BOL: Related items

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.

The Brent Lab

Fri, 09 Feb 2018 10:55:27 -0600

The Brent Lab is developing and applying computational methods for mapping gene regulation networks, modeling them quantitatively, and engineering new behaviors into them.

Liu Lab

Tue, 04 Feb 2020 06:27:02 -0600

Shirley is a computational biologist with expertise in cancer epigenetics. Her research focuses on algorithm development and integrative mining from big data generated on microarrays, massively parallel sequencing, and other high throughput techniques to model the specificity and function of transcription factors, chromatin regulators and lncRNAs in tumor development, progression, drug response and resistance.

https://liulab-dfci.github.io/software/

ALE: a Generic Assembly Likelihood Evaluation Framework for Assessing the Accuracy of Genome and Metagenome Assemblies

Neel — Tue, 26 Apr 2016 03:38:43 -0500

Assembly Likelihood Evaluation (ALE) framework that overcomes these limitations, systematically evaluating the accuracy of an assembly in a reference-independent manner using rigorous statistical methods. This framework is comprehensive, and integrates read quality, mate pair orientation and insert length (for paired-end reads), sequencing coverage, read alignment and k-mer frequency. ALE pinpoints synthetic errors in both single and metagenomic assemblies, including single-base errors, insertions/deletions, genome rearrangements and chimeric assemblies presented in metagenomes. At the genome level with real-world data, ALE identifies three large misassemblies from the Spirochaeta smaragdinae finished genome, which were all independently validated by Pacific Biosciences sequencing. At the single-base level with Illumina data, ALE recovers 215 of 222 (97%) single nucleotide variants in a training set from a GC-rich Rhodobacter sphaeroides genome. Using real Pacific Biosciences data, ALE identifies 12 of 12 synthetic errors in a Lambda Phage genome, surpassing even Pacific Biosciences' own variant caller, EviCons. In summary, the ALE framework provides a comprehensive, reference-independent and statistically rigorous measure of single genome and metagenome assembly accuracy, which can be used to identify misassemblies or to optimize the assembly process.

More at http://www.ncbi.nlm.nih.gov/pubmed/23303509

Address of the bookmark: http://sc932.github.io/ALE/about.html

Frontend: Perl Web framework documentation - Andrej Sali Lab

Jit — Mon, 08 Jan 2018 22:32:03 -0600

The frontend is a set of Perl classes that displays the web interface, allowing a user to upload their input files, start a job, display a list of all jobs in the system, and get back job results. The main saliwebfrontend class must be subclassed for each web service. This class is then used to display the web pages using a set of CGI scripts that are set up automatically by the build system.

Address of the bookmark: https://saliweb.readthedocs.io/en/latest/frontend.html

PilonGrid: parallel wrapper around the Pilon framework

Rahul Nayak — Thu, 13 Dec 2018 09:35:40 -0600

The distribution is a parallel wrapper around the Pilon framework The pipeline is composed of bash scripts, an example mapping.fofn which shows how to input your fastq files (you give paths to the R1 file), and how to launch the pipeline.

Address of the bookmark: https://github.com/skoren/PilonGrid

Dash: a web application framework that provides pure Python abstraction around HTML, CSS, and JavaScript.

Jit — Tue, 05 Nov 2019 06:39:48 -0600

Dash is a web application framework that provides pure Python abstraction around HTML, CSS, and JavaScript.

Dash Bio is a suite of bioinformatics components that make it simpler to analyze and visualize bioinformatics data and interact with them in a Dash application.

The source can be found on GitHub at plotly/dash-bio.

These docs are using Dash Bio version 0.1.4.

Address of the bookmark: https://dash.plot.ly/dash-bio