Monsanto is seeking a very talented Genomics Scientistto become an integral member of our Global Pipeline Analytics team with a focus on quantitative genetics. The ideal candidate will have familiarity with modeling and analysis of genetic data sets using a variety of statistical techniques.

Major Responsibilities:
- Provide guidance on experimental design for genomic-related experiments
- Familiarity with analysis of the following methods: GWS, QTL, eQTL, RNA-Seq
- Provide written and oral presentations of methods, results, conclusions, and recommendations to peer and management groups.
- Ensure timely delivery and clear communication of results
- Develop strong and successful collaborations among various Monsanto enabling teams.

Required Skills:

- PhD degree in Statistics, Biostatistics, Statistical Genetics, Quantitative Genetics, Breeding, Bioinformatics or a related field with 2 years of experience
- Working knowledge and experience with one of the following quantitative languages:R, Python, Perl, SAS
- Background in Windows and Linux operating systems
- Very strong problem solving skills will be required to work well as a member of a dynamic team
- Strong verbal and written communication skills.
- Demonstrated ability to deliver timely results and be results oriented.
- Extensive knowledge of quantitative genetics and experimental design. 
- Demonstrated track record of solving challenging and complex problems.

Desired Skills/Experience:

- Excellent communication skills, with the ability to summarize complex concepts in language understandable by scientists from a variety of disciplines.
- Experience in agronomy and/or plant breeding in vegetables or row crops.

Please apply to
https://jobs.monsanto.com/job/st-louis/genomics-scientist/769/2081771

Cleaning your data in this way is often required: Reads from small-RNA sequencing contain the 3’ sequencing adapter because the read is longer than the molecule that is sequenced. Amplicon reads start with a primer sequence. Poly-A tails are useful for pulling out RNA from your sample, but often you don’t want them to be in your reads.

Cutadapt helps with these trimming tasks by finding the adapter or primer sequences in an error-tolerant way. It can also modify and filter reads in various ways. Adapter sequences can contain IUPAC wildcard characters. Also, paired-end reads and even colorspace data is supported. If you want, you can also just demultiplex your input data, without removing adapter sequences at all.

Cutadapt comes with an extensive suite of automated tests and is available under the terms of the MIT license.

If you use cutadapt, please cite DOI:10.14806/ej.17.1.200 .

Address of the bookmark: https://cutadapt.readthedocs.io/en/stable/installation.html#quickstart

ORFfinder is a graphical analysis tool for finding open reading frames (ORFs). We’ve been working on a few updates, and we’d like to find out what you think about them. Read on to find out what you can do with the new ORFfinder.

Smart BLAST (https://ncbiinsights.ncbi.nlm.nih.gov/2015/07/29/smartblast/)

Select one or a group of ORFs and BLAST several databases at once, and use the newly developed SmartBLAST to verify protein names. Looking for the traditional results from BLAST? They’re there too.

More at https://bitbucket.org/henryhzhang/rcircos/src

Address of the bookmark: https://bitbucket.org/henryhzhang/rcircos/src

http://biogps.org/#goto=welcome

Address of the bookmark: http://biogps.org/#goto=welcome

Address of the bookmark: http://www.ici.upmc.fr/cluego/

Address of the bookmark: https://github.com/al2na/methylKit

Address of the bookmark: http://4dgenome.research.chop.edu/

More at http://www.pondiuni.edu.in/news?quicktabs_2=5#quicktabs-2