bedtools is developed in the Quinlan laboratory at the University of Utah and benefits from fantastic contributions made by scientists worldwide.

Address of the bookmark: http://bedtools.readthedocs.io/en/latest/index.html

More at https://bioinformaticsonline.com/snippets/view/42919/perl-ipcopen2-module

2. exec””

 syntax: exec "command";

3. Backticks “ or qx//

syntax: `command`;

syntax: qx/command/;

4. IPC::Open3

syntax: $output = open3(\*CHLD_IN, \*CHLD_OUT, \*CHLD_ERR, 'command arg1 arg2', 'optarg',...);

It is very similar to IPC::Open2 with the capability to capture all three file handles of the process, i.e., STDIN, STDOUT, and STDERR. It can also be used with or without the shell. You can read about it more in the documentation: IPC::Open3.

$output = open3(my $o ut, my $in, 'command arg1 arg2');

OR without using the shell

$output = open3(my $out, my $in, 'command', 'arg1', 'arg2');

5.a2p

syntax: a2p [options] [awkscript]

There is a Perl utility known as a2p which translates awk command to Perl. It takes awk script as input and generates a comparable Perl script as the output. Suppose, you want to execute the following awk statement

awk '{$1 = ""; $2 = ""; print}' f1.txt

This statement gives error sometimes even after escaping the variables (\$1, \$2) but by using a2p it can be easily converted to Perl script:

For further information, you can read it’s documentation: a2p

More help at https://bioinformaticsonline.com/snippets/view/42920/perl-script-to-run-awk-inside-perl

6. system()

syntax: system( "command" );

It is also a Perl function (perlfunc) that executes a command and waits for it to get finished first and then resume the Perl script. The return value is the exit status of the command. It can be called in two ways:

system( "command arg1 arg2" );

OR

system( "command", "arg1", "arg2" );

HELP

Here are some useful Perl cheat sheets that can be used as a quick reference guide.-- https://www.pcwdld.com/perl-cheat-sheet

Address of the bookmark: http://kakitone.github.io/finishingTool/

Master's degree in bioinformatics, computer science, molecular biology, or related field
One year of experience in analyzing RNA-Seq data
In lieu of a degree, consideration will be given to an equivalent combination of related education and required work experience.
Understanding of molecular biology, biochemistry, and genetics
Proficiency in writing scripts using Perl, Python, Java, or equivalent languages
Proficiency in R and UNIX/LINUX
Knowledge of genomics, alignment, annotation, bioinformatics, concepts of sequence assembly
Highly motivated and detail-oriented
Ability, interest, and curiosity to learn new skills
Must possess strong communication skills to work effectively with users across disciplines
Ability to work independently and as part of a multi-disciplinary team
Strong visual, verbal, and written communication skills
Excellent time organizational skills
Preferred:

Experience writing software or building software pipelines
Experience with oncology-specific public databases including TCGA
Experience with deploying and/or running software on high-performance computational systems
Statistical and experimental design knowledge
Additional Information:

This position is contingent on the availability of funding
Purdue will not sponsor employment authorization for this position
A background check will be required for employment in this position
FLSA: Exempt (Not Eligible For Overtime)
Retirement Eligibility: Defined Contribution Waiting Period
Purdue University is an EOE/AA employer. All individuals, including minorities, women, individuals with disabilities, and veterans are encouraged to apply

More at https://careers.purdue.edu/job/West-Lafayette-Bioinformatician-Purdue-Cancer-Center-IN-47906/686617600/

Grouping large genomic fragments assembled from shotgun metagenomic sequences to deconvolute complex microbial communities, or metagenome binning, enables the study of individual organisms and their interactions. Here we developed an automated metagenome binning software, called MetaBAT, which integrates empirical probabilistic distances of genome abundance and tetranucleotide frequency. Tested on both synthetic and real metagenome datasets, MetaBAT outperforms alternative methods in both accuracy and computational efficiency. Applying MetaBAT to an assembly from 1,704 human gut samples formed 1,634 genome bins (>200kb) in 3 hours, where 621 genome bins are >50% complete with <5% contamination from other species. Further analysis shows that the quality of these genome bins approaches manually curated genomes.

