Protein-protein and protein-DNA/RNA docking based on a hybrid algorithm of template-based modeling and ab initio free docking.

The HDOCK server distinguishes itself from similar docking servers in its ability to support amino acid sequences as input and a hybrid docking strategy in which experimental information about the protein–protein binding site and small-angle X-ray scattering can be incorporated during the docking and post-docking processes.

Address of the bookmark: http://hdock.phys.hust.edu.cn/

Why Normalize RNA-Seq Data?

Normalization is a crucial step in RNA-Seq analysis for the following reasons:

• Sequencing depth: Different RNA-Seq experiments produce varying numbers of reads, making direct comparisons between samples misleading.

• Gene length: Longer genes inherently generate more reads, irrespective of their actual expression level.

• Bias reduction: Normalization mitigates technical biases, enabling meaningful biological interpretation.

TPM (Transcripts Per Million)

TPM measures the proportion of reads mapped to a transcript, normalized by transcript length and sequencing depth. It is calculated as:

Key Features:

1. Proportionality: TPM values sum to 1,000,000 across all transcripts in a sample, making it easier to compare between samples.

2. Intuitive interpretation: TPM values directly represent the abundance of transcripts in a sample.

3. Preferred for comparisons: TPM facilitates between-sample comparisons better than FPKM.

FPKM (Fragments Per Kilobase Million)

FPKM normalizes read counts by transcript length and sequencing depth, but without enforcing proportionality like TPM. It is defined as:

Key Features:

1. Historical significance: FPKM was one of the first normalization methods used for RNA-Seq.

2. Single-end vs. paired-end: In paired-end sequencing, FPKM becomes RPKM (Reads Per Kilobase Million).

3. Limited utility: FPKM values are not as robust as TPM for cross-sample comparisons due to lack of proportionality.

CPM (Counts Per Million)

CPM normalizes raw read counts by sequencing depth, without considering gene length. It is expressed as:

Key Features:

1. Simplicity: CPM is straightforward and computationally less intensive.

2. Application: Suitable for non-length-dependent analyses, such as comparing total expression levels or differential expression analysis.

3. Gene length agnostic: CPM does not correct for gene length, making it less ideal for measuring expression levels.

When to Use Each Method

• TPM: Best for comparing expression levels between samples, especially when transcript length and sequencing depth vary.

• FPKM: Useful for historical consistency but generally replaced by TPM.

• CPM: Ideal for differential expression analysis when gene length normalization is unnecessary.

Conclusion

Choosing the right normalization method depends on the specific objectives of your RNA-Seq analysis. TPM’s proportionality and robustness make it the preferred choice for most applications, while CPM serves well for differential expression studies. Although FPKM paved the way for RNA-Seq normalization, it has largely been supplanted by TPM in modern workflows. Understanding these methods and their nuances ensures accurate and meaningful interpretations of RNA-Seq data.

References:

1. Li, B., & Dewey, C. N. (2011). RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics.

2. Trapnell, C., et al. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology.

3. Law, C. W., et al. (2014). voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biology.

Code in almost all popular languages using Coding Ground. Edit, compile, execute and share your projects, 100% cloud.

http://www.tutorialspoint.com/codingground.htm

Address of the bookmark: http://www.tutorialspoint.com/codingground.htm

1. Understand the Landscape

Before diving into applications, take the time to understand the bioinformatics job market. Common roles include:

• Bioinformatics Analyst/Scientist: Focused on data analysis and interpretation.
• Computational Biologist: Combines computational techniques with biological research.
• Data Scientist in Genomics: Applies machine learning and statistical models to genomic data.
• Software Developer in Bioinformatics: Designs and develops tools and pipelines for biological research.

Familiarize yourself with the key industries hiring bioinformaticians, such as academia, biotech, pharmaceuticals, healthcare, and agriculture.

2. Build a Strong Foundation

Bioinformatics demands a diverse skill set. Ensure you have a solid foundation in the following areas:

• Programming Skills: Proficiency in Python, R, or Perl is often required. Familiarity with tools like Bash scripting and version control systems (e.g., Git) is a plus.
• Statistics and Data Analysis: Knowledge of statistical methods, machine learning, and data visualization is crucial.
• Biological Knowledge: Understanding genomics, transcriptomics, and proteomics will help you communicate effectively with biologists.
• Specialized Tools and Databases: Be comfortable using tools like BLAST, Bowtie, and databases like NCBI and Ensembl.

