Starting with your current or most recent employment provide details of your position as follows:

• A chronological CV should be arranged in reverse chronological order. It should be apparent immediately where you are now.
• Remember that an application form is limited to the few things that a particular institution wants to know about everybody. A CV lets you give information that is unique to you. Add all your key accomplishments and activities in the initial draft. In subsequent drafts, you can remove information that may not be pertinent.
• Resist the temptation to append explanatory sentences or language, which will distract the reader from the basic information being presented. The language of a CV is abbreviated and succinct. When applying for residency training, you will have the opportunity to express yourself in a personal or biographical statement. In the future, when applying for a job or some other type of position, you will want to include an appropriate cover letter with your CV to explain your particular qualifications and strengths for the position.
• Don’t despair if your CV doesn’t resemble those of other students who are applying to the same residency program. Everybody’s CV is different. Even if everyone used the same format suggested in this section, your CV will not resemble others’ because it doesn’t have the same content. No residency program director is looking for a specific CV style. You will receive points for neatness, and readability.
• Be honest. If you haven’t accomplished anything in a particular category, leave it out. Don’t create accomplishments to fill in the spaces. You can be specific about your level of participation in a project or activity, but don’t be misleading (i.e., you can say you coordinated membership recruitment for your AMSA chapter, but don’t say you were “president” unless you were).

• Job title - time that you have held this position
• The key tasks and responsibilities that comprise this role's requirements - notable achievements whilst in the role
• Where possible quantify your achievements with precise facts and figures, e.g. managed junior staff, handled department budget, prepared management reports
• Expand on the skills you are using in your current job which you believe will be valuable in the position(s) for which you are applying

It is not necessary to state the reason you are leaving your current position. This will be a topic for conversation when you are invited for interview or can be covered in your letter of application.
For all previous employment, unless one appointment was more significant than your current or last position, keep details brief i.e. the name of the company, job title, period of employment and the job.
Be sure there are no gaps in your career history - unless for example you took a year out to travel, in which case make reference to this under Interests/Hobbies.
If you are a student just starting work, give any evidence you can to demonstrate your practical skills e.g. school prefect, event organisation, member of sports team, contributor to college magazine or voluntary work.

You are under no obligation to disclose marital status, age or whether or not you have children unless these are specific criteria for selection for a position that you are interested in.
Consider what examples (interests/ hobbies) you can give to show that you match the selection criteria.

If they want someone to work as part of a large team, remember to say if you belong to a local organisation or if you are part of a sports team.

If they want someone who will work on their own for large periods of time, make reference to an Open University course you are considering undertaking.

Your primary objective is to convince the prospective employer that you have the requisite skills, experience and hunger to do the job.

Your CV should be no more than two A4 pages and as every employer is different remember to customise your CV to every job you go after.

There are abundant books on the contents and presentation of a general CV. A BMJ article published in 1978, offering doctors guidance on how to prepare a CV, has been reprinted in the widely read How to do it series.1 2 A survey among postgraduate deans and training advisers at regional colleges found that the contents and presentation of a model CV for doctors in training has been published.3 It is perhaps surprising to note that these models differ significantly from one another. Although they may be useful as starting points, their differences tend to create confusion and anxiety among students. I would argue that these differences exist because the content and presentation of the "ideal" CV vary considerably among individual applicants, the stage of their careers, and the purposes for which the CV is used. It is impossible to create a generic CV. I have therefore not attempted to draw up another model CV. Rather, the purpose of this article is to outline the general principles and important practical points in preparing a good CV. General principles on contents Before finalising your CV for a particular purpose you must be sure of your objectives, whether it is used as an initial screening or the only selection instrument, and the criteria against which it is judged. What details, and how many of them, to include in your CV depends on these factors. I shall illustrate with examples relevant to medical students. 

(1) Job application Your objective is to get the job. In a job application, the CV is used for two purposes: as an initial screening instrument for shortlisting candidates and as a framework for discussion during the interview. The explicit criteria used for shortlisting are usually given in the job advertisement. For some organisations, separate lists of essential and desirable criteria are given. Alternatively, you can get a good idea of the basic requirements from the job description. Your CV must clearly highlight these criteria, preferably on the first page. These usually include: formal qualifications; registration with the General Medical Council; and the prescribed experience. It is sometimes easy to forget to mention items specifically asked for in the job description (for example, a valid driving licence). The implicit criteria are less easy to pinpoint. For example, how much detail on your BSc dissertation and publications should you include? Should you make a long list of extracurricular activities, interests outside medicine, and positions of responsibility? If you admit to a wide range of extracurricular activities and interests, would you be considered as a candidate with a well balanced mature personality or will it be interpreted to mean that you will have little time or interest to do your job? There are no easy answers. Common sense might tell you that BSc dissertation and publications are more important in application for teaching hospital or research posts, but less important for district hospital posts. Conversely, you might think that interests outside medicine are more important for posts in district hospitals or in general practice. This is, however, not always true. A few consultants at district hospital are highly academic. Information gathered from students and doctors working under the professor or consultant concerned may be vital. Alternatively, it is worth while doing your homework by looking up your prospective consultant in the medical directory. This may occasionally prompt you to include information that you might otherwise have left out. For example, you may find that the consultant qualified in Scotland and has previously worked in Scotland for a considerable time, and you may decide to add in your CV that you studied in a Scottish secondary school. Since the CV is only used as for initial screening, you need not go into your previous experience or extracurricular activities in too much detail. Highlight only the most significant points, and leave the details for the interview. If you are applying for a clinical post, one of your referees should be a consultant for whom you have worked as a student. You should ask for permission to use his/her name before submitting your application. 

