BOL: Related items

JRF Bioinformatics at CUK

Thu, 03 Dec 2015 23:40:38 -0600

JRF Bioinformatics

Eligibility : MSc(Bio-Informatics), BE/B.Tech

Location : Kasaragod

Last Date : 20 Dec 2015

Hiring Process : Face to Face Interview
Central University of Kerala

JRF job opportunity in Central University of Kerala (CUK) on temporary basis

Project Title : "Targeting TAL effector mediated susceptibility for durable and broad-spectrum resistance to bacterial blight in Rice"

No. of Post : 01

Qualification : MSc in any subject under Life Science or Bioinformatics/ B.Tech in Bioinformatics + 1 yr experience

Stipend : Rs. 14,000/-
How to apply

Interested candidates are requested to send their applications explaining their interest in the position with an updated CV to Dr. Ginny Antony, Assistant Professor, Department of Plant Science, School of Biological Sciences, Central University of Kerala, Padannakkad, Kasaragod, Kerala - 671 314 email: ginnycuk2013@gmail.com on or before 20th December, 2015.

Project Assistant at IISER Mohali

Fri, 29 Jan 2016 11:04:27 -0600

Project Assistant Job position in Indian Institute of Science Education & Research (IISER) Mohali

Title : In silico understanding of molecular basis of recognition, binding, and regulation of mRNA by STAR family of transcriptional regulators.

No. of Post : 01

Department : Science and Technology

Qualifications : M.Sc./B.Tech in computational life sciences, computational chemistry, computational natural sciences or allied areas. Working experience in MD simulations, bioinformatics, molecular modeling, and drug designing is desirable and plus

Emoluments : As per DST norms
How to apply

Applicants are requested to send application along with bio-data and a summary of previous projects (if any) as a PDF file with the e-mail to Dr. Monika Sharma, Email: mnsharma@iisermohali.ac.in. Last date of applications is 17:00 IST. Feb 15, 2016. Shortlisted candidates will be called for interview on Feb 22, 2016.

More at http://14.139.227.202/tenders/tenderinvite/index.php/iiserm-project-openings/554-applications-are-invited-to-work-as-project-assistant-in-a-dst-inspire-research-project-funded-by-department-of-science-and-technology-india

Statistics Using R with Biological Examples

Neel — Thu, 03 Nov 2016 04:55:41 -0500

This book is a manifestation of my desire to teach researchers in biology a bit more about statistics than an ordinary introductory course covers and to introduce the utilization of R as a tool for analyzing their data. My goal is to reach those with little or no training in higher level statistics so that they can do more of their own data analysis, communicate more with statisticians, and appreciate the great potential statistics has to offer as a tool to answer biological questions.

This is necessary in light of the increasing use of higher level statistics in biomedical research. I hope it accomplishes this mission and encourage its free distribution and use as a course text or supplement.

K Seefeld, May 2007

Senior Scientist Computational Biology at NIPGR

Sun, 19 Jul 2020 23:30:04 -0500

Senior Scientist Computational Biology
Level 13

₹ 1,66,378 (Consolidated)

(UR)

Ph.D. in the area of Computational Biology/Bioinformatics/Biotechnology/Life Sciences with at least 6 years of relevant experience.
OR
M. Tech with 8 years of relevant experience.

The relevant experience shall be in the area of sequencing/genome assembly and annotation and high throughput genotyping for facilitating Genomic assisted Breeding
Age: Not exceeding 50 years

• The incumbent will assist the Programme Director of the Facility to discharge various activities of the NGGF

• Co-ordination with the service provider, DBT/academic institutions/anchoring institute (NIPGR) for execution of activities of the Facility.

• The incumbent will be expected to identify the requirements of Private Sector and Government laboratories in the area of Marker assisted selection and develop linkages to facilitate the same.

• Interact with multiple stake holders including Government and Private Sector in the area of agriculture Biotechnology.

• Oversee the establishment of relevant Standard Operational Procedures (SOP), Quality Accreditation of Genomics and Genotyping facility.

