BOL: Related items

Postdoctoral Fellowship in Bioinformatics at pesolelab

Thu, 01 Oct 2015 07:20:48 -0500

Job Description: Bioinformatics postdoc positions are available in the area of genomics with main focus on exome and RNAseq technologies by ultra high-throughput sequencing platforms. Successful applicants should have the following qualities:

1) demonstrated experience in Bioinformatics research,
2) programing experience (python and/or R, C and C++ are very welcome),
3) knowledge of Linux/Unix environment,
4) experience in handling deep-seq data,
5) highly motivated and hard working, and
6) interested to work with a multi-disciplinary team combining bioinformatics, genomics, computational biology approaches with experimental biology.

Our research interest covers different areas of bioinformatics and genomics in order to achieve a deeper understanding of gene and genome structure and function (please look at our PubMed publications for more details about our research http://www.ncbi.nlm.nih.gov/pubmed/?term=pesole+g).

Interested applicants should email the curriculum vitae to Prof. Graziano Pesole at graziano.pesole@uniba.it or Dr. Ernesto Picardi at Ernesto.picardi@uniba.it.

Start date: immediate

Duration: up to 24 months
Contact Person (Referent): Ernesto Picardi
Ref. E-Mail: ernesto.picardi@uniba.it
Tel: +390805443308
Fax: +390805443317

Group Web Page: http://www.pesolelab.it/

Ensembl comparative genomics resources

Jitendra Narayan — Sun, 28 Feb 2016 17:10:20 -0600

The Ensembl comparative genomics resources are one such reference set that facilitates comprehensive and reproducible analysis of chordate genome data. Ensembl computes pairwise and multiple whole-genome alignments from which large-scale synteny, per-base conservation scores and constrained elements are obtained. Gene alignments are used to define Ensembl Protein Families, GeneTrees and homologies for both protein-coding and non-coding RNA genes. These resources are updated frequently and have a consistent informatics infrastructure and data presentation across all supported species. Specialized web-based visualizations are also available including synteny displays, collapsible gene tree plots, a gene family locator and different alignment views. The Ensembl comparative genomics infrastructure is extensively reused for the analysis of non-vertebrate species by other projects including Ensembl Genomes and Gramene and much of the information here is relevant to these projects. The consistency of the annotation across species and the focus on vertebrates makes Ensembl an ideal system to perform and support vertebrate comparative genomic analyses. We use robust software and pipelines to produce reference comparative data and make it freely available.

Database URL: http://www.ensembl.org.

Address of the bookmark: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4761110/

MetaPred2CS

Manisha Mishra — Fri, 03 Mar 2017 05:15:07 -0600

MetaPred2CS Web server is a meta-predictor based on Support Vector Machine (SVM) that combines 6 individual sequence based protein-protein interaction prediction methods to predict prokaryotic two-component system protein-protein interactions (PPIs). The methods implemented in MetaPred2CS are 2 co-evolutionary methods: in-silico two hybrid (i2h) and mirror tree (MT) methods and 4 genomics context based methods: phylogenetic profiling (PP), gene fusion (GF), gene neighbourhood (GN) and and gene operon methods (GO).

http://metapred2cs.ibers.aber.ac.uk/

Address of the bookmark: https://github.com/martinjvickers/MetaPred2CS

Anvio

Shruti Paniwala — Thu, 16 Jun 2016 18:15:41 -0500

In a nutshell

Anvi’o is an analysis and visualization platform for ‘omics data.

Please find the methods paper here: https://peerj.com/articles/1319/

Anvi’o would not have been possible without the help of many people who directly or indirectly contributed to its development. Here is the acknowledgements section of our methods paper

An analysis and visualization platform for 'omics data http://merenlab.org/projects/anvio

Paper https://peerj.com/articles/1839/

Address of the bookmark: https://github.com/meren/anvio

Genome Browser : GBrowse

Jit — Fri, 19 Feb 2016 09:22:43 -0600

Generic Genome Browser Version 2: A Tutorial for Administrators

This is an extensive tutorial to take you through the main features and gotchas of configuring GBrowse as a server. This tutorial assumes that you have successfully set up Perl, GD, BioPerl and the other GBrowse dependencies. If you haven't, please see the GBrowse HOWTO During most of the tutorial, we will be using the "in-memory" GBrowse database (no relational database required!) Later we will show how to set up a genome size database using the berkeleydb and MySQL adaptors.

