BOL: Related items

GAGE : Genome Assembly Gold-standard Evaluation

Jit — Wed, 07 Sep 2016 07:35:49 -0500

GAGE is an evaluation of the very latest large-scale genome assembly algorithms. We have organized this "bake-off" as an attempt to produce a realistic assessment of genome assembly software in a rapidly changing field of next-generation sequencing. The main results of GAGE have now been published in the journal Genome Research: GAGE: A critical evaluation of genome assemblies and assembly algorithms.

http://genome.cshlp.org/content/early/2012/01/12/gr.131383.111

Address of the bookmark: http://gage.cbcb.umd.edu/index.html

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

VirMet

Jit — Mon, 10 Oct 2016 08:27:19 -0500

Watch out: only a few files are counted in coverage statistics.

Full documentation on Read the Docs.

A set of tools for viral metagenomics.

virmet is called with a command subcommand syntax: virmet fetch --viral n, for example, downloads the bacterial database. Other available subcommands so far are

fetch download genomes
update update viral/bacterial database
index index genomes
wolfpack analyze a Miseq run
covplot plot coverage for a specific organism

A short help is obtained with virmet subcommand -h.

More at https://github.com/ozagordi/VirMet

Address of the bookmark: https://github.com/ozagordi/VirMet

Ribbon !!

Jit — Fri, 21 Oct 2016 04:54:30 -0500

Visualization has played an extremely important role in the current genomic revolution to inspect and understand variants, expression patterns, evolutionary changes, and a number of other relationships. However, most of the information in read-to-reference or genome-genome alignments is lost for structural variations in the one-dimensional views of most genome browsers showing only reference coordinates. Instead, structural variations captured by long reads or assembled contigs often need more context to understand, including alignments and other genomic information from multiple chromosomes. We have addressed this problem by creating Ribbon (genomeribbon.com) an interactive online visualization tool that displays alignments along both reference and query sequences, along with any associated variant calls in the sample. This way Ribbon shows patterns in alignments of many reads across multiple chromosomes, while allowing detailed inspection of individual reads (Supplementary Note 1). For example, here we show a gene fusion in the SK-BR-3 breast cancer cell line linking the genes CYTH1 and EIF3H. While it has been found in the transcriptome previously, genome sequencing did not identify a direct chromosomal fusion between these two genes. After SMRT sequencing, Ribbon shows that there are indeed long reads that span from one gene to the other, going through not one but two variants, for the first time showing the genomic link between these two genes (Figure 1a). More gene fusions of this cancer cell line are investigated in Supplementary Note 2. Figure 1b shows another complex event in this sample made simple in Ribbon: the translocation of a 4.4 kb sequence deleted from chr19 and inserted into chr16 (Figure 1b). Thus, Ribbon enables understanding of complex variants, and it may also help in the detection of sequencing and sample preparation issues, testing of aligners and variant-callers, and rapid curation of structural variant candidates (Supplementary Note 3). In addition to SAM and BAM files with long, short, or paired-end reads, Ribbon can also load coordinate files from whole genome aligners such as MUMmer. Therefore, Ribbon can be used to test assembly algorithms or inspect the similarity between species. Supplementary Note 4 shows a comparison of gorilla and human genomes using Ribbon, highlighting major structural differences. In conclusion, Ribbon is a powerful interactive web tool for viewing complex genomic alignments.

Script at https://github.com/MariaNattestad/ribbon

Address of the bookmark: http://genomeribbon.com/

eFORGE.v1.2

Jit — Fri, 28 Oct 2016 09:06:59 -0500

The eFORGE tool provides a method to view the tissue specific regulatory component of a set of EWAS DMPs. eFORGE analysis takes a set of DMPs, such as those hits above genome-wide significance threshold in an EWAS study, and analyses whether there is enrichment for overlap of putative functional elements compared to matched background DMPs. It assesses enrichment on a per cell type basis, since functional elements are differentially active in different cell types, and hence can expose tissue-specific signals of enrichment for the given test DMP set. This can reveal the sites of action underlying the EWAS signal, and provide confirmation of the validity of the EWAS where a tissue-specific mechanism is known or expected for the phenotype. Conversely unknown tissue involvements can also be revealed.

Address of the bookmark: http://eforge.cs.ucl.ac.uk/eFORGE.v1.2/?documentation

Minia

Jit — Thu, 08 Dec 2016 05:07:00 -0600

Minia is a short-read assembler based on a de Bruijn graph, capable of assembling a human genome on a desktop computer in a day. The output of Minia is a set of contigs. Minia produces results of similar contiguity and accuracy to other de Bruijn assemblers (e.g. Velvet).

Download

Minia 2.0.7 Linux 64-bits binaries (Source code) (Legacy codebase)

For the impatient

A typical Minia command line looks like:

./minia -in reads.fa -kmer-size 31 -abundance-min 3 -out output_prefix

Type

./minia

for a quick explanation of the parameters.

For more information, refer to the manual.

KmerGenie can be used to determine the best k-mer size, minimum abundance of correct k-mers, and genome size estimation for your dataset.

Address of the bookmark: http://minia.genouest.org/

Standardized velvet assembly report

Poonam Mahapatra — Fri, 09 Dec 2016 03:59:59 -0600

Requirements:

velvet (velveth velvetg should be in your PATH)
R (with Sweave)
pdflatex (usually part of TeTeX)
ggplot2 (from R prompt type install.packages("ggplot2","proto","xtable"))
Perl

Optional:

BLAT or BLAST (to generate alignments against a reference genome). If using BLAT, add faToTwoBit,gfClient,gfServer to your PATH. If using BLAST, add blastall and formatdb.

Edit permute.sh to your liking, paying particular attention to the kmer, cvCut, expCov, and other flags

To Run:

perl fastaAllSize mysequences.fa > mysequences.stat or gunzip -c mysequences.fa.gz | fastaAllSize > mysequences.stat Substitute fastqAllSize for fastq files.
./permute.sh mysequences (leave out the .fa)

https://github.com/leipzig/standardized-velvet-assembly-report

Address of the bookmark: https://github.com/leipzig/standardized-velvet-assembly-report

EAGER

Jit — Sat, 10 Dec 2016 18:07:23 -0600

The automated reconstruction of genome sequences in ancient genome analysis is a multifaceted process.

EAGER encompasses both state-of-the-art tools for each step as well as new complementary tools tailored for ancient DNA data within a single integrated solution in an easily accessible format.

https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-0918-z

Address of the bookmark: https://github.com/apeltzer/EAGER-GUI

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

GAM-NGS: genomic assemblies merger for next generation sequencing

Jit — Mon, 19 Dec 2016 06:07:05 -0600

GAM-NGS (Genomic Assemblies Merger for Next Generation Sequencing), whose primary goal is to merge two or more assemblies in order to enhance contiguity and correctness of both. GAM-NGS does not rely on global alignment: regions of the two assemblies representing the same genomic locus (called blocks) are identified through reads' alignments and stored in a weightedgraph. The merging phase is carried out with the help of this weighted graph that allows an optimal resolution of local problematic regions.

Address of the bookmark: https://github.com/vice87/gam-ngs