3. Create a Winning Resume and Portfolio

Highlight your technical skills, biological knowledge, and relevant experience. Tips for a standout application:

• Tailor your resume to each job, emphasizing skills mentioned in the job description.
• Showcase your experience with real-world datasets by linking to your GitHub profile or online portfolio.
• Include details of any publications, presentations, or significant projects.

4. Network Actively

Networking is often the key to discovering opportunities. Here’s how to build connections:

• Attend Conferences and Workshops: Events like ISMB or specialized bioinformatics workshops are great for meeting professionals.
• Engage Online: Join LinkedIn groups, participate in bioinformatics forums, and follow relevant hashtags on Twitter.
• Leverage Alumni Networks: Connect with alumni from your university who are working in the field.

5. Gain Relevant Experience

Experience is a major factor for hiring managers. Ways to enhance your profile include:

• Internships: Seek out internships in research labs or biotech companies.
• Collaborations: Volunteer to work on projects with professors or peers.
• Open Source Contributions: Participate in bioinformatics software development on platforms like GitHub.

6. Prepare for Interviews

Bioinformatics interviews often combine technical and behavioral questions. Prepare by:

• Reviewing Key Concepts: Refresh your knowledge of algorithms, sequence analysis, and statistical methods.
• Practicing Coding: Be ready to solve coding challenges or discuss code snippets.
• Understanding the Organization: Research their recent projects, publications, or products.
• Preparing Questions: Demonstrate interest by asking about their tools, workflows, or team structure.

7. Stay Resilient and Persistent

Job hunting can be a long process, but persistence pays off. Tips to keep moving forward:

• Keep improving your skills by taking online courses or certifications.
• Stay updated with advancements in bioinformatics by following journals and blogs.
• Apply to multiple positions and don’t get discouraged by rejections. Each application is a learning experience.

Closing Thoughts

Landing a bioinformatics job requires a mix of technical expertise, networking, and resilience. By understanding the market, showcasing your skills effectively, and continuously learning, you’ll be well on your way to a rewarding career in this dynamic field. Remember, the key to cracking the code is perseverance—stay curious, stay determined, and success will follow.

2.Mapmygenome™
Royal Demeure,HUDA Techno Enclave,
Plot No. 12/2, Sector-1 500 081 
Madhapur,Hyderabad
AP, India

3. DNA Labs India

http://www.dnalabsindia.com/lab.php

4.MedGenome Labs Pvt Ltd
(Division of SciGenom Labs Pvt Ltd.)
Plot no: 43A,SDF, 3rd floor
A Block,CSEZ, Kakanad, Cochin
Kerala - 682037 
Phone: 0484 - 2413399
Fax: 0484 - 2413398
Email: info@medgenome.com

5.Narayana Nethralaya

Narayana Hrudayalaya Campus
Narayana Health City
# 258/A, Bommasandra, Hosur Road, 
Bangalore - 560 099 - INDIA.
TEL: +91-80-66660655-0658 
FAX: +91-80-66660650 
Mobile: 9902 821128 (Emergency Only)
e-mail: info@narayananethralaya.com

6.BioAxis DNA Research Centre Private Limited
13-51,Sri Lakshmi Nagar colony,
Besides Big Bazar, Near Kamineni Hospitals
GSI Post BandalGuda (L B Nagar) Hydeabad-500068
Andhra Pradesh (India).
Phone : +91-40-24034503/+91-9246338983

7.Gene Guiide

8th Floor, Embassy Towers, 7 Bungalows Rd, Versova, Andheri West, Mumbai-61 
 09167 117799 
 info@geneguiide.com 

See more at: http://www.geneguiide.com

8.INDIAN BIOSCIENCES
Regd. Office:
G-2 (Ground Floor Rear), Kailash Colony, New Delhi - 110048, India.
Phone: +91 (0)11 29236088, Email: info@inbdna.com.

9.SRL Limited

GP-26, MARUTI INDUSTRIAL ESTATE,

UDYOG VIHAR,SECTOR-18,

GURGAON - 122015

Tel: 0124-3001243 / 0124-3001209

SRL Limited
VASANT VIHAR, 8, PALAM MARG,
NEW DELHI - 110057
Tel: 011 - 4229 5333 

Website: http://www.srlworld.com
National Customer care number:
Call Toll Free : 1800-222-660/1800-102-8282 
E-mail id: customercare@srl.in

10.Tata Memorial Centre,

Advanced Centre for Treatment, Research and Education in Cancer

Kharghar, Navi Mumbai - 410 210, INDIA.

