Genome Annotation and Functional Genomics

Bergman Lab is actively engaged in the development and application of computational methods to improve the annotation of functional biological features in genome sequences. Bergman Lab work focuses on improving annotation of non-protein-coding regions of the genome including conserved noncoding sequences (CNSs), cis-regulatory modules (CRMs), transcription factor binding sites (TFBSs), transposable elements (TEs) and noncoding RNA (ncRNA) genes. Current projects include improving the (i) annotation of TEs in the fly and yeast genomes, (ii) annotation of CRMs and TFBSs in the fly genome, and (iii) analysis of transposon knockout collections in flies. Research in this area is supported by the EC FP7 programme.

Genome and Molecular Evolution
Text and Data Mining

More @ http://bergmanlab.smith.man.ac.uk/

• the Gene Ontology (GO), which provides the logical structure of the biological functions (‘terms’) and their relationships to one another, manifested as a directed acyclic graph
• the corpus of GO annotations, evidence-based statements relating a specific gene product (a protein, non-coding RNA, or macromolecular complex, which we often refer to as ‘genes’ for simplicity) to a specific ontology term

Together, the ontology and annotations aim to describe a comprehensive model of biological systems. Currently, the GO knowledgebase includes experimental findings from over 140 000 published papers, represented as over 600 000 experimentally-supported GO annotations. These provide the core dataset for additional inference of over 6 million functional annotations for a diverse set of organisms spanning the tree of life.

In addition to this core knowledgebase, GOC resources also include software to edit and perform logical reasoning over the ontologies, web access to the ontology and annotations, and analytical tools that use the GO knowledgebase to support biomedical research.

Address of the bookmark: http://www.geneontology.org/

Nature Biotechnology spoke with Altschul and several other originators of computational biology software programs widely used today (Table 1). The conversations explored what makes certain software tools successful, the unique challenges of developing them for biological research and how the field of computational biology, as a whole, can move research agendas forward. What follows is an edited compilation of interviews.

Detail @ http://www.nature.com/nbt/journal/v31/n10/full/nbt.2721.html

News Source @ Nature

2013 Oct Advertisement from Indian Statistical Institute

Post: Network Analysis

No. of Positions: 01

Educational Qualifications:

Candidate should have passed BE/B.Tech Or Equivalent in Computer Science / Electrical Engineering / Electronics / Information Technology / Bioinformatics / Biotechnology with throughout first Class
Experience:

(details of experience required)
Pay Scale: INR Rs.16000-20000/-P.M.

Walk-In-Interview : 22 Oct 2013 at 10:30 AM

Download Official Notification:
http://www.isical.ac.in/JobApplicationFiles/MIU_0310201311433700.pdf

The Power of Evo 2: AI Meets DNA

Evo 2 is the largest AI model for biology ever created, trained on an astonishing 9.3 trillion DNA "letters" (nucleotides) carefully selected from genomes spanning the entire tree of life. This massive dataset ensures that Evo 2 can recognize patterns and relationships in genetic sequences at an unparalleled scale.

For the first time, scientists can design DNA with AI, moving beyond simple sequence analysis to active DNA generation. Evo 2 enables researchers to predict, modify, and even create entire genetic sequences, opening new possibilities in medicine, agriculture, and synthetic biology.

Decoding the Dark Genome

One of the biggest challenges in genetics is understanding the non-coding regions of DNA—vast stretches of the genome that do not code for proteins but play crucial roles in regulating gene expression. These regions control when and how genes are activated, influencing everything from development to disease.

Evo 2 is designed to decode these non-coding elements, helping researchers uncover their functions and use this knowledge to develop gene-based therapies, synthetic life forms, and precision agriculture solutions.

From Reading DNA to Writing It

To put Evo 2’s impact into perspective:

• Previous AI models could "read" DNA like a book, analyzing genetic sequences and identifying patterns.
• Evo 2 can "write" entirely new DNA, designing functional genes, chromosomes, and even full genomes from scratch.

This means scientists can now engineer biological systems with AI, designing new proteins, metabolic pathways, and genetic circuits to address real-world challenges.

