BOL: All site blogs

Computer simulation of genetic mechanism !!

Jit — Sun, 13 Mar 2016 09:29:56 -0500

Computer simulation is the discipline of designing a model of an actual or theoretical physical/biological system, executing the model on a digital computer, and analyzing the execution output. Simulation embodies the principle of ``learning by doing'' --- to learn about the system we must first build a model of some sort and then operate the model. The use of simulation is an activity that is as natural as a child who role plays. Children understand the world around them by simulating (with toys and figurines) most of their interactions with other people, animals and objects. As adults, we lose some of this childlike behavior but recapture it later on through computer simulation. To understand reality and all of its complexity, we must build artificial objects and dynamically act out roles with them. Computer simulation is the electronic equivalent of this type of role playing and it serves to drive synthetic environments and virtual worlds. Within the overall task of simulation, there are three primary sub-fields: model design, model execution and model analysis

Simulation models have become important tools in Bioinformatics studies. There are many reasons for this, but we emphasize three of the more important:

(1) they enable exploration of hypotheses, and as such, have become invaluable means to guide research;

(2) they are unique approaches to integrate (in the literal term of the word) biological knowledge, in the form of experimental results; and

(3) they enable connecting biology with other fields of study ranging from physiology to genomics;

This blog, and this software list, is intended to guide the potential user of simulation models.
It is not, in any way, meant to be comprehensive on the very diverse simulation tools that already exist, but focuses on mechanistic, dynamic models. Similarly, it is not meant to provide any coverage of the breadth of applications; however, for interested readers, we provide references to use as a possible starting point.

Simulation models are meant to answer questions which scientists have in a dynamic, quantitative, and often, a pictorial way. Much of the bioinformatics research and its applications, in particular, involve a large number of components, actors, and factors. Assembling these in a coherent framework may seem a daunting task, especially for beginners, and can lead to confusion, even for experienced scientists, especially if the objectives of such an exercise are not well defined. Followings are the list of tools bioinformatician may use to analyze and provide answers to complex biological mechanisms and related problems.