Tel: +91-22-2740 5000

Fax: +91-22-2740 5085

E-mail: mail@actrec.gov.in

Scientists with a long-running Framingham Heart Study looked at 1,932 people (examination of about 1.5 million markers of genetic variations), comparing unrelated friends to unrelated strangers. They found that friends shared about 1% of their genes — a percentage much higher than those shared with strangers.This new findings made it clear that people have more DNA in common with those who are selected as friends than with strangers in the same population. 

The genes that lined up the most were olfactory genes, which deal with smell. The ones that lined up the least were immune system genes. The researchers weren't sure why that happened :/. Olfactory genes might be a straightforward explanation: People who like the same smells tend to be drawn to similar environments, where they meet others with the same tendencies.

Reference:

http://www.pnas.org/content/111/Supplement_3/10796.full

Image : http://i.kinja-img.com

Applications are invited for pre-selection of candidates for Ministry of Science and Technology, Department of Science and Technology INSPIRE Faculty Scheme: a component of “Assured Opportunity for Research Career (AORC)” under INSPIRE in the area of computational Biology/Systems Biology/Bioinformatics.

Candidates having done their B.Tech/B.E. and or M.Sc./M.Tech in Computer Science or Biotechnology and Ph.D. in Systems/ Computational Biology or Bioinformatics may apply in the following format prescribed by DST to the Director, Cluster Innovation Center, University Stadium, GC Narang Marg, University of Delhi, Delhi -11107. Detials of other qualification, age limits etc., please visit www.inspire-dst.gov.in.

Application on the prescribed format may be submitted by email to director@cic.du.ac.in before October 25, 2014. Selected candidates shall be called for an interview. The date, time and venue of the interview shall be informed by email/telephone. For more information about Cluster Innovation Center, please visit https://ducic.ac.in.

ancestor -- Any organism, population, or species from which some other organism, population, or species is descended by reproduction.

apomorphy -- specialized (=derived) characters of an organism.

basal group -- The earliest diverging group within a clade; for instance, to hypothesize that sponges are basal animals is to suggest that the lineage(s) leading to sponges diverged from the lineage that gave rise to all other animals.

biological classification -- The orderly arrangement of organisms in hierarchical system that ideally reflects evolutionary history.

cDNA -- Complementary DNA; DNA that is synthesized, by reverse transcriptase, from a Messenger RNA template ( Messenger RNA contains the coded information for protein synthesis).

character -- Heritable trait possessed by an organism.

character state -- characters are usually described in terms of their states, for example: "hair present" vs. "hair absent," where "hair" is the character, and "present" and "absent" are its states.

clade -- A monophyletic taxon; a group of organisms which includes the most recent common ancestor of all of its members and all of the descendants of that most recent common ancestor. From the Greek word "klados", meaning branch or twig.

cladogenesis -- The development of a new clade; the splitting of a single lineage into two distinct lineages; speciation.

cladogram -- A diagram, resulting from a cladistic analysis, which depicts a hypothetical branching sequence of lineages leading to the taxa under consideration. The points of branching within a cladogram are called nodes. All taxa occur at the endpoints of the cladogram.

convergence -- Similarities which have arisen independently in two or more organisms that are not closely related. Contrast with homology. 

crown group -- All the taxa descended from a major cladogenesis event, recognized by possessing the clade's synapomorphy. See: stem group.

derived -- Describes a character state that is present in one or more subclades, but not all, of a clade under consideration. A derived character state is inferred to be a modified version of the primitive condition of that character, and to have arisen later in the evolution of the clade. For example, "presence of hair" is a primitive character state for all mammals, whereas the "hairlessness" of whales is a derived state for one subclade within the Mammalia.

diversity -- Term used to describe numbers of taxa, or variation in morphology. 

evolution -- Darwin's definition: descent with modification. The term has been variously used and abused since Darwin to include everything from the origin of man to the origin of life.

evolutionary tree -- A diagram which depicts the hypothetical phylogeny of the taxa under consideration. The points at which lineages split represent ancestor taxa to the descendant taxa appearing at the terminal points of the cladogram.

expressed sequence tag (EST) -- A partial coding sequence isolated at random from a cDNA library, used for identification and mapping of coding sequences, for discovery of new genes and (by reference to sequence data banks) for discovery of identities with other genes.