A Step Toward Generative Biology

The Arc Institute describes Evo 2 as a major step toward "generative biology"—a revolutionary approach where AI is used to create novel biological structures rather than just analyzing existing ones. This could lead to breakthroughs such as:

• New medicines: AI-generated enzymes and proteins tailored for targeted therapies.
• Disease-resistant crops: Genetically optimized plants for higher yield and climate resilience.
• Synthetic organisms: Custom-designed microbes for bioremediation, biofuel production, and industrial applications.

An Open-Source Revolution

Unlike many proprietary AI models, Evo 2 is open source, making its capabilities accessible to researchers worldwide. This democratization of AI-driven biology means that scientists from different disciplines can collaborate, experiment, and innovate, accelerating discoveries in genetic engineering and synthetic biology.

With Evo 2, the boundaries of what’s possible in DNA design, genetic engineering, and biological innovation are being redrawn. The future of life sciences is no longer just about understanding life’s code—it’s about writing it.

Research Interest
Genomic Data Integration

Microarray Analysis

Gene and Protein Function Prediction

Detection and Analysis of Chromosomal Abnormalities and Functional Evolution

Integration of Computation and Experiments

Identification of Biological Networks and Pathways

Evaluation and Validation of Computational Predictions

Scalable Visualization-Based Data Analysis

More @ http://reducio.princeton.edu/cm/
PI page @ http://reducio.princeton.edu/cm/ogt

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

Details of the Positions and Pay Structure:

03 Posts for Assistant Professor in Molecular Synthesis for Drug Discovery and Development

Essential Qualifications and Requirements:

1. PhD in Synthetic Organic Chemistry/Medicinal Chemistry with research publications in high quality international journals and first class grade at the preceding degree from recognised University/Institute in India or abroad with consistently good academic record.
2. Three Yrs of Post-doctoral experience in relevant area.
3. Below 35 Yrs of age at the time of application

Desirable Experience: Candidates having strong research background in organic synthesis, total synthesis of structurally complex and medicinally important natural products/drugs related to cancer, neurodegenerative diseases (neurotropically active molecules for Alzheimer's, Parkinson’s, dementia etc) and infectious diseases such as malaria, TB etc. will be preferred.

Interested candidates may apply with:

1. Filled up Application Form (download from CBMR Website: http://www.cbmr.res.in) along with the Cover Letter, Curriculum Vitae including academic record (Bachelor degree onwards), awards, honours, list of Publications and reprints of 5 best publications.
2. Proposed research plan (max 3-4 pages).
3. Names and address (with valid e-mail and Phone number) of at least 3 academic referees.
4. Online Payment Receipt with transaction reference no. of Rs. 1000/- (USD 100 or equivalent foreign currency) on following details.
Account Number: 30054847814 Name: Director, Centre of Biomedical Research
Bank: STATE BANK OF INDIA, SGPGI Campus Branch, LUCKNOW

IFSC Code: SBIN0007789
MICR No: 22602034

Applications can be sent by registered/speed post or by e-mail to the following address:

The Director,
Centre of Biomedical Research (CBMR),
Sanjay Gandhi PGI Campus,
Raebareli Road, Lucknow-226014
e-mail: cbmr.admin@cbmr.res.in,
gp.pandey@cbmr.res.in

More Info:

http://www.cbmr.res.in/career/Advertisement%20for%20the%20post%20of%20Professors%20and%20Assistant%20Professors.pdf

Features

• Linear representation of several segments of DNA
• Comparisons represented by areas between the segments (like Artemis, for example)
• Reads from common formats: Genbank, EMBL, blast, Mauve, and from user-generated tab files
• Plot several subsegments of the same segment on the same line, separated by a //
• Automatic or manual placement of the segments on the plot
• Add annotations to all the lines
• Create smart, automatic annotations for genomes, based on gene names
• Add a user-generated tree
• Add a global scale or a scale to each line
• Use user-defined graphical functions to represent genes

Address of the bookmark: http://genoplotr.r-forge.r-project.org/

We are seeking a highly motivated young PhD student with strong interest in high throughput data analysis.
Detailed descriptions of our recent research activities may be found here:
http://www.humanitas-research.org/condorelli-gianluigi-md-phd/

Position is available starting from October/November. A probationary period of one month will be required.

Please send a CV along with a cover letter stating the reasons for applying and contact details of one or more referees to Dr. Paolo Kunderfranco (paolo.kunderfranco@humanitasresearch.it).