(2) Application for research scholarship or PhD studentship The CV and application form are sometimes used as the sole selection instrument, and you must make enquiries before you submit your application. Clearly, academic ability is the main criterion for selection, and you should include as much information relevant to your academic ability and interests as you can. Examples are your A levels, your BSc dissertation, any publications (even in the form of a letter in newspaper), any experience as an editor (for example, for your school magazine). Your extracurricular activities are less important, and you can simply give a short list. At least one of the referees should be an academic - for example, your previous supervisor in your BSc degree.
 
(3) Application to join a clinical course in another medical school Preclinical students who have completed an intercalated degree often have the option of applying to join a clinical course in another medical school, although it may become more difficult to do so with the introduction of the new GMC curriculum. Your CV is usually used for shortlisting candidates for interview. The criteria differ slightly among medical schools, but both academic ability and contribution to university life are important. Hence, not only should you highlight your academic achievements but you must also highlight your participation in the university (for example, in sports or music). 

(4) For the information of your tutor or counsellor For most medical schools, you are allocated a tutor who provides both academic and non-academic support and monitors your progress throughout your study on a confidential basis. Students are sometimes asked to submit their up to date CV to their tutors for information. Assuming that the tutors are helpful there is little to gain from over emphasising your strengths or hiding your weaknesses. Once you become a doctor in training after you qualify you will need to undergo an annual assessment of your progress. It serves to certify that you have reached a satisfactory standard, but it is also used as an aid to identify and help with your weaknesses. You may find it difficult to balance these two purposes in presenting your CV. General principles on presentation Now that all students are computer literate, there should be few problems in preparing a well presented CV. The following list serves as a reminder on how to present your CV effectively: Spelling or grammar mistakes - do not rely purely on the spell check on your computer. Ask friends to proofread your CV for you. Consistency - The use of punctuation to open and close sentences, justification, and fonts should be consistent. Readability - The headings should be clear. The font size should be no less than 12 point. Basic criteria - The basic criteria should be easily located, preferably on the first page. Length - The length of your CV increases as you progress up the professional ladder. For students, it should generally be no more than three pages. Quality of print - The CV should be printed on good quality paper, preferably using a laser printer. Practical points In this article, I argue that different versions of a CV may be required for different purposes. Even applications for different posts in the same specialty may require slightly different versions. Also, CVs need to be updated regularly. This would have been time consuming to achieve in the past, but it is now quite simple, with the aid of a basic word processing package. A master CV containing all relevant information should be prepared and saved as a computer file. This should be continuously kept up to date. When the need for a CV arises, it can be tailormade by editing the master document. It is important to save each of these edited versions separately, with the file names indicating the date when it was created and the purpose. It is also important to prepare a cover letter to go with the CV. Key messages A good CV is essential for successful progression up the medical professional ladder The contents of the CV should be tailormade for the purpose it is used for and the criteria against which it is judged. It is important to gather information about these criteria first The CV must be technically well presented, with the basic criteria easily located Information technology has made it simple to regularly update our CVs and allows preparation of different versions of a CV for different purposes with relative ease.

Reference:

Prepare a curriculum vitae. BMJ 1978;25(2):1478-9.
O'Brien E. Prepare a curriculum vitae. In: Reece D, ed. 
How to do it. Vol 1. London: BMJ Publishing Group, 1995 Chambler AF, Chapman-Sheath PJ, Pearse MF. 
A model curriculum vitae: what are the trainers looking for? Hosp Med 1998;59(4):324-6.

