BOL: Related items

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

e-RGA: enhanced Reference Guided Assembly of Complex Genomes

Jit — Mon, 19 Dec 2016 05:56:14 -0600

Next Generation Sequencing has totally changed genomics: we are able to produce huge amounts of data at an incredibly low cost compared to Sanger sequencing. Despite this, some old problems have become even more difficult, de novo assembly being on top of this list. Despite efforts to design tools able to assemble, de novo, an organism sequenced with short reads, the results are still far from those achievable with long reads. In this paper, we propose a novel method that aims to improve de novo assembly in the presence of a closely related reference. The idea is to combine de novo and reference-guided assembly in order to obtain enhanced results.

Address of the bookmark: http://journal.embnet.org/index.php/embnetjournal/article/view/208

BIMA V3: an aligner customized for mate pair library sequencing

Abhimanyu Singh — Wed, 14 Dec 2016 15:20:00 -0600

Summary: Mate pair library sequencing is an effective and economical method for detecting genomic structural variants and chromosomal abnormalities. Unfortunately, the mapping and alignment of mate pair read pairs to a reference genome is a challenging and
time consuming process for most NGS alignment programs. Large insert sizes, introduction of library preparation protocol artifacts (biotin junction reads, paired-end read contamination, chimeras, etc.), and presence of structural variant breakpoints within reads increases mapping and alignment complexity. We describe an algorithm that is up to 20 times faster and 25% more accurate than popular NGS alignment programs when processing mate pair sequencing.
Availability: http://bioinformaticstools.mayo.edu/research/bima/
Contact: vasmatzis.george@mayo.edu

Address of the bookmark: http://bioinformatics.oxfordjournals.org/content/early/2014/02/12/bioinformatics.btu078.full.pdf

CONCOCT: Clustering cONtigs with COverage and ComposiTion

Jit — Mon, 06 Mar 2017 04:08:16 -0600

A program for unsupervised binning of metagenomic contigs by using nucleotide composition, coverage data in multiple samples and linkage data from paired end reads.

Warning! This software is to be considered under development. Functionality and the user interface may still change significantly from one version to another. If you want to use this software, please stay up to date with the list of known issues:https://github.com/BinPro/CONCOCT/issues

Address of the bookmark: https://github.com/BinPro/CONCOCT

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

MEDEA: Comparative Genomic Visualization with Adobe Flash

Jit — Tue, 26 Apr 2016 12:15:16 -0500

As the number of sequence and annotated genomes grows larger, the need to understand, compare, and contrast the data becomes increasingly important. Using the power of the human visual system to detect trends and spot outliers is necessary in such large and complex data sets.

More at http://www.broadinstitute.org/annotation/medea/

Address of the bookmark: http://www.broadinstitute.org/annotation/medea/

Cytoscape

Anjana — Mon, 23 May 2016 02:32:00 -0500

Cytoscape is an open source software platform for visualizing complex networks and integrating these with any type of attribute data. A lot of Apps are available for various kinds of problem domains, including bioinformatics, social network analysis, and semantic web.

Address of the bookmark: http://www.cytoscape.org/

KisSplice

Jit — Tue, 16 Aug 2016 08:34:19 -0500

KisSplice is a software that enables to analyse RNA-seq data with or without a reference genome. It is an exact local transcriptome assembler that allows to identify SNPs, indels and alternative splicing events. It can deal with an arbitrary number of biological conditions, and will quantify each variant in each condition. It has been tested on Illumina datasets of up to 1G reads. Its memory consumption is around 5Gb for 100M reads.

KisSplice is not a full-length transcriptome assembler. This means that it will output the variable regions of the transcripts, not reconstruct them entirely.

KisSplice comes as a workflow, with several possible post-treatments meant to facilitate the analysis of the results. The choice of the post-treatment depends on the availability of a reference genome/transcriptome and on the need to perform a differential analysis, as summarised in the following table.

Address of the bookmark: http://kissplice.prabi.fr/

Machine Learning !!!

Gudiya Pal — Fri, 01 Jul 2016 12:57:12 -0500

In machine learning, computers apply statistical learning techniques to automatically identify patterns in data. These techniques can be used to make highly accurate predictions.

Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

WiseScaffolder

Poonam Mahapatra — Wed, 13 Jul 2016 08:08:57 -0500

Function

WiseScaffolder is a stand-alone semi-automatic application for genome scaffolding of pre-assembled contigs using mate-pair data. It also produces editable scaffold maps, allowing either to build gapped scaffolds or usable as a common thread for the manual improvement of scaffolds.

Description

WiseScaffolder includes 4 subcommands: dumpconfig generates a configuration file that notably specifies the average insert size of the mate-pair library preprocess allows the detection and correction of chimerae, the estimation of contigs copy number and produces valuable outputs for the manual improvement of scaffolds scaffold constitutes the central scaffold-builder and comprises two modules:

i) the interative_scaffold_extender, which works with big, unambiguous contigs, or when they run out, single copy contigs, and

ii) the small_contig_inserter, which inserts the small contigs within scaffolds buildfasta converts the scaffold(s) map(s) into Fasta sequences.

Address of the bookmark: http://abims.sb-roscoff.fr/wisescaffolder