More at http://www.nipgr.ac.in/careers/vacancies_latest.php#vacancy2

Computational Biology Summer Research Programme

Mon, 20 Jan 2020 23:38:44 -0600

IMSc has a limited programme for highly motivated bachelors and masters students interested in research in the areas of Theoretical Physics, Mathematics, Theoretical Computer Science and Computational Biology to visit the Institute over their summer vacation. In addition, IMSc also accepts students through the summer program organized by the joint Indian Academies of Science.
General Structure
This is a limited programme, depending on the availability of infrastructure and faculty advisors. We typically select about 25 students across disciplines although this number varies a bit from year to year. These visits typically span 6-8 weeks during the summer (May-July). There is also a provision for a 4-6 month visit, typically during January-April or August-November for extended project work.

Qualifications
Students currently in their pre-final or final year of BSc/BE/BTech or first year MSc/ME/MTech or equivalent with a good academic record are encouraged to apply through IMSc's formal application process.

To apply through the summer program jointly organized by the Academies of Science, please check the Indian Academy of Sciences for their application process: http://web-japps.ias.ac.in:8080/fellowship2018/index.html.
Stipend
Selected students will be paid 2nd class round trip train fare plus Rs.200 per diem. Accommodation will be provided in the hostel during summer, subject to availability. Since our ability to provide accomodation is often limited, we suggest that students also explore alternative possibilities for stay in Chennai. Accommodation will not be provided for longer visits.

Application Process
To apply for our summer programs please follow the instructions for the respective fields:
Theoretical Physics
Mathematics
Theoretical Computer Science
Computational Biology

Other information
If you have more questions about our application procedures, about your eligibility or simply about life and work at IMSc, do write to any of the faculty members listed on our home page.

Lecturer @ UEssex in Evolutionary Biology

Tue, 06 Aug 2024 02:41:17 -0500

The University of Essex, UK, is seeking a Lecturer conducting research in
evolutionary biology or a related field. The new Lecturer will join the
collegial and supportive environment of the School of Life Sciences,
contributing to teaching and leading a research programme, ideally
relating to any aspect of evolutionary biology.

Application closing date 16/09/2024

Details of the post and application process can be found at the following
url:

https://vacancies.essex.ac.uk/tlive_webrecruitment/wrd/run/ETREC107GF.open?VACANCY_ID=202575WW5Z&WVID=9918109NEm&LANG=USA

Informal enquiries may be made by email to the Head of the School of
Life Sciences Prof Terence McGenity at tjmcgen@essex.ac.uk(all formal
applications for the post should be made online through the University
of Essex website).

Biotite: A general framework for computational biology

Jit — Mon, 17 Dec 2018 18:52:27 -0600

The package is open source and freely available at GitHub (https://github.com/biotite-dev/biotite). This package is simple to use especially for the beginners in programming and computationally efficient because of the implementation of Numpy and Cython. Biotite consists of four sub packages: sequence, structure, databases, and application. The sequence and structure modules serve for the analysis of sequence and structural data analysis respectively, database downloads files from the other databases such as RCSB PDB, and application provides interface for external software.

The Biotite package bundles popular tasks in computational biology into an unifying framework, which is easy to use on the one hand side, but is also computationally efficient due to intensive usage of NumPy and Cython. This package focuses on working with sequence and structure data and supports various file formats and analysis and manipulation functions.

Address of the bookmark: https://github.com/biotite-dev/biotite

Online resources on must-read papers in evolutionary biology, for a literature club

Shruti Paniwala — Tue, 28 Jun 2022 07:29:08 -0500

1.       *Nick Barton:*

- The textbook "Evolution" by Nick Barton, with resources for
  exploring the literature: Barton, N. H., Briggs, D. E. G., Eisen, J.
  A., Goldstein, D. B., & Patel, N. H. (2007). Evolution. Cold Spring
  Harbor Laboratory Press.

- Papers from a course named "Classics in Evolutionary Biology":

Evolutionary Synthesis
1. Haldane, J. B. S. 1932. The causes of evolution. Longmans. New York.
   (esp. Ch. IV).
2. Fisher, R. A. 1930. The genetical theory of natural selection. Oxford
   University Press, Oxford. Selected Sections - Fundamental Theorem.