• BigDataScript – A cross-system scripting language for working with big data pipelines in computer systems of different sizes and capabilities. [ paper-2014 | web ]
• Bpipe – A small language for defining pipeline stages and linking them together to make pipelines. [ web ]
• Common Workflow Language – a specification for describing analysis workflows and tools that are portable and scalable across a variety of software and hardware environments, from workstations to cluster, cloud, and high performance computing (HPC) environments. [ web ]
• Cromwell – A Workflow Management System geared towards scientific workflows. [ web ]
• Galaxy – a popular open-source, web-based platform for data intensive biomedical research. Has several features, from data analysis to workflow management to visualization tools. [ paper-2018 | web ]
• Nextflow (recommended) – A fluent DSL modelled around the UNIX pipe concept, that simplifies writing parallel and scalable pipelines in a portable manner. [ paper-2018 | web ]
• Ruffus – Computation Pipeline library for python widely used in science and bioinformatics. [ paper-2010 | web ]
• SeqWare – Hadoop Oozie-based workflow system focused on genomics data analysis in cloud environments. [ paper-2010 | web ]
• Snakemake – A workflow management system in Python that aims to reduce the complexity of creating workflows by providing a fast and comfortable execution environment. [ paper-2018 | web ]
• Workflow Descriptor Language – Workflow standard developed by the Broad. [ web ]

Pune University Vacancy Details:
Total No. of Posts: 03
Name of the Post: Project Assistant

Educational Qualification: Candidates should possess M.Sc Degree in Biotechnology/ Biochemistry/ Virology/ Microbiology/ Bioinformatics/ Botany/ Zoology/ Molecular Biology/ Genetics with first class or equivalent GPA with relevant experience.

Selection Process: Shortlisted candidates will be called for interview.

How to Apply: Eligible candidates may send their applications along with detailed biodata with recent passport size photo & self attested copies of relevant documents as mentioned in the notification in an envelope should be superscribed with specialization of subject & cast addressed to UPE Phase II, Focus Area, Biotechnology at Savitribai Phule Pune University on or before 28-02-2015.

Last Date for Receipt of Applications: 28-02-2015.

Read more: Pune University Recruitment 2015 - Project Asst Posts http://www.freejobalert.com/university-of-pune/6941/#ixzz3Sfhtn4hb

http://www.freejobalert.com/wp-content/uploads/2015/01/Notification-Pune-University-Project-Asst-Posts.pdf

The prepint version is found on http://www.biorxiv.org/content/early/2017/08/09/173872

It uses the pyPaSWAS framework for sequence alignment (https://github.com/swarris/pyPaSWAS)

Address of the bookmark: https://github.com/swarris/Pacasus

R-language: http://www.r-project.org

BioConductor: http://www.bioconductor.org

Advantages of R

• Free!
• Powerful, many libraries have been created to perform application specific tasks. e.g. analysis of microarray experiments and Next-Gen sequencing (bioconductor: including Bioseq group).
• Presentation quality graphics
  • Save as a png, pdf or svg
• History
  • What you do can be saved for the next time you use R.
  • Ability to turn it into an automated script to perform again and again on different data

Disadvantages

• Lack of a comprehensive graphical user interface, but two do exist: However some do exist: R commander: http://socserv.mcmaster.ca/jfox/Misc/Rcmdr/ and Limma-gui (microarrays) : http://bioinf.wehi.edu.au/limmaGUI/

Preparation

• (Optional) Download and save the tutorial data set from
  • http://bioinformatics.knowledgeblog.org/wp-content/uploads/bioinf/kerr/data.tsv
  • Start R (type R on a Linux or Mac terminal, or find the starting link from PC)

Getting More Help

• Project Home page
  • http://www.r-project.org/
  • Check out the ‘introduction to R’, which is a much more in depth guide .
  • Also R has a built-in help system (see later)

Working directory

This is the directory used to store your data and results. It is useful if it is also the directory where your input data is stored.

• Mac/Linux: this is the directory where you typed in R
• PC: Change using the change working directory option

• Binaries for Linux/macOS
• From sources for Linux/macOS
• From sources for Windows

Address of the bookmark: https://github.com/imgag/ngs-bits

Walk-in-Interview will be held on 04th March 2015 at 11.30 A.M. in the Bio- Informatics Centre of Bose Institute, P-1/12, C.I.T. Scheme VII-M, Kolkata- 700054 for two (02) positions of Research Associate/ Extended Senior Research Fellow in the DBT sponsored following two projects running under the CoE- Bioinformatics under the guidance of Prof. Pinakpani Chakrabarti, Bioinformatics Centre.

