BOL: Related items

Artemis Comparison Tool (ACT)

Shruti Paniwala — Wed, 07 Sep 2016 03:54:41 -0500

ACT is a Java application for displaying pairwise comparisons between two or more DNA sequences. It can be used to identify and analyse regions of similarity and difference between genomes and to explore conservation of synteny, in the context of the entire sequences and their annotation. It can read complete EMBL, GENBANK and GFF entries or sequences in FASTA or raw format.

Address of the bookmark: http://www.sanger.ac.uk/science/tools/artemis-comparison-tool-act

CIRCOS Visualize !!

Jit — Fri, 02 Sep 2016 08:29:26 -0500

Before uploading a data file, check the samples gallery to make sure that your data format is compatible.

Your file must be plain text.
Your data values must be non-negative integers.
Data must be space-separated (one or more tab or space, which will be collapsed).
No two rows or columns may have the same name.
Column and row names must begin with a letter (e.g. 'A', 'A0', 'A-0') and can only contain letters, numbers and _. No punctuation!
Maximum row + column total is 150 — if exceeded, rows and columns are limited to 75.
If you are using order, size and color rows/columns in combination they must appear in that order.

Need help? Post questions to the Circos Google Group.

http://mkweb.bcgsc.ca/tableviewer/visualize/

Address of the bookmark: http://mkweb.bcgsc.ca/tableviewer/visualize/

Sybil

Jit — Wed, 07 Sep 2016 03:20:44 -0500

The Sybil software package provides a primarily web-based front-end to comparative genome datasets warehoused in a chado relational database. It was developed by the bioinformatics department at The Institute for Genomic Research (TIGR) and development continues at the J. Craig Venter Institute (JCVI) and the Institute for Genome Sciences (IGS) at the University of Maryland: Baltimore. Sybil has been used at TIGR/JCVI, IGS, NYU, New York Medical College, Novartis Vaccines and University of Maryland: College Park to support a number of research projects that involve comparative genome analysis. The following sections provide some high-level technical details about the overall architecture and external dependencies of the Sybil package.

Address of the bookmark: http://sybil.sourceforge.net/

Nemo – A stochastic, individual-base, genetically explicit simulation platform

Jit — Sat, 01 Oct 2016 14:45:02 -0500

A recombination map has been added for all multi-locus traits. The map positions (chromosomal) for neutral markers (e.g. SNPs) and loci under selection (QTLs, deleterious mutations, DMIs) can now be specified explicitly, or set at random. The map can hold an unlimited number of loci of different types jointly, at any recombination scale (cM or lower). The effects of linkage can thus be finely explored.
A new trait coding for (Bateson-)Dobzhansky-Muller incompatibility loci. Multiple haploid or diploid pairs of incompatible loci can be spread throughout the genome and affect individual fitness.
Multi-type selection: Individual fitness can be jointly determined by different types of loci under selectinon, such as QTLs coding for quantitative traits under spatially variable selection, universally deleterious mutations, and Dobzhansky-Muller incompatibility loci.
An unlimited number of quantitative traits under different forms of selection can be modelled, based on universally pleiotropic loci with several bi- or multi-allelic models.
Spatial and temporal variation of selection on quantitative traits is possible, modelling shifts of environmental conditions over time.
The dispersal matrix describing the movement of individuals among sub-populations can be replaced by a connectivity matrix and a reduced dispersal matrix describing migration only among the connected sub-populations. This offers a substantial gain in computing time and system memory when simulating very large grids.
Input parameters' arguments may be specified in separate files. This is particularly convenient when specifying large matrices.
Many adjustments have been made for refined control of the input of parameters and data output. See updates in the manual.

Address of the bookmark: http://nemo2.sourceforge.net/index.html

Live Webinar on RNA-Seq Data Analysis on 9 Nov 2016

Strand — Wed, 19 Oct 2016 05:25:27 -0500

Live Webinar on RNA-Seq Data Analysis

Abstract: Strand NGS supports an extensive workflow for the analysis and visualization of RNA-Seq data. The workflow includes Transcriptome / Genome alignment, Differential expression analysis with Statistical approach and Splicing events detection. Strand NGS also supports novel discovery like identification of novel genes, exons and Novel splice junctions, alongside it can also detect gene fusion events. Further downstream analysis such as GO and pathway analysis can be performed on the set of interesting genes. The product has an option to create pipelines for time consuming jobs which automates analysis and leaves more time for end data interpretation. This webinar will give an overview of the features in the RNA-Seq data analysis workflow in Strand NGS and also highlights on parameters within each feature that can be optimized depending on datasets and analysis needs.

Speaker: Mr. Sugandan Sivamani, Senior Application Scientist, Strand Life Sciences

Date: 9th Nov, Session 1 for SAPK/ APFO: 2:30 PM IST Date: 9th Nov, Session 2 for AFO/ EMEA: 9:00 AM PST

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/

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

RECORD

Bulbul — Fri, 25 Nov 2016 08:23:36 -0600

Background. Next-generation sequencing technologies are now producing multiple times the genome size in total reads from a single experiment. This is enough information to reconstruct at least some of the differences between the individual genome studied in the experiment and the reference genome of the species. However, in most typical protocols, this information is disregarded and the reference genome is used. Results. We provide a new approach that allows researchers to reconstruct genomes very closely related to the reference genome (e.g., mutants of the same species) directly from the reads used in the experiment. Our approach applies de novo assembly software to experimental reads and so-called pseudoreads and uses the resulting contigs to generate a modified reference sequence. In this way, it can very quickly, and at no additional sequencing cost, generate new, modified reference sequence that is closer to the actual sequenced genome and has a full coverage. In this paper, we describe our approach and test its implementation called RECORD. We evaluate RECORD on both simulated and real data. We made our software publicly available on sourceforge. Conclusion. Our tests show that on closely related sequences RECORD outperforms more general assisted-assembly software.

More at https://sourceforge.net/projects/record-genome-assembler/files/

Address of the bookmark: https://www.ncbi.nlm.nih.gov/pubmed/26558255

EXCAVATOR2tool

Bulbul — Wed, 30 Nov 2016 04:09:19 -0600

EXCAVATOR2 is a collection of bash, R and Fortran scripts and codes that analyses Whole Exome Sequencing (WES) data to identify CNVs. EXCAVATOR2 enhances the identification of all genomic CNVs, both overlapping and non-overlapping targeted exons by integrating the analysis of In-targets and Off- targets reads. Specifically, it improves the precision of calling CNVs overlapping targeted exons from WES data and enlarges the spectrum of detectable CNVs to off-target events.
EXCAVATOR2 can be effectively employed for the identification of CNVs in small as well as large-scale re-sequencing population and cancer studies. Lastly, it’s of particular interest that all WES experiments can be re-analysed using our method with the beneficial effect to identify novelCNVs in extra-exonic regions by having the full-genome CN profile.

Address of the bookmark: https://sourceforge.net/projects/excavator2tool/

SGA: String Graph Assembler

Jit — Thu, 08 Dec 2016 05:08:59 -0600

SGA is a de novo genome assembler based on the concept of string graphs. The major goal of SGA is to be very memory efficient, which is achieved by using a compressed representation of DNA sequence reads.

More at

https://github.com/jts/sga

SGA dependencies:
-google sparse hash library (http://code.google.com/p/google-sparsehash/)
-the bamtools library (https://github.com/pezmaster31/bamtools)
-zlib (http://www.zlib.net/)
-(optional but suggested) the jemalloc memory allocator (http://www.canonware.com/jemalloc/download.html)

Address of the bookmark: https://github.com/jts/sga