Software Resource	Brief Description and Homepage
Aladyn	Tools to investigate how demographic parameters, populations genetics and abiotic conditions affect the rate of adaptation http://www.katja-schiffers.eu/research.html
ALF	A Simulation Framework for Genome Evolution http://www.cbrg.ethz.ch/alf
ART	ART is a set of simulation tools to generate synthetic next-generation sequencing data by mimicking real sequencing process with empirical error models or quality profiles. http://www.niehs.nih.gov/research/resources/software/biostatistics/art/
BAMSurgeon	Methods for realistic simulation of mutations in real data. https://github.com/adamewing/bamsurgeon
Bayesian Serial SimCoal	Bayesian Serial SimCoal, (BayeSSC) is a modification of SIMCOAL 1.0, a program written by Laurent Excoffier, John Novembre, and Stefan Schneider. http://www.stanford.edu/group/hadlylab/ssc/index.html
BaySICS	An integral platform with a graphical interface for statistical inference based on approximate Bayesian computation. https://sites.google.com/site/baysicsabc/
BEERS	BEERS was designed to benchmark RNA-Seq alignment algorithms and also algorithms that aim to reconstruct different isoforms and alternate splicing from RNA-Seq data http://cbil.upenn.edu/beers/
BOTTLENECK	Bottleneck is a program for detecting recent effective population size reductions from allele data frequencies http://www.ensam.inra.fr/urlb/bottleneck/bottleneck.html
BottleSim	BottleSim is a computer simulation program for simulating the process of population bottlenecks http://chkuo.name/software/bottlesim.html
CASS	Protein Sequence Simulation https://liberles.cst.temple.edu/software/cass/index.html
CDPOP	CDPOP is a landscape genetics tool for simulating the emergence of spatial genetic structure in populations resulting from specified landscape processes governing organism movement behavior. http://cel.dbs.umt.edu/cdpop
Classical Genetics Simulator	Web-based simulation software http://www.cgslab.com/
CoaSim	CoaSim is a tool for simulating the coalescent process with recombination and geneconversion under various demographic models. http://users-birc.au.dk/mailund/coasim/index.html
cosi	The cosi package is written in C and is available as a tar file. http://www.broadinstitute.org/~sfs/cosi/
CS-PSeq-Gen	A program to simulate the evolution of protein sequences under the constraints of the information of a particular reconstructed phylogeny http://bioserv.rpbs.univ-paris-diderot.fr/software/cs-pseq-gen/
DAWG	An application designed to simulate the evolution of recombinant DNA sequences in continuous time http://scit.us/projects/dawg
Easypop	EASYPOP is an individual based model intended to simulate datasets under a very broad range of conditions http://www.unil.ch/dee/en/home/menuinst/softwares--dataset/softwares/easypop.html
EggLib	EggLib is a C++/Python library and program package for evolutionary genetics and genomics. http://egglib.sourceforge.net/
EpiSIM	EpiSIM: simulation of multiple epistasis, linkage disequilibrium patterns and haplotype blocks for genome-wide interaction analysis https://sourceforge.net/projects/episimsimulator/files/
EvolSimulator	A simulation test bed for hypotheses of genome evolution http://acb.qfab.org/acb/evolsim/
EvolveAGene	A realistic coding sequence simulation program that separates mutation from selection and allows the user to set selection conditions http://bellinghamresearchinstitute.com/software/index.html
fastsimcoal	A continuous-‐time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios http://cmpg.unibe.ch/software/fastsimcoal/
FastSLINK	Simulation of Marker and Phenotype Data in Pedigrees https://watson.hgen.pitt.edu/
FFPopSim	C++/Python library for population genetics. http://webdav.tuebingen.mpg.de/ffpopsim/
FLUX SIMULATOR	The Flux Simulator aims at providing a deterministic in silico reproduction of the experimental pipelines for RNA-Seq, employing a minimal set of parameters. http://sammeth.net/confluence/display/sim/home
forqs	Forward-in-time simulation of Recombination, Quantitative Traits, and Selection https://bitbucket.org/dkessner/forqs
ForSim	ForSim: A Forward Evolutionary Computer Simulation http://anth.la.psu.edu/research/weiss-lab/research/research
ForwSim	The program given below is based on the algorithm described in Padhukasahasram et al. 2008 to simulate genetic drift in a standard Wright-Fisher process. http://badri-populationgeneticsimulators.blogspot.com/
FPG	Forward Population Genetic simulation https://bio.cst.temple.edu/~hey/software/software.htm#fpg
FREGENE	FREGENE is a C++ program that simulates sequence-like data over large genomic regions in large diploid populations. http://www.ebi.ac.uk/projects/bargen
FIGG	FIGG is a genome simulation tool that uses known or theorized variation frequency, per a given fragment size and grouped by GC content across a genome to model new genomes in FASTA format while tracking applied mutations for use in analysis http://insilicogenome.sourceforge.net/
fwdpp	A C++ template library for implementing efficient forward simulations. http://molpopgen.github.io/fwdpp/
GAMETES	Genetic Architecture Model Emulator for Testing and Evaluating Software: Simulates complex SNP models with pure, strict epistatic interactions with n-loci. http://sourceforge.net/projects/gametes/?source=navbar
GASP	Genometric Analysis Simulation Program. A software tool for testing and investigating methods in statistical genetics by generating samples of family data based on user specified models. http://research.nhgri.nih.gov/gasp/
GCTA	Genome-wide Complex Trait Analysis http://www.complextraitgenomics.com/software/gcta/download.html
GemSIM	Next generation sequencing read simulator http://sourceforge.net/projects/gemsim/
GeneArtisan	Simulation of Markers in Case-Control Study Designs http://www.rannala.org/?page_id=241
GENOME	A rapid coalescent-based whole genome simulator http://www.sph.umich.edu/csg/liang/genome/
GenomePop2	GenomePop2 is a specialization of the program GenomePop just to manage SNPs under more flexible and useful settings. If you need models with more than 2 alleles please use the GenomePop program version. https://ritchielab.psu.edu/research/research-areas/statistical-genetics-and-gen-epi/methods/genomesimla
GenomeSimla	GenomeSIMLA is currently under development- however, we have a beta release that we are asking to be tested http://chgr.mc.vanderbilt.edu/genomesimla/
GENS2	Simulates interactions among two genetic and one environmental factor and also allows for epistatic interactions. https://sourceforge.net/projects/gensim/
GWAsimulator	A rapid whole genome simulation program http://biostat.mc.vanderbilt.edu/wiki/main/gwasimulator
HAP-SAMPLE	An association simulator for candidate regions or genome scans http://www.hapsample.org/
HAPGEN	A simulator for the simulation of case control datasets at SNP markers https://mathgen.stats.ox.ac.uk/genetics_software/hapgen/hapgen2.html
HapSim	A simulation tool for generating haplotype data with pre-specified allele frequencies and LD coefficients http://cran.r-project.org/web/packages/hapsim/index.html
HAPSIMU	A program that simulates heterogeneous populations with various known and controllable structures under the continuous migration model or the discrete model http://l.web.umkc.edu/liujian/
IBDsim	IBDSim is a computer package for the simulation of genotypic data under general isolation by distance models. http://raphael.leblois.free.fr/
indel-Seq-Gen	A biological sequence simulation program that simulates highly divergent DNA sequences and protein superfamilies http://bioinfolab.unl.edu/~cstrope/isg/
Indelible	A powerful and flexible simulator of biological evolution http://abacus.gene.ucl.ac.uk/software/indelible/
invertFREGENE	InvertFREGENE is a forward-in-time simulator of inversions in population genetic data http://www.ebi.ac.uk/projects/bargen/
kernalPop	A spatially explicit population genetic simulation engine http://cran.r-project.org/src/contrib/archive/kernelpop/
MaCS	Markovian Coalescent Simulator http://www-hsc.usc.edu/~garykche/