Position : RA/SRF
Project title : 1. "Centre of Excellence (CoE) in Bioinformatics at Bose Institute”,2. Project entitled “Setting up of National Facility on Interactive Graphysics Computer System (IGCS) for Biomolecular Modeling, Molecular Dynamics & Structures”

Desired Profile : Ph.D degree in Biological or Chemical Sciences with in-depth understanding of protein structure and dynamics for R.A. position.Those who have submitted thesis can be considered for Extended SRF position
Preferred : Knowledge of computer programming and bioinformatics softwares.
Stipend : For R.A- Rs. 22,000/- p.m., plus admissible H.R.A. and Medical benefit. For Extended SRF - Rs. 20,000/- p.m., plus admissible H.R.A.and Medical benefit.
Age : For R.A- Below 35 years; For Extended SRF - Below 33 years
Interested and eligible candidates should appear before the Selection Committee with atyped application addressed to the Sr.Prof. & In-Charge, Registrar's Office, Bose Institute, P- 1/12, CIT Scheme VII-M, Kankurgachi, Kolkata-700054 along with Bio-data giving details of qualification i.e. examination passed, year, division, percentage of marks from Secondary onwards with attested copies of Certificates, Mark-Sheet and testimonials. The candidates should also bring the original mark-sheets, certificates etc. at the time of Interview.

Walk in Interview : 04.03.15

More at http://www.boseinst.ernet.in/ADVT/14/p_34.pdf

CASTp : http://sts.bioengr.uic.edu/castp/  Computed Atlas of Surface Topography of proteins (CASTp) provides an online resource for locating, delineating and measuring concave surface regions on three-dimensional structures of proteins. These include pockets located on protein surfaces and voids buried in the interior of proteins. The measurement includes the area and volume of pocket or void by solvent accessible surface model (Richards' surface) and by molecular surface model (Connolly's surface), all calculated analytically. CASTp can be used to study surface features and functional regions of proteins. CASTp includes a graphical user interface, flexible interactive visualization, as well as on-the-fly calculation for user uploaded structures. CASTp is updated daily and can be accessed at http://cast.engr.uic.edu.

LigASite: http://www.bigre.ulb.ac.be/Users/benoit/LigASite/index.php?home is a gold-standard dataset of biologically relevant binding sites in protein structures. It consists of proteins with one unbound structure and at least one structure of the protein-ligand complex. Both a redundant and a non-redundant (sequence identity lower than 25%) version is available. Quaternary structures proposed by PISA (3) are used for all structures in the dataset.

PDBeMotif: http://www.ebi.ac.uk/pdbe-site/pdbemotif/ is an extremely fast and powerful search tool that facilitates exploration of the Protein Data Bank (PDB) by combining protein sequence, chemical structure and 3D data in a single search. Currently it is the only tool that offers this kind of integration at this speed. PDBeMotif can be used to examine the characteristics of the binding sites of single proteins or classes of proteins such as Kinases and the conserved structural features of their immediate environments either within the same specie or across different species. For example, it can highlight a conserved activation loop common to protein kinases, which is important in regulating activity and is marked by conserved DFG and APE motifs at the start and end of the loop, respectively. The prediction of the effect of modifications to small molecules that bind to the active and/or regulatory sites of proteins on their efficacy can be based on the outcome of analytic work done using PDBeMotif.

fPOP: http://pocket.uchicago.edu/fpop/ (footprinting Pockets Of Proteins, http://pocket.uchicago.edu/fpop/) is a database of the protein functional surfaces identified by shape analysis. In this relational database, we collected the spatial patterns of protein binding sites including both holo and apo forms from more than 40,000 structures. To identify protein binding sites, we model the shape of a split pocket induced by a binding ligand(s). Essentially, we use a purely geometric method to extract site-specific spatial patterns of split pockets as templates to match those from unbound structures. To perform an effective shape comparison, we utilize the Smith-Waterman algorithm to footprint an unbound pocket fragment with those selected from the canonical functional surfaces of >19,000 structures in the SplitPocket (http://pocket.uchicago.edu/). The pairwise alignment of the unbound and split-pocket fragments is superimposed to evaluate the local structural similarity for detecting the unbound split characteristic through the RMSD measurement. Furthermore, we conduct a large-scale computation to systematically identify binding sites of proteins. In addition to the geometric measurements, we extensively measure the propensity of surface conservation encapsulated in the evolutionary history.(more)

metaPocket: http://metapocket.eml.org/  is a meta server to identify pockets on protein surface to predict ligand-binding sites. The identification of ligand-binding sites is often the starting point for protein function annotation and structure-based drug design. Many computational methods for the prediction of ligand-binding sites have been developed in recent decades. Here we present a consensus method metaPocket, in which the predicted sites from four methods: LIGSITE^cs, PASS, Q-SiteFinder, and SURFNET are combined together to improve the prediction success rate. All these methods are evaluated on two datasets of 48 unbound/bound structures and 210 bound structures. The comparison results show that metaPocket improves the success rate from 70 to 75% at the top 1 prediction. MetaPocket is available at http://metapocket.eml.org.

