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

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.

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.

Read more about it @

https://link.springer.com/article/10.1186/s12859-024-05953-w

Shell history search command

Type history at a shell prompt:
$ history

It will display the list of all used commandline history with an serial number.

To search particular command, enter:
$ history | grep command-name
$ history | egrep -i 'scp|ssh|ftp'
Emacs Line-Edit Mode Command History Searching

To get previous command containing string, hit [CTRL]+[r] followed by search string:

(reverse-i-search):

To get previous command, hit [CTRL]+[p]. You can also use up arrow key.

CTRL-p

To get next command, hit [CTRL]+[n]. You can also use down arrow key.

CTRL-n

fc command

Apart from hostory command there are fc command to extract the command from history. The fc stands for either "find command" or "fix command.

For example list last 10 command, enter:
$ fc -l 10
To list commands 130 through 150, enter:
$ fc -l 130 150
To list all commands since the last command beginning with ssh, enter:
$ fc -l ssh
You can edit commands 1 through 5 using vi text editor, enter:
$ fc -e vi 1 5

Delete command history

The -c option causes the history list to be cleared by deleting all of the entries:
$ history -c

Check out the changes they made.

They added the cleanup-blastdb-volumes.py script to remove unused BLAST database volumes. Read the documentation here.

They also switched the protocol from ftp to https to access BLAST databases for increased performance and reliability when downloading data from the NCBI with the update_blastdb.pl script.

And fixed a few bugs related to downloading data from the NCBI, and mt_mode crashing blastn and blastx.

Check out the release notes.

Download BLAST+ 2.14.1

Questions or comments? Please write the BLAST help desk.

More info and download: https://blast.ncbi.nlm.nih.gov/doc/blast-news/2023-BLAST-News.html

The following 5 questions are those that I have heard many times during the job-search process. There is no reason for not planning responses in such situations.

1. Tell Us About Yourself
This is a very typical opener in interviews. It's a perfect question to ask, and getting something planned will really help you concentrate and ease in the conversation. However, you need to make sure that your response is applicable to the job you're interviewing.
It's probably better to keep your answer professional. Try to include these in the answer as well: where did your love of science and bioinformatics come from? How the heck did you end up in this field? Why programming and scripting ?

2. What is your plan for your bioinformatics career? / How do you look at yourself in five years? / How are your personal objectives to accomplish these goals / What are the plan for your research fundings ?

Your CV/resume has already impressed the selection panel if you have been invited for an interview. The questions from the bioinformatics interview team provide an incentive for you to market yourself and illustrate the work in question with the most appropriate knowledge.

3. What do you understand about the job description/What would your suggested research path be if you were a successful candidate?
Summarize the specifics of the advertised bioinformatics position in your own words. Follow on with some suggestions of how you want to extend your research and create your own projects within the community.

4. Will you work as a group or do you want to work on your own?
This requirement can vary from jobs to job, so when addressing, be alert. A company/research PI may need a bioinformatician that is able to work on a single project autonomously, or they may need a person who can help direct and organize a team. In your response, refer to the job description.

5. What particular methods have you used to date with your experiments?
You might have experience with all the laboratory techniques described in the job description, but stress the ones you highly experienced with. Highlight your professional abilities and stress that you are extremely capable of mastering new techniques with others ...

At the end of the day, remember that you're questioning the jury as well as they're interviewing you. You will ought to think of any questions you would like the interview panel to pose. This indicates that you have done your homework and serious about the position.

All the best for your future job interview.

Address of the bookmark: https://github.com/isovic/graphmap

Address of the bookmark: https://github.com/Reedwarbler/GAPPadder

Address of the bookmark: https://github.com/dentearl/evolverSimControl

MGI, a subsidiary of global genomics leader BGI Group, announced pricing and its first early access customer for the new ultra high-throughput sequencer, MGISEQ-T7, saying it has driven down sequencing cost to $5 per gigabyte, with exceptionally high accuracy. Such innovations are helping more people to realize the benefits of genomic information.

In October, MGI launched the MGISEQ-T7, a highly flexible production-scale platform that is the most powerful sequencer to date. It can produce as many as 60 whole human genomes in one day. The instrument sells for $1 million.

The T7 enables simultaneous but independent operation of up to four flow cells, which means different applications such as single-cell RNA sequencing, whole exome sequencing and whole genome sequencing can be run in different flow cells at the same time. This helps to reduce costs, allowing MGI to offer the most competitive sequencing price in the market.

Powered by DNBseq™, MGISEQ delivers quality data with accuracy for SNP and Indel calling rate of 99.9% and 99%, respectively, along with decreased duplication rate down to less than 2 percent, and almost zero Index mis-assignment rate.

SOURCE MGI

https://www.bgi.com/global/company/news/bgis-mgi-tech-launches-two-new-ngs-platforms/

http://en.mgitech.cn/