Address of the bookmark: https://bitbucket.org/berkeleylab/metabat

My client provide innovative biomarker discovery and development services to the pharmaceutical industry. They partner with the pharmaceutical industry to develop and implement biomarker strategies, providing a full range of biomarker services from pre-clinical biomarker discovery, assay development, right through to the delivery of clinical tests in their CLIA lab.

As a Senior Statistician you would support this effort and be responsible for the management of technical experimental study design and data handling processes required for the discovery, development and commercial delivery of multiplex clinical diagnostic assays; You will:

Develop analytical experimental designs for multiplex clinical diagnostic assays in accordance with regulatory requirements (e.g. CLIA, FDA)
Lead and coordinate the evaluation of analytical studies including characterization, verification, and validation studies
Lead specification setting and specification alterations
Ensure DOE methodology is routinely used in analytical studies.
Work with the Operations Department to ensure robust, reproducible and precise assay development
Provide expertise of general aspects for Statistical Process Control
Provide statistical expertise for R&D, Quality, and Manufacturing
You will work in a fast-paced, project orientated environment and the ability to plan and execute objectives under tight timelines is a must. This is a unique opportunity suited for a qualified statistician with an interest in working to deliver first class data analysis support and solutions in a clinical setting.

Requirements

MSc or PhD in statistics or a related discipline
In depth knowledge of DOE methods to analytically validate, monitor and trouble shoot multiplex clinical diagnostic assays, ideally in a commercial/industrial setting
Experienced in the analysis of statistical technology evaluation, independent data and dependent data analysis, medical diagnostic accuracy, statistical graphics and reproducible reporting.
Excellent interpersonal, communication (including written and spoken English)
Ability to independently manage multiple projects and to deliver results on time per project deadlines
Proficient programming and analysis skills in one or more statistical package (e.g. R, Stata, SAS)
The following skills, while not mandatory, are highly desirable:

Development and validation of predictive models
Experience of clinical epidemiology, survival analysis, biomarker research, Bayesian methods, quantifying predictive accuracy.
Knowledge of regulatory standards for CLIA and/or FDA IVD tests
Reward

An attractive remuneration package will reflect the importance of this role and will include 6.8 weeks annual leave (pro rata, including fixed closure days), company pension scheme, enhanced sick pay and maternity entitlements, healthcare plan and opportunities for learning and development, as well as access to a company restaurant and parking facilities

Address of the bookmark: http://ritchielab.psu.edu/software/phenogram-downloads

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements.

Knowledge in System Biology/ Statistical and computational Genomics/ Bioinformatics
Knowledge in computer programming, LINUX OS.
Expertise in use of R/other Bioinformatics software

More at http://iasri.res.in/employment/2017/cabin_advertisement_RA_SRF_YP_Mar2017.pdf

Phenomics of Moisture Deficit Stress Tolerance and Nitrogen Use December 31, 2019

Research Associate (RA) Two (2)

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent or System Administrator/ Computer expert for database development, development of phenome data bank and virtual phenomics facility, data archiving and Efficiency in Rice and Wheat-Phase II (Funded by National Agricultural Science Fund, ICAR) Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements. maintenance; Development of image analysis algorithms, APIs and IAPs.

Knowledge in System Biology/ Statistical and computational Genomics/ Bioinformatics
Knowledge of programming in LINUX/R/Perl/JAVA/PHP/JSP and use of various software & tools.
December 31, 2019

Ph.D. in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science / Computer Application or equivalent or Master’s in Bioinformatics/ Agricultural Statistics/ Statistics/ Computer Science/ Computer Application or equivalent with 4 years or 5 years of Bachelor’s degree having 1st Division or 60% marks or equivalent overall grade point average, with at least two years of research experience as evidenced from fellowship/ associateship/ training/ other engagements.

Knowledge of Statistical and Computational Genomics/ Bioinformatics.
Knowledge of programming in LINUX/R/Perl/JAVA/PHP/JSP and use of various software & tools.
March 31, 2020