debug: Connecting to remotehost.example.com, port 22... (SOCKS not used)
        warning: Connecting to remotehost.example.com failed: No address associated to

5. Escape Character: (Toggle SSH session, SSH session statistics etc.)

Escape character ~ get’s SSH clients attention and the character following the ~ determines the escape command.
Toggle SSH Session: When you’ve logged on to the remotehost using ssh from the localhost, you may want to come back to the localhost to perform some activity and go back to remote host again. In this case, you don’t need to disconnect the ssh session to the remote host. Instead follow the steps below.

i. Login to remotehost from localhost: localhost$ssh -l jit remotehost
ii. Now you are connected to the remotehost: remotehost$
iii. To come back to the localhost temporarily, type the escape character ~ and Control-Z. When you type ~ you will not see that immediately on the screen until you press and press enter. So, on the remotehost in a new line enter the following key strokes for the below to work: ~

    remotehost$ ~^Z
    [1]+  Stopped                 ssh -l jit remotehost
    localhost$

iv. Now you are back to the localhost and the ssh remotehost client session runs as a typical unix background job, which you can check as shown below:

    localhost$ jobs
    [1]+  Stopped                 ssh -l jit remotehost

v. You can go back to the remote host ssh without entering the password again by bringing the background ssh remotehost session job to foreground on the localhost

    localhost$ fg %1
    ssh -l jit remotehost
    remotehost$

Project Scientist-I
Experimental / Computation analysis experience in highthroughput genomics/ clinical application.

Project Manager
Experience in handling large biological projects involving high-throughput genomics/ clinical application.

Scientific Administrative Assistant
Lab Work.

More at https://vinodscaria.genomes.in/positionsopen

The "Ifs" of NGS QC and Trimming

1. Ensures Data Integrity
   If you want to minimize errors in downstream analyses, QC and trimming remove low-quality reads and bases, ensuring high-confidence data. This step is essential for reliable variant calling, assembly, and other applications.

2. Removes Contaminants
   If adapter sequences or contaminants are present in the raw reads, trimming can eliminate them. This prevents issues like misalignment or incorrect biological interpretations, ensuring cleaner data for analysis.

3. Improves Mapping and Assembly
   If your goal is better alignment to a reference genome or improved de novo assembly, trimming low-quality bases and adapters is critical. High-quality reads map more efficiently and generate more accurate assemblies.

4. Reduces Computational Load
   If you want to save computational resources, trimming reduces the dataset size, which speeds up processing and analysis. Clean datasets mean less computational time spent on processing low-quality data.

5. Prepares for Standardized Analyses
   If your project involves multiple datasets, QC and trimming ensure uniformity across them. This standardization makes comparisons valid and reproducible, particularly in large collaborative studies.

The "Buts" of NGS QC and Trimming

1. Risk of Over-Trimming
   But excessive trimming can lead to the loss of informative sequences, reducing read depth and potentially discarding biologically relevant data. This is especially critical in studies with limited sequencing depth.

2. Bias Introduction
   But trimming algorithms might introduce biases, especially if they inadvertently remove sequences with specific biological patterns. This can skew results and compromise biological insights.

3. Loss of Context in Paired-End Reads
   But trimming one read in a pair more than the other can lead to loss of pairing information. This complicates downstream analyses that rely on paired-end data, such as structural variant detection.

4. Time and Resource Intensive
   But running QC and trimming for large datasets can be computationally expensive and time-consuming. As sequencing depth increases, preprocessing becomes a bottleneck in the analysis pipeline.

5. Variable Standards
   But the criteria for trimming (e.g., quality threshold, minimum read length) can vary between tools and datasets. This variability may affect reproducibility and comparability of results across studies.

Balancing the "Ifs" and "Buts"

To maximize the benefits of QC and trimming while mitigating the challenges, consider the following best practices:

• Use QC Tools Wisely: Start with tools like FastQC to identify quality issues in your raw data. Visualizing quality metrics helps tailor your trimming parameters.

• Choose Reliable Trimming Tools: Tools like Trimmomatic, Cutadapt, and BBduk offer adaptive and customizable trimming options. Select one that aligns with your dataset and project goals.

• Set Reasonable Parameters: Avoid over-trimming by setting quality thresholds and minimum read lengths that balance data retention and quality improvement.

• Test Downstream Effects: Validate the impact of QC and trimming on downstream analyses, such as alignment efficiency, variant calling accuracy, or assembly quality.

• Document Your Workflow: Maintain detailed records of the parameters and tools used for QC and trimming. This ensures reproducibility and enables better troubleshooting.

Conclusion

NGS quality control and trimming are essential steps to ensure reliable and accurate data for analysis. While the "ifs" highlight the clear benefits of these steps, the "buts" remind us of the potential pitfalls. By adopting best practices and carefully balancing these considerations, you can optimize your preprocessing workflow and unlock the full potential of your sequencing data.