The Indian scientists working in Bangalore, along with their American counterparts, have mapped more than 17,000 proteins in 30 organs of the human body. Just like the human genome was sequenced around the turn of the millennium, this is an equivalent mapping of the human proteome.

The researcher estimated there are around 20,500 proteins in the human body. These scientists have profiled around 17,294, which account for around 84% of the total proteins. Apart from this, the team also traced around 2,500 of 3,000 proteins that had been categorised as "missing proteins".

The work, done by group of Indian scientists, and Johns Hopkins University, published in the renowned journal Nature ( http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html ). Of the 72 people who worked on the project, 46 are Indians.

Reference:

http://www.nature.com/nature/journal/v509/n7502/full/nature13302.html

http://www.proteinatlas.org/ -The antibody-based Human Protein Atlas programme

http://www.humanproteomemap.org/ -Proteogenomic analysis by identifying translated proteins from annotated pseudogenes, non-coding RNAs and untranslated regions.

https://www.proteomicsdb.org/ -Assembled protein evidence for 18,097 genes in ProteomicsDB

Smudgeplots are computed from raw or even better from trimmed reads and show the haplotype structure using heterozygous kmer pairs. For example:

Address of the bookmark: https://github.com/KamilSJaron/smudgeplot

Address of the bookmark: https://www.ncbi.nlm.nih.gov/nuccore/ON563414

What is PhyloHerb: PhyloHerb is a wrapper program to process genome skimming data collected from plant materials. The outcomes include the plastid genome (plastome) assemblies, mitochondrial genome assemblies, nuclear ribosomal DNAs (NTS+ETS+18S+ITS1+5.8S+ITS2+28S), alignments of gene and intergenic regions, and a species tree. It is designed to be a high throughput program dealing with lower quality data. Examples include low-coverage (5x cpDNA) plastome phylogeny, recycling plastid genes from target enrichment data, retrieving low-copy nuclear genes from medium coverage (5x nucDNA) genome skimming.

License: GNU General Public License

Citation:

• Cai, Liming, Hongrui Zhang, and Charles C. Davis. 2022. PhyloHerb: A high‐throughput phylogenomic pipeline for processing genome‐skimming data. Applications in Plant Sciences 10(3): 1–9. https://doi.org/10.1002/aps3.11475

Address of the bookmark: https://github.com/lmcai/PhyloHerb/

No.NITW/BT/2014/adhoc

APPLICATIONS ARE INVITED FOR THE APPOINTMENT OF ADHOC FACULTY ON CONTRACT BASIS IN THE DEAPARTMENT OF BIOTECHNOLOGY

Period of Contract: Initially the appointment is for one semester i.e., from July 2014 up to December 2014 only.

Essential Qualifications:

i) B. Tech or equivalent in Biotechnology/ Industrial Biotechnology/ Biochemical Engineering / Chemical Engg. Or M. Sc in Microbiology/ Botany/ Zoology/ Biochemistry/Biotechnology and ii) M. Tech or equivalent in Biotechnology/Industrial Biotechnology/Bioinformatics

Or

Integrated M. Tech in Biotechnology/Industrial Biotechnology/ Bioinformatics

Candidates must possess First class (60% aggregate marks or 6.5 CGPA) at B. Tech/ M. Sc and M. Tech.

Desirable: Ph. D Pay Package: All selected candidates shall be eligible for a consolidated pay of Rs.30, 000/- per month. Candidates with Ph. D shall be eligible for an additional amount of Rs.5, 000/- per month.

How to apply : Applications on plain paper with attested photocopies of certificate and bio data along with justification for eligibility should reach to the Head, Department of Biotechnology, National Institute of Technology, Warangal AP 506004 in the form of soft or hard copy on or before 21st June 2014 email : biotech_hod@nitw.ac.in

Intimation: No separate call letters will be sent to the candidates. All the eligible candidates will be notified in the institute web site on 23rd June 2014. All the eligible candidates are requested to report for the interview to the Head, Department of Biotechnology at 9:00 AM on 27th June 2014

Joining: Selected candidates will be informed and they are expected to join immediately.

Advertisement:

http://www.nitw.ac.in/nitw/announcements/2014/Bio-Adhoc%20Advt.%20May-2014.pdf

Key Findings of the Study:

1. Alu Insertion and Tail Loss:
   The researchers discovered an Alu-mediated genetic change in a common ancestor of modern apes and humans. This change disrupted the normal function of a gene involved in tail development, leading to the suppression of tail formation.

2. Gene Disruption Mechanism:
   The Alu insertion was found within a regulatory region of the TBXT gene (also known as T or Brachyury), which is crucial for tail development in vertebrates. This insertion likely altered the gene's expression patterns, leading to tail reduction over evolutionary time.

3. Functional Evidence from Model Organisms:
   To test their hypothesis, the researchers introduced similar genetic modifications in mice. The modified mice exhibited shortened or absent tails, supporting the idea that the identified mutation played a role in tail loss in hominoids.

4. Evolutionary Implications:
   The findings suggest that small, random genomic changes—such as transposable element insertions—can have profound effects on body morphology. This study provides evidence that mobile DNA elements (like Alu) can drive major evolutionary transitions.

5. Relevance to Human Evolution:
   Understanding the genetic basis of tail loss helps in reconstructing the evolutionary history of hominins (the lineage that includes humans and our extinct relatives). It also sheds light on how genetic variations contribute to anatomical diversity among primates.

Significance of the Study:

This research highlights the role of transposable elements in shaping evolutionary traits and provides a concrete genetic explanation for a defining characteristic of humans and great apes. It also demonstrates how mutations in regulatory regions of developmental genes can lead to significant anatomical changes.

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

The authors tested the pipeline across 5 datasets from 4 species, spanning an almost 80-fold difference in sample size (from 136 to over 10,700 individuals) and found that the predictive accuracy was within 0.02-0.05 of the standard GBLUP and Bayesian ridge regression approaches, again without the user having to manually select a model. More importantly the pipeline was able to recover known biological signals (FLC, FT and DOG1 in Arabidopsis, Waxy and GW5 in rice and Glu-D1 in wheat) and produced well calibrated association statistics. The most interesting observation though is that prediction accuracy and biological association are not necessarily the same thing. Two traits with identical predictive performance could have association significance that varied by over 60 orders of magnitude. So knowing how well we can predict a trait doesn't necessarily tell us anything about the distribution of the genetic architecture of that trait across the genome. That's where GenoBridge is actually of value. It's not just another machine learning pipeline. It's an example of an adaptive analytical workflow that doesn't just throw algorithms at the data but asks whether the data actually justify the next analytical step. As the amount of genomic data we generate continues to grow and the number of computational methods available to us increases, the bottleneck may not be in finding the right algorithm at all. The bottleneck may be in deciding which algorithm we can actually trust.

GenoBridge offers a potential solution: let the data tell you which model is appropriate, validate the prediction rigorously and only then move from prediction to biological discovery. That may be where computational genomics is heading. Rather than a future in which there is one single 'best' model, we may be heading towards a future of intelligent systems that know when a model is appropriate, when a result is reliable and when the biology is actually telling us something that we should be listening to.

More at https://link.springer.com/article/10.1186/s13007-026-01596-5

Address of the bookmark: http://infoplatter.wordpress.com/2014/04/06/bioinformaticians-pocket-reference/