Genetic Variation
1a. Lewontin, R. C., and J. L. Hubby. 1966. A molecular approach to
the study of genic heterozygosity in natural populations. II. Amount
of variation and degree of heterozygosity in natural populations of
Drosophila pseudoobscura. Genetics. 54:595-609.

1b. Sachidandam et al. 2001. A map of human genome sequence variation
containing 1.42 million single nucleotide polymorphisms. 409: 928-33.

2. Wright S., Dobzhansky T., Hovanitz W. 1942 Genetics of natural
populations VII The allelism of lethals in the third chromosome of
Drosophila pseudoobscura. Genetics 27: 363-394.

Recombination and evolution
1. Hill, W. G., and A. Robertson. 1966. The effect of linkage on limits
to artificial selection. Genet. Res. 8:269-294.

2. Maynard Smith and Haigh. 1974. The hitch-hiking effect of a favourable
gene. Genet. Res. 23: 23-35.

Understanding sequence variation
1. Begun D. J., Aquadro C. F., 1992 Levels of naturally occurring DNA
polymorphism correlate with recombination rate in Drosophila melanogaster.
Nature 356: 519-520.

2. Green R. E., Reich D., Pääbo S., 2010 A draft sequence of the
Neandertal genome. Science 328: 710-722.

Quantitative Genetics:  variation in complex traits
1. Galton F., 1877 Typical laws of heredity. Nature 15: 492-495-
512-514- 532-533.

2. Turelli M., 1984 Heritable genetic variation via
mutation-selection balance: Lerch's Zeta meets the abdominal
bristle. Theor. Popul. Biol. 25: 138-193.

Quantitative Genetics:  finding the genes
1. Shrimpton A. E., Robertson A., 1988 The Isolation of polygenic factors
controlling bristle score in Drosophila melanogaster II Distribution of
third chromosome bristle effects within chromosome sections. Genetics
118: 445-459.

2. Boyle E. A., Li Y. I., Pritchard J. K., 2017 An expanded view of
complex traits: from polygenic to omnigenic. Cell 169: 1177-1186.

Neutral Evolution
1. Kimura, M. 1968. Evolutionary rate at the molecular level. Science.
217:624-626.

2a. Kern A. D., Hahn M. W., 2018 The Neutral Theory in Light of Natural
Selection. Molecular Biology and Evolution 110: 21077-6.

2b. Jensen J. D., Payseur B. A., Stephan W., Aquadro C. F., Lynch M.,
Charlesworth D., Charlesworth B., 2018 The importance of the Neutral Theory
in 1968 and 50 years on: a response to Kern and Hahn 2018. Evolution 112:
2109-4.

2c. Ellegren & Galtier. 2016. Determinants of genetic diversity. Nature
Reviews Genetics.

Mutation and Genetic Variability
1. Luria, S. E., and M. Delbrück. 1943. Mutations of Bacteria from Virus
Sensitivity to Virus Resistance. Genetics. 28(6):491-511.

2. Hill, W G. 1982. "Rates of Change in Quantitative Traits From Fixation
of New Mutations." Proceedings of the National Academy of Sciences (U.S.A.)
79: 142-45.

Testing for selection
1. McDonald & Kreitman. 1991. Adaptive protein evolution at the Adh locus
in Drosophila. Nature.

2. Begun, et al. Mol. Biol. Evol. 16, 1816-1819 (1999).

3. Siddiq et al. 2016. Experimental test and refutation of a classic case
of molecular adaptation in Drosophila melanogaster.  Nature Ecology &
Evolution.

The shifting balance
1. Wright, S. 1932. The roles of mutation, inbreeding, crossbreeding and
selection in evolution. Proceedings of the VI International Congress of
Genetics: 1. pp 356-366.

2. Coyne, J.A., N.H. Barton, and M. Turelli. 1997. A critique of Wright's
shifting balance theory of evolution.  Evolution 51: 643-671.

3. Barton. 2016. Sewall Wright on Evolution in Mendelian Populations and
the "Shifting Balance". Genetics.

Evolution of Sex
1.  Muller, H.J. 1964. The relation of recombination to mutational advance.
Mutation Res. 1(1):2-9

2. McDonald et al. 2016. Sex speeds adaptation by altering the dynamics of
molecular evolution. Nature.

Kin Selection, Cooperation, and Conflict
1. Hamilton, W. D. 1964. The genetical evolution of social behaviour I.
Journal of Theoretical Biology. 7:1-52.

