BOL: Related items

deepTools

Martin Jones — Sat, 08 Nov 2014 15:02:08 -0600

deepTools addresses the challenge of handling the large amounts of data that are now routinely generated from DNA sequencing centers. To do so, deepTools contains useful modules to process the mapped reads data to create coverage files in standard bedGraph and bigWig file formats. By doing so, deepTools allows the creation of normalized coverage files or the comparison between two files (for example, treatment and control). Finally, using such normalized and standardized files, multiple visualizations can be created to identify enrichments with functional annotations of the genome.

Publicaton: http://nar.oxfordjournals.org/content/early/2014/05/05/nar.gku365.full

Source Code and Wiki: https://github.com/fidelram/deepTools/wiki

Galaxy Tool Shed repository: http://toolshed.g2.bx.psu.edu/view/bgruening/deeptools

and example Galaxy workflows: http://toolshed.g2.bx.psu.edu/view/bgruening/deeptools_workflows

Vital-IT

Abhi — Thu, 18 Dec 2014 10:46:59 -0600

Vital-IT is a bioinformatics competence center that supports and collaborates with life scientists in Switzerland and beyond. The multi-disciplinary team provides expertise, training and maintains a high-performance computing (HPC) and storage infrastructure, so as to help develop, maintain and extend life science and medical research (activities).

Address of the bookmark: http://www.vital-it.ch/

LASTZ

Abhi — Mon, 18 Apr 2016 04:41:55 -0500

LASTZ is a program for aligning DNA sequences, a pairwise aligner. Originally designed to handle sequences the size of human chromosomes and from different species, it is also useful for sequences produced by NGS sequencing technologies such as Roche 454.

More at http://www.bx.psu.edu/~rsharris/lastz/

Thesis: http://www.bx.psu.edu/~rsharris/rsharris_phd_thesis_2007.pdf

Address of the bookmark: http://www.bx.psu.edu/~rsharris/lastz/

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/

Alignment of closely related whole genomes/scaffolds

Rahul Nayak — Fri, 29 Jan 2016 10:37:27 -0600

With the relative ease and low cost of current generation sequencing technologies has led to a dramatic increase in the number of sequenced genomes for species across the tree of life. This increasing volume of data requires tools that can quickly compare multiple whole-genome sequences, millions of base pairs in length, to aid in the study of populations, pan-genomes, and genome evolution.This bookmaks have been created to report new tools for whole genome alignments.

Please report new whole genome alignment tools under comment sections.

Address of the bookmark: http://www.cs.utoronto.ca/~brudno/721.full.pdf

UCSC Genome Browser and Blat software !

Jit — Thu, 18 Feb 2016 03:18:57 -0600

This directory contains Genome Browser and Blat application binaries built for standalone
command-line use on various supported Linux and UNIX platforms. To determine which set of binaries
to download, type "uname -a" on the command line to display your machine type. In most cases the
usage statement for the application can be viewed by running the binary with no arguments.

The UCSC Genome Browser and Blat software are free for academic, nonprofit, and personal use. A
license is required for commercial download and installation of these binaries, with the exception
of items built from the following source code directories, which are freely available for all uses:

- kent/src/utils (includes big* tools)
- kent/src/lib
- kent/src/hg/autoSql
- kent/src/hg/autoXml

For information about commercial licensing of the Genome Browser software, see
http://genome.ucsc.edu/license/. The Blat and In-Silico PCR software may be commercially
licensed through Kent Informatics (http://www.kentinformatics.com).

More at http://hgdownload.cse.ucsc.edu/admin/exe/

Address of the bookmark: http://hgdownload.cse.ucsc.edu/admin/exe/

CANU: Assembling Large Genomes with Single-Molecule Sequencing and Locality Sensitive Hashing.

Jit — Tue, 26 Apr 2016 11:38:10 -0500

Canu is a fork of the Celera Assembler designed for high-noise single-molecule sequencing (such as the PacBio RSII or Oxford Nanopore MinION). The software is currently alpha level, feel free to use and report issues encountered.

Canu is a hierachical assembly pipeline which runs in four steps:

Detect overlaps in high-noise sequences using MHAP
Generate corrected sequence consensus
Trim corrected sequences
Assemble trimmed corrected sequences

Read the documentation

New release https://github.com/marbl/canu/releases

Address of the bookmark: https://github.com/marbl/canu

4DGenome

Jitendra Narayan — Mon, 04 Jul 2016 00:44:55 -0500

Records in 4DGenome are compiled through comprehensive literature curation of experimentally-derived and computationally-predicted interactions. The current release contains 4,433,071 experimentally-derived and 3,605,176 computationally-predicted interactions in 5 organisms. Experimental data cover both high throughput datasets and individiual focused studies.

All interaction data are freely available in a standardized file format. Records can be queried by genomic regions, gene names, organism, and detection technology.

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

DECIPHER

Anjana — Fri, 30 Sep 2016 09:33:12 -0500

DECIPHER is a software toolset that can be used to maintain, analyze, and decipher large amounts of DNA sequence data. To install DECIPHER, see the Downloads page.

To begin using DECIPHER read the "Getting Started DECIPHERing" tutorial. Refer to the PDF documents below for instructions on how to use DECIPHER for various tasks.

Address of the bookmark: http://decipher.cee.wisc.edu/Documentation.html

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