BOL: Related items

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.

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/

DeCoSTAR - Detection of Co-evolution

Jit — Fri, 14 Apr 2017 06:27:25 -0500

DeCoSTAR is a software which aims at reconstructing ancestral gene or genome organizations, in the form of sets of neighborhood relations -adjacencies- between pairs of ancestral genes or gene domains.
Ancestral genes or domains are deduced from reconciled gene trees in a context of birth, speciation, duplication, loss, transfer, which are either given as input or computed with the ecceTERA package, to which DeCoSTAR is integrated. DeCoSTAR constructs parsimonious scenarios of gains and breakages of adjacencies, and contains in particular all the features of previous software DeCo, DeCoLT, ArtDeCo and DeClone. It provides statistical supports on ancestral adjacencies, or the possibility to handle badly assembled genomes.
DeCoSTAR is able to reconstruct the histories of domains inside genes, including gene fusion and fission events, as well as ancestral genome structures for dozens of whole genomes from all kingdoms of life in a few minutes.

Address of the bookmark: http://pbil.univ-lyon1.fr/software/DeCoSTAR/

MALVA: Genotyping by Mapping-free ALlele Detection of Known VAriants

Jit — Tue, 28 Jan 2020 03:39:22 -0600

MALVA is able to genotype multi-allelic SNPs and indels without mapping reads

MALVA calls correctly more indels than the most widely adopted genotyping pipelines

Mapping-free approaches are as accurate as alignment-based ones, while being faster

More at https://www.sciencedirect.com/science/article/pii/S2589004219302366

https://www.sciencedirect.com/science/article/pii/S2589004219302366

Address of the bookmark: https://github.com/AlgoLab/malva

Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data

Jit — Thu, 19 Apr 2018 08:06:03 -0500

Heap, that enables robustly sensitive and accurate calling of SNPs, particularly with a low coverage NGS data, which must be aligned to the reference genome sequences in advance. To reduce false positive SNPs, Heap determines genotypes and calls SNPs at each site except for sites at the both end of reads or containing a minor allele supported by only one read. Performance comparison with existing tools showed that Heap achieved the highest F-scores with low coverage (7X) restriction-site associated DNA sequencing reads of sorghum and rice individuals. This will facilitate cost-effective GWAS and GP studies in this NGS era. Code and documentation of Heap are freely available from https://github.com/meiji-bioinf/heap and our web site (http://bioinf.mind.meiji.ac.jp/lab/en/tools.html).

Address of the bookmark: https://github.com/meiji-bioinf/heap

SvABA: Genome-wide detection of structural variants and indels by local assembly

Abhimanyu Singh — Mon, 21 Jan 2019 17:58:56 -0600

SvABA is a method for detecting structural variants in sequencing data using genome-wide local assembly. Under the hood, SvABA uses a custom implementation of SGA (String Graph Assembler) by Jared Simpson, and BWA-MEM by Heng Li. Contigs are assembled for every 25kb window (with some small overlap) for every region in the genome. The default is to use only clipped, discordant, unmapped and indel reads, although this can be customized to any set of reads at the command line using VariantBam rules. These contigs are then immediately aligned to the reference with BWA-MEM and parsed to identify variants. Sequencing reads are then realigned to the contigs with BWA-MEM, and variants are scored by their read support.

Address of the bookmark: https://github.com/walaj/svaba

Platypus – R package for object detection and image segmentation.

BioStar — Mon, 09 Nov 2020 02:56:25 -0600

platypus is an R package for object detection and semantic segmentation. Currently using

platypus you can perform:

multi-class semantic segmentation using U-Net architecture
multi-class object detection using YOLOv3 architecture

You can install the latest version of platypus with remotes package:

remotes::install_github("maju116/platypus")

Note that in order to install platypus you need to install keras and tensorflow packages and Tensorflow version >= 2.0.0 ( Tensorflow 1.x will not be supported!)

Address of the bookmark: https://github.com/maju116/platypus

Scalpel

Shruti Paniwala — Wed, 20 Aug 2014 02:07:58 -0500

A team from Cold Spring Harbor Laboratory has released an algorithm, called Scalpel, for finding insertions and deletions in next generation sequencing data sets. Scalpel, which is open source and available for download on SourceForge, outperformed the popular tools GATK HaplotypeCaller and SOAPindel in test runs on both simulated and real whole human exomes.

Like other indel callers, Scalpel works by performing de novo assembly of regions of interest, so that misalignment to the reference genome cannot obscure the presence of an insertion or deletion. Scalpel's innovation is to repeatedly check its assembly before comparing to the reference genome, to account for simple sequence repeats that are a regular source of error in indel calling. When Scalpel assembles an exon, it collects reads that map to that exon (including partial matches), splits them into k-mers, and creates a de Bruijn graph to span the exon; however, if it detects repeats in the map, it iteratively increases the size of the k-mers by one base until the repeats are eliminated. This ensures that the final assembly of the exon is highly accurate while minimizing compute time.

The Cold Spring Harbor team's validation of Scalpel, published over the weekend in Nature Methods, compares Scalpel's performance on a live whole exome against HaplotypeCaller and SOAPindel. The donor is an individual with serious neurological disorders, which may be linked to a high incidence of indels. One thousand indels from this individual's exome, called by one or more of the informatics pipelines, were selected for focused resequencing. This resequencing revealed a 77% true positive rate for Scalpel calls, dramatically better than the rates for either of the competing tools; Scalpel performed especially well with indels longer than five base pairs, a traditional weak point for indel callers.

Finally, the authors demonstrate Scalpel's use on a large set of genetic data from nearly 600 families who donated samples to the Simons Simplex Collection, a project of the Simons Foundation Autism Research Initiative. Scalpel found a very high enrichment for indels in children affected by autism, compared with their unaffected siblings, a pattern that persisted even after excluding common variants.