2. Trivers, R. L. 1974 Parent-offspring conflict. American Zoologist.
14(1):249-264.

Sexual Selection
1. Zahavi, A. 1975. Mate selection - a selection of a handicap. J. Theor.
Biol. 53:205-214.

2. Kirkpatrick, M., and Ryan, M.J. 1991. The evolution of mating
preferences and the paradox of the lek. Nature. 350:33-38.

Fitness Landscapes
1. Dean, A. 1995. A Molecular Investigation of Genotype by Environment
Interactions. Genetics. 139:19-33.

2. Costanzo et al. 2010. The Genetic Landscape of a Cell. Science.

Speciation
1. Coyne, J. A., and H. A. Orr. 1989. Patterns of speciation in Drosophila.
Evolution. 43:362-381.

2. Corbett-Detig et al. 2013. Genetic incompatibilities are widespread
within species. Nature.

2.       *Marcos Antezana:*

Valen, L. v. 1975. Energy and Evolution. University of Chicago, Department
of Biology.

3.       *Remco Folkertsma:*

1. The work by Hopi Hoekstra on local adaptation and oldfield mice

2. Poelstra, J. W., Vijay, N., Bossu, C. M., Lantz, H., Ryll, B., Müller,
I., ... & Wolf, J. B. (2014). The genomic landscape underlying phenotypic
integrity in the face of gene flow in crows. Science, 344(6190), 1410-1414.

4.       *Joshka Kaufmann and Leslie Turner*

They offer us a link to 'papers every evolutionary biologist should read',
the papers are collected by Leslie Turner.
https://static1.squarespace.com/static/53e8cb7ce4b02c4bc3aeeee4/t/5ab8fcb670a6ad55c67fcdf4/1522072758665/EvoBioClassicsRefList.pdf

5.       *Sarah Stockwell*

Matt Ridley collected classic papers in evolutionary biology and printed
part of these papers in his book Evolution (see Matt Ridley. Evolution
(Univ. of Oxford Press, 2nd edition, 2004))

HybPiper

Jit — Fri, 04 Nov 2016 05:02:10 -0500

HybPiper was designed for targeted sequence capture, in which DNA sequencing libraries are enriched for gene regions of interest, especially for phylogenetics. HybPiper is a suite of Python scripts that wrap and connect bioinformatics tools in order to extract target sequences from high-throughput DNA sequencing reads.

Targeted bait capture is a technique for sequencing many loci simultaneously based on bait sequences. HybPiper pipeline starts with high-throughput sequencing reads (for example from Illumina MiSeq), and assigns them to target genes using BLASTx or BWA. The reads are distributed to separate directories, where they are assembled separately using SPAdes. The main output is a FASTA file of the (in frame) CDS portion of the sample for each target region, and a separate file with the translated protein sequence.

HybPiper also includes post-processing scripts, run after the main pipeline, to also extract the intronic regions flanking each exon, investigate putative paralogs, and calculate sequencing depth. For more information, please see our wiki.

HybPiper is run separately for each sample (single or paired-end sequence reads). When HybPiper generates sequence files from the reads, it does so in a standardized directory hierarchy. Many of the post-processing scripts rely on this directory hierarchy, so do not modify it after running the initial pipeline. It is a good idea to run the pipeline for each sample from the same directory. You will end up with one directory per run of HybPiper, and some of the later scripts take advantage of this predictable directory structure.

Address of the bookmark: https://github.com/mossmatters/HybPiper

DeepHiC: A Generative Adversarial Network for Enhancing Hi-C Data Resolution

Rahul Nayak — Tue, 03 Mar 2020 01:12:47 -0600

DeepHiC is a GAN-based model for enhancing Hi-C data resolution. We developed this server for helping researchers to enhance their own low-resolution data by a few steps of clicks. Ab initio training could be performed according to our published code. We provided trained models for various depth of low-coverage sequencing Hi-C data. The depth of input data is estimated by its distribution comparing with those of the downsampled Hi-C data we used in training

Address of the bookmark: http://sysomics.com/deephic