PocketQuery: http://pocketquery.csb.pitt.edu/  is a web service for interactively exploring not only hot spot and anchor residues, but hot regions, defined by clusters of residues, at the interface of protein-protein interactions. An assortment of metrics, including changes in solvent accessible surface area, energy-based scores, and sequence conservation, are available to screen and sort clusters of residues. PocketQuery was developed by David Koes from the Camacho Lab in the Department of Computational and System Biology at the University of Pittsburgh.

IBIS: http://www.ncbi.nlm.nih.gov/Structure/ibis/ibis.cgi is the NCBI Inferred Biomolecular Interactions Server. For a given protein sequence or structure query, IBIS reports physical interactions observed in experimentally-determined structures for this protein. IBIS also infers/predicts interacting partners and binding sites by homology, by inspecting the protein complexes formed by close homologs of a given query. To ensure biological relevance of inferred binding sites, the IBIS algorithm clusters binding sites formed by homologs based on binding site sequence and structure conservation.

3DLigandStie: http://www.sbg.bio.ic.ac.uk/~3dligandsite/ is an automated method for the prediction of ligand binding sites. Users can either submit a sequence or a protein structure. If a sequence is submitted then Phyre is run to predict the structure. The structure is then ussed to search a structural library to identify homologous structures with bound ligands. These ligands are superimposed onto the protein structure to predict a ligand binding site.

SitesBase: http://www.modelling.leeds.ac.uk/sb/ is a database of known ligand binding sites within the PDB which is navigable by PDB identifier or ligand 3 letter code e.g. NAD. Each binding site has a frequently updated register of structurally similar binding sites sharing atomic similarity detected by geometric hashing (Brakoulias and Jackson 2004). Multiple alignments, structural superpositions and links to other structural databases are also available enabling further analysis.

PROSURFER: http://163.43.140.95/top contains information about structural similarities with respect to the query surfaces. A pocket search algorithm detected 48,347 potential ligand binding sites from the 9,708 non-redundant protein entries in the PDB database. All-against-all structural comparison was performed for the predicted sites, and the similar sites with the Z-score ≥ 2.5 were selected. These results can be accessed by the PDB code or ligand name.

KBDOCK: http://kbdock.loria.fr/index.php is a 3D database system that defines and spatially clusters protein binding sites for knowledge-based protein docking. KBDOCK integrates protein domain-domain interaction information from 3DID and sequence alignments from PFAM together with structural information from the PDB in order to analyse the spatial arrangements of DDIs by Pfam family, and to propose structural templates for protein docking. [More]

Pocketome: http://www.pocketome.org/ The Pocketome is an encyclopedia of conformational ensembles of all druggable binding sites that can be identified experimentally from co-crystal structures in the Protein Data Bank.

sc-PDB: http://cheminfo.u-strasbg.fr:8080/scPDB/2011/db_search/about_scpdb.html  To assist structure-based approaches in drug design, we have processed the PDB to identify binding sites suitable for the docking of a drug-like ligand and we have so created a database called sc-PDB. The sc-PDB database provides separated MOL2 files for the ligand, its binding site and the corresponding protein chain(s). Ions and cofactors at the vicinity of the ligand are included in the protein. More details about the sc-PDB scope, its content and its evolution during the 2004-2009 period are provided in a pdf document.

The FunFOLD Binding Site Residue Prediction Server: BACKGROUND: The accurate prediction of ligand binding residues from amino acid sequences is important for the automated functional annotation of novel proteins. In the previous two CASP experiments, the most successful methods in the function prediction category were those which used structural superpositions of 3D models and related templates with bound ligands in order to identify putative contacting residues. However, whilst most of this prediction process can be automated, visual inspection and manual adjustments of parameters, such as the distance thresholds used for each target, have often been required to prevent over prediction. Here we describe a novel method FunFOLD, which uses an automatic approach for cluster identification and residue selection. The software provided can easily be integrated into existing fold recognition servers, requiring only a 3D model and list of templates as inputs. A simple web interface is also provided allowing access to non-expert users. The method has been benchmarked against the top servers and manual prediction groups tested at both CASP8 and CASP9.RESULTS: The FunFOLD method shows a significant improvement over the best available servers and is shown to be competitive with the top manual prediction groups that were tested at CASP8. The FunFOLD method is also competitive with both the top server and manual methods tested at CASP9. When tested using common subsets of targets, the predictions from FunFOLD are shown to achieve a significantly higher mean Matthews Correlation Coefficient (MCC) scores and Binding-site Distance Test (BDT) scores than all server methods that were tested at CASP8. Testing on the CASP9 set showed no statistically significant separation in performance between FunFOLD and the other top server groups tested. CONCLUSIONS: The FunFOLD software is freely available as both a standalone package and a prediction server, providing competitive ligand binding site residue predictions for expert and non-expert users alike. The software provides a new fully automated approach for structure based function prediction using 3D models of proteins.