Marlin	Marlin provides a user-friendly interface for performing forward-in-time population genetic simulations. http://www.patrickmeirmans.com/software/marlin.html
Mason	A package for the simulation of nucleotide data. http://www.seqan.de/projects/mason/
mbs	modifying Hudson's ms software to generate samples of DNA sequences with a biallelic site under selection http://www.sendou.soken.ac.jp/esb/innan/innanlab/software.html
Mendel's Accountant	Mendel's Accountant (MENDEL) is an advanced numerical simulation program for modeling genetic change over time and was developed collaboratively by Sanford, Baumgardner, Brewer, Gibson and ReMine http://mendelsaccount.sourceforge.net/
MetaPopGen	Simulates genetics in large size metapopulations https://sites.google.com/site/marcoandrello/metapopgen
MetaSim	A tool to generate collections of synthetic reads that reflect the diverse taxonomical composition of typical metagenome data sets http://ab.inf.uni-tuebingen.de/software/metasim/
mlcoalsim	Multilocus Coalescent Simulations http://code.google.com/p/mlcoalsim-v1/
ms	The purpose of this program is to allow one to investigate the statistical properties of such samples, to evaluate estimators or statistical tests, and generally to aid in the interpretation of polymorphism data sets. http://home.uchicago.edu/~rhudson1/source/mksamples.html
msHOT	The purpose of this program is to allow one to investigate the statistical properties of such samples, to evaluate estimators or statistical tests, and generally to aid in the interpretation of polymorphism data sets. http://home.uchicago.edu/~rhudson1/
msms	A coalescent Simlation tool with selection. http://www.mabs.at/ewing/msms/index.shtml
MySSP	A program for the simulation of DNA sequence evolution across a phylogenetic tree http://www.rosenberglab.net/software.html
Nemo	A forward-time, individual-based, genetically explicit, and stochastic simulation program designed to study the evolution of genetic markers, life history traits, and phenotypic traits in a flexible (meta-)population framework. http://nemo2.sourceforge.net/
NetRecodon	Coalescent simulation of coding DNA sequences with recombination (inter and intracodon), migration and demography http://code.google.com/p/netrecodon/
OncoSimulR	BioConductor package for Forward Genetic Simulation of Cancer Progresion with Epistasis https://github.com/rdiaz02/oncosimul
PEDAGOG	Software for simulating eco-evolutionary population dynamics https://bcrc.bio.umass.edu/pedigreesoftware/node/5
phenosim	A tool to add phenotypes to simulated genotypes http://evoplant.uni-hohenheim.de/doku.php?id=software:software
PhyloSim	An R package for the Monte Carlo simulation of sequence evolution http://www.ebi.ac.uk/goldman-srv/phylosim/
pIRS	Profile-based Illumina pair-end reads simulator https://code.google.com/p/pirs/
ProteinEvolver	Simulation of protein evolution along phylogenies under structure-based substitution models http://code.google.com/p/proteinevolver/
QMSim	QTL and Marker Simulator http://www.aps.uoguelph.ca/~msargol/qmsim/
quantiNEMO	An individual-based program for the analysis of quantitative traits with explicit genetic architecture potentially under selection in a structured population http://www2.unil.ch/popgen/softwares/quantinemo/
RECOAL	Simulates new haplotype data from a reference population of haplotypes. ftp://popgen.usc.edu/
Recodon	Coalescent simulation of coding DNA sequences with recombination, migration and demography http://code.google.com/p/recodon/
rlsim	A package for simulating RNA-seq library preparation with parameter estimation http://bit.ly/rlsim-git
Rmetasim	Rmetasim is a front-end for the metasim engine that is implemented as a package that runs in the statistical computing environment R http://cran.r-project.org/web/packages/rmetasim/index.html
RNA Seq Simulator	RSS takes SAM alignment files from RNA-Seq data and simulates over dispersed, multiple replica, differential, non-stranded RNA-Seq datasets. http://useq.sourceforge.net/cmdlnmenus.html#rnaseqsimulator
Rose	Random model of sequence evolution http://bibiserv.techfak.uni-bielefeld.de/rose/
scrm	A coalescent simulator optimized for long sequences and large samples. https://scrm.github.io/
SelSim	SelSim is a program for Monte Carlo simulation of DNA polymorphism data for a recom- bining region within which a single bi-allelic site has experienced natural selection http://www.well.ox.ac.uk/~spencer/selsim/
Seq-Gen	An application for the Monte Carlo simulation of molecular sequence evolution along phylogenetic trees. http://tree.bio.ed.ac.uk/software/seqgen/
SEQPower	Statistical power analysis for sequence-based association studies http://bioinformatics.org/spower/
SeqSIMLA	SeqSIMLA can simulate sequence data with user-specified disease and quantitative trait models. Family or unrelated case-control data can be simulated. http://seqsimla.sourceforge.net/
Serial NetEvolve	A flexible utility for generating serially-sampled sequences along a tree or recombinant network http://biorg.cis.fiu.edu/sne/
SFS_CODE	SFS_CODE can perform forward population genetic simulations under a general Wright-Fisher model with arbitrary migration, demographic, selective, and mutational effects. http://sfscode.sourceforge.net/sfs_code/index/index.html
SIBSIM	Quantitative phenotype simulation in extended pedigrees http://sourceforge.net/projects/sibsim/
SimAdapt	A spatially explicit, individual-based, forward-time, landscape-genetic simulation model combined with a landscape cellular automaton. https://www.openabm.org/model/3137
SIMCOAL2	A coalescent program for the simulation of complex recombination patterns over large genomic regions under various demographic models http://cmpg.unibe.ch/software/simcoal2/
SimCopy	An R package simulating the evolution of copy number profiles along a tree. http://bit.ly/simcopy
SIMLA	SIMLA is a SIMuLAtion program that generates data sets of families for use in Linkage and Association studies. http://dmpi.duke.edu/simla-simulation-software-version-32
SimPed	A Simulation Program to Generate Haplotype and Genotype Data for Pedigree Structures http://bioinformatics.org/simped/
Simprot	A program to simulate protein evolution by substitution, insertion and deletion http://www.uhnresearch.ca/labs/tillier/software.htm#3
SimRare	Rare variant simulation and analysis tool http://code.google.com/p/simrare/
simuGWAS	A forward-time simulator that simulates realistic samples for genome-wide association studies. http://simupop.sourceforge.net/cookbook/simugwas
simuPOP	simuPOP is a general-purpose individual-based forward-time population genetics simulation environment. http://simupop.sourceforge.net/
SISSI	A software tool to generate data of related sequences along a given phylogeny, taking into account user defined system of neighbourhoods and instantaneous rate matrices. http://www.cibiv.at/software/sissi/
SMARTPOP	Simulating Mating Alliance as a Reproductive Tactic for Populations http://smartpop.sourceforge.net/
SNPsim	Coalescent simulation of hotspot recombination http://code.google.com/p/phylosoftware/
SPIP	SPIP simulates the transmission of genes from parents to offspring in a population having demographic structure defined by the user http://swfsc.noaa.gov/textblock.aspx?division=fed&id=3434
Splatche	Spatial and Temporal Coalescences in Heterogeneous Environment http://www.splatche.com/
srv	Simulator of Rare Varaints (srv) is a simulator for the simulation of the introduction and evolution of (rare) genetic variants. http://simupop.sourceforge.net/cookbook/simurarevariants
SUP	SLINK/FastSLINK utility program http://mlemire.freeshell.org/software.html
TreesimJ	A flexible, forward-time population genetic simulator http://code.google.com/p/treesimj/
Variant Simulation Tools	A simulation tool for post-GWAS genetic epidemiological studies using whole-genome or whole-exome next-gen sequencing data, with an emphasis on user-friendliness and reproducibility. http://varianttools.sourceforge.net/simulation/homepage
Vortex	VORTEX is an individual-based simulation model for population viability analysis (PVA). http://www.vortex9.org/vortex.html
Wessim	Whole Exome Sequencing SIMulator http://sak042.github.io/wessim/