More at http://elp.ucdavis.edu/tutorial/tutorial.html

Address of the bookmark: http://elp.ucdavis.edu/tutorial/tutorial.html

Scarpa

Poonam Mahapatra — Wed, 13 Jul 2016 07:59:25 -0500

Scarpa is a stand-alone scaffolding tool for NGS data. It can be used together with virtually any genome assembler and any NGS read mapper that supports SAM format. Other features include support for multiple libraries and an option to estimate insert size distributions from data. Scarpa is available free of charge for academic and commercial use under the GNU General Public License (GPL).

See the user manual or the paper for more information about Scarpa. Click here for the supplementary material.

Address of the bookmark: http://compbio.cs.toronto.edu/hapsembler/scarpa.html

Graph Genome Suite

Jit — Fri, 28 Oct 2016 07:59:54 -0500

Seven Bridges is the biomedical data analysis company accelerating breakthroughs in genomics research for cancer, drug development and precision medicine. We build self-improving systems to analyze millions of genomes, including the Graph Genome Suite — the most advanced population genomics tools in the world.

Address of the bookmark: https://www.sbgenomics.com/graph/

Statistics and probability

Jit — Tue, 08 Nov 2016 07:34:25 -0600

Topics

Displaying and describing data

Modeling distributions of data

Describing relationships in quantitative data

Designing studies

Probability

Random variables

Sampling distributions

Confidence intervals (one sample)

Significance tests (one sample)

Significance tests and confidence intervals (two samples)

Inference for categorical data (chi-square tests)

Advanced regression (inference and tran

Address of the bookmark: https://www.khanacademy.org/math/statistics-probability

sequenceserver

Jit — Fri, 10 Mar 2017 08:51:55 -0600

SequenceServer lets you rapidly set up a BLAST+ server with an intuitive user interface for use locally or over the web.

More at http://sequenceserver.com.

Address of the bookmark: https://github.com/wurmlab/sequenceserver

GeneBreak: a tool to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach

Jit — Sat, 01 Oct 2016 15:15:29 -0500

Development of cancer is driven by somatic alterations, including numerical and structural chromosomal aberrations. Currently, several computational methods are available and are widely applied to detect numerical copy number aberrations (CNAs) of chromosomal segments in tumor genomes. However, there is lack of computational methods that systematically detect structural chromosomal aberrations by virtue of the genomic location of CNA-associated chromosomal breaks and identify genes that appear non-randomly affected by chromosomal breakpoints across (large) series of tumor samples. ‘GeneBreak’ is developed to systematically identify genes recurrently affected by the genomic location of chromosomal CNA-associated breaks by a genome-wide approach, which can be applied to DNA copy number data obtained by array-Comparative Genomic Hybridization (CGH) or by (low-pass) whole genome sequencing (WGS). First, ‘GeneBreak’ collects the genomic locations of chromosomal CNA-associated breaks that were previously pinpointed by the segmentation algorithm that was applied to obtain CNA profiles. Next, a tailored annotation approach for breakpoint-to-gene mapping is implemented. Finally, dedicated cohort-based statistics is incorporated with correction for covariates that influence the probability to be a breakpoint gene. In addition, multiple testing correction is integrated to reveal recurrent breakpoint events. This easy-to-use algorithm, ‘GeneBreak’, is implemented in R (www.cran.r-project.org) and is available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/GeneBreak.html).

Address of the bookmark: http://www.bioconductor.org/packages/release/bioc/html/GeneBreak.html