ProBiS: http://probis.cmm.ki.si/index.php  algorithm for detection of structurally similar protein binding sites by local structural alignment. Motivation: Exploitation of locally similar 3D patterns of physicochemical properties on the surface of a protein for detection of binding sites that may lack sequence and global structural conservation. Results: An algorithm, ProBiS is described that detects structurally similar sites on protein surfaces by local surface structure alignment. It compares the query protein to members of a database of protein 3D structures and detects with sub-residue precision, structurally similar sites as patterns of physicochemical properties on the protein surface. Using an efficient maximum clique algorithm, the program identifies proteins that share local structural similarities with the query protein and generates structure-based alignments of these proteins with the query. Structural similarity scores are calculated for the query protein's surface residues, and are expressed as different colors on the query protein surface. The algorithm has been used successfully for the detection of protein–protein, protein–small ligand and protein–DNA binding sites. Availability: The software is available, as a web tool, free of charge for academic users at http://probis.cmm.ki.si

Active Site prediction: http://www.scfbio-iitd.res.in/dock/ActiveSite_new.jsp Active Site Prediction of Protein server computes the cavities in a given protein.

DEPTH: http://mspc.bii.a-star.edu.sg/tankp/run_depth.html Depth measures the closest distance of a residue/atom to bulk solvent. Accessible surface area is a parameter that is widely used in analyses of protein structure and stability. However accessible surface area does not distinguish between atoms just below the protein surface and those in the core of the protein. In order to differentiate between such buried residues, we describe a computational procedure for calculating the depth of a residue from the protein surface. A detailed description of the computation of depth can be found here.

FINDSITE: http://cssb.biology.gatech.edu/findsite  FINDSITE is a threading-based binding site prediction/protein functional inference/ligand screening algorithm that detects common ligand binding sites in a set of evolutionarily related proteins. Crystal structures as well as protein models can be used as the target structures.

PocketDepth: http://proline.physics.iisc.ernet.in/pocketdepth/  A new depth based algortihm for identification of ligand binding sites. Abstract: Computational methods for identifying and predicting functional sites in protein structures are increasingly becoming important in structural biology and bioinformatics not only for understanding the function of the molecule in detail but also for structure-based design of possible ligands and potential drugs as well as modified protein molecules. While there are a few structure based prediction methods already available, given the complexity and diversity of protein structural types, there is still a great need to explore newer methods and concepts to develop accurate, versatile and efficient binding site prediction algorithms. We have developed a new method PocketDepth, for identification of binding sites in proteins. The method is purely geometry-based and proceeds in two stages, labeling of grid cells with depth factors followed by a depth based clustering that uses neighbourhood information. Depth is an important parameter considered during protein structure visualization and analysis but has been used more often intuitively than systematically. Our current implementation of depth reflects how central a given sub-space is to a putative pocket rather than reflecting merely how far away it is situated from the nearest external surface of the protein. We have tested the algorithm against PDBbind, a large curated set of 1091 proteins obtained from PDB. A prediction was considered a true-positive if the predicted pocket had at-least 10% overlap with the actual ligand. The prediction accuracy using this set was about 96%. Moreover, 87% of the true-positives were identified within the first five ranks for each protein, of which 55% are in the first rank itself. 77% of the predictions had at least 50% overlap with the experimentally observed ligand. High prediction rates were again observed, when the method was tested against a data-set of apo-proteins and compared with their respective ligand complexes. A comparison of our method with four other widely used methods for a chosen representative set is also presented.

GHECOM 1.0 : http://strcomp.protein.osaka-u.ac.jp/ghecom/  Grid-based HECOMi finder. A program for finding multi-scale pockets on protein surfaces using mathematical morphology

Pocket-Finder: http://www.modelling.leeds.ac.uk/pocketfinder/ is based on the Ligsite algorithm written by Hendlich et al. (1997). Pocket-Finder was written to compare pocket detection with our new ligand binding site detction algorithm Q-SiteFinder.

Screen2: http://luna.bioc.columbia.edu/honiglab/screen2/cgi-bin/screen2.cgi  is a tool for identifying protein cavities and computing cavity attributes that can be applied for classification and analysis. The original Screen, written by Murad Nayal, was dependent on the obsolete Irix platform and is no longer available. Screen2 was reengineered by Brian Y. Chen for efficiency and compatibility, and made accessible as a web service by Raquel Norel.

ConCavity: http://compbio.cs.princeton.edu/concavity/ Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavit​y/).