BioLinux Ubuntu Desktop folder and files disappeared !!

Jit — Tue, 16 Feb 2016 08:40:41 -0600

Restarted my BioLinux ubuntu computer after an update, and when I logged back in, I noticed that all of my files went missing. Instead of Desktop folder, icons of all of my home folder are showed on desktop.

Then I thaught it migh be a problem of graphical display and I opened the terminal out of curiosity, and I found out that there is no ~/Desktop folder at all. What happened? What do I need to do?

Then I google the problem and found this is a very common problem after updates. To fix this problem, follow these steps:

You need to edit the ~/.config/user-dirs.dirs file, and make sure the contents of the file are like the following:

XDG_DESKTOP_DIR="$HOME/Desktop"
XDG_DOWNLOAD_DIR="$HOME/Downloads"
XDG_TEMPLATES_DIR="$HOME/"
XDG_PUBLICSHARE_DIR="$HOME/Share"
XDG_DOCUMENTS_DIR="$HOME/Documents"
XDG_MUSIC_DIR="$HOME/Music"
XDG_PICTURES_DIR="$HOME/Pictures"
XDG_VIDEOS_DIR="$HOME/Videos"

Then restart nautilus:

killall nautilus

or

nautilus -q

Then, open nautilus via Unity menu (press the Super key) or using the run command (Alt+F2)

BGAgenomicsDB: Acaryochloris marina MBIC11017 - A cyanobacterial organim

Arijit — Thu, 03 Dec 2015 01:38:52 -0600

We have developed a database system cyanobacterial organims called bgagenomicsDB. Cyanobacteria or some time called blue gree algee(BGA) is found in almost every teristorial .They have application in industrial product, health application, food technology etc.

One of the cyanobacterial organism is Acaryochloris marina MBIC11017. This organism has one circular chromosome and 9 plasmids.

Chromosome's circular view

Want to know more about this organism visit : bgagenomics.iicb.res.in

Five key traits to seek out in potential bioinformatics candidates !!!

Jit — Mon, 10 Aug 2015 12:53:50 -0500

Genomics and proteomics data are being collected in bulk, but mostly, traditional biologist don’t know what to do with it. Perhaps this is the reason why (not only this!!! ) computational biologist/bioinformatics scientists are hot commodities in the research world.

In fact, there are huge demands for expert biological data analyst. It’s a fairly new (not exactly) hot area, these bioinformatician are invaluable because they know and understand the significance of biological data for your research and how you can use it for better understanding of biological problems.

The bioinformatics can discover biological patterns and stories in genomic and proteomics data. They can develop the pipeline needed to properly collect, store and analyse it.

Once your research group is ready to make a larger investment and hire a bioinformatician to gain a competitive edge, there are several key traits to seek out in potential candidates. The best bioinformatician are:

1. Highly Skilled - programming skills, experience with the biological software and tools.

The biological data won’t illuminate much if the scientist analysing it doesn’t possess practical programming skills, experience with the biological software and tools and a thorough understanding of basic biological stuff. A solid background in mathematics and statistics is also an indispensable trait.