extinction -- When all the members of a clade or taxon die, the group is said to be extinct.

genetic marker -- A DNA sequence that can be recognized and thus used to characterize the larger DNA sequence and the chromosome in which it occurs. 

homolog -- A feature that appears similar in two or more taxa with a common ancestor that also possessed that feature.

homology -- Two structures are considered homologous when they are inherited from a common ancestor which possessed the structure. This may be difficult to determine when the structure has been modified through descent.

hypothesis -- A concept or idea that can be falsified by various scientific methods.

ingroup -- In a cladistic analysis, the set of taxa which are hypothesized to be more closely related to each other than any are to the outgroup.

lineage -- Any continuous line of descent; any series of organisms connected by reproduction by parent of offspring.

monophyletic -- Term applied to a group of organisms which includes the most recent common ancestor of all of its members and all of the descendants of that most recent common ancestor. A monophyletic group is called a clade.

outgroup -- In a cladistic analysis, any taxon used to help resolve the polarity of characters, and which is hypothesized to be less closely related to each of the taxa under consideration than any are to each other.

paraphyletic -- Term applied to a group of organisms which includes the most recent common ancestor of all of its members, but not all of the descendants of that most recent common ancestor.

parsimony -- Refers to a rule used to choose among possible cladograms, which states that the cladogram implying the least number of changes in character states is the best.

phylogenetics -- Field of biology that deals with the relationships between organisms. It includes the discovery of these relationships, and the study of the causes behind this pattern.

phylogeny -- The evolutionary relationships among organisms; the patterns of lineage branching produced by the true evolutionary history of the organisms being considered.

plesiomorphy -- A primitive character state for the taxa under consideration.

polarity of characters -- The states of characters used in a cladistic analysis, either original or derived. Original characters are those acquired by an ancestor deeper in the phylogeny than the most recent common ancestor of the taxa under consideration. Derived characters are those acquired by the most recent common ancestor of the taxa under consideration.

polyphyletic -- Term applied to a group of organisms which does not include the most recent common ancestor of those organisms; the ancestor does not possess the character shared by members of the group.

primitive -- Describes a character state that is present in the common ancestor of a clade. A primitive character state is inferred to be the original condition of that character within the clade under consideration. For example, "presence of hair" is a primitive character state for all mammals, whereas the "hairlessness" of whales is a derived state for one subclade within the Mammalia.

radiation -- Event of rapid cladogenesis, believed to occur under conditions where a new feature permits a lineage to move into a new niche or new habitat, and is then called an adaptive radiation.

rank -- In traditional taxonomy, taxa are ranked according to their level of inclusiveness. Thus a genus contains one or more species, a family includes one or more genera, and so on.

relatedness -- Two clades are more closely related when they share a more recent common ancestor between them than they do with any other clade.

repetitive DNA -- Sequences of DNA that are found to be repeated, sometimes thousands of times over.  

reticulation -- Joining of separate lineages on a phylogenetic tree, generally through hybridization or through lateral gene transfer. Fairly common in certain land plant clades; reticulation is thought to be rare among metazoans.

selection -- Process which favors one feature of organisms in a population over another feature found in the population. This occurs through differential reproduction -- those with the favored feature produce more offspring than those with the other feature, such that they become a greater percentage of the population in the next generation.

sister group -- The two clades resulting from the splitting of a single lineage.

stem group -- All the taxa in a clade preceding a major cladogenesis event. They are often difficult to recognize because they may not possess synapomorpies found in the crown group.

sympleisiomorphy – A ancestral character shared by the taxa under consideration

synapomorphy -- A character which is derived, and because it is shared by the taxa under consideration, is used to infer common ancestry (shared derived state).

synteny -- Portions of chromosomes in which gene order is conserved. 

systematics -- Field of biology that deals with the diversity of life. Systematics is usually divided into the two areas of phylogenetics and taxonomy.

taxon -- Any named group of organisms, not necessarily a clade

taxonomy -- The science of naming and classifying organisms. 

"Robert Sapolsky is an American neuroendocrinologist, professor of biology, neuroscience, and neurosurgery at Stanford University, researcher and author" ----Wikipedia

Address of the bookmark: http://www.youtube.com/watch?v=_dRXA1_e30o

Directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.

http://www.nature.com/nature/journal/vaop/ncurrent/full/nature14618.html

Address of the bookmark: http://www.nature.com/nature/journal/vaop/ncurrent/full/nature14618.html