MultiBind and MAPPIS: http://bioinfo3d.cs.tau.ac.il/MultiBind/index.html Web servers for multiple alignment of protein 3D binding sites and their interactions. Analysis of protein–ligand complexes and recognition of spatially conserved physico-chemical properties is important for the prediction of binding and function. Here, we present two webservers for multiple alignment and recognition of binding patterns shared by a set of protein structures. The first webserver, MultiBind (http://bioinfo3d.cs.tau.ac.il/MultiBind), performs multiple alignment of protein binding sites. It recognizes the common spatial chemical binding patterns even in the absence of similarity of the sequences or the folds of the compared proteins. The input to the MultiBind server is a set of protein-binding sites defined by interactions with small molecules. The output is a detailed list of the shared physico-chemical binding site properties. The second webserver, MAPPIS (http://bioinfo3d.cs.tau.ac.il/MAPPIS), aims to analyze protein–protein interactions. It performs multiple alignment of protein–protein interfaces (PPIs), which are regions of interaction between two protein molecules. MAPPIS recognizes the spatially conserved physico-chemical interactions, which often involve energetically important hot-spot residues that are crucial for protein–protein associations. The input to the MAPPIS server is a set of protein-protein complexes. The output is a detailed list of the shared interaction properties of the interfaces.

MolAxis: http://bioinfo3d.cs.tau.ac.il/MolAxis/  is a tool for the identification of high clearance pathways or corridors which represent molecular channels in the complement space of proteins. It is extremely efficient because it samples the medial axis of the complement of the molecule, reducing the problem dimension to two, since the medial axis is composed of surface patches. It is designed to analyze proteins channels, calculate pore dimensions and analyze atom accessibility. MolAxis reads files in the standard Protein Data Bank format (PDB) containing a single frame or multiple frames generated by molecular dynamics (MD) simulations. MolAxis handles two distinct scenarios: It computes channels that connect a single point (like an inner chamber) to the bulk solvent, and it also computes transmembrane (TM) channels. MolAxis has a friendly web interface (see the Web Server tab). It also has a stand-alone version, statically compiled for linux, which can be downloaded from the Download tab.

fpocket: http://fpocket.sourceforge.net/ fpocket is a very fast open source protein pocket (cavity) detection algorithm based on Voronoi tessellation. It was developed in the C programming language and is currently available as command line driven program. A GUI is in development and mdpocket (fpocket on md trajectories) is out now. fpocket includes two other programs (dpocket & tpocket) that allow you to extract pocket descriptors and test own scoring functions respectively. Furthermore a nifty druggability prediction score has been added to fpocket recently. As the algorithm is very fast it can be used on a large scale level (PDB size for instance). If you use fpocket for publication, please cite : Vincent Le Guilloux, Peter Schmidtke and Pierre Tuffery, "Fpocket: An open source platform for ligand pocket detection", BMC Bioinformatics, 2009, 10:168

SuMo: http://sumo-pbil.ibcp.fr/cgi-bin/sumo-welcome allows you to screen the Protein Data Bank (PDB) for finding ligand binding sites matching your protein structure or inversely, for finding protein structures matching a given site in your protein. This method is neither based on aminoacid sequence nor on fold comparisons. Priority is given to biological relevance. SuMo uses its own heuristics for defining ligand binding sites. Automatically selected ligand binding sites are extracted from PDB structure files and stored into SuMo's own database.

CAVER: http://www.caver.cz/ CAVER is a software tool for analysis and visualization of tunnels and channels in protein structures. Tunnels are void pathways leading from a cavity buried in a protein core to the surrounding solvent. Unlike tunnels, channels lead through the protein structure and their both endings are opened to the surrounding solvent. Studying of these pathways is highly important for drug design and molecular enzymology.

SiteHound: http://scbx.mssm.edu/sitehound/sitehound-download/download.html SiteHound identifies protein regions that are likely to interact with ligands. The only input files required by SITEHOUND are the PDB file of the protein and the Molecular Interaction Field (MIFs) or Affinity Map for that protein structure structure. EasyMIFs is provided as a tool to calculate MIFs, alternatively AutoGrid (part of the AutoDock suite developed by Arthur Olson’s group at The Scripps Research Insitute) or the SiteHound-web server can be used to produce Affinity maps or MIFs. A python script named 'auto.py' is provided in the package and can be used to perform binding site identification in a fully automated fashion. The script will prepare the protein PDB file, compute a Molecular Interaction Fields map with EasyMIFs and carry out binding site identification using SiteHound. It is also possible to use EasyMIFs and SiteHound separately.

SURFNET: http://www.biochem.ucl.ac.uk/~roman/surfnet/surfnet.html The SURFNET program generates surfaces and void regions between surfaces from coordinate data supplied in a PDB file.