2. Insight - Real vision, robust understanding and deep insight.

In order to hire the best bioinformatics and computational biologist scientist for your needs, it is always recommended and mostly practiced by the recruiters, to ask each contender to write and develop a sample script/presentation based on a specific set of data you provide. Then, explore the approaches used to deal with data provided and pick up those candidates who convey real vision, robust understanding and deep insight.

3. Energetic – Curiosity to explore

Mostly natural curiosity and enthusiasm for solving big biological problems coupled with an ability to transform data into a scientific stories may place one candidate above the rest. In addition to achieve that, the bioinformatician should be agile enough to quickly modify their methods to suit changes within a particular research.

4. Researcher – Publications

Look for someone who has a keen sense and understanding of concern biological problems. You can judge it by looking at previously published papers and data. It is always recommended to have a look at GitHub and other repository for codes written by her/him.

5. Impressive communicator - Insight that can’t be expressed is worthless.

Good bioinformatics scientists are able to uncover biological patterns and are willing to explain those patterns in clear and helpful ways through thoughtful and open communication. In other words, they should must have good scientific writing skills. A computational biologis/bioinformatician should know how to present the data and tell a scientific story through numbers/images.

Calling narrow and broad peaks from ChIP-Seq data

Strand — Tue, 04 Aug 2015 05:06:03 -0500

Know about the state-of-the-art algorithms implemented in Strand NGS for detecting the binding sites of transcription factor (narrow peaks) and enriched regions of histone modification (broad peaks) from ChIP-Seq data.
Read the benchmarking study on Calling narrow and broad peaks from ChIP-Seq data in Strand NGS by Rohit Gupta and Anita Sathyanarayanan. For more information, please contact us

Integrated mRNA and microRNA transcriptome analysis in Strand NGS

Strand — Tue, 04 Aug 2015 05:04:46 -0500

Using a nasopharyngeal carcinoma case study, this paper highlights the integrated transcriptome analysis capabilities of Strand NGS demonstrating the identification of miRNA – mRNA interactions in regulatory networks.
Read the application note on Integrated mRNA and microRNA transcriptome analysis in Strand NGS by Veena Hedatale and Rohit Gupta. For more information, please contact us

Integrative RNA and ChIP-Seq analysis of regulatory T-cells

Strand — Tue, 04 Aug 2015 05:03:19 -0500

Integrative RNA and ChIP-Seq analysis of regulatory T-cells , a Strand NGS application note describes how integrated multi-omics functionality in Strand NGS was used to find the regulatory role of FoxP3 in T-regulatory and T-helper cells. Learn how the gene expression profiles from RNA-Seq and FoxP3 DNA-protein binding sites from ChIP-Seq are integrated. For mor information, please write to us

Bioinformatics PhD / PostDoc / Job Rejection

Jit — Thu, 02 Jul 2015 08:52:46 -0500

While your PhD or PostDoc application, it is more common that you got rejected by many professors. Don't disappoint reply it calmly.

In grad school, I shared a house with three Bioinformatics PhD students. One, when he applied to a particular professor, received a letter that said, essentially, "If you are applying because you want to enrich yourself, great. If you are applying because you want a job, you should know that you won't get one." I am trying to tell you this is because if you, with a good background in Bioinformatics, are passing up opportunities, you must be a strong candidate in many areas. Enrich yourself.

So, my suggestion is take a deep breath, forgot about all. Don’t take it personally. It's been usual processes while hunting for a good lab and professor. Take is positive, I am not sure why they reject, but don't worry perhaps the lab don't deserve you. Always remember there are billions of reasons not to hire someone for projects, especially in a research sector.

My suggestion, please do not whine about how you were a great research candidate for the post, and you just can't understand why they were so stupid as to have rejected you! This feeling will not win you any points in research, community. Especially, when in todays socially connected era everyone is linked. Remember, a nice E-mail saying, "I really wished to working with you on this project and I hope we cross paths again," is all you need to send to the professor. Send a thank you note to the professor. Thank them for the time they spend to judge you. In the future, If you and the professor (of your dream) are attending a bioinformatics conference, invite him/her to lunch (please remember to pay the bill). In today evolving scientific ere, always remember to build your solid network in order to get a job of interest. Join all possible networking sites like LinkedIn, ResearchGate, Acamedia, FB for the same reason. You as a researcher always build a bridge with student/researcher/colleague/professor who have the research potential to lead in research and hire you. Just because you didn't get this project, doesn't mean there isn't another that will open up in couple of month.

Mostly, jobs that are hard to get are hard to get. Only you can decide if the continued sacrifices are worth the expected payout. If it is, keep on plowing. Build relationships. Attend conferences.

Image ref @ JaSonYa

One page R survival guide !!

Rahul Nayak — Thu, 28 May 2015 21:10:12 -0500

There any many of the documents have been developed and tested by scientist around the world. I found this one really useful. The data used is available for download as data.zip.

Reference@http://www.datasciencecentral.com/profiles/blogs/one-page-r-a-survival-guide-to-data-science-with-r

Templates for the Data Scientist
1. A Template for Preparing Data: *OnePageR - *R
2. A Template for Building Models: *OnePageR - *R
Getting Started as a Data Scientist
1. Getting Started with R and Rattle: *Lecture - *Laboratory
2. Introducing and Interacting with R: *Lecture - *Laboratory
3. BasicR - OnePage(R) - Writing R scripts
Dealing With Data
1. Read Data into R: *OnePageR - *R
2. Explore and Summarise Data: *OnePageR - *R
3. Transform Data: *OnePageR - *R
4. Dealing with Dates and Time: (PDF, R) Dates and Time
5. Visualising Data with GGPlot2: *OnePageR - *R
6. Visualising Data with Maps *OnePageR - *R
7. Spatial (R) Spatial Analysis
8. Handling Big Data *OnePageR - *R
Descriptive Analytics
1. Cluster Analysis: *Lecture - *OnePageR - *R
2. Association Analysis: *Lecture
Predictive Analytics
1. Decision Trees: *Lecture - *OnePageR - *R - *Rattle
2. Ensembles of Decision Trees: *Lecture - *OnePageR - *R
3. SVM (R)
4. KernLab (R)
5. NeuralNetworks (R)
6. NNet (R)
Model Delivery
1. Evaluating Models: *OnePageR - *R
2. Evaluation (R)
3. Scoring (R)
4. PMML (R) Exporting Models for Deployment
Advanced Topics
1. Text Mining: *OnePageR - *R
Advanced R Topics
1. Plots (PDF, R) Miscellaneous Plots
2. Functions (PDF, R) Writing Functions in R
3. Parallel (PDF, R) Parallel Execution
4. Packaging (R) Pulling it Together into a Package
5. Doing R with Style: *OnePageR - *R
6. Literate Data Mining with KnitR: *Lecture - *OnePageR - *

Bioinformatics PhDs - Cover Letter

Martin Jones — Mon, 16 Feb 2015 12:48:52 -0600

Overview
The reception your cover letter will receive is more varied and unpredictable than the other elements of your application packet. Some readers, especially at large research universities, will skip it entirely, and focus instead on more direct indicators of your academic achievements and potential: the CV, letters, and writing sample. Most often, however, your cover letter works in tandem with your CV, and represents your best opportunity to communicate directly with your target audience prior to an interview.

The cover letter should not simply repeat the information contained in the CV; rather it should elaborate and frame the aspects of your academic training and background that you want search committee members to have uppermost in their minds as they consider your candidacy. At a minimum, it should contain a clear statement of your research and teaching interests, and how your qualifications match the requirements of the job description. Many disciplines have their own conventions and protocols for application materials including the cover letter. Be sure to show a draft of your letter to your chair or the department's placement advisor.

There is no perfect or even preferred style of cover letter save that tone should be that of a confident professional. Departments aren't interested in hiring graduate students (they already have enough of those). They are looking for serious scholars/teachers who will make interesting, congenial, and productive colleagues. While it is probably not a good use of your time to tailor your letter for each opening, you will probably need at least two base versions that emphasize different elements (You would not want to use the same cover letter to apply to Oberlin as UC Irvine). You may want to customize the letters for the three to five jobs most attractive to you.

Your cover letter should not exceed a page and a half or two at the most. No one will read a four-page letter, and your apparent inability to communicate your credentials in a concise manner is not to your advantage. Every line of your letter should serve a demonstrable purpose. Some people have gotten excellent jobs with a brief, boilerplate letter containing no more than a short intro, a paragraph on their research, and a couple of sentences on their teaching. Others use letters that discuss in very specific terms how they, their research, and their teaching would "fit" within the existing department and institutional setting. The choice of style is up to you, and should reflect what makes you feel most comfortable and most positive about your credentials.

There are, however, circumstances where a longer, more annotated version is more likely to be helpful. The issues you should take into account when making this decision are:

    The size of the department/institution
    The extent to which your research is mainstream and its significance readily apparent
    The extent to which your qualifications diverge from those mentioned in the job ad
    The extent to which the institution to which you are applying differs from UC Berkeley and the relative importance of teaching versus research
    Unusual circumstances or career paths

Size matters. The larger the department, the less detailed your letter needs to be. If there are already six people working in your sub-field, they can assess and translate the significance of your work to others in the department. If, however, you will be the only history of science person or the only physical anthropologist in the department, the search committee and others will likely need more help from you to understand the import of your work. Remember, if they are searching for your specialty they are understaffed in your area, and even if there is more than one person in your sub-field she/he may be on leave or otherwise uninvolved in the search.

Similarly, smaller departments are more likely to be concerned than larger ones about the specific courses you are prepared to teach. If there are certain core courses your position needs to cover, make it unambiguously clear that you are prepared to do so even if you haven't taught them already.

As a rule of thumb, the more your profile diverges from the specifics of the job description (or the norm, such that it exists), the more likely you will want to say more about why you are nonetheless a strong candidate. By doing so you can turn a question mark into an advantage. Highlight your strengths, and if you have time perform a little research on the department (see below) so you can explain how you would add to the department in ways that they might otherwise not have considered. Job descriptions are not always etched in stone. If you're very interested in the job, call the department and ask to speak to someone on the search committee. If that's too intimidating, call the departmental assistant or secretary. They can at least tell you if it’s a new position or replacement which in turn can indicate whether they really need someone who can teach The Politics of Southern Africa or if Middle Eastern Politics would do just as well. They may well be willing to sacrifice a course in an area that the ad specified in order to get the extra teaching experience or innovative research that you have to offer, but only if you give them the opportunity and explain what they'd get in return.