MSPocket: http://appserver.biotec.tu-dresden.de/MSPocket/ is an orientation independent program for the detection and graphical analysis of protein surface pockets [Zhu2011]. The approach is based on the solvent excluded surfaces generated by MSMS [Sanner1996].

Pfinder : http://pdbfun.uniroma2.it/pfinder/index.html  Pfinder is a bioinformatic method for the prediction of phosphate-binding sites in protein structures. Given a protein structure, Pfinder compares it with a set of 215 highly conserved structural motifs known to bind the phosphate moiety of phosphorylated ligands.

VOIDOO: http://xray.bmc.uu.se/usf/voidoo.html is a program for detection of cavities in macromolecular structures. It uses an algorithm that makes it possible to detect even certain types of cavities that are connected to "the outside world". Three different types of cavity can be handled by VOIDOO: Vanderwaals cavities (the complement of the molecular Vanderwaals surface), probe-accessible cavities (the cavity volume that can be occupied by the centres of probe atoms) and MS-like probe-occupied cavities (the volume that can be occupied by probe atoms, i.e. including their radii).

PocketPicker: http://gecco.org.chemie.uni-frankfurt.de/pocketpicker/index.html Background: Identification and evaluation of surface binding-pockets and occluded cavities are initial steps in protein structure-based drug design. Characterizing the active site's shape as well as the distribution of surrounding residues plays an important role for a variety of applications such as automated ligand docking or in situ modeling. Comparing the shape similarity of binding site geometries of related proteins provides further insights into the mechanisms of ligand binding. Results: We present PocketPicker, an automated grid-based technique for the prediction of protein binding pockets that specifies the shape of a potential binding-site with regard to its buriedness. The method was applied to a representative set of protein-ligand complexes and their corresponding apo-protein structures to evaluate the quality of binding-site predictions. The performance of the pocket detection routine was compared to results achieved with the existing methods CAST, LIGSITE, LIGSITE^cs, PASS and SURFNET. Success rates PocketPicker were comparable to those of LIGSITE^cs and outperformed the other tools. We introduce a descriptor that translates the arrangement of grid points delineating a detected binding-site into a correlation vector. We show that this shape descriptor is suited for comparative analyses of similar binding-site geometry by examining induced-fit phenomena in aldose reductase. This new method uses information derived from calculations of the buriedness of potential binding-sites. Conclusion: The pocket prediction routine of PocketPicker is a useful tool for identification of potential protein binding-pockets. It produces a convenient representation of binding-site shapes including an intuitive description of their accessibility. The shape-descriptor for automated classification of binding-site geometries can be used as an additional tool complementing elaborate manual inspections.

McVol: http://www.bisb.uni-bayreuth.de/index.php?page=data/mcvol/mcvol  This program was developed to integrate the molecular volume, solven accessible volume an Van der Waals volume of proteins using a Monte carlo algorithm. Based on this calculations, McVol is also able to identify internal cavities as well as surface clefts und fill these cavities with water molecules. Additionally, a membrane of dummy atoms can be placed as a disc atound the protein. The program is available under the Gnu Public Licence. A precompiled binary (X86) can be downloaded free of charge from here (when the associated paper is published).

Research Associate Biotechnology /JRF / Lab. Assistant recruitment in Indian Institute of Vegetable Research

Project:
Genomics assisted selection of Solanum chilense introgression lines for enhancing drought tolerance in tomato
Post Name : Research Associate
Qualification : Ph.D in Biotechnology/ Bioinformatics/Genetics & Plant Breeding. M. Tech in Computer Science with at least one research paper in science citation indexed journal. Desirable: Experience in bioinformatics and next generation sequence data handling. Familiarity in Linux, R, Perl/Phython or other programming languages. Willingness to travel to European partner centers.

Pay Scale : Rs. 36000 for 1st and 2nd year as per rules for Research Associate. Rs. 25000/- for 1st and 2nd year and Rs. 28000 as per rules for Junior Research Fellow. Rs. 7000/- for Lab. Assistant.

Age : Not more than 35 years for Men and 40 years for Women (Relaxable for SC/ST/OBC/PH candidates as per rules) for Research Associate/ Junior Research Fellow. Minimum age will be 21 years and maximum age will be 45 years (Relaxable for SC/ST/OBC/PH candidates as per rules) for Lab.Assistant.

More at http://iivr.org.in/Job%20Oppurtunities/RA20.03.2015.pdf

The standalone version (Linux-based command-line) of RNAcon is freely available for the global scientific community.

Reference: Panwar, B.; Arora, A. and Raghava, G.P.S. (2014) Prediction and classification of ncRNAs using structural informationBMC Genomics 2014, 15:127

Address of the bookmark: http://crdd.osdd.net/raghava/rnacon/