If you've taken an unusually long time to finish because you spent three years learning a new language in order to prepare for two years of original research or some other factor that enriches what you have to offer as a teacher or a scholar, let them know about it. Learning a new language, for example, is an indicator of your commitment to serious scholarship. Think of a way to put a positive valence on an aspect of your CV that you believe is likely to raise questions or doubts.

As hard as it may be for not-yet-employed-but-soon-to-be academics like yourselves to believe, there are some individuals and departments that are intimidated by you and your institutional pedigree. There are many fine schools and departments that are convinced that every Berkeley graduate student is only interested in a position at Harvard, Princeton, or (hold your nose) Stanford. If it is a small liberal arts college, they may also assume that you view teaching and advising undergraduates strictly as a necessary evil. Ergo, why interview someone who will either look down their nose at us, and/or desert us for greener pastures as soon as their third book has been published and Yale comes calling. They often don't seem to realize that Berkeley graduate students are sincerely interested in a wide range of academic settings for a variety of reasons personal and professional, and, perhaps more to the point, Yale rarely "comes calling." An even greater burden of proof exists if a quick scan of your CV reveals that your all of your experience in higher education has occurred in a large, elite, research-oriented setting. How can you overcome this potential obstacle especially if you're very interested in a school that you have reason to believe may be suspicious of Berkeley PhDs?

It's probably not advisable to write in a cover letter that they shouldn't be intimidated by lil' old you (i.e., "I'm really not very good; in fact, my advisor says I'm her worst graduate student in twenty years."), but there are ways to indicate a sincere interest in their department. One means is to do a little research, and briefly discuss how you might fit into the department and the institutional community-at-large. There are numerous articles written by faculty about the search process that state explicitly the positive impact it can have on their impression of a candidate to see evidence that that he/she has taken the time to learn something about them. Researching a department and its associated programs/research centers has never been easier. Virtually all colleges and universities are online, and their web addresses can be easily located using Education/Universities link.

A second way to address fears of imminent flight is to provide evidence of ties to the area, the university, or lifestyle.   If you are from the Midwest, have family nearby, or even if you just spent an enjoyable summer there, add a line about it and your desire to return to your cover letter for Purdue or Wayne State. If you did your undergraduate work at a similar type of institution, draw their attention to that link. Don't assume that they will notice where you got your BA on your CV, and make the connection.

If you are applying to small, liberal arts colleges, don't just list the courses you've taught with a line of boilerplate about how important you take your teaching responsibilities to be. These days, a good, small college has a list price of about $30,000 a year, and close contact with people like you is one of their key selling points to parents and potential students. You are expected to be more than a lecturer, and your ability to convey your recognition of that fact and a sincere interest in working closely with students matters. Advising, participating in non-academic activities, watching your students grow and mature inside the classroom and out (and having an influence on that process) can be among the most rewarding aspects of the profession. If you value these broader elements of being a professor, let them know. In particular, if your own experience has been limited to large universities, think about describing an episode where you had a positive impact on the development of a student and the satisfaction you derived from helping him or her. At many small colleges, and large universities (including Berkeley) as well, one of the most significant pedagogical trends is fostering greater student involvement in faculty research. If you can articulate how this might occur for you and their students in a brief but thoughtful manner, it can alleviate some of the concerns occasioned by your Berkeley background.

Your letter typically will have four segments: the introduction, research, teaching, and the closing.

Introduction
Be sure to identify the position you are applying for by rank and sub-field in the first sentence or two. It is not uncommon for departments to be engaged in more than one search in a given year, and large departments may have more than one in your sub-field. If it is an open rank position and you are applying as an assistant professor, in most cases you and your record of accomplishments will not be directly compared with those of more experienced candidates applying for it at the associate or full professor level. Rather, applicants are assessed based on where they are in their career path, and you have every chance of successfully competing against them.

If you have finished, mention it up front. If not, state when you expect to file - no later than June 2003. Many readers will view your optimistic prediction with a skeptical eye, and anything you can say that makes the claim appear more credible (e.g., "I have written and my chair has reviewed four of the six chapters.") can help mitigate their concerns. You can put such a statement in the introduction or wait until you discuss the dissertation itself.

If your discipline holds its annual meeting in the summer or early fall (i.e., before application deadlines) and you had an excellent conference interview, make reference to it in the cover letter including the names of the faculty with whom you spoke. By the time people actually start to read files, months may have passed and even the strongest of impressions can fade. But they can be rehabilitated and revived, especially if you can remind them of a specific strength, ability, or issue that seemed particularly salient during the interview.
Research
Unless you are applying to a school that cares only about your teaching (increasingly rare), a description of your research generally follows next. The challenge here is not simply to describe your research, but to frame it in terms of your sub-field and discipline. The search committee, hasn't lived, eaten and breathed Post-Edwardian Hermeneutics for the past five years the way you have. And they haven't been there every step of the torturous process like your friends and significant others. So forgive them for not immediately recognizing your research at first blush for the path-breaking work that it is.

Departments want to know that in hiring you, they are adding someone who will make a future contribution to the discipline and enhance the reputation of the department. However, they are often ill-equipped to understand exactly how that will be true in your particular case. Many disciplines are sufficiently broad that leading or cutting-edge research in one sub-field is barely intelligible to those in others. In addition, when you became a candidate for the short list, your file will be read by department members outside your specialty, and, oftentimes later on in the process, by individuals outside your discipline as well. You need to describe the forest in which your tree resides, and explain why it matters in terms of the broader trends and issues within your discipline. Obviously, if your work is focused on one of the classic conundrums of your field, much less in the way of providing perspective is necessary than if you are addressing an emergent issue or employing an unconventional approach.

If your research is particularly novel or cutting-edge, any markers of broader acceptance by other, more established scholars or scholarly organs in your field can ameliorate possible concerns about its relevance and potential importance. In discussing your work, note the recognition it has received in the form of competitive grants, awards, publications in refereed journals, and/or presentations at major conferences.

It is also important to mention where you expect your research to go after the completion of your dissertation and the publications that will flow from it. They, especially at research-oriented institutions, want to know at least in broad terms where you expect to go from here. They want to see evidence of a scholarly agenda that extends beyond the dissertation. You don't need to have pages written or titles blocked out, but you need to tell them in a paragraph, (two at the most) about what questions intrigue you, and how you expect to go about finding the answers to them. These questions for future research may have been generated by the findings of your dissertation, unusual data uncovered during your fieldwork, or interesting side issues that you were forced to put off in order to keep your dissertation taut and focused. Think twice about mentioning future projects that appear entirely unrelated to your current work. Departments will want you to be firmly established in one area before you go off into another.

The relative importance of teaching versus research is a continuous and not a discrete variable. Even schools that emphasize teaching in their job listing will generally want evidence of scholarly engagement and publishing potential. It is a very competitive market for students out there, and virtually all institutions are under pressure to sell themselves via the quality of their faculty to the limited number of good students in the applicant pool. A good marker for the relative importance of teaching versus research is the teaching load. An institution with a 3-2 teaching load (a total of five courses taught per two-semester year) will expect more in the way of and value research more highly than one with a 3-3 load.

If you work in a capital-intensive area, mention your track record of gaining grants and other sources of external funding.
Teaching
In a similar fashion, the more the institution you are applying to diverges from Berkeley and the more your profile differs from the job description, the more expansive you should be in talking about your teaching and what you have to offer their students. If you have won a teaching award at Berkeley, don't make them wait until they read your CV to discover that fact.

For letters sent to large universities where you will be expected to teach large, lecture courses and graduate seminars, little is needed except to convey that you have the necessary experience and/or background. Since the dominant pedagogical style is the same as that found at Berkeley, it will be assumed that you will be able to do the same for them.

If you have TA'd some of the courses that (based on the job description) you would be expected to offer, let them know. If you have not had that opportunity, but your fields and research fall within the domain of the job description, you may want to add a line about how well prepared you are to teach such courses based on your training and research.

If your profile does not correspond exactly to the description found in the job announcement, but you believe that you could nonetheless teach the required courses, explain the basis for your confidence. What strength would you bring to the department that would more than make up for your "otherness"? Departments may decide that they don't "need" another conventional European historian if you can convince them that you can handle the core courses and offer something new that they don't currently cover as well. In this case especially, think about doing some research so you can speak in more specific terms about how you would fit into and enhance the department's offerings. Help them visualize how you would strengthen them as a department.

Small colleges will want to know that you are able to teach on a more intimate basis and are prepared to take the time to do it well. If you give them three generic lines about how "important" you consider your teaching responsibilities, don't expect much of a positive response. Spend a couple of paragraphs describing your teaching experience and philosophy, and how you would take advantage of the opportunity to create you own courses.

If you've had the opportunity to design and teach your own course, tell them, briefly, how you went about it and the choices you made. Don't just say you're a good teacher, tell them why. Look over your teaching evaluations and pick out one or two consistent strengths to highlight. Drawing on your experience, talk about how you engage students and enhance their skills (especially writing) and intellectual development.

For good, small colleges, the quality of the teaching offered to students is their stock-in- trade. Even the most research-oriented, small colleges take teaching undergraduates very seriously. You need to demonstrate that you take it seriously as well, and can talk about it using more than vague generalities and shop-worn cliches.
Closing/Enclosures
Wrap it up quickly. If you are going to be traveling for any significant portion of the job search season be sure to let them know how they can contact you (email, cellphone and/or a Pacific Bell message center account are particularly good options).

Before closing, include a sentence where you list the materials you have enclosed (e.g., CV, dissertation abstract, transcripts, teaching statement/portfolio, writing samples, etc.) and are having sent separately (letters of recommendation). You will often be assembling multiple packets at the same time (as in midnight October 14th, midnight November 14th, etc.) for jobs that ask for different combinations of enclosures. In addition to telling them what they should have received, it serves as a handy list for you to check before sealing the envelope.

Should you send a writing sample, even if they haven't asked for one? Unless the norm in your discipline is to never send more than they ask for, if you've been smart and have already run off thirty copies at two cents per page (that's only sixty cents for a thirty page chapter) by all means send it. That way it's in the file should someone